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Founder populations and large pedigrees offer many well-known advantages for genetic mapping studies , including cost-efficient study designs . Here , we describe PRIMAL ( PedigRee IMputation ALgorithm ) , a fast and accurate pedigree-based phasing and imputation algorithm for founder populations . PRIMAL incorporates both existing and original ideas , such as a novel indexing strategy of Identity-By-Descent ( IBD ) segments based on clique graphs . We were able to impute the genomes of 1 , 317 South Dakota Hutterites , who had genome-wide genotypes for ~300 , 000 common single nucleotide variants ( SNVs ) , from 98 whole genome sequences . Using a combination of pedigree-based and LD-based imputation , we were able to assign 87% of genotypes with >99% accuracy over the full range of allele frequencies . Using the IBD cliques we were also able to infer the parental origin of 83% of alleles , and genotypes of deceased recent ancestors for whom no genotype information was available . This imputed data set will enable us to better study the relative contribution of rare and common variants on human phenotypes , as well as parental origin effect of disease risk alleles in >1 , 000 individuals at minimal cost .
Despite decreasing costs of whole exome and whole genome sequencing , the role of rare genetic variants in common disease risk remains hard to assess due to the very large sample sizes required for such studies [1 , 2] . Therefore , approaches that allow accurate imputation of rare variants to large numbers of individuals based on the sequences of relatively few individuals could address this important question at minimal cost . Founder populations are particularly suitable to this strategy because pedigree relationships are either known or can be inferred from genotypes , facilitating imputation approaches that incorporate identity by descent ( IBD ) relationships between chromosomal segments and improving imputation accuracy . Moreover , variants that occur at low frequency ( <5% ) or are rare ( <1% ) in large outbred populations , may occur at common frequencies ( >5% ) in founder populations due to the bottleneck at the time of their founding followed by random genetic drift effects in subsequent generations . Similar to mutations for rare monogenic disorders reaching relatively common frequencies in founder populations [3–6] , subsets of the rare variants contributing to common complex diseases are also expected to occur at higher frequencies in these populations . This provides a unique opportunity to study the relative roles of rare and common variants on common disease risk in individuals exposed to similar environments , which further minimizes the contribution of non-genetic factors to inter-individual variation in disease risk and facilitates identification of disease-associated alleles . Methodological approaches to genotype imputation fall into two general categories depending on whether they are based on linkage disequilibrium ( LD ) or on genetic relationships ( i . e . , pedigrees ) [7] . LD-based imputation methods require a reference panel of genotype training data , usually from unrelated individuals , to infer local haplotype structure , and sharing of haplotype stretches are used for filling in missing genotypes [8–11] . These approaches typically result in high call rates at the expense of lower accuracy , especially for rare alleles [12] . In contrast , pedigree-based imputation approaches are more accurate because they rely on identifying regions of IBD sharing among the study subjects [13 , 14] . However , call rates are typically lower than from LD-based methods , and pedigree-based imputation can be significantly slower to implement due to complex pedigree structures , which often pose limitations on maximum family sizes and minimum relatedness of individuals [15] . To address the limitations of LD- and pedigree-based imputation methods , we developed PRIMAL ( PedigRee IMputation ALgorithm ) , a fast phasing and imputation algorithm , to assign genotypes at 7 million bi-allelic variants that were discovered in the whole genome sequences of 98 Hutterites to an additional set of 1 , 317 Hutterites who had genome-wide genotypes for ~300 , 000 common single nucleotide variants ( SNVs ) . We first phased the SNV genotypes using pedigree-based phasing algorithms [16 , 17] and determined IBD segments between each pair of haplotypes using a Hidden-Markov Model [18] . We then organized IBD segments into an IBD clique dictionary , a novel data structure for efficient IBD lookup queries that enables fast pedigree-based imputation of the variants identified in the 98 genomes . We demonstrate that the accuracy of the algorithm is above 99% regardless of minor allele frequency , with a call rate of approximately 77% . To improve the call rate , the missing genotypes were imputed using the LD-based IMPUTE2 program [11] , with the phased haplotypes of the 98 whole genome sequenced Hutterites as the reference panel . The result is a hybrid method that combines the benefits of pedigree- and LD-based strategies to obtain similar accuracy ( > 99% ) , and higher call rates ( 87 . 3% ) . Moreover , using the IBD clique dictionary implemented in PRIMAL , we can infer the parental origin of 83% of alleles . We are also able to impute whole genome genotypes to recent ancestors with no available DNA . The PRIMAL algorithm and software will facilitate genetic studies of rare variants and parent-of-origin effects in the Hutterites and in other founder populations with similar data .
This study was conducted according to the principles expressed in the Declaration of Helsinki . All participants in the experiment provided written informed consent in approval with the University of Chicago Institutional Review Board . The Hutterites originated in central Europe in the 1500s . After a series of migrations and population bottlenecks , they settled in what is now South Dakota in the 1870s , and currently live on communal farms in the northern U . S . plains states and western Canadian provinces [19] . At present , there are over 14 , 000 Hutterites living in South Dakota , all of whom are descendants of just 64 founders and related to each other with a mean kinship coefficient of 3 . 4% [20] . This study includes 1 , 415 Hutterites who previously participated in one or more of our studies of Mendelian and common diseases and associated phenotypes ( e . g . , [5 , 21] ) . These individuals are related to each other through multiple lines of descent in a 3 , 671-person minimum pedigree . We genotyped DNA from Hutterite individuals using one of three Affymetrix arrays ( 500k , 5 . 0 and 6 . 0 ) , as previously described [21 , 22 , 23] . As part of our quality control ( QC ) process , we removed SNVs with five or more Mendelian errors , Hardy-Weinberg p-values < 0 . 001 , or call rates <95% , resulting in 332 , 242 SNVs present on all three platforms . The final sample included 1 , 415 Hutterites with genotype call rate > 95% . We used the subset of 271 , 486 SNVs with minor allele frequency ( MAF ) ≥ 5% for phasing and imputation in this study . These SNVs are referred to as the “framework SNVs” , and genotyped individuals for whom both parents were not genotyped are referred to as the “quasi-founders” of this sub-pedigree . Ninety-eight Hutterites were selected from the 1 , 415 for whole genome sequencing ( WGS ) to maximize their relatedness to the other 1 , 317 Hutterites ( and thus leading to high pedigree-based imputation call rates ) , while minimizing the pairwise relatedness among the 98 ( to reduce the amount of redundant sequencing ) . To achieve this we used a greedy algorithm described elsewhere [16] where subjects were selected sequentially to maximize the average kinship to the non-sequenced individuals , while imposing a kinship smaller than 0 . 1 with the sequenced individuals . Sequencing was performed by Complete Genomics , Inc . ( Mountain View , CA ) . A total of 18 . 2 million variants ( 14 . 0M SNVs , 2 . 7M insertions , 1 . 4M deletions; Table 1 ) were discovered in the 98 WGS , including 11 . 6 million variants ( 9 . 2M SNVs , 1 . 3M insertions and 1 . 1M deletions ) for which both alleles were called as high quality by Complete Genomics . Using the 332 , 242 SNVs , the concordance between the genotypes from the whole genome sequences and those determined by genotyping with the Affymetrix arrays was 99 . 8% . To investigate the quality of sequencing-based genotypes for classes of variants ( for example , all novel singletons ) , we developed a pedigree-based method to assess genotyping errors . The method is an extension of the classical Mendelian error checking in families . However , in contrast to Mendelian checks that use parents and their offspring , our approach includes all pairs of related individuals , regardless of the distance of the relationship , using their IBD segments . High confidence IBD2 segments ( i . e . , IBD = 2 , or regions where two individuals inherited the same chromosomal segments from a common ancestor ) were previously calculated between each pair of individuals among the 98 Hutterites using the 332 , 242 framework SNVs [24] . Next , for each sequenced variant , we determined the number of IBD2 segments shared between pairs of individuals that contain the variant and counted the number of discordances ( the number of pairs of IBD2 segments in which the genotypes for the variant under investigation did not match ) . We then estimated the variant calling discordant rate ( the proportion of discordances ) for each class of variants as the total number of discordances divided by the total number of pairs of IBD2 segments in that category . Discordant rates increased with decreasing call rate , suggesting poorer quality of genotype calls for variants with more missing data . Thus , we determined call rate cut-offs for each variant class to maintain a less than 0 . 5% discordant rate . This resulted in a final set of variants that included all non-singletons ( i . e . , variants in which the rare allele occurred at least twice ) with rs numbers ( in dbSNP135 ) with call rates > 90% and novel variants ( no rs number in dbSNP135 ) with call rates > 99% ( i . e . , at most one missing call ) . Among singletons ( variants with one copy of the rare allele in the sequenced subjects ) , we retained novel insertions with call rates > 90% and all other variant types with call rates > 99% ( Table 1 , and Fig S2 in S1 Text ) . The allele frequency distribution and functional annotation of the final set of 7 , 008 , 666 variants in the 98 Hutterites with WGS are shown in Fig S2 in S1 Text . The quality of imputed genotypes was assessed by comparing them to data from a different whole-genome sequencing study in five parent-offspring trios who were among the 1 , 317 Hutterites in our study [25] . These 15 individuals were sequenced on the Illumina platform at a 10–17x coverage . High quality ( as determined by Illumina ) genotypes were extracted for all the SNVs imputed using PRIMAL and that passed QC . One of the 15 subjects was sequenced on both platforms and this allowed us to estimate the joint sequencing error rate . Discordance rates between the Illumina sequence-based and PRIMAL-imputed genotypes were calculated as the proportion of differences in genotypes in each of the remaining 14 individuals using these two methods . The algorithm described in Results is implemented in software , PRIMAL v1 , that is freely available for academic use from the website: https://github . com/orenlivne/ober
Our imputation algorithm consists of five main stages ( Fig . 1 , steps 4–8 ) . The first four require only the framework SNVs: ( i ) phasing; ( ii ) identifying IBD segments among all haplotype pairs; ( iii ) indexing IBD segments into a dictionary of IBD cliques; and ( iv ) assigning parental origin to haplotypes . In the fifth step , we phase the WGS-derived genotypes , and then perform fast pedigree-based imputation of all variants present in the WGS using the IBD clique dictionary . Our phasing method is similar to the long-range phasing algorithms described by Kong et al . [17] and Glodzik et al . [13] and to our earlier phasing algorithm for Hutterite genotype data [16] , but introduces two key improvements that boost its quality . First , we use a phased proband as a template to phase siblings in nuclear families as in Coop et al . [26] ( Supplementary Materials S1 Text ) , and second , we employ a Hidden Markov Model ( HMM ) similar to the IBDLD model [24] to identify IBD segments between a proband and his/her surrogate parents ( Fig S3 in S1 Text ) . The phasing workflow is outlined in Fig S3 and described in detail in S1 Text . Using this approach , only 0 . 5% of the framework genotypes remained unphased , 99 . 2% of the genotypes were correctly phased , and the remaining 0 . 3% of the framework genotypes were discordant . During the phasing step , IBD segments are identified , but only between the individual being phased and his/her surrogate parents . Therefore , we created a complete IBD dictionary by identifying IBD segments between each pair of the 2×1 , 415 = 2 , 830 haplotypes in the sample ( S1 Text ) . Computational complexity prevented us from using available software to estimate IBD segments in related individuals [27–29] . Our HMM is the haplotype analogue of the genotype HMM used for phasing , and is similar to the HBD-HMM developed previously [18] . However , only kinship coefficients are used instead of condensed identity coefficients . The complexity is quadratic in the number of samples , but the hidden constant is small because only two states ( IBD or not IBD ) are possible instead of the nine in the genotype HMM ( Table S1 and S1 Text ) . A total of 97 , 821 , 947 IBD segments were identified among the 1 , 415 Hutterites ( ~1 . 1 segment per haplotype pair on average , because there are 2830×2829/2 = 4 , 003 , 035 individual pairs and 22 chromosomes ) . To verify the overall quality of the detected IBD segments , our fraction of the genome covered by IBD segments was compared to the fraction calculated by IBDLD [24] . The methods were concordant ( correlation coefficient r = 0 . 96 with a slope of β = 1 . 01 ) and the length distribution followed an exponential distribution , in accordance with theory [30] . We organize IBD segments in an IBD segment index data structure , which consists of a set of IBD cliques at each SNV and allows a quick O ( 1 ) -time queries of whether a pair of haplotypes is IBD at a certain SNV . At each SNV , we build a weighted , undirected pairwise IBD graph G ( Fig . 2 ) whose nodes are the 2 , 830 haplotypes of the 1 , 415 Hutterites , an edge indicates the two haplotypes are IBD , and the edge weight is the HMM posterior probability of IBD ( S1 Text , Eq . ( 19c ) ) . Large weights are thus given to haplotype pairs that have a higher probability of being IBD . Because IBD is a transitive relation , G must be a union of disjoint cliques ( fully connected sub-graphs ) , one for each ancestral haplotype present in the population . In practice , G is a perturbation of a clique union due to very low HMM certainty near segment ends and genotyping errors , and we would like to recover a “reasonable'' set of cliques from it . Cluster editing methods ( see for example [31] ) find the minimum number of edges ( or total edge weight ) that need to be added or removed to transform G to a clique union . This is an NP-hard problem , and practical heuristic-based algorithms run in superlinear time in the number of edges . We chose a different heuristic inspired by the graph algebraic multigrid literature [32–34] that resulted in good imputation cross-validation accuracy and has linear complexity ( S1 Text ) . First , we calculate new edge weights called affinities that measure the connectedness or affinity between the graph neighborhoods of the nodes ( Fig . 2 ) . A large affinity means that the nodes share many common neighbors , i . e . , they are connected via many short paths . Next , we removed graph edges with weight < 0 . 85 or affinity < 0 . 9 . These thresholds were chosen to minimize imputation errors in a cross-validation of several framework SNVs representing the entire MAF spectrum . Finally , each of the resulting graph’s connected components is transformed to a clique by adding links between all nodes that are not yet connected ( Fig . 2 and Fig S4 in S1 Text ) . This method worked well for our data set , and these thresholds should be good default values for other data sets . However , threshold determination and a comparison with other clique-generation methods undoubtedly need to be further investigated in a future research . The use of cliques significantly speeds up imputation because all haplotypes in a clique are imputed simultaneously . In addition , cliques allow the derivation of the maximum call rate obtainable per SNV from imputation , which is the ratio of the number of haplotypes in cliques containing haplotypes of sequenced individuals to the total number of haplotypes . The predicted imputation rate was 85% ± 9% for the framework SNVs . Note that , using pedigree-based imputation , the accuracy approaches 100% ( because we rely on Mendelian rules ) . Genotyped individuals are considered “quasi-founders” if either of their parents were not genotyped . Haplotypes of non-quasi-founders can be automatically labeled as paternal and maternal because their parents are included in our sample and haplotypes are assigned using Mendelian rules . However , because the quasi-founders do not have genotyped parents , the parental origin of the quasi-founder haplotypes is assigned in two stages . First , during phasing , we do not determine which haplotypes are paternal and maternal , but we ensure that the first haplotype of every child comes from the same parent ( arbitrarily denoted A ) , and the second haplotype from the other parent ( arbitrarily denoted B ) . This is achieved using the following steps: a ) Regions of the children’s haplotypes are assigned to four different “bins” ( illustrated as four colors in Fig S5 in S1 Text ) that represent the four parental haplotypes . Regions that are IBD are in the same bin , under the constraint that the number of recombinations be minimized . b ) There are three possible assignments of four parental haplotypes to parents A and B . For each assignment , we calculate for each child C with haplotypes C1 , C2 a separation measure as follows: let F1 be the fraction of C1 covered by A’s haplotypes plus the fraction of C2 covered by B’s haplotypes , and F2 be the fraction of C1 covered by B’s haplotypes plus the fraction of C2 covered by A’s haplotypes . The separation is the ratio max ( F1 , F2 ) , which measures how decisively C’s haplotypes can be identified as paternal or maternal haplotypes . c ) We pick the parental assignment that maximizes the minimum child separation , and order C1 , C2 in all children so that the first always corresponds to parent A and the second to parent B . The separation measure is defined in S1 Text . Next , after parental origin is assigned to haplotypes within each nuclear family ( both parents and their children ) , we calculate a different separation measure at each SNV for each quasi-founder C . Let 1 and 2 denote the child’s haplotypes , C1 and C2 the corresponding IBD cliques , and A and B representing C’s untyped parents . For each parent and each clique , we calculate the median of the set of kinship coefficients between the parent and all quasi-founders in the clique that are not siblings of the proband ( the quasi-founder in question ) , resulting in a 2×2 matrix ( Fig . 3; siblings and non-quasi-founders are excluded to minimize bias ) . For each SNV , indexed by s , we define a separation measure m ( C , s ) ( precisely defined in the Supplementary S1 Text , Eq . ( 4 ) ) such that-1 ≤ m ( C , s ) ≤ 1 . The measure approaches-1 when the off-diagonal matrix elements are much larger than the diagonal elements , and approaches 1 when the diagonal elements dominate . If the proband is properly phased , m ( C , s ) must be consistently positive or negative across the chromosome . We consider only “informative variants” as those where |m ( C , s ) | > 0 . 25 is separated from 0 . Suppose there are n+ informative variants with m ( C , s ) > 0 and n- with m ( C , s ) < 0; the sample separation measure M ( C ) is defined as max ( n+ , n- ) / ( n+ + n- ) . That is , the fraction of variants exhibiting the “majority sign” . We assign parental origin when M ( C ) > 0 . 75 . Using this approach we were able to assign parental origin to 76% ( 313 out of 411 ) of the quasi-founders’ chromosomes , with 279 having M ( C ) > 0 . 99 ( Fig S6 in S1 Text ) . Including non-quasi-founders , we were able to assign parental origin to 93% of the sample . Once the IBD clique dictionary is constructed , imputation is performed separately and in parallel for each variant present in one or more of the 98 whole genome sequences . The main idea behind the approach is that each sequencing-based allele that is phased on a particular haplotype can be imputed to all the haplotypes in its IBD clique . First , homozygous genotypes are phased , and the alleles and indices of the two haplotypes are placed into a queue . We remove the first haplotype from the queue , and impute all haplotypes in its IBD clique with the same allele . If these include haplotypes of heterozygous genotypes in the 98 sequenced individuals , they can now be phased . For each such individual , we add its other haplotype index and allele to the end of the queue . The next entry in the queue is then similarly processed , except that , when there is conflicting allele information within a clique ( when a two-third majority vote does not exist ) , no haplotype is imputed . We process queue entries one by one until the queue becomes empty . Using this approach , we imputed 7M variants ( Table 2 , columns 4–5 ) in about 75 , 000 CPU on Beagle , a 150 teraflops , 18 , 000-core Cray XE6 supercomputer at the Computation Institute , a joint initiative between The University of Chicago and Argonne National Laboratory [35] . Finding and indexing IBD segments into cliques takes the majority of computing time in the PRIMAL pipeline . The dominant complexity term is O ( n2s ) , where n = 1415 is the number of genotyped individuals and s = 271 , 486 is the number of framework markers ( S1 Table in S1 Text , columns 2–3 ) . The overall genotype call rate was 76 . 2% . The mean individual call rate was 75 . 5%; 547 out of 1317 individuals ( 41% ) had call rate ≥ 80% . Call rates were higher in regions with higher framework SNV density , lower recombination rate and farther from the telomeres ( Fig S7a in S1 Text ) . Fig S8a in S1 Text shows that the MAF distributions of European ultra-rare SNVs ( MAF = 0 in the 1000 genomes CEU database ) are comparable in both the 98 sequenced Hutterite sample set and the 98 sequenced + 1 , 317 imputed Hutterites ( n = 1 , 415 ) . Furthermore , we compared the Alternative Allele Frequency ( AAF ) in the Hutterites and CEU sample set . The Hutterite and CEU AAF were highly correlated ( Fig S8b-d in S1 Text ) . Out of 6 , 715 , 275 variants that were not A/T or C/G SNVs , 5 , 299 , 330 had similar CEU and Hutterite AAFs ( absolute difference < 0 . 1 ) ; there were more variants with larger AAF in the Hutterites than in CEU compared to the opposite case ( 880 , 912 vs . 534 , 012 variants ) . To check the accuracy of PRIMAL imputed genotypes , their concordance with the framework genotypes was assessed . First , we phased the framework ( Affymetrix ) genotypes , identified IBD segments and indexed them into cliques . We then masked the framework genotypes of the 1 , 317 individuals whose genomes were not sequenced , imputed the framework genotypes , and calculated the concordance between the imputed and true genotypes over a sample of 53 , 861 framework SNVs ( sorted by base-pair position , every 5th framework SNV was picked instead of using all SNVs to save computing time ) . The concordance was close to a 100% regardless of MAF ( Fig S7c in S1 Text ) . In addition , we also tested for heterozygote concordance rate within the variants with MAF < 5% because the concordance over all genotypes would be high even if they were randomly imputed . The heterozygous concordance also approached 100% . We also calculated concordance rates between imputed genotypes based on the 98 Hutterites sequenced by Complete Genomics and genotype calls for 14 Hutterites who were sequenced on the Illumina platform as part of a separate study [25] . The concordance rate for each subject was larger than 99% ( the concordance rates ranged from 99 . 3% to 99 . 8% ) with an overall average of 99 . 7% . This overall rate is very similar to the rate of concordance obtained from the subject sequenced on both platforms . The use of cliques significantly speeds up imputation and also allowed us to determine that the maximum predicted imputation rate is 85% for the framework SNVs . However , while genotypes imputed by PRIMAL had high accuracy , the call rate ( 77% ) is lower than the maximum predicted rate , most likely due to imperfect phasing of variants without a consensus allele . To mitigate this problem , we filled in as many genotypes as possible for the remaining 23% of variants using LD-based imputation . We chose IMPUTE2 [11] because of its ease of use , high speed and high imputation accuracy . Importantly , we used the high quality pedigree-based phased haplotypes from the 98 whole genome sequenced individuals as the reference panel . This boosted the IMPUTE2 accuracy ( evidenced by the measures described below ) and reflects the accuracy of our phasing . To obtain data that are consistent in format and accuracy to those generated by PRIMAL , IMPUTE2 genotype probabilities were converted to hard genotype calls only if the maximum probability among the three possible genotypes was > 99%; otherwise , they were not called . When using this criterion , the concordance rates between IMPUTE2 genotypes and those based on sequencing in the 14 individuals range between 99 . 5 and 99 . 8% with an overall average of 99 . 7% ( identical to PRIMAL ) . As a QC check on this second round of imputation , we calculated overall as well as heterozygote concordance rates between PRIMAL and IMPUTE2 imputed SNVs . All genotypes called by both methods and called as heterozygous by at least one of them were included . IMPUTE2-imputed genotypes were retained only if the heterozygous concordance rate was ≥ 99% and the MAF ≥ 1% ( heterozygous concordance rate drops significantly for variants with MAF <1%—Fig S9 in S1 Text ) . Finally , the PRIMAL+IMPUTE2 combined method yielded an overall call rate of 87 . 3% with > 99% estimated accuracy ( Table 2 ) . We also used LD-based imputation to increase the parent of origin ( PO ) assignment for each allele . First , we created a data set with twice the number of samples ( 2N ) . For each subject , we created “paternal haploid” and “maternal haploid” sets . For unphased genotypes , the haploid entries were set to missing . We ran IMPUTE2 on the haploid data set . We then assigned parental origin to each genotype called by IMPUTE2 in the original data set only if both the PO of the paternal and maternal haplotypes were imputed with maximum probability > 99% and were compatible with the genotype . PRIMAL alone assigned PO to 80% of alleles , but with IMPUTE2 directly imputing from PO-assigned haplotypes , we increase the PO call rate to 83% .
Despite trends over the past nearly 20 years toward genetic association studies in large case-control samples [36] , there have been strong arguments for , and a recent re-appreciation of the advantages of family studies for understanding the genetic architecture of complex phenotypes [37–39] . For example , family-based studies are particularly well suited for discovery of rare disease-associated variants and revealing parent-of-origin effects while minimizing potential confounding due to population substructure and genetic and environmental heterogeneity . Moreover , the family structure itself allows more extensive quality control checks of genotype data and ultimately more accurate genotype calls . Now , in the era of whole exome and whole genome sequencing , studies in families and founder populations offer a new , powerful framework for mapping studies because the genome or exome sequences of relatively few ‘founders’ are needed to impute highly accurate whole genome genotypes to other members of the pedigree with only framework genotypes . We describe in this paper a fast phasing and computationally efficient imputation method ( PRIMAL ) that combines the advantages of pedigree-based and LD-based methods and obtains accurate genotypes ( >99% ) and high ( 87% ) call rates in 1 , 317 related Hutterites using whole genome sequencing data on only 98 related individuals , providing unprecedented coverage of genetic variation in a population sample with extensive phenotyping and demographic data . The call rates and , to a lesser degree the concordance rates , are correlated to the degree of relatedness between the imputed individuals and the sequenced subjects . Fig S16 in S1 Text illustrates these relationships , and suggest that the rates are mostly influenced by the few sequenced subjects who are most related to the imputed individual . Note that similar accuracy can be achieved using IMPUTE2 ( as detailed above ) , with a call rate of 84% when restricting to the high quality called genotypes . In addition , PRIMAL allows accurate parent-of-origin assignments for each allele as well as imputed genotypes of recent ancestors ( or other members of the pedigree ) with no DNA or available genotype information . This additional information is unique to this approach , and is crucial for many analyses , such as those looking for parent-of-origin effects in associated variants , and imprinting . PRIMAL can be applied to other founder populations or to large families to provide accurate and nearly complete genotype coverage for relatively very small cost and minimal computation time . The quantity and quality of the genotypes generated using PRIMAL will depend on several factors including the family structures , the extent of IBD sharing between the reference and target subjects , and the quality of framework genotypes that are used for inferring the IBD cliques . In addition to comprehensive surveys of the effects of all variants present in the Hutterite genomes on risk for common and Mendelian diseases and on disease-associated quantitative phenotypes , these data will facilitate association studies with the > 460 , 000 variants that are rare ( <1% ) in European populations but have risen to common ( >5% ) frequencies in the Hutterites and investigations of the effects of maternally-inherited versus paternally-inherited alleles on disease risks and quantitative trait values , and will allow the incorporation of the additional information from IBD sharing in more efficient genetic association studies . Such studies in the Hutterites and other founder populations should yield new insights into the genetic architecture of common diseases , gene expression traits , and clinically relevant biomarkers of disease , and ultimately provide outstanding opportunities for personalized medicine in these well-characterized populations . | The recent availability of whole genome and whole exome sequencing allows genetic studies of human diseases and traits at an unprecedented resolution , although their cost limits the size of the studied sample . To overcome this limitation and design cost-efficient studies , we developed a two step method: sequencing of relatively few members of a well-characterized founder population followed by pedigree-based whole genome imputation of many other individuals with genome-wide genotype data . We show that by sequencing only 98 Hutterites , we can impute 7 million variants in an additional 1 , 317 Hutterites with >99% accuracy and an average call rate of 87% . Furthermore , parental origin was assigned to 83% of the alleles . Such studies in the Hutterites and other founder populations should yield new insights into the genetic architecture of common diseases , gene expression traits , and clinically relevant biomarkers of disease , and ultimately provide outstanding opportunities for personalized medicine in these well-characterized populations . | [
"Abstract",
"Introduction",
"Materials",
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"Results",
"Discussion"
] | [] | 2015 | PRIMAL: Fast and Accurate Pedigree-based Imputation from Sequence Data in a Founder Population |
Cell proliferation has generally been considered dispensable for anteroposterior extension of embryonic axis during vertebrate gastrulation . Signal transducer and activator of transcription 3 ( Stat3 ) , a conserved controller of cell proliferation , survival and regeneration , is associated with human scoliosis , cancer and Hyper IgE Syndrome . Zebrafish Stat3 was proposed to govern convergence and extension gastrulation movements in part by promoting Wnt/Planar Cell Polarity ( PCP ) signaling , a conserved regulator of mediolaterally polarized cell behaviors . Here , using zebrafish stat3 null mutants and pharmacological tools , we demonstrate that cell proliferation contributes to anteroposterior embryonic axis extension . Zebrafish embryos lacking maternal and zygotic Stat3 expression exhibit normal convergence movements and planar cell polarity signaling , but transient axis elongation defect due to insufficient number of cells resulting largely from reduced cell proliferation and increased apoptosis . Pharmacologic inhibition of cell proliferation during gastrulation phenocopied axis elongation defects . Stat3 regulates cell proliferation and axis extension in part via upregulation of Cdc25a expression during oogenesis . Accordingly , restoring Cdc25a expression in stat3 mutants partially suppressed cell proliferation and gastrulation defects . During later development , stat3 mutant zebrafish exhibit stunted growth , scoliosis , excessive inflammation , and fail to thrive , affording a genetic tool to study Stat3 function in vertebrate development , regeneration , and disease .
Signal transducer and activator of transcription 3 ( STAT3 ) is an essential mediator of cytokine and growth factor signaling involved in animal development , homeostasis and disease [1 , 2] . Primarily a transcription factor , STAT3 activates or inhibits expression of downstream genes involved in cell proliferation , apoptosis , stem cell maintenance , differentiation , and migration in normal tissues . Non-transcriptional functions of STAT3 in microtubule , mitochondria , and chromatin regulation have also been reported [3 , 4] . In tumors , constitutively active STAT3 drives cell proliferation through upregulation of cell cycle-regulators such as c-Myc and Cyclin D , promotes pluripotency of cancer stem cells , and potentiates metastasis by modulating cytoskeleton and extracellular matrix [4 , 5] . Further underscoring its role in human disease , dominant autosomal STAT3 mutations account for numerous symptoms in Hyper-IgE syndrome ( HIES ) patients such as misregulated TNFα levels and scoliosis [6–8] . Disruption of murine Stat3 in hematopoietic cells causes Crohn’s disease-like immunodeficiency [9] . Stat3 also has essential functions in development . Firstly , STAT signaling regulates border cell migration in the developing Drosophila egg chamber [10] . Secondly , Stat3 knockout mice die by early gastrulation [11] , implying critical but yet undefined roles of Stat3 in embryogenesis . Thirdly , morpholino studies in zebrafish indicated a requirement for Stat3 in planar cell polarity ( PCP ) signaling and gastrulation movements [12 , 13] . Later during development , Stat3 promotes bone formation , as its deletion in mouse osteoclasts and osteoblasts decreased bone density and volume [14 , 15] . Dominant negative and morpholino-interference approaches also implicated zebrafish Stat3 in heart and eye regeneration [16 , 17] . Here , we report analyses of zebrafish stat3 mutants that lead us to propose a different model wherein Stat3 regulates embryonic axis extension by ensuring sufficient number of cells through its role as promoter of cell proliferation during blastula and gastrula stages and cell survival during gastrulation . The cell cycle during zebrafish embryogenesis is complex . Early embryos undergo rapid and synchronous cell cleavages [18] consisting of DNA synthesis ( S ) and mitosis ( M ) phases without transcription or cell growth . After mid-blastula transition ( MBT ) and activation of the zygotic genome , cell cycles slow down and become asynchronous with the acquisition of a G2 phase but little growth [19] . Conserved from fly to mammals , Cdc25a phosphatase is a positive regulator of cell cycle progression during embryogenesis [20–23] . Through activation of Cyclin B/Cdk1 complexes , Cdc25a synthesized from both maternal and zygotic transcripts propels mitotic entry [23] . But how Cdc25a is activated in early vertebrate embryos is unclear . MBT is followed by gastrulation , during which cells engage in large-scale migrations and rearrangements to establish future body plan . Convergence and extension ( C&E ) are conserved gastrulation movements that narrow the germ layers mediolaterally and lengthen them along the anteroposterior ( AP ) axis [24] . Under the influence of Wnt/PCP signaling , cells become mediolaterally elongated and either migrate dorsally ( convergence ) or engage in polarized intercalations that preferentially separate anterior and posterior neighbors to drive simultaneous mediolateral ( ML ) convergence and AP axis extension [24 , 25] . Disruption of Wnt/PCP signaling in zebrafish mutants such as silberblick ( slb ) /wnt11 and trilobite ( tri ) /vangl2 impairs ML cell elongation and polarized cell behaviors , consequently producing a shorter and wider embryonic body [26 , 27] . Interestingly , impaired cell elongation and ML alignment , and consequently defective C&E were also reported in stat3 morphants , implicating Stat3 as a regulator of PCP signaling during zebrafish C&E [12 , 13] . Cell proliferation and gastrulation movements must be coordinated to achieve proper embryogenesis . Indeed , rapid cell proliferation usually precedes gastrulation , during which cell divisions occur infrequently [28] . Gastrulating cells divide at the expense of migration by rounding up and abolishing their planar polarized asymmetries [29] , likely because cell division and motility utilize common cytoskeletal machineries . Limiting cell divisions is required for normal C&E of the paraxial mesoderm in Xenopus [28] and posterior body elongation in zebrafish [30] . Conversely , cell proliferation appears dispensable for axis elongation during gastrulation . Complete gastrulation featuring elongated bodies occurs in the zebrafish emi mutants in which mitosis ceases from early gastrulation , and in embryos where cell proliferation is chemically inhibited during gastrulation [31 , 32] . However , without quantitative assessment of C&E movements in these embryos , some contribution of cell proliferation to gastrulation cannot be excluded . In addition , the relationship between cell division orientation and axis elongation in zebrafish remains unresolved . While some studies pointed out that oriented cell division under the regulation of Wnt/PCP signaling is a driving force for axis elongation [33] , others argued against the importance of cell division orientation in axis extension [32] . Here , we report that Stat3 contributes to extension movements during zebrafish gastrulation by ensuring sufficient number of cells through its role as a promoter of cell proliferation and survival . Investigating null stat3 mutations generated with transcription activator-like effector nuclease ( TALEN ) method , we found that neither maternal nor zygotic stat3 functions are essential for the completion of embryogenesis . However , stat3 mutants die during juvenile stages exhibiting scoliosis and excessive inflammation , warranting evaluation as a model for diseases such as scoliosis , cancer , and inflammatory diseases ( e . g . HIES ) . Strikingly , rather than typical Wnt/PCP-based strong C&E defects , MZstat3 gastrulae manifested transient and mild extension defects in the axial and paraxial mesoderm . As the underlying cellular mechanism , we demonstrate that reduced cell number , due to decreased cell proliferation at blastula and gastrula stages and increased cell death during late gastrulation account for stat3 mutant extension defects . We further show that the evolutionarily conserved Stat3 function of promoting cell cycle progression through upregulation of cdc25a expression is required for axis elongation during gastrulation .
To extend functional studies of Stat3 in zebrafish , we generated mutations in the stat3 gene using TALEN method ( Fig 1A , see also Materials and methods ) . The stat3stl27 and stat3stl28 alleles contain 7- and 2-base pair deletions in Exon 5 , respectively , resulting in frameshift and premature stop codons , encoding proteins predicted to lack almost all the critical functional domains of the Stat3 protein ( Fig 1B ) . Surprisingly , neither zygotic stat3stl27/stl27 nor stat3stl28/stl28 mutant embryos showed overt gastrulation defects described in the previous morpholino studies [12] , and displayed normal morphology and notochord formation until 15 dpf ( Fig 2A and 2D ) . During later larval stages , stat3stl28/stl28 ( Fig 2A ) and stat3stl27/stl27 ( Fig 2B and 2F ) mutant larvae appeared significantly smaller compared to siblings and manifested spinal curvatures in all dimensions ( Fig 2E–2G ) . Scoliotic phenotypes could be discerned as early as 20 dpf ( Fig 2E and 2N ) . Using Alizarin Red staining and micro-computed tomographic ( micro-CT ) imaging , we further detected three major categories of structural abnormalities in the vertebrae that contributed to spinal curvatures in stat3 mutants ( S2 Fig , S1–S3 Movies ) . In the first group , vertebrae with a straight vertebral body and perpendicular end plates display either gentle or sharp turns ( S2B and S2C Fig ) . In the second group , abnormal vertebral morphology such as bent vertebral body and non-perpendicular end plates were observed in stat3 mutant larvae ( S2D and S2E Fig ) . Lastly , in two out of ten mutant larvae , we observed fractures and extra bony matrix ( S2F–S2H Fig ) . Mutant animals were fragile and lethargic , and progressively died by 1 . 5 to 2 months of age ( Fig 2C ) . Among progenies of stat3stl27/+ incross , mutants constituted 25% at 15 dpf; their proportion decreased over time , such that no mutants were detected at 45 dpf ( Fig 2C ) . Micro-CT analyses revealed reduced bone mineral density ( Fig 2I ) and a nearly 40% decrease in the total bone volume in stat3 juveniles ( Fig 2J ) . Consistent with the roles of Stat3 in immune responses [6 , 8] , using qRT-PCR we observed a significant upregulation of transcripts encoding pro-inflammatory factors Tnfα and Interleukin ( Il ) -6 in stat3stl27/stl27 larvae and juveniles at 35 dpf ( Fig 2K and 2L ) , subsequent to the manifestation of stunted growth and scoliotic phenotypes ( Fig 2A , 2B and 2E ) . Given that both stl27 and stl28 alleles are predicted to cause frame shifts and produce similarly truncated proteins ( Fig 1B ) , and that they resulted in similar growth defects , scoliotic phenotypes , and lethality ( Fig 2 ) , we conclude that they similarly disrupt stat3 gene function . We also characterized , an additional stat3sa15744 allele , generated by chemical mutagenesis and high throughput sequencing , which is predicted to introduce a stop codon at amino acid 73 ( Fig 1B ) [34] . Fish homozygous for this allele exhibited phenotypes similar to the TALEN created alleles , including runting , scoliosis , gaping jaw , and larval stage lethality starting at about 19 dpf ( Fig 2H ) . Given the similar phenotypes of the three TALEN stat3 mutant alleles , we focused most of our studies on the stat3stl27 allele . One hypothesis is that the growth defect observed in stat3 homozygous mutants is due to failure to compete for food with their heterozygous and wild-type ( WT ) siblings in the same tank as a result of scoliosis and hence reduced mobility . To test this , we grew progeny of stat3stl27/+ heterozygotes on rotifer diet to reduce food competition with WT siblings ( see Materials and methods ) . Larvae were fixed on 18 dpf , measured for body length , and genotyped . Homozygous zygotic stat3 mutant larvae appeared to be 9% shorter than their siblings at 18 dpf ( Fig 2B ) , indicating that growth retardation is a primary phenotype of stat3 mutants . Interestingly , only one out of four scoliotic larvae was among the shortest; and in other experiments we also observed very short larvae of all genotypes without body curvatures ( S2I Fig ) , arguing against a simple causal relationship between stunted growth and scoliosis phenotypes . Together these observations reveal that zygotic stat3 function is dispensable for embryonic and early larval development in zebrafish . At juvenile stages loss of stat3 function results in stunted growth , scoliosis , and inflammation that are not clearly causally related , and eventually death before sexual maturity . The lack of an overt embryogenesis defects in zygotic stat3 mutants generated by heterozygous parents was surprising given previous reports of strong gastrulation defects in stat3 morphants [12] and whole-mount in situ hybridization ( WISH ) studies that detected only zygotic expression of stat3 in zebrafish embryos [35] . However , using WISH and RT-PCR we detected high levels of maternal stat3 transcripts ( Fig 1C and 1D ) . WISH further showed that stat3 mRNA was expressed ubiquitously during blastula and gastrula stages and became enriched in the head and in neuromasts at 5 dpf ( Fig 1C ) . To test whether maternal Stat3 function contributes to gastrulation , we generated maternal zygotic ( MZ ) stat3stl27/stl27 mutants by germline transplantations [29] . For simplicity , hereafter we use MZstat3 , Zstat3 , and Mstat3 to refer to maternal zygotic ( MZstat3 , from incrosses of WT fish harboring stat3stl27/stl27 germline ) , zygotic ( Zstat3 , from stat3stl27/+ incrosses ) , and maternal ( Mstat3stl27/+ , from crosses of WT females harboring stat3stl27/stl27 germline with stat3stl27/+ or stat3+/+ males ) mutant , respectively , unless indicated otherwise . Unexpectedly , MZstat3 mutants progressed through embryogenesis and later exhibited phenotypes described above for Zstat3 mutants with no observable changes in the onset , severity or survival ( Fig 2C and 2M ) . Using qRT-PCR and four different primer pairs spanning regions at , upstream of , or downstream of the stat3stl27 deletion in all three stat3 transcript sequences annotated in the Zv9 zebrafish genome assembly , we observed significant reduction of stat3 transcripts in MZstat3 embryos ( Fig 1E , S1A–S1C Fig ) . In agreement , Western blotting with an antibody against the C-terminus of zebrafish Stat3 failed to detect Stat3 protein in MZstat3 gastrulae ( Fig 1F , S1D Fig , Materials and methods ) . Based on these results we conclude that stl27 is a strong/null allele . We first aimed to verify the scoliosis phenotype observed in zygotic stat3 mutant larvae by growing 39 WT and 210 MZstat3 embryos in separate tanks . Whereas over 90% of WT larvae were alive at various time points of interest , survival of MZstat3 fish declined sharply over time , with 66% mutants alive at 20 dpf and only 7% at 43 dpf ( Fig 2M ) . We also observed spinal curvatures in 100% of MZstat3 mutants from as early as 20 dpf , with majority of the larvae showing two , three , or four curves by 26 dpf ( Fig 2N ) . Together , these results further confirmed that stat3 function is essential for survival and spinal development and showed that the scoliosis phenotype and failure to thrive are completely penetrant . Previous studies that employed antisense morpholino oligonucleotides ( MO1-stat3 ) proposed a requirement of stat3 in Wnt/PCP signaling and ML cell elongation essential for C&E gastrulation movements [13] . However , MZstat3 gastrulae did not exhibit severe C&E defects ( Fig 3Aa and 3Ab ) and showed normal AP body length by 30 hpf ( Fig 3Ac and 3B ) . To detect any subtle morphogenetic defects , we visualized the nascent embryonic tissues that undergo dynamic C&E gastrulation movements using WISH [27 , 36] . Whereas we failed to detect any defects in convergence of paraxial mesoderm assessed by the ML dimension of the paraxial protocadherin ( papc ) -expression domain ( Fig 3C and 3D ) , we noticed 13 . 2% and 14 . 2% reduction compared to WT in the AP extension of the notochord marked by expression of no tail ( ntl ) in Mstat3 and MZstat3 mutants , respectively ( Fig 3E and 3F ) . Phenotypic differences between morphants and mutants have been described for many zebrafish genes [37] . One possible mechanism involves genetic compensation , in which other genes alter their expression in the presence of a mutation , resulting in mild or no phenotypes [38] . To test whether such genetic compensation could explain the milder phenotypes of our MZstat3 mutants compared to stat3 morphants [12] , we injected MO1-stat3 into both WT and MZstat3 one-celled embryos . If the lack of phenotype in MZstat3 embryos were due to genetic compensation , then they should show no additional C&E phenotypes when injected with morpholinos or even milder phenotype than WT zygotes injected with the same morpholino dose [38] . While injection of MO1-stat3 into WT embryos resulted in dose-dependent body length reduction ( S3A Fig ) , MZstat3 mutants showed comparable severe C&E phenotypes compared to WT gastrulae upon injection of the same MO1-stat3 dose ( Fig 3E and 3F ) . These results support the notion that the discrepancy between stat3 morpholino- and mutation-induced phenotypes is likely due to off-target effects of MO1-stat3 rather than to genetic compensation ( see Discussion ) [38] . During zebrafish gastrulation , ML intercalation of cells is the main driving force for C&E of the axial mesoderm tissue [39] . Hence , reduced AP length of axial mesoderm tissue in MZstat3 gastrulae ( Fig 3E and 3F ) could result from defective ML intercalation of cells . Against this notion , the notochord in WT , Mstat3 and MZstat3 mutant gastrulae at 1-somite stage ( Fig 4A ) showed comparable ML dimension ( Fig 4B ) , an equivalent number of cells across the notochord in WT versus mutants ( Fig 4C ) . Moreover , by mid-segmentation stage notochord in both Mstat3 and MZstat3 mutants converged to a single-cell column as observed in WT ( S4 Fig ) . We next asked whether ML cell elongation , the hallmark of Wnt/PCP signaling [25] , was affected in stat3-deficient gastrulae . Our analyses revealed that at 1-somite stage the notochord cells in Mstat3 and MZstat3 mutants had a reduced length-to-width ratio ( LWR ) ( 2 . 0±0 . 0 ) compared to WT cells with LWR of 2 . 5±0 . 0 ( Fig 4A , 4D and 4F–4H ) . However , contrasting a typical Wnt/PCP defect [27] , the MZstat3 mutant notochord cells aligned normally their long axes with the ML embryonic axis ( Fig 4E ) . Moreover , we noted that Mstat3 and MZstat3 mutant cells were 11 . 6% and 17 . 3% larger compared to their WT counterparts , respectively ( Fig 4I ) . These results argue against Stat3 being a key regulator of Wnt/PCP signaling during zebrafish gastrulation , but also suggest a role of Stat3 in cell shape and size ( see Discussion ) . To query further whether Stat3 plays any role in Wnt/PCP signaling , we asked whether phenotypes of mutations disrupting Wnt/PCP components such as trilobite ( tri ) /vangl2 [27] or silberblick ( slb ) /wnt11 [26] could be exacerbated by simultaneous reduction of Stat3 function . A spectrum of eye separation phenotypes from partial to complete fusion of the eyes ( Fig 4J ) , often associated with C&E defects , are commonly seen in both tri and slb embryos and are exacerbated in compound PCP mutants [36 , 40] . However , we found that Zstat3;Ztrivu67/vu67 and Zstat3;MZslbtz216/tz216 double mutants exhibited similar penetrance and expressivity of the eye separation defect compared to their single mutant siblings ( Fig 4K and 4L ) . Together , our data provide genetic evidence for an essential role of Stat3 in normal AP axis extension during zebrafish gastrulation . Moreover , Stat3 regulates cell size and shape during gastrulation without significantly affecting Wnt/PCP signaling . In the above morphometric analyses , the enlarged cell size in MZ/Mstat3 mutant gastrulae stood out ( Fig 4I ) . Consistent with reports of Stat3 promoting cell proliferation in many biological contexts [5] , we detected 31 . 2% and 33 . 7% reduction in mitosis at early gastrulation ( 6 hpf ) in Mstat3 and MZstat3 mutants compared to WT , respectively , as revealed by phosphorylated Histone H3 ( pH3 ) immunostaining ( Fig 5A and 5B ) . Total number of cells , determined by counting DAPI-stained nuclei , was also decreased by 12 . 5% and 14 . 1% in Mstat3 and MZstat3 mutants ( Fig 5C ) , indicating comparable cell proliferation defects in these mutants at early gastrulation . By late gastrulation , however , Mstat3 gastrulae exhibited similar level of cell proliferation to that seen in WT ( Fig 5D ) , whereas their total cell number continued to be reduced ( Fig 5E ) , suggesting only a partial rescue of the cell proliferation defect by zygotic stat3 expression . By contrast , both proliferation rate and total cell number in MZstat3 embryos remained low throughout gastrulation ( Fig 5A , 5D and 5E ) . Together , these data indicate that Stat3 regulates cell proliferation during zebrafish embryogenesis . Moreover , one zygotic WT allele is not sufficient to compensate the cell number deficit caused by reduced cell proliferation in maternal stat3 mutants , revealing a crucial role of maternal Stat3 . To investigate the maternal Stat3 function in cell proliferation , we analyzed the rapid cleavages prior to MBT that depend exclusively on maternally deposited proteins and RNAs [41] , via in vivo confocal time-lapse imaging of embryos with Histone2B-RFP ( H2B-RFP ) -labeled nuclei ( Materials and methods ) . Whereas WT blastomeres divided every 15 . 8 ~ 16 . 6 min from Cycle 5 ( from 16 to 32 cells ) to Cycle 9 ( from 256 to 512 cells ) ( Fig 5F , S5 Fig , S4 Movie ) consistent with previous reports [18] , pre-MBT cycles ranged from 17 . 6 min to 19 . 2 min in embryos lacking maternal stat3 function ( Fig 5F , S5 Movie ) , a nearly 13% increase . Cumulatively , Mstat3 mutants took significantly longer to complete five pre-MBT cell cycles compared to progeny of WT and heterozygous females ( Fig 5F and 5G ) . Interestingly , embryos obtained from heterozygous females exhibited normal length of the early cleavage cycles ( Fig 5F ) , indicating that one WT stat3 allele in the female germline is sufficient to ensure in the progeny normal embryonic cleavages before the initiation of zygotic transcription . We next asked whether Stat3 is required for post-MBT cell divisions . Our manual lineage tracing of individual blastomeres for the duration of five post-MBT divisions ( Fig 6A–6C , Materials and methods ) revealed that cell cycles gradually lengthened from MBT onward in both WT and MZstat3 embryos , consistent with previous observations [19] . Furthermore , cycles 10 through 13 were significantly longer in MZstat3 than in WT embryos ( Fig 6D ) , demonstrating Stat3 was required for post-MBT cell cycle progression . Together , these results establish a key role of Stat3 in promoting cell proliferation throughout early embryogenesis . Alignment of cell division with the AP embryonic axis during early gastrulation has been shown to be regulated by Wnt/PCP pathway , although it remains unresolved whether polarized cell division contributes to axis extension [32 , 33] . Oriented cell division during late gastrulation and early segmentation is essential for morphogenesis such as neural rod midline formation [32 , 33] . To test Stat3’s role in cell division orientation , we analyzed time-lapse movies of WT and MZstat3 embryos at late gastrulation and early segmentation stages ( 1–3 somite stage ) . Cell divisions of dorsal neuroectodermal cells were mediolaterally aligned in both WT and mutant gastrulae ( Fig 6E–6G ) . Together these results indicate a requirement for both maternal and zygotic Stat3 function in promoting cell divisions before and after MBT , but argue against a significant role of Stat3 in cell division orientation during early zebrafish embryogenesis . Stat3 suppresses apoptosis in various biological contexts through transcriptional activation of anti-apoptotic genes [5] . During zebrafish embryogenesis , apoptosis can be detected from late gastrulation/early segmentation stages [42] . Terminal deoxynucleotidyl transferase mediated dUTP Nick End Labeling ( TUNEL ) assay did not detect apoptotic cells in WT or MZstat3 embryos at midgastrulation ( 60% epiboly or 6 . 5 hpf and 80% epiboly or 8 . 3hpf ) , but revealed a nearly 70% increase in the number of apoptotic cells in MZstat3 embryos compared to WT at both late gastrulation ( 10 hpf; S6A–S6C Fig ) and early somitogenesis ( 11hpf; S6D–S6F Fig ) . Using qRT-PCR , we further detected substantial downregulation of birc5a , the zebrafish homolog of known anti-apoptotic target of Stat3 , Survivin [43] , in MZstat3 embryos ( S6G Fig ) , whereas expression of bcl2a was not significantly altered ( S6H Fig ) . Together , these data indicate that Stat3 suppresses apoptosis in zebrafish embryos likely through activating anti-apoptotic genes such as birc5a/survivin . Moreover , elevated apoptosis may further decrease the number of cells in MZstat3 mutants at late gastrulation ( Fig 5E ) and thus contribute to the axis extension defect ( Figs 3E and 7 ) . During ML cell intercalation in vertebrate gastrulae cells move medially or laterally to separate anterior and posterior neighbors and align one after another anteroposteriorly , simultaneously producing AP extension and ML convergence [24] . Confocal imaging at 1 somite stage revealed normal width and 2–4 rows of cells in the notochords of both MZstat3 and WT gastrulae ( Fig 4A–4C ) . At the 3-somite stage , we observed two rows of ML elongated axial mesodermal cells in the notochords of both MZstat3 and WT gastrulae ( Fig 7A–7C ) , arguing against defective ML intercalation . Rather , we reasoned that a shorter notochord in MZstat3 gastrulae results from fewer cells that participate in ML intercalation , which drives the anteroposterior extension of dorsal mesoderm , due to decreased cell proliferation ( Fig 5A–5E ) and increased apoptosis ( S6 Fig ) . To test this , we analyzed the dimensions and numbers of cells in one optical section of the notochord and adjacent first three somites in 3-somite stage embryos ( see Materials and methods ) . Within each somite , flanking the notochord are the adaxial cells that later give rise to slow muscles [44] . We found that somites were 8 . 9% and 14 . 7% shorter in AP dimension in Mstat3 and MZstat3 mutants compared to WT , respectively ( Fig 7E ) . Correlated with the AP extension defect , somites exhibited 11 . 4% ( Mstat3 ) and 16 . 1% ( MZstat3 ) fewer adaxial cells compared to WT somites , which contained 4 . 9 ± 0 . 1 adaxial cells . Likewise , the adjacent notochord tissue contained 12 . 2% ( Mstat3 ) and 21 . 7% ( MZstat3 ) fewer cells than in WT embryos , in which the corresponding notochord fragment featured 8 . 8 ± 0 . 2 cells ( Fig 7F and 7G ) . These results support the model whereby reduction of cell number in Mstat3 and MZstat3 mutants contributes to morphogenetic defects in extension of axial and presomitic mesoderm ( Fig 7H–7J ) , despite enlarged cell size ( Fig 4I ) . To test this model further , we asked whether chemical inhibition of cell proliferation in WT gastrulae could phenocopy the MZstat3 axis extension defect . Inhibiting proliferation in WT embryos with 150 μM aphidicolin and 20 mM hydroxyurea from early shield stage ( 5 . 7 hpf , Fig 8A–8D ) [31] , resulted in 22% reduction of total cell number by late gastrulation ( 10 hpf , Fig 8C–8E ) , similar to that in MZstat3 mutants ( Fig 5E ) . Moreover , compared to DMSO-treated controls , ntl-expressing notochord was 10% shorter in drug-treated embryos at 1-somite stage ( Fig 8F–8H ) . At 3-somite stage , somites were 20% shorter in AP dimension and had 27 . 5% fewer adaxial cells ( Fig 8I–8L ) , with the corresponding notochord fragment possessing 34 . 3% fewer cells ( Fig 8M ) . Together , these results indicate that drug inhibition of cell proliferation phenocopied both proliferation and morphogenetic defects caused by loss of stat3 function , supporting the model whereby reduced cell number in MZstat3 embryos due to proliferation defect could account for impaired extension . Hence , cell proliferation is required for normal axis extension during zebrafish gastrulation . We next asked if the altered cell shape observed in MZstat3 gastrulae was due to increased cell size ( Fig 4F–4I ) . Similarly to MZstat3 mutants , drug treatment increased notochord cell size ( Fig 8P , 8Q and 8V ) with only slight change in their ML alignment ( Fig 8R ) . However , the enlarged cells in drug-treated embryos featured greater long and short axes , and their cell elongation was only slightly reduced ( LWR = 2 . 3 ) compared to that of cells from DMSO-treated controls ( LWR = 2 . 6 , Fig 8S , 8T and 8U ) . This contrasted the cell shape defect of MZstat3 mutants , where enlarged cells showed diminished long but increased short axes , and therefore strongly reduced LWR ( 2 . 0±0 . 0 , Fig 4F–4H ) . These results argue against the cell size increase alone causing the cell shape defect in MZstat3 gastrulae . We next asked whether restoring Stat3 expression could rescue proliferation and/or axis extension phenotypes in stat3-deficient embryos by injecting into 1-celled zygotes synthetic RNA encoding zebrafish Stat3 with FLAG tag at the C-terminus ( Stat3-F ) . However , injection of neither 10 pg nor 25 pg stat3-F RNA altered the lengths of the cell cycles preceding initiation of zygotic transcription in MZstat3 mutants ( Fig 5H–5K ) . For post-MBT cell divisions , we mosaically overexpressed Stat3-F in MZstat3 embryos labeled ubiquitously with H2B-RFP ( Fig 6A–6C; Materials and methods ) . Notably , in MZstat3 cells overexpressing Stat3-F , Cycles 11~13 were shorter compared to those in uninjected mutant cells , but still longer than WT cycles ( Fig 6D ) , indicating a partial rescue of post-MBT cell division defect . Interestingly , we did not observe significant changes of cell cycle length in WT cells overexpressing Stat3-F ( Fig 6D ) . We also verified in WT and MZstat3 embryos that post-MBT cell cycle lengths were not altered in cells injected with RNA encoding fluorescent proteins ( S7A and S7B Fig ) . Although injection at 1-cell stage of 25 pg stat3-F RNA did not significantly rescue reduced axis extension in MZstat3 mutants as assayed by ntl WISH at 1-somite stage ( Fig 3E and 3F ) , it partially rescued the somite AP extension and notochord cell number phenotypes and completely normalized adaxial cell number at 3-somite stage ( Fig 7D–7G and 7K ) . Hence , restoring Stat3 expression in MZstat3 mutants could partially rescue phenotypes caused by defects in post-MBT processes such as post-MBT cell divisions; but failed to rescue the deficits caused by pre-MBT defects . These observations corroborate the critical function of maternally contributed Stat3 , and imply that the role of Stat3 in cell proliferation during zebrafish embryogenesis is transcription-dependent . We next wished to define the molecular mechanism through which Stat3 regulates cell proliferation . Stat3 is known to regulate transcription of many cell cycle regulators [5] . Accordingly , qRT-PCR revealed significant downregulation of cdc25a RNA in MZstat3 mutants during cleavage and gastrula stages ( Fig 9A and 9B ) . In addition , expression of genes encoding Cyclins , such as ccna2 , ccnb1 , and ccnb2 , was slightly but not statistically significantly reduced with exception of cyclinD1 ( S8A Fig ) . Cdc25a has a conserved role in promoting mitotic entry in early animal development [19–21] . Hence , we asked if restoring cdc25a expression could suppress cell cycle and axis extension defects in MZstat3 mutants . Accordingly , we observed shortening of pre-MBT cycles ( Cycle 7~9 ) in mutant embryos injected with 50 pg cdc25a RNA ( but not with 25 pg cdc25 RNA ) , with cells dividing every 17 . 7~17 . 9 min compared to 18 . 4~18 . 8 min in control MZstat3 embryos ( Fig 9C ) . Further , injection of either 25 pg or 50 pg cdc25a RNA fully suppressed post-MBT cell cycle phenotype ( Fig 9D ) , and partially suppressed notochord extension defect in MZstat3 gastrulae ( Fig 9E and 9F ) . Notably , injection of 50 pg cdc25a RNA also resulted in excess notochord extension in WT gastrulae ( Fig 9E and 9F ) . Injection of 25 to 50 pg cdc25a RNA significantly reduced size and width of the notochord cells in 1-somite stage WT gastrulae without affecting cell body orientation ( S9A–S9H Fig ) . Although not statistically significant , there appeared to be more notochord cells lined up between adjacent somitic furrows in cdc25a-overexpressing WT embryos at 3-somite stage ( S9M Fig ) , opposite to what we observed in MZstat3 or cell division inhibitor-treated embryos ( Figs 4 and 8 ) . Based on these results we propose that Stat3 regulates cell proliferation in zebrafish embryogenesis in part by regulating cdc25a expression , and that Stat3/Cdc25a-dependent cell proliferation promotes axis extension during gastrulation .
The frame-shift mutations we generated in the zebrafish stat3 gene predicted to encode truncated proteins lacking all functional domains are likely strong/null mutations as evidenced by significant reduction of stat3 transcripts and undetectable level of Stat3 protein in the MZstat3stl27/stl27 mutants ( Fig 1C , 1E and 1F ) . In contrast to previous findings [35] , we show that stat3 is also maternally expressed . Nevertheless , both zygotic and MZ stat3 mutants complete embryogenesis ( Fig 1C ) , indicating that stat3 is not essential for embryonic development in zebrafish . Contrasting the stat3 morpholino studies [12] , MZstat3 gastrulae exhibited normal convergence and only mild and transient axis extension defects largely due to reduced cell proliferation and increased apoptosis . Post-MBT cell proliferation and axis extension defects could be partially rescued by Stat3 overexpression , which further supports that the phenotypes caused by stl27 and stl28 alleles are specific to loss of stat3 function ( Figs 5 and 6 ) . The elevated apoptosis at late gastrulation and early segmentation ( S6 Fig ) exacerbates cell deficit in MZstat3 mutants and likely contributes to the axis extension defect . However , cell death is unlikely a major contributor to the axis extension defect in MZstat3 embryos , as C&E of the axial mesodermal tissue starts as early as 7 . 3 hpf [39] , but apoptosis only becomes detectable from 10 hpf in both WT and MZstat3 embryos ( S6 Fig ) . Therefore , apoptosis is less likely to have a significant impact on axis extension at the time of our analyses ( 1-somite stage , or 10 . 3 hpf ) . In addition , there are nearly ca . 100 and 50 fewer cells undergoing mitosis as revealed by pH3 staining at 6 and 10 hpf ( Fig 5B and 5D ) , respectively , compared to only ca . 30 more cells undergoing apoptosis in MZstat3 mutant embryos at 10 hpf ( S6C Fig ) . Together , whereas increased cell death likely contributes to the axial extension defect in MZstat3 gastrulae , we consider reduced cell proliferation as the primary cellular basis for the axis extension defect in M and MZstat3 mutants . Neither cell proliferation defect nor apoptosis was reported for stat3 morphant gastrulae . Injection of 10 pg stat3 morpholino as reported in Yamashita et al [12] or 5 pg morpholino as tested in our study , led to significant developmental delay , increased apoptosis , and later necrosis in the head region at 1 dpf; with morphants dying between 1 and 5 dpf [12] . The discrepancy between the reported stat3 morphant and stat3 mutant phenotypes described here resonates with recent reports of poor correlation between morpholino-induced and mutant phenotypes in zebrafish [37] . There could be several causes for this phenotypic discrepancy between stat3 morphant and mutant phenotypes . Firstly , different strategies may result in different degrees of functional stat3 inactivation . Whereas both MO1-stat3 [12] and mutations were able to deplete Stat3 protein during gastrulation ( Fig 1 , S1 Fig ) , only inactivation of stat3 in the female germline ( by transplanting stat3 mutant germline into WT blastulae ) but not injections of MO1-stat3 into fertilized zygotes , can inactivate stat3 function during oogenesis . An essential function of Stat3 in the pre-MBT cleavages is underscored by the observation that injections of RNA encoding Stat3-F into MZstat3 one-celled zygote failed to normalize pre-MBT cell divisions ( Fig 5H–5K ) , while being able to partially rescue the post-MBT cell division defects ( Fig 6A–6D ) . We propose that Stat3 promotes the transcription of cdc25a during oogenesis to ensure normal early cleavages before MBT . This proposed mechanism can explain why the cell proliferation defect was clear in MZstat3 mutants ( Fig 5 ) but was not reported in the morphants [12] , as translation-blocking MO1-stat3 injected after fertilization could not interfere with the function of stat3 during oogenesis . Secondly , a recent study reported that phenotypic differences between zebrafish morpholino knockdown and mutants could be explained by genetic compensation induced by deleterious mutations with transcriptome being fine-tuned for adaptation [38] . For example , in the egfl case , where such genetic compensation was observed in the mutants , egfl mutants appeared less sensitive than WT to egfl morpholino injections due to upregulation of downstream genes in the mutants compensating for loss of the egfl function . However , the fact that MZstat3 mutant embryos were not less sensitive than WT to MO1-stat3 injection argues against such genetic compensation accounting for the mild gastrulation phenotypes observed in MZstat3 mutants ( Fig 3E and 3F ) [38] . Therefore , the phenotypic discrepancy between stat3 morphants and mutants is likely due to off-target effects of MO1-stat3 . Further , the zebrafish stat3 mutants described here afford a reliable tool to verify and investigate other proposed functions of Stat3 , such as in retina regeneration [17] . We have established a requirement of Stat3 , particularly maternal Stat3 , in both pre- and post-MBT cell proliferation during zebrafish embryogenesis . In the absence of both maternal and zygotic Stat3 functions , cell cycles are longer ( Figs 5 and 6 ) . Moreover , stat3 mutants exhibited severe growth defects from late larval stage ( Fig 2A and 2B ) , suggesting a continuous requirement of Stat3 for cell proliferation throughout zebrafish development . Several observations argue that the cell proliferation defects and reduced axis extension in MZstat3 mutants are not associated with or caused by developmental delay . First , a number of key morphogenetic processes occurred in MZstat3 mutants contemporaneously with such events in WT embryos . For example , the dorsal embryonic shield formed on time and epiboly progression was not delayed in MZstat3 embryos despite elongated cell cycles ( Fig 3A ) . Convergence movements of lateral mesodermal cells , which are initiated at midgastrulation and narrow mesoderm mediolaterally , occurred normally in MZstat3 mutants ( Fig 3C and 3D ) . In addition , segmentation is considered a key staging index in zebrafish and other vertebrates , and MZstat3 mutants exhibited the same number of somites as time-matched WT embryos ( Figs 3Ab and 7A–7C ) . Second , tissue-specific gene expression occurred at equivalent times in MZstat3 mutants compared to time-matched WT embryos . For example , zygotic gene expression of bozozok/dharma at 4 hpf and floatinghead at 6 hpf occurred on time in MZstat3 embryos ( S10 Fig ) [45] . Further the mediolateral dimension of the floating head expression domain , which is shaped by dynamic anterior migration and convergence movements was not significantly different than in control WT embryos . In addition , MZstat3 mutants at 10 . 3 hpf exhibited equivalent to WT expression of papc presomitic and dlx3b ectodermal marker genes ( Fig 3C ) . All these lines of evidence indicate that the axial extension defects of MZstat3 mutant gastrulae do not reflect developmental delay , but rather a specific morphogenetic defect . Therefore , our studies support the notion that when the cell cycle length and consequently cell division number are uncoupled from the normal developmental schedule , this leads to morphogenetic defects like axis shortening . Such morphogenetic defects , even if transient , can impact inductive interactions between tissues . For example , we previously reported that C&E movements regulate the number of adaxial cells , slow muscle fiber precursors that are specified during gastrulation , by determining the size of the interface between the inductive axial and target presomitic tissues [46] . Given that MZstat3 mutants exhibit smaller number of larger adaxial cells during segmentation ( Fig 7C and 7F ) , it will be interesting to investigate development of slow muscle fibers in these mutants . Cell cycle control is a conserved role of Stat3 in animal development and cancer [5] . Our data support a model whereby Stat3 promotes cell divisions during zebrafish embryogenesis in part through transcriptional activation of Cdc25a , as in MZstat3 embryos cdc25a transcripts were significantly downregulated ( Fig 9A and 9B ) . Moreover , ectopic cdc25a RNA suppressed both pre-MBT and post-MBT cell cycle phenotypes ( Fig 9C and 9D ) while providing ectopic stat3 RNA from 1-cell stage rescued only post-MBT but not pre-MBT cell cycle defect in MZstat3 mutants ( Figs 5 and 6 ) . A key regulator of G1-S and G2-M transitions , CDC25a is overexpressed in human cancers driving abnormal cell proliferation downstream of multiple signaling pathways including STAT3 [47] . In HepG2 carcinoma cells , for example , STAT3 binds directly to CDC25a promoter and activates its expression [48] . Cdc25a is also a conserved regulator of cell divisions during embryogenesis from Drosophila to Xenopus , where pre-MBT mitotic entry is propelled by Cdc25a synthesized from maternal RNAs through activation of Cyclin B/Cdk1 complexes [20–23] . Cdc25a activity is continuously required after MBT , as cells are arrested in G2 in the Drosophila cdc25/string mutant [49] and zebrafish cdc25a/standstill mutant [50] . Whereas it was unclear how cdc25a expression is activated in these early embryos , our studies point to Stat3 as a regulator of cdc25a during zebrafish development , paralleling this role in cancer [48] . Furthermore , Stat3/Cdc25a pathway may be conserved in mammalian embryogenesis . First , Stat3 and Cdc25a knockout mice both die by early gastrulation; when cultured , both Stat3-/- and Cdc25a-/- mouse blastocysts exhibit growth defects [11 , 51] . Second , STAT3 mutant homozygotes have never been reported in human , while spontaneous dominant-negative STAT3 mutations have been linked to autosomal dominant HIES [6] , suggesting that STAT3 inactivation causes embryonic lethality in humans . Hence , the Stat3/Cdc25a pathway may serve as a universal mechanism regulating cell proliferation during animal embryogenesis . However , our results imply other players downstream of Stat3 are involved . First , we detected downregulation of other cell cycle-regulating genes in MZstat3 embryos including ccnd1 encoding Cyclin D1 ( S8A Fig ) . Second , Stat3 overexpression failed to normalize cdc25a transcript level in whole MZstat3 gastrulae ( S8B Fig ) . Given the tissue-specific requirement of Stat3 we observed ( Fig 7 ) , Stat3-dependent cdc25a activation may only occur within certain tissues and would be difficult to detect in the context of a whole embryo . Cell proliferation has been generally considered dispensable or even prohibitive for gastrulation movements and morphogenesis . For example , cell shape changes and ventral furrow formation in Drosophila require the inhibition of ventral cell proliferation through String/Cdc25 inhibitors Tribbles and Frühstart [52] . In Xenopus , increased cell proliferation induced by inhibition of Wee2 , a Cdk negative regulator , impaired C&E in the paraxial mesoderm [28] . Conversely , zebrafish gastrulae still achieved relatively normal AP axis extension when mitosis was chemically inhibited [32] , although morphometric analyses have not been carried out . Indeed , a mathematical modeling of zebrafish gastrulation indicated that directed cell migration and polarized cell intercalation , the motile cell behaviors mediated by Wnt/PCP pathway , are largely sufficient to account for the morphogenesis of paraxial mesoderm given that cell divisions are very infrequent in the course of this process , although a minor role of cell proliferation could not be excluded [53] . We present evidence in support of a small but significant contribution of cell proliferation to zebrafish gastrulation by showing that cell proliferation during blastula and/or gastrula stages promotes and is required for AP extension of both the axial and paraxial mesoderm . The most compelling corroboration of our MZstat3 mutant analyses comes from pharmacological experiments where we inhibited mitosis in WT zebrafish embryos during gastrulation with hydroxyurea and aphidicolin [32] . Drug treatment during gastrulation recapitulated both proliferation and morphogenetic defects seen in MZstat3 gastrulae , as manifested by a shorter AP axis , as well as reduced AP dimensions of both axial and paraxial mesoderm cells , albeit larger in size , along the AP axis in these tissues ( Fig 8 ) . Moreover , cell intercalation seemed normal in MZstat3 embryos as evidenced by normal notochord width and the number of cells across the notochord at early segmentation ( Fig 4B and 4C ) , as well as a single-cell column notochord formed subsequently in both WT and stat3 mutant ( S4 Fig ) . Therefore , we conclude that Stat3-mediated cell proliferation during blastula and gastrula stages promotes extension during zebrafish gastrulation , most likely by providing sufficient building blocks necessary for the ML intercalation-based AP extension ( Fig 10A ) . Consistent with this model , loss of cdc25a function in the zebrafish standstill mutant led to a bent and shorter body at 1 dpf [50] . We observed that ectopic cdc25a expression partially suppressed the extension phenotype in MZstat3 mutants and produced excess extension in WT gastrulae ( Fig 9E and 9F ) . However , although trending , notochord AP dimension and number of notochord cells along AP axis were not significantly increased in WT embryos injected with cdc25a RNA . We attribute this to large variation between injections , suggesting that the axis elongation is sensitive to the dose of ectopic cdc25a . Whereas overexpression of Stat3 in MZstat3 could only rescue somite AP dimension and notochord cell number defects to Mstat3 level ( the zygotic portion ) , reduction in adaxial cell number was fully normalized ( Fig 7 ) , suggesting a tissue-specific requirement of Stat3-dependent cell proliferation during morphogenesis . The significance and novelty of the role cell proliferation plays during vertebrate gastrulation is further underscored by a recent publication providing evidence that cell division coupled with intercalations powers morphogenesis of chick epiblast before primitive streak formation [54] . Our studies in zebrafish demonstrate a key role of cell proliferation in producing sufficient number of cells needed for cell intercalations of mesenchymal cells that drive axial extension ( Fig 10A ) . We gathered several lines of evidence arguing against Stat3 regulating C&E by promoting Wnt/PCP signaling and ML cell orientation [13] . First , the enlarged MZstat3 notochord cells , although rounder , exhibited normal ML orientation ( Fig 4A and 4D–4H ) . Second , MZstat3 mutants displayed normal convergence of axial and paraxial tissues ( Figs 4B , 4C , 3C and 3D ) . Third , we failed to detect any enhancement of cyclopia or axis extension phenotypes when zygotic stat3 function was inactivated in Wnt/PCP pathway components mutants ( Fig 4K and 4L ) , with a caveat that maternal stat3 function was not removed in these experiments . In addition , cell division orientation of neuroectodermal cells shown to be regulated by Wnt/PCP signaling appeared normal in MZstat3 gastrulae ( Fig 6E–6G ) . However , our morphometric analyses implicate Stat3 in regulation of cell shape as MZstat3 notochord cells were rounder compared to WT with a bigger AP and a shorter ML dimension ( Fig 4G and 4H , Fig 10B ) . One possibility is that the slightly reduced LWR is due to increased cell size . However , our observations support an alternative model where Stat3 plays a more direct role of Stat3 in cell shape regulation , as the enlarged cells resulting from the chemical inhibition of cell division increased in both AP and ML cell dimensions compared to WT cells ( Fig 8J and 8K , Fig 10B ) . Indeed , in mouse keratinocytes and fibroblasts cytoplasmic Stat3 regulates microtubule and actin cytoskeleton through its interaction with Stathmin , a microtubule-destabilizing protein , and small Rho-GTPases , respectively [3 , 55] . Given that inhibition of other regulators of Rho such as Rho kinase [56] and Gα12/13 heterotrimeric G proteins [57] impairs cell elongation during C&E , it will be interesting to investigate whether Stat3 utilizes similar mechanisms to shape gastrulating zebrafish cells . We describe the first vertebrate stat3 mutant being capable of surviving beyond embryonic stages , opening new avenues for functional studies of Stat3 in later developmental processes and disease . Before they perished as juveniles , stat3 mutants exhibited scoliosis and excessive inflammation ( Fig 2 and S2 Fig ) . Work from our and other laboratories linked early notochord malformations at embryonic and larval stages with scoliosis in juveniles and adults [58 , 59] . However , stat3 mutants showed normal notochord morphology during embryogenesis ( Figs 4A , 3C and S4 Fig ) . Moreover , Alizarin Red staining at 15 dpf failed to reveal any structural abnormalities in stat3 mutants in the notochord or differentiating vertebrae ( between the swim bladder and the cloaca ) ( Fig 2D ) , indicating that the scoliosis phenotype in stat3 mutant fish is likely not of congenital but of idiopathic type . As a key regulator of immune responses , abnormal Stat3 activity has been associated with immunodeficiency such as HIES in human [6] and Crohn’s disease-like conditions in mouse Stat3 CKO [9] . With a global disruption of the stat3 gene , our stat3 mutant zebrafish warrants further characterization as a new candidate tool for studies of Stat3-related diseases in human . In summary , we generated and characterized a valuable vertebrate stat3 genetic model for further studies of development and disease . Our work provides direct evidence that cell proliferation promotes zebrafish axis extension , and clarifies the role of Stat3 in zebrafish C&E gastrulation movements as proliferation regulator , in part through Cdc25a activation . Further studies will verify whether cell cycle regulation function of Stat3 is conserved in larval and juvenile stages , and address the mechanisms underlying scoliosis and other phenotypes associated with stat3 zebrafish mutations .
Zebrafish are housed and handled under protocols approved by the Washington University Animal Studies Committee . AB* or AB*/Tubingen WT , trivu67 , stat3sa15744 , and slbtz216 mutant zebrafish ( Danio rerio ) lines were used . Fish were normally fed with rotifers during larval stages followed by a mixture of rotifers and artemia during juvenile stages and adulthood . Some fish ( as indicated in text ) were fed exclusively with rotifers at all times to diminish food competition . Embryos were collected from natural matings , maintained in 28 . 5°C , and staged according to [18] . A TALEN pair was designed to target the boundary of Intron 4 and Exon 5 of the zebrafish stat3 gene . The targeting sequences for the TALEN arms were 5’- TAACCTCTTACTCATCCTCCA -3’ and 5’-AAGAGGTTGTAGAAGTAGA-3’ , respectively . An NlaIII restriction site within the 15-base pair long spacer between the two TALEN arms was used for assaying disruption of this sequence in genomic DNA ( Fig 1A ) . TALEN constructs were assembled using the Golden Gate method [60] and used to generate indels in stat3 target sequences as described [61] . Two alleles , stl27 and stl28 , containing a 7-base pair and a 2-base pair deletion in Exon 5 , respectively , were originally confirmed by sequencing and identified using PCR-based genotyping ( forward primer 5’-AGCTATTGCTTGGGTATAACCTCTTACTC-3’ , reverse primer 5’-GCAGTCATACCTCCAGCACTC-3’ , followed by NlaIII digestion ) . However , this genotyping method is not recommended to identify stat3 mutation carriers as biased amplification of the mutant DNA possibly due to allelic competition during PCR may confound genotyping . Instead , we used allele-specific PCR amplification to identify stl27 heterozygous and homozygous fish ( shared forward primer 5’-CCACCTGTGACCATATGACTGAA-3’ , WT allele reverse primer 5’-CTCCAACATCTTCATCTTCTGCTCCA-3’ , stl27 allele reverse primer 5’-CTCCAACATCTTCATCTTCTGTCCTG-3’ ) . stl27 and stl28 alleles are predicted to encode truncated proteins of 158 and 168 amino acids , respectively . Full-length coding sequence of zebrafish stat3 was subcloned from the previously published stat3 construct [35] into pCS2 plasmid and FLAG-tagged at the C-terminus . Full length coding sequence of zebrafish cdc25a was subcloned from cdc25 Tol2 construct [30] into pCS2 plasmid . Capped RNAs were synthesized using mMessage mMachine kit ( Ambion ) , and injected at 1- or 8-cell stage with doses specified in Results . 5 ng of stat3 morpholino ( MO1-stat3 , http://zfin . org/action/marker/view/ZDB-MRPHLNO-051004-1 ) was injected at 1-cell stage as previously described [12] . Embryos were fixed in 4% paraformaldehyde ( PFA ) in PBS . In situ hybridization was carried out as described [62] . Images were acquired and morphometric measurements were carried out manually with Fiji software . Immunostaining was performed using a standard protocol . The following antibodies were used: anti-phospho-Histone H3 antibody ( 1:3 , 000 , rabbit , Upstate , 06–570 ) , and Alexa Fluor 488 or 568 goat anti-rabbit ( 1:500 , Invitrogen ) . Embryos were counterstained with 4' , 6-diamidino-2-phenylindole ( DAPI , 0 . 1 μg/mL , Invitrogen ) , mounted in 0 . 75% low melting temperature agarose ( Lonza ) in 0 . 3% Danieau’s solution ( LMTD agarose ) , and imaged with the Quorum spinning disk confocal microscope ( SDCM ) using a 10x objective lens ( 10x ) . A Z-stack of over 200 μm was acquired at a step size of 3 μm and projected in Fiji . The number of nuclei was quantified using Analyze Particles plugin in Fiji . Five to six embryos ( 6 hpf ) were deyolked and homogenized in a modified RIPA buffer [16] . Proteins were resolved in 4–12% NuPage Bis-Tris gels ( Invitrogen ) and transferred to PVDF membrane blocked with 10% milk in Phosphate Buffered Saline with Tween ( PBS-T ) . Primary antibodies used were: anti-Stat3 ( 1:250 , AnaSpec , 55861 ) , anti-FLAG ( 1:1 , 000 , Sigma-Aldrich , F1804 ) , and anti-β-actin ( 1:1 , 000 , Sigma-Aldrich , A5441 ) . Secondary antibodies used were: donkey anti-mouse HRP ( 1:5 , 000 , Fisher Scientific , SA1100 ) and goat anti-rabbit horseradish peroxidase ( HRP ) ( 1:5 , 000 , Fisher Scientific , PR-W4011 ) . Signals were detected with an enhanced chemiluminescence ( ECL ) kit ( Perkin Elmer ) and imaged using film . Total RNA was isolated from 30–50 embryos with Trizol ( Ambion ) and treated with DNase ( Zymo Research ) . For larvae and juveniles , the whole animals were subjected to snap freezing in liquid nitrogen and homogenized using a mortar and pestle . cDNA was synthesized using iScript kit ( Bio-Rad ) . qRT-PCR was performed using CFX Connect Real-Time system and SYBR green ( Bio-Rad ) , with at least three independent biological samples for each experiment . Primers are listed in S1 Table ) . WT embryos were dechorionated in 0 . 3x Danieau solution and incubated with 20 mM hydroxyurea ( Sigma-Aldrich ) and 150 μM aphidicolin ( Sigma-Aldrich ) in 4% dimethyl sulfoxide ( DMSO , Sigma-Aldrich ) from 5 . 7 hpf until desired stages [31] . Incubation in 4% DMSO was used as control . Embryos were injected with 200 pg membraneEGFP ( mEGFP ) RNA at 1-cell stage , mounted in 0 . 5% LMTD agarose at desired stages and imaged on Quorum SDCM with a 40x objective ( N . A . 0 . 75 ) . For cell body alignment and shape , image stacks were acquired and the top layer of the notochord cells were analyzed in Fiji [27] . To measure the AP dimension of the somite , five lines parallel to the notochord were drawn randomly in Fiji between two adjacent somitic furrows . For pre-MBT cell divisions , zygotes were injected within 20 minutes post-fertilization ( mpf ) with 70 pg H2B-RFP RNA and mounted in 0 . 3% LMTD agarose at 4–8 cell stage . Time-lapse movies were taken at 28 . 5°C with Quorum SDCM using a 10x objective lens . A z stack covering 200 μm at a 3–4 μm step distance was acquired every 1–2 min for at least 4 hours . Cell divisions were manually tracked in Fiji by quantifying the length from telophase to telophase . As H2B-RFP signal became clearly visible only from 8–16 cell stage , Cycles 5 ( 16 cells to 32 cells ) to 9 ( 256 cells to 512 cells ) were recorded and analyzed . Post-MBT cell division experimental design was adapted with modifications from Dalle Nogare et al . [19] . To minimize inter-individual and experimental variability , we performed a combination of global and mosaic labeling , which allowed us to compare experiment and control lineages within the same embryo . At 1 cell stage , embryos were injected with H2B-RFP RNA . At 8-cell stage , one blastomere was injected with 18 . 8 pg ( a dose equivalent to 150 pg at 1-cell stage ) membraneCherry ( mCherry ) as control . An adjacent blastomere was injected with 18 . 8 pg mEGFP with or without 3 . 1 pg ( a dose equivalent to 25 pg at 1-cell stage ) stat3-FLAG ( stat3-F ) or cdc25a RNA . Time-lapse movies were recorded separately for mEGFP- ( Fig 5B , see also S6 Movie ) or mCherry- ( Fig 5C , see also S7 Movie ) clones of each embryo with Quorum SDCM and a 40x objective at 2-minute interval for the duration of 5–6 post-MBT cycles . At each time point , a z stack spanning 100 μm was acquired at a step size of 4 μm . Movies were converted to hyperstacks in Fiji . Cell divisions were manually tracked using MtrackJ plugin in Fiji . The orientation of cell division of dorsal neuroectodermal cells was determined as previously described [63] . Juvenile fish were fixed in 4% PFA , bleached in 3% hydrogen peroxide/1% KOH , and stained with 1 mg/mL Alizarin Red in 1% KOH overnight for whole-mount bone staining . Soft tissues were cleared with 1% trypsin in 2% borax for up to a week . Larval vertebrae were stained in vivo by Alizarin Red ( 1 mg/mL ) for 2 hours before imaging live on Quorum SDCM . Microcomputed tomography ( Scanco uCT40 ) was used for 3D reconstruction and analyses of bone parameters ( threshold set as ~150 ) of the juvenile vertebrae . WISH and immunostaining quantification , and morphometric analysis were performed blindly , followed by genotyping for the stat3stl27 allele . Data were collected in Excel ( Microsoft ) , analyzed and graphed with GraphPad Prism ( GraphPad Software ) . Student’s t test was applied to determine statistical significance ( p<0 . 05 ) between two datasets . Kolmogorov-Smirnov test was used to compare angle distributions . All results are shown as Mean ± Standard Error of the Mean ( SEM ) . | During vertebrate embryogenesis , cell proliferation , fate specification and cell movements are key processes that transform a fertilized egg into an embryo with head , trunk and tail . Cell proliferation is orchestrated by maternal and zygotic functions of conserved regulators including Cdc25a , and has generally been considered dispensable for embryonic axis elongation . Stat3 transcriptional factor , a known promoter of cell proliferation , is associated with human scoliosis , inflammation and cancer . Based on morpholino-mediated downregulation of Stat3 during zebrafish embryogenesis , Stat3 was previously proposed to regulate convergence and extension cell movements that narrow the embryonic body and elongate it from head to tail partially through planar cell polarity signaling and unknown transcriptional targets . Here , we report that zebrafish mutants lacking maternal and zygotic Stat3 expression exhibit normal convergence movements and planar cell polarity signaling , but transient axis elongation defect due to insufficient number of cells resulting largely from reduced cell proliferation and increased cell death . Accordingly , pharmacologic inhibition of cell proliferation also hinders axis elongation . Further experiments indicate that Stat3 promotes head- to -tail axis elongation by stimulating cell proliferation in part via upregulation of Cdc25a expression during oogenesis . During later development , zebrafish stat3 mutants exhibit scoliosis and inflammation , potentially affording a new tool to study related human diseases . | [
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... | 2017 | Stat3/Cdc25a-dependent cell proliferation promotes embryonic axis extension during zebrafish gastrulation |
Genetic variants altering cis-regulation of normal gene expression ( cis-eQTLs ) have been extensively mapped in human cells and tissues , but the extent by which controlled , environmental perturbation influences cis-eQTLs is unclear . We carried out large-scale induction experiments using primary human bone cells derived from unrelated donors of Swedish origin treated with 18 different stimuli ( 7 treatments and 2 controls , each assessed at 2 time points ) . The treatments with the largest impact on the transcriptome , verified on two independent expression arrays , included BMP-2 ( t = 2h ) , dexamethasone ( DEX ) ( t = 24h ) , and PGE2 ( t = 24h ) . Using these treatments and control , we performed expression profiling for 18 , 144 RefSeq transcripts on biological replicates of the complete study cohort of 113 individuals ( ntotal = 782 ) and combined it with genome-wide SNP-genotyping data in order to map treatment-specific cis-eQTLs ( defined as SNPs located within the gene ±250 kb ) . We found that 93% of cis-eQTLs at 1% FDR were observed in at least one additional treatment , and in fact , on average , only 1 . 4% of the cis-eQTLs were considered as treatment-specific at high confidence . The relative invariability of cis-regulation following perturbation was reiterated independently by genome-wide allelic expression tests where only a small proportion of variance could be attributed to treatment . Treatment-specific cis-regulatory effects were , however , 2- to 6-fold more abundant among differently expressed genes upon treatment . We further followed-up and validated the DEX–specific cis-regulation of the MYO6 and TNC loci and found top cis-regulatory variants located 180 kb and 250 kb upstream of the transcription start sites , respectively . Our results suggest that , as opposed to tissue-specificity of cis-eQTLs , the interactions between cellular environment and cis-variants are relatively rare ( ∼1 . 5% ) , but that detection of such specific interactions can be achieved by a combination of functional genomic approaches as described here .
The genetic contribution to population variation in cis-regulation of gene expression has been well studied in expression QTL ( eQTL ) studies where genome-wide expression profiles in cells or tissues of interest are statistically linked to sequence variants , known as “expression SNPs” or eSNPs . Following the pioneering of mapping eQTLs using Epstein-Barr virus transformed lymphoblastoid cell lines ( LCLs ) [1] such as those analyzed in the HapMap project [2] , [3] , various primary cells [4]–[8] and complex tissues [5] , [8]–[10] have been used for the identification of cis-regulatory variants . These eQTL studies have all been able to show a strong effect of common cis-variants on gene expression as compared to trans-effects that are more difficult to detect due to smaller effect sizes [7] . More recently , eQTL studies have included multiple cell types from the same study population where results have pointed towards a substantial proportion of cis-effects being reproducible across different cell types [11] . However , although a large proportion of the cis-effects seem to be cell-type invariant , others may act in a cell-type specific manner [4] , [11] . Environmental factors influence gene expression , and undoubtedly interaction between sequence variants and environmental stimuli represents a critical step to cellular development and disease pathogenesis . Modeling gene-environment interactions in a clinical setting is challenging , but eQTL mapping may represent an excellent model for identifying these important interactions that impact phenotype . Smirnov et al [12] studied inter-individual differences in expression in response to radiation in LCLs and found that most regulators influencing radiation-induced gene expression act in trans with very few cis-regulatory effects . Similarly , in a recent report studying the effect of pro-inflammatory oxidized phospholipids on global gene expression in human primary endothelial cell lines , the majority of the regulated transcripts were shown to be influenced by trans-acting loci [13] . In an attempt to elucidate the impact of non-genetic , experimental factors such as growth conditions on eQTL mapping in cultured cells , we previously studied multiple primary cell lines derived independently from the same individual , and assessed the replicability of cis- versus trans-associations using these biological replicates . We found that cis-eQTLs are highly reproducible across biological replicates , as compared to trans-eQTLs that showed much lower than expected overlap across replicates [7] . Clearly , larger sample sizes are needed in order to find SNPs with true trans regulatory effects on gene expression . To explore the impact of environmental perturbation on cis-regulation ( defined here as variants located within the gene or in a ±250kb window flanking the gene ) of human gene expression , we performed large-scale induction experiments using human primary osteoblasts ( HOb ) from a population panel described previously [7] , [14] . Environmental stimuli included growth factors [15] , [16] , cytokines [17] , [18] and hormones [19]–[21] , all with previous known effects on the osteoblast transcriptome . We verified that the response upon treatment was robust before proceeding with cis-eQTL analysis . In addition to standard eQTL mapping , we applied an alternative approach with improved sensitivity for mapping cis-regulatory variants based on the measurement of allele-specific expression ( AE ) that directly demonstrates that a variant acts in cis [11] , [22] , [23] . We employed the Illumina HumanOmni1-Quad BeadChip for global assessment of AE - an approach based on the quantitative assessment of allele ratios in expressed heterozygous SNPs in RNA samples , which are then normalized to corresponding genomic DNA heterozygote ratios . An outline of the study is presented in Figure 1 . Using these approaches we find that the cis-regulatory landscape within a cell is very stable , with only a small proportion of the identified cis-eQTLs being specific to environmental stimuli .
Human osteoblast-like cells ( HOb ) derived from 113 unrelated Swedish donors , each with independently derived primary cell lines ( n = 3 ) , were cultured under 18 different conditions: seven different treatments ( BMP-2 , dexamethasone ( DEX ) , IGF1 , PTH , PGE2 , TNFα , 1 . 25VitD3 ) and two controls , each at two time points ( 2h and 24h , respectively ) . To identify those conditions that most clearly influenced global gene expression , we first performed a pilot study , assessing the response of each treatment on gene expression upon treatment in three biological replicates for one individual using Affymetrix GeneChip U133+2 arrays . In general , all treatments demonstrated significantly more genes that were up- or down-regulated after 24h exposure than at the earlier time point ( FDR adjusted P value<0 . 05 and Fold change >2 ) ( Figure 2A and Figure S1A ) . Based on both magnitude and biological relevance of regulated genes , BMP-2 , DEX and PGE2 were chosen for the subsequent global analysis of the effect of environmental perturbation on cis-regulation . DEX and BMP-2 treatment regulate the expression of several immediate-early ( 2h exposure ) and late ( 24h exposure ) genes and pathways related to bone cell function as previously described [14] . For instance , upon stimulation with BMP-2 for 2h , a large number of negative regulators are up-regulated including the inhibitory Smads ( SMAD6 and SMAD7 ) and the BMP inhibitor , Noggin . In DEX-treated samples , the IGF1 signaling pathway is one of the top canonical signaling pathways down-regulated following stimulation . We further analyzed the genes regulated by PGE2 and found them significantly associated with skeletal development and function including growth ( P = 3×10−4 , Fisher's exact test ) , differentiation ( P = 3×10−4 , Fisher's exact test ) and formation of bone cells ( P = 9×10−4 , Fisher's exact test ) . To verify that the observed responses upon treatment were robust across expression platforms , we profiled one additional individual using Illumina HumanRef8 v2 BeadChips and again found that stimulation by DEX , BMP-2 and PGE2 resulted in the most striking gene expression changes ( Figure 2B ) with modest to weak effects of the remaining treatments ( Figure S1B ) . The proportions of differentially expressed genes ( 1-pi0 ) among all tested genes for the three treatments are presented in Table S1 . In addition , complete lists of response genes upon treatment are shown in Tables S2 , S3 , S4 . We have previously validated response genes upon DEX and BMP-2 treatment [14] by real-time RT-PCR , and upon PGE2 stimulation in this study , and in all cases the direction of effect was the same ( Figure S2 ) . We then obtained whole genome expression profiles from the cultured primary cells that were untreated ( t = 24h , N = 94 ) and treated with DEX ( t = 24h , N = 107 ) , BMP-2 ( t = 2h , N = 101 ) , PGE2 ( t = 24h , N = 100 ) , each with at least two biological replicates , using Illumina HumanRef8 v2 arrays . For each individual , we averaged the normalized expression scores across biological replicates to obtain a single measure for each of the 18 , 144 genes included on the array . We further studied whether responses upon DEX , BMP-2 and PGE2 treatments seen in a single sample as described above using Affymetrix GeneChips accurately represented a general effect in the study population . Approximately 13 , 000 RefSeq transcripts overlapped the two studies and the expression response for each treatment from the different studies was used in a correlation analysis ( median log2 fold change was used as an estimate of the general effect in the study population ) . We found strong correlations ( r = 0 . 5–0 . 6 ) of expression changes by individual genes from the different studies ( Figure S3 ) indicating that the selected treatments robustly impact the expression profiles in the study population . The samples included here have previously been genotyped using the Illumina Hap550K arrays and were included in our recent work of cis-eQTL analysis focused on the untreated control samples from the same study population [7] . All probes overlapping a SNP ( dbSNP 126 ) were removed as previously described resulting in ∼17 , 000 probes included in the eQTL analyses . The conditioned expression traits were used as dependent variables in linear regression models and adjusted for sex and year of birth . Our analysis focused on cis-regulatory variants defined as SNPs ( N = 388 , 946 ) located in a 250kb window flanking the target transcript [7] . Summary statistics from the cis-eQTL analyses are presented in Table 1 and in Figure S4 using all samples ( n = 94–101 ) and in Table S5 separating the biological replicates in identical samples across treatments ( n = 80 ) . Characteristics of all significant , independent cis-variants ( 5% FDR ) including distance to transcription start site ( TSS ) and SNP category ( i . e . intronic , 3′UTR , coding etc ) are presented in Table S6 and Figure S5 . At all three significance levels ( Bonferroni P<3 . 5×10−8 , 1% FDR , and 5% FDR ) , we identified on average ∼40% more cis-eQTLs in induced samples as compared to the untreated controls . However , when we compared top ranked cis-eQTLs between treatments we found a high overlap of significant associations across treatments ( Figure 3 ) . Despite clear global effects of each treatment to expression profiles , genes under genetic cis-regulatory component demonstrate very similar dependence on local variants . For example , 93% of cis-eQTLs identified in DEX treated samples at 1% FDR ( P<2 . 5×10−5 ) are observed in at least one other condition at a slightly less stringent P-value threshold of P = 5×10−4 . The overlap changes only slightly when considering independent signals ( 87% versus 93% ) . This degree of overlap across treatment groups is similar to the proportion of shared cis-eQTLs between two biological replicates , as previously described [7] . Similarly , when we restrict analysis to significant cis-eQTLs ( 1% FDR ) in DEX-treated samples , we note strong enrichments of low P-values in the eQTL analysis for both of the other treatments ( BMP-2 , PGE2 ) and in the untreated control samples , confirming that the vast majority of observed eQTL associations are observed regardless of experimental condition ( Figure S6 ) . We then asked whether the shared cis-eQTLs across treatments are for genes that significantly respond to each treatment or if they are expressed , but not differentially expressed . We classified the genes as responders ( significant >1 . 5-fold difference in expression upon induction ) and non-responders based on the pilot data ( Table S2 , S3 , S4 ) but found no difference in the proportion of overlapped cis-eQTLs between the two lists of genes . We defined high-confidence environmental-specific cis-eQTLs as being 1 ) significant in one treatment at 1% FDR or 5% FDR , respectively , and 2 ) non-significant ( P>0 . 05 ) in all three remaining conditions . As expected based on the results from the initial analysis described above , treatment-specific cis-eQTLs were found to be rare . At 1% FDR , we identified on average 1 . 4% treatment-specific cis-eQTLs , representing only a slight excess to the expected false discovery rate ( Table 2 ) . When restricting to independent cis-eQTLs only , the treatment specificity at 1% FDR was increased to ∼2 . 5% ( all treatment-specific cis-eQTLs at 5% FDR are presented in Table S7 , S8 , S9 ) . The identified high-confidence cis-eQTLs at 5% FDR corresponded to 72–143 genes per treatment group ( Table 2 ) where we found evidence of a ∼2-fold enrichment of treatment-specific expression pattern for the DEX-specific genes harboring an eQTL , than expected by chance ( binomial P<0 . 05 ) . No such enrichment was seen for the BMP-2 or PGE2 specific associations . We further analyzed the genes harboring treatment-specific cis-eQTLs for enrichment in biological processes or pathways using the Ingenuity Pathway Analysis software and the results are presented in Table S10 . Interestingly , the top mapped canonical pathway for DEX-specific genes is MIF ( macrophage migration inhibitory factor ) -mediated glucocorticoid regulation ( P = 7 . 43E-04 , Fisher's exact test ) . We also analyzed the data sets jointly by fitting a linear mixed model where the treatment*SNP interaction term was included and we found similar proportions as above; i . e . at 5% FDR we found a total of 853 cis-eQTLs interacting with any treatment compared to 932 at similar FDR using the approach described above ( Table S11 ) . We then sought to validate our top high-confidence DEX-specific cis-eQTLs using low-throughput sequencing-based AE assessment [24] , [25] in samples heterozygous for 1 ) the top cis-eSNP and 2 ) an intragenic exonic marker either included on the Illumina Hap550K array or imputed from the HapMapII panel . We selected the top five ( MYO6 , CDSN , ZNF480 , LSM16 , TMBIM1 ) DEX-specific genes ( Table 3 ) where the expression levels were detectable by RT-PCR . CA2 was excluded due to absence of an exonic marker , and PLEKHA6 and USP10 were excluded due to undetected expression in BMP-2 and PGE2 treated samples , respectively . Of the five selected genes , one failed in sequencing reaction ( CDSN ) . Of the remaining four genes we were able to successfully validate treatment specific cis-regulation of the MYO6 locus ( Figure 4 and Table S12 ) . Specifically , DEX-treated samples heterozygous for the top eSNP from the cis-eQTL analysis ( rs646967; DEX eQTL P = 8 . 8×10−10 , BMP-2 eQTL P = 0 . 8 , PGE2 eQTL P = 0 . 06; Figure 4A ) showed allele-specific expression revealed by differences in RNA ( cDNA ) allele ratios at the marker heterozygous site . The normalized allele ratio within each DEX-treated sample deviated greater than 2SD from the corresponding BMP-2 , PGE2 , and genomic DNA heterozygote ratio , respectively , and we found an overall significant difference in mean |Δ het ratio| between treatments ( rs12606; |Δ het ratio|DEX = 0 . 52±0 . 26 and |Δ het ratio|BMP-2+PGE2 = 0 . 04±0 . 02; P = 2 . 7×10−4 , t-test; Figure 4B ) . We fine-mapped the candidate region upstream of the MYO6 gene by including imputed , untyped HapMapII SNPs and found the rs584677 SNP , located ∼160kb upstream of the TSS , to have the strongest effect on MYO6 expression ( Figure 4C ) . To directly investigate the impact of environmental perturbation to allelic cis-regulation in primary cells , we carried out an independent AE assessment in a subset of five randomly selected samples included in the eQTL study . This was achieved by applying a direct method for measuring allele-specific cis-regulation in a genome-wide manner as we described recently [26] . The genome-wide AE test was carried out on Illumina HumanOmni1-Quad genotyping chips by interrogating differences in RNA ( cDNA ) allele ratios at heterozygous sites across primary transcripts normalized to genomic DNA allele ratios from the same individual . All four cell-culture conditions ( DEX , BMP-2 , PGE2 and untreated control ) were employed in the experiment with the DEX treatment carried out in duplicate , resulting in a total of 25 samples from five individuals included in the analysis . We verified that these newly cultured cells responded to each treatment as expected ( Table S13 , see Methods ) . A duplicate DEX and PGE2 sample failed for one individual leaving a total of 23 AE profiles for analysis . The cDNA data at each heterozygous site were subjected to expression ( signal intensity ) and allele resolution filters [26] , respectively , along with a novel method for normalization of signal intensity induced biases in allele ratio estimates ( see Methods ) . Initially , we looked for convergence of the AE data with our cis-eQTL data . Using five individuals we did not have power to independently map cis-rSNPs [26] , therefore we focused on samples heterozygous for significant treatment-specific cis-eSNPs ( defined as being significant in one treatment at 5% FDR and non-significant , P>0 . 05 , in all three remaining conditions ) . In total , 162/485 genes ( Table S14 ) with treatment-specific cis-regulation from the eQTL study were informative for AE test , i . e . at least one out of five samples being heterozygous for a cis-eSNP and with the region being robustly expressed . We then used windows of three consecutive heterozygous SNPs ( at least three of four informative SNPs above signal threshold in each sample ) to detect differential allelic expression , defined as an average |Δ het ratio|>0 . 05 ( corresponding to 1 . 2-fold difference between alleles ) among SNPs showing empirical probability of P<0 . 05 ( see Methods ) . Of the 162 tested regions , evidence of a cis-regulatory effect was independently confirmed for 73 ( 45% ) genes ( Table S14 ) . However , of these , we were able to confirm treatment-specific effects for only a minority ( 5 of 73 loci , 7% ) . For the remaining 68 loci ( 93% ) , allele-specific expression was observable across treatments ( Table S14 ) . The five validated treatment-specific cis-eSNPs were shown to have a greater effect on induced gene expression indicated by differences in fold change ( AA versus BB ) than the 68 treatment-independent loci ( Figure S7 ) . Next , we used our AE data set to assess the global impact of treatment in allelic cis-regulation by measuring AE differences within ( for duplicate measurements used in the case of DEX ) or between treatments in one individual or across samples . The genome-wide profiles of treatment induced allelic cis-variation measured the difference of average allele-ratios [mean ( |Δ het ratio|test−|Δ het ratio|ref ) ] using windows of three consecutive heterozygous SNPs in robustly expressed RefSeq regions ( at least three of four informative SNPs above signal threshold in both samples ) . Each treatment ( “test” ) was correlated to the same DEX ( “reference” ) sample and only windows expressed in all treatments were used for within individual comparisons . Treatment explained ∼10–15% of variance of AE within an individual ( r = 0 . 87–0 . 89 DEX versus DEX , r = 0 . 77–0 . 79 DEX versus other treatment ) ( Figure 5A ) . In contrast , the variance of allelic expression among individuals was considerably higher ( average r = 0 . 4 ) , which eliminated any observable difference within ( Figure 5B ) or between treatments ( Figure 5C ) . Overall , these results reiterate the relative stability of allelic cis-regulation upon environmental perturbation within a cell type as observed in our eQTL survey as well as heritability of AE phenotypes observed earlier by us [26] and others [11] . Finally , we explored the possibility of identifying loci accounting for the 10–15% higher variance in cis-regulation observed upon perturbation by observing outliers in treatment comparisons within samples . We used three SNP windows where all measurement points consistently showed either lower or higher deviation from equal expression in reference ( DEX ) versus test samples ( BMP-2 , PGE2 or untreated control ) , with 40K window pairs available on average . Genome-wide distribution of pair-wise differences allowed us to assign a probability ( empirical significance ) for observing three consecutive SNPs with a certain degree of directional difference ( consistently greater or smaller deviation from equal expression in consecutive SNPs ) between reference and test sample . On average , window pairs reaching permutation significance of 0 . 05 or lower ( 1 . 8% on average ) were further assessed to look for genes with DEX-specific allelic expression differences . We observed 16–84 genes ( at 12–25% FDR , see Methods ) per sample ( Table S15 ) where DEX treatment consistently differed from all three other cell culture conditions suggesting a direct interaction between cis-variants and environmental perturbation . Notably , these genes were 2 . 3–6 . 2-fold more common among the DEX response genes ( significant >1 . 5-fold difference in expression upon DEX induction ) ( Table S2 and S15 ) than expected by chance ( binomial P<0 . 05 for each sample ) . These results suggest that specific interaction of cis-regulatory sequences with the environment can be directly identified in vivo by genome-wide AE measurements . We followed-up and validated our top DEX-dependent cis-regulated locus , the Tenascin-C ( TNC ) locus , identified in the global AE test ( Figure 6A ) using low-throughput methods in an extended set of samples as well as at different time points ( t = 2h and t = 24h ) . We were able to successfully confirm the treatment-specific effect after 24h but no difference in AE was seen after 2h ( Figure 6B ) indicating a time-dependent effect of the gene regulation . We extended our cis-eQTL analysis in DEX-treated samples by including imputed HapMapII SNPs in order to fine-map the association and found the top SNP ( rs7850103 , P = 2×10−7 ) located ∼250 kb upstream of the TSS ( Figure 6C ) . Further real-time RT-PCR experiments confirmed that down-regulation of TNC expression by DEX is genotype-dependent ( Figure S8 ) . We then asked whether DEX-dependent heritable cis-regulation of TNC expression could underlie the difference in inhaled corticosteroid treatment response in the treatment of childhood asthma . We chose asthma as a clinical model given that ( 1 ) inhaled corticosteroids represent the most commonly prescribed and efficacious asthma controller medication; ( 2 ) clinical response to inhaled corticosteroids in asthma is variable between subjects; and ( 3 ) TNC expression is known to be increased in lung tissue of asthmatics , which is modulated by corticosteroid treatment [27] . We tested six SNPs located in our candidate region in children with mild-to-moderate persistent asthma enrolled in a multicenter , randomized , placebo-controlled trial of inhaled anti-inflammatory medication . The analysis was limited to 170 children of self-reported non-hispanic white ancestry randomized to daily , inhaled budesonide treatment , on whom lung function ( forced expiratory volume in one second ( FEV1 ) ) had been measured before and after two months of corticosteroid treatment . We found suggestive associations between the TNC cis-variants and response to inhaled corticosteroid ( rs955387-A , beta = −6 . 99 , P = 0 . 005; rs10982634-C , beta = −6 . 01 , P = 0 . 01; rs10817727-G , beta = −5 . 78 , P = 0 . 02; rs12380804-A , beta = −8 . 09 P = 0 . 02; rs10982611-G , beta = −2 . 87 , P = 0 . 07; rs10817762-C , beta = −2 . 785 , P = 0 . 08 ) ( Figure 6D ) although independent replication is needed to confirm these findings .
We have studied how environmental perturbation impacts genetic cis-regulation of global gene expression in primary human osteoblasts . To our knowledge , this is the first study exploring the cis-regulatory landscape in a population panel of human cells cultured under multiple conditions and time points . Environmental stimuli included growth factors , cytokines and steroid hormones known to be relevant to the cell type studied and with previous known effects on the osteoblast transcriptome [15]–[21] . The robust response to treatment was verified both within and across samples using different expression platforms and clearly showed evidence of biologically relevant transcript changes such as the significant down-regulation of the IGF1 signaling pathway following glucocorticoid stimulation [14] . However , despite the large impact on the transcriptome by the treatments , we found that only a small proportion of the identified cis-eQTLs can be considered as true treatment-specific , indicating that global changes in gene expression may be governed more by subtle heritable and environmental effects . Our experimental design with the inclusion of multiple independently derived cell lines as biological replicates improved not only the detection of response genes but also the discovery of treatment-specific and treatment-independent cis-eQTLs . We have previously shown that by including such replicates , approximately 60% more cis-eQTLs can be identified compared to the use of single replicates [7] . Using this design in multiple conditions , we identified ∼40% more cis-eQTLs in conditioned samples as compared to untreated controls but interestingly , when we compared them across treatments we found that the majority ( >90% ) of them were seen in more than one condition . These findings were confirmed in a slightly different study design separating the biological replicates and performing two independent cis-eQTL analyses per treatment . This indicates that environmental perturbation seems to allow a higher discovery rate of genetic cis-effects not because of treatment-specific regulatory variants but rather due to increased power in cis-eQTL analysis in conditioning cells , perhaps attributable to a higher level of coordination of gene regulatory activity between treatments ( i . e . reducing environmental variability of “resting cell culture” ) . In contrast to environmental-dependence , cell-type specific cis-regulation of gene expression has previously been studied with contrasting results . Using human primary fibroblasts and immortalized B-lymphocytes , Lee et al [11] found that up to 10% of the genes studied might be influenced by tissue-specific cis-regulatory variants whereas Dimas et al [4] reported that 69–80% of regulatory variants operate in a cell type-specific manner using similar human cell panels . Notably , the former study utilized AE monitoring and the latter applied eQTL-mapping . Lee et al [11] also explored how cis-regulation is affected by experimental conditions following iPS reprogramming , and their results indicated that allele-specific expression remained largely invariable . Our results are consistent with the more conservative estimates of context specificity of cis-regulatory variation reported by others [11] , [28] . In line with this are the recent observations indicating that at genome-wide level , trans-variants predominate when hypothesis free mapping of perturbation specific effects are mapped [12] , [13] , [29] . Based on our earlier results [7] , we chose not to pursue analyses including trans variants , since even among biological replicates these cannot be reproduced in the current sample size . However , we followed-up and validated our findings from the cis-eQTL analysis by measuring global allele-specific expression pattern in the different conditioned cells . This allowed us to study treatment-specific cis-effects in more detail due to higher sensitivity and specificity of the approach [26] . We were able to validate ∼45% of the cis-regulatory effects identified in the eQTL analysis which is in fact similar to what has been shown previously comparing AE mapping with traditional cis-eQTL mapping [26] . The reason for the remaining 55% of the cis-eQTL not being validated can be due to insufficient power in AE test , false positives in eQTL mapping , or measurement of mRNA versus pre-mRNA in eQTL and AE studies , respectively . Nevertheless , the results from these allele-specific expression assessments confirmed our previous findings from eQTL analysis and strengthened the hypothesis that a large proportion of cis-regulatory variants are shared across treatments . In fact , the majority of the environmental-dependent regulatory variants that were identified in eQTL analysis were shown to be shared across multiple treatments . The reason for these numerous false-negatives could be explained by the sensitivity of the eQTL approach where identification of cis-regulation of low expression transcripts is challenging . An example of this occurrence is the cis-acting variant regulating the expression of TNFSF4 that has been associated with susceptibility to systemic lupus erythematosus and whose cis-regulatory effect is largely pronounced in activated cells as compared to non-activated cells due to up-regulation of TNFSF4 expression upon activation [30] . In addition to validating our findings from cis-eQTL analysis , we used the AE assessments to try and further detect environmental-dependent cis-regulatory effects , focusing on the DEX treatment . DEX is a synthetic glucocorticoid steroid hormone that regulates gene expression through binding to its nuclear receptor , the glucocorticoid receptor ( GR ) . The ligand-receptor complex binds the DNA at specific glucocorticoid response elements ( GRE ) resulting in activation or repression of gene expression [31] . Here , we were able to detect on average ∼60 genes/sample with evidence of DEX-specific AE differences enriched among DEX-responsive genes ( i . e . showed a significant >1 . 5-fold difference in expression upon DEX stimulation ) allowing us to speculate whether the causative eSNP may affect the GR-GRE binding and subsequent the DEX-GR ability to regulate gene expression . Recently a comprehensive ChIP-seq study was presented of DEX-GR binding and its effect on gene expression throughout the human genome [32] . Reddy et al showed that while genes activated with DEX treatment have GR bound in proximity to the TSS , genes repressed with DEX treatment have GR bound >100kb from the TSS . Moreover , another striking difference between genes activated and repressed by DEX was the time required for gene expression response to DEX with repression beginning much later than activation following DEX exposure . Interestingly , these features were seen for our top significant DEX-specific cis-regulated locus , the Tenascin-C ( TNC ) locus . Tenascin-C is an extracellular matrix protein whose expression is up-regulated in inflammatory conditions , such as rheumatoid arthritis [33] and asthma [27] and known to be down-regulated by DEX [27] , [34] . Here we showed that the DEX-specific down-regulation of TNC is both genotype- and time-dependent with a significant cis-regulatory effect seen only after the later time point of DEX exposure and with the top SNP being located ∼250 kb upstream of the TSS . In fact , the same region on chromosome 9 was identified by ChIP-seq to be bound by GR in human A549 lung epithelial carcinoma in response to DEX treatment [32] . In conclusion , our results indicate that qualitative ( “on/off” ) interactions between controlled environmental perturbation and heritable cis-regulatory SNPs are uncommon . Therefore , uncovering true interactions requires either multi-pronged approaches where independent tools are used to assess treatment specificity on genetically controlled expression , as employed here . Alternatively , larger sample sizes with adequate replication in independent cohorts are required for establishing cis-regulatory variant – environment interaction , due to much smaller effect sizes than those observed for natural variation in gene expression among populations . At the same time , the existence of validated , genetically controlled , treatment cis-specific effects shown here suggests that systematic functional genomic screens may yield a valuable alternative approach for identifying pharmacogenomic biomarkers altering gene regulation [35] , often difficult to access in clinical cohorts due to limited sample sizes .
All research involving human participants have been approved by institutional review boards ( Dnr Ups 03-561 , McGill IRB A10-M121-06B ) and conducted according to the principles expressed in the Declaration of Helsinki . Human trabecular bone from the proximal femoral shaft was collected from 113 donors ( 51 female and 62 male donors , respectively ) undergoing total hip or knee replacement at the Uppsala University Hospital , Uppsala , Sweden . The bone samples from each donor were thoroughly minced and cultured in three biological replicates . The cells were grown in medium containing α-MEM ( SigmaAldrich , Suffolk , UK ) supplemented with 2 mmol/l L-Glutamine , 100U/mL penicillin , 100mg/mL streptomycin ( National Veterinary Institute of Sweden , Uppsala , Sweden ) , and 10% fetal bovine serum ( SigmaAldrich , Suffolk , UK ) at 37°C with 5% CO2 . At 70–80% confluence , the cells were passaged and sub-cultured in 6-well plates ( 100 , 000 cells/well ) for 12 days . The culture medium was changed twice weekly . Prior to treatment , the cells were starved for 20h by adding complete cell medium containing 0 . 5% fetal bovine serum . The cells were then incubated for 2h and 24h with 0 . 1 µg/ml of rhBMP-2 , 100 nM of dexamethasone , 100 nM of IGF-1 , 1µM of PGE2 , 100 nM of PTH ( 1–34 ) , 0 . 1 nM of TNF-α , 100 nM of 1 . 25 VitD3 and with the same concentration of control , respectively ( Table S16 ) . At the two time points , the cell medium was removed and the cells were harvested by adding 600µL of RLT buffer ( Qiagen , GmbH , Germany ) . The cell lysates were homogenized by using QIAshredder ( Qiagen , GmbH , Germany ) homogenizers and stored in −70°C until RNA extraction . The study was approved by the local ethics committees ( Dnr Ups 03-561 , McGill IRB A10-M121-06B ) . RNA was extracted from cell lysates using the RNeasy Mini Kit ( Qiagen , Mississauga , Canada ) . High RNA quality was confirmed for all samples using the Agilent 2100 BioAnalyzer ( Agilent technologies , Palo Alto , CA , USA ) and the concentrations were determined using NanoDrop ND-1000 ( NanoDrop Technologies , Wilmington , DE , USA ) . Expression profiling of one complete sample was performed in triplicate ( biological replicates ) using the Affymetrix Human Genome U133 plus 2 . 0 Array ( Affymetrix , Santa Clara , CA , USA ) . One microgram of RNA was reverse transcribed into cDNA and in vitro transcription was performed to generate biotin-labeled cRNA for subsequent hybridization . Hybridized target cRNA was then stained with streptavidin phycoerythrin , and arrays were scanned using a GeneArray Scanner at an excitation wavelength of 488nm . The raw data was imported to BioConductor [36] using the R 2 . 5 . 0 package and normalized mean expression values were generated by the Robust Multichip Average algorithm [37] , [38] . The microarray data have been deposited in the Gene Expression Omnibus ( GEO ) , www . ncbi . nlm . nih . gov/geo ( accession no . GSE10311 ) . Expression profiling of one complete sample and of all BMP-2 ( 2h , n = 101 ) , dexamethasone ( 24h , n = 106 ) , PGE2 ( 24h , n = 105 ) and untreated control ( 24h , n = 95 ) samples , each with at least two biological replicates , was performed using the Illumina HumRef-8v2 BeadChips ( Illumina Inc . San Diego , CA , US ) where 200ng of total RNA was processed according to the protocol supplied by Illumina . The raw data was imported to BioConductor [36] using the R 2 . 5 . 0 lumi package for variance-stabilizing transformation and robust spline normalization to obtain normalized mean expression values . The detectionCall algorithm in the lumi package was used to find genes uniquely expressed in one condition . A gene was considered expressed if present in at least 10% of the measured samples . The microarray data has been deposited in the Gene Expression Omnibus ( GEO ) , www . ncbi . nlm . nih . gov/geo ( accession no . GSE15678 , GSE21410 , GSE21725 , GSE21726 , GSE21727 ) . In order to visualize whole-genome expression data in the context of biological networks , functions or pathways data were analyzed through the use of Ingenuity Pathway Analysis ( IPA ) system ( Ingenuity Systems , Mountain View , CA , USA , www . ingenuity . com ) . The datasets containing differently expressed genes ( FDR adjusted P<0 . 05 and Fold change >2 ) or genes harboring a treatment-specific cis-eQTL were uploaded to the application . Each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base . A fold change cutoff was set to identify genes whose expression was significantly differentially up or down-regulated . These genes , called Focus Genes , were overlaid onto a global molecular network developed from information contained in the Ingenuity Pathways Knowledge Base . Networks of these Focus Genes were then algorithmically generated based on their connectivity . The Functional Analysis identified the biological functions that were most significant to the dataset . Genes from the dataset that met the cutoff and were associated with biological functions in the Ingenuity Pathways Knowledge Base were considered for the analysis . Fisher's exact test was used to calculate a p-value determining the probability that each biological function assigned to the dataset is due to chance only . The Cyber-t test was used to determine the significance between the observed differences in gene expression . The Cyber-t test is based on simple t-tests and uses the observed variance of gene measurements across replicate experiments , thereby accommodating noise , variability , and low replication , often typical of microarray data [39] . Since the number of tests ( genes ) is large , the p-values are adjusted for multiple comparisons using the false discovery rate ( FDR ) algorithm [40] in Bioconductor using the R 2 . 5 . 0-package [36] . For the proportion of differentially expressed genes among all tested genes for each treatment ( 1-pi0 ) the qvalue package implemented in R was used [40] . DNA was successfully extracted from cell lysates of 109/113 samples and re-suspended in 200µl PBS using the GenElute DNA Miniprep Kit ( SigmaAldrich , Suffolk , UK ) . Concentrations were determined using the Quant-iT PicoGreen kit ( Molecular Probes ) . Genotyping was performed on 106 samples using the Illumina HapMap 550k Duo chip ( Illumina Inc . , San Diego , CA , US ) according to the protocol provided by the manufacturer . The total number of SNPs included on the chip is 561 , 303 . Individuals with low genotyping rate ( <90% ) and SNPs showing significant deviation from Hardy-Weinberg equilibrium ( P<0 . 05 ) were excluded . Similarly , low frequency SNPs ( MAF<0 . 10 ) and SNPs with high rates of missing data ( >5% ) were excluded . The average success rate of the genotyping of the 561 , 303 SNPs across all individuals was 99% . Of the 22 , 184 Illumina probes ( corresponding to 18 , 144 genes ) , those with SNPs ( dbSNP build 126 ) within them were excluded . We tested for association of the induced expression levels to SNPs using the linear regression model implemented in the PLINK software ( http://pngu . mgh . harvard . edu/purcell/plink/ ) [41] assuming additive effect of the SNPs . Two covariates were also included in the regression model; year of birth and sex ( mRNA = a+b1SNP1+b2cov12+b3cov23+e ) . Cis-regulatory effects were tested using SNPs mapping ±250kb flanking the gene or within the gene itself . In order to study whether array hybridization time point ( i . e . batch ) biased our data and masked weak treatment-specific effects we also analyzed the data including batch as a cofactor in exactly overlapping samples across treatments ( n = 80 ) keeping the biological replicates separate in independent cis-eQTL analyses . We also analyzed the data sets jointly by fitting a linear mixed model using the SAS 9 . 1 software ( SAS institute Inc . , Cary , NC , US ) . Treatment group was included as a random effect and year of birth and sex as fixed effects . Two interactions terms were also included; treatment*SNP and sex*SNP , respectively . No significant sex*SNP interaction effects were found . Independent cis-eQTLs were identified by first obtaining genome-wide recombination hotspot coordinates based on the HapMap Phase 2 ( release 22 , NCBI 36 ) data ( http://hapmap . ncbi . nlm . nih . gov/downloads/recombination ) . Within each locus we first identified top associations within each interval between recombination hotspots and retained the top ranking association . Next we tested for potential residual LD ( D′>0 . 5 ) between significant associations mapping to independent recombination hotspot intervals in our population using our genotyping as input in the Haploview software ( version 4 . 2 ) ( www . broadinstitute . org/haploview ) . Only the top association from SNPs showing pairwise D′>0 . 5 was kept for each locus to identify unique cis-eQTLs . Genotypes from 103 samples that passed quality control were imputed for all SNPs ( n = 478 , 805 ) oriented to the positive strand from phased ( autosomal ) chromosomes of the HapMap CEU Phase II panel ( release 22 , build 36 ) . Untyped markers were inferred using algorithms implemented in MACH 1 . 0 [42] , [43] . R2 was used as imputation quality control metrics and estimates the squared correlation between imputed and true genotypes . A cut-off of R2<0 . 3 was used to remove poorly imputed markers . Association of imputed genotypes using estimated genotype probabilities with expression traits was performed using a linear regression model implemented in the MACH2QTL software [42]–[44] with sex and year of birth as covariates . The genomic DNA data collected in parallel from 1 . 1 M SNPs in these individuals also allowed us to measure imputation accuracy across the dataset using a common set of 636 , 676 SNPs . Overall , 88% of the HapMap SNPs were imputed at 100% accuracy across samples and the error rate within a sample was 3% which is similar to what has been reported previously [45] . Primers were designed using the Primer3 v . 0 . 4 . 0 software ( http://frodo . wi . mit . edu/ ) and all primer sequences can be found in Table S17 . Allele-specific expression was assessed by quantitative sequencing [24] . High-quality RNA was used to synthesize first strand cDNA with random hexamers ( Invitrogen , Burlington , Canada ) and Superscript II reverse transcriptase ( Invitrogen , Burlington , Canada ) . For each locus , we designed exonic primers and we used PeakPicker v . 2 . 115 with the default settings to quantify the relative amount of the two alleles at the heterozygote site measured from the chromatogram after peak intensity normalization . The normalized heterozygote ratios of genomic DNA samples were used to calculate mean and SD for each SNP . If all heterozygote ratios from three technical replicates showed concordant deviation greater than two SDs from the genomic DNA heterozygote ratio mean , the sample was called to have allele-specific expression . The PA317-neo packaging cells ( ATCC Inc , Manassas , VA , US ) expressing pLXSN hTERT and pLXSN HPV16-E7 were kindly provided by Dr Eric Shoubridge ( McGill University , Montreal , QC , CA ) . The packaging cells were grown to near confluence in DMEM containing 10% FBS ( SigmaAldrich , Suffolk , UK ) . After 24 h to 48h , the medium containing retroviral particles was harvested and filtered through a 0 . 45-µm filter and mixed with complete cell medium . Polybrene was added to a final concentration of 4ug/mL . Five of the cultured human osteoblast lines ( see above ) were plated in 75-cm2 culture flasks and cultured in α-MEM ( SigmaAldrich , Suffolk , UK ) supplemented with 2 mmol/l L-Glutamine , and 10% fetal bovine serum ( SigmaAldrich , Suffolk , UK ) . When cells reached 30–40% confluence , the media were removed and 6 ml of fresh prepared retroviral suspension ( 1 . 5 ml of hTERT suspension , and 1 . 5 ml of E7 suspension and 3 ml of medium , respectively ) was added to the cells and incubated 1–2 hours at 37°C . Growth medium was then added with polybrene to bring up to usual flask volume and maintain incubating overnight at 37°C . The media were then removed and the cells were rinsed once with fresh medium and new culture medium without polybrene was added . After 48h , the media were changed to selection media containing 400µg/ml of G418 ( Invitrogen , Burlington , ON , CA ) and the cells were cultured under these selection conditions for 2–3 weeks . The immortalized osteoblasts were seeded in 75-cm2 and cultured in complete cell medium . At 80% confluence , the cells were starved for 20h by adding complete cell medium containing 0 . 5% fetal bovine serum ( SigmaAldrich , Suffolk , UK ) . The cells were then incubated with 10−4 mg/ml of rhBMP-2 ( 2h ) , 10−7 M of dexamethasone ( 24h ) and 10−6 M of PGE2 ( 24h ) and with the same concentration of control , respectively . At the different time points , the cell medium was removed and the cells were harvested by adding 2mL of TRIzol Reagent ( Invitrogen , Burlington , ON , CA ) and stored in −70°C until RNA extraction . Robust responses to each treatment of these newly cultured cells were confirmed by comparison of gene expression assessed by real-time RT-PCR experiments in both immortalized and the corresponding primary treated HObs , respectively . Genes validated were selected from expression profiling using the Illumina Ref8 Beadarrays of primary cells . Aliquots of the different RNA from the primary or immortalized cell , respectively , were each annealed to 500 ng of random hexamers ( Invitrogen Corporation , Carlsbad , CA , USA ) at 70°C in 10 min . First-strand cDNA synthesis was performed using SuperScript II reverse transcriptase ( Invitrogen Corporation , Carlsbad , CA , USA ) according to manufacturer's instructions . The target gene as well as the 18S housekeeping gene were analyzed in triplicates as well as a calibration curve from a two-fold dilution series of control cDNA and non-template control ( NTC ) samples . The real-time PCR assays were performed on the Rotor-Gene™ 6000 real-time rotary analyzer ( Corbett Life Sciences , Sydney , Australia ) using the Platinum SYBR Green qPCR SuperMix-UDG ( Invitrogen Corporation , Carlsbad , CA , USA ) according to manufacturer's recommendation . The cycling conditions on the Rotor-Gene™ 6000 real-time rotary analyzer were: 4 minutes at 95°C , 40 cycles x ( 20 seconds at 95°C , 30 seconds at 58°C and 30 seconds at 72°C ) followed by the dissociation protocol at 72°C . Results of the experimental samples were analyzed using the comparative CT method . The CT mean and standard deviation value of each technical replicate sample was calculated and the mean CT value was then normalized to the 18S mean CT value . All statistical analyses for the associations of delta CT values with genetic variants were performed using a general linear model in SAS 9 . 1 ( SAS institute Inc . , Cary , NC , US ) . Approximately 150µg of total RNA was extracted from the cells using the commercially available TRIzol reagent protocol ( Invitrogen , Burlington , ON , CA ) and subsequently treated with 18 U DNaseI ( Ambion Inc . , Austin , TX , USA ) for 30 min at 37°C and further extracted with phenol/chloroform . High RNA quality was confirmed for all samples using the Agilent 2100 BioAnalyzer ( Agilent Technologies , Palo Alto , CA , USA ) and the concentrations were determined using the NanoDrop ND-1000 ( NanoDrop Technologies , Wilmington , DE , USA ) . Poly ( A ) RNA was then isolated using the MicroPoly ( A ) Purist protocol ( Ambion Inc . , Austin , Tx , USA ) and poly-A enriched RNA quality and quantity was measured using the Agilent 2100 BioAnalyzer and NanoDrop ND-1000 , respectively . DNA from the cell lysates was extracted using the GenElute DNA Miniprep Kit ( SigmaAldrich ) according to the protocol provided by the manufacturer and concentrations were determined using the NanoDrop ND-1000 . Approximately 1µg poly-A enriched RNA was annealed to 50ng of random hexamers ( Invitrogen , Burlington , ON , CA ) at 70°C for 10 min . First- and second strand cDNA synthesis was performed using the Superscript Double-Stranded cDNA Synthesis Kit ( Invitrogen , Burlington , ON , CA ) according to the manufacturer's instructions . The double-stranded cDNA was extracted with phenol∶chloroform∶isoamyl alcohol and dissolved in 12ul DEPC-treated water . The size distribution of the double-stranded cDNA samples ( average 1 . 2–1 . 5kb ) was confirmed using the Agilent BioAnalyzer DNA Kit . Approximately 200ng of genomic DNA and 50–300ng double-stranded cDNA sample was used for the parallel genotyping and AE analysis on the Illumina Infinium Omni1-Quad BeadArrays according to the manufacturer's instructions ( Illumina Inc . , San Diego , CA , US ) . Genotypes in genomic DNA were extracted using BeadStudio . The parallel assessment of genomic DNA and cDNA heterozygote ratios was carried out essentially as described earlier [26] , but signal intensity normalization at heterozygous sites followed a slightly modified approach . For AE analysis we utilized the Xraw and Yraw signal intensities and since the variances in the two channels are not same ( i . e . it is a function of total intensity from both channels ) we need to correct this variation through normalization to allow comparison between gDNA and cDNA allele ratios . In this study , we only normalized β ratio ( Xraw/ ( Xraw+Yraw ) ) from heterozygous SNPs with total intensity ( Xraw+Yraw ) higher than the threshold value of 3000 . The scatter plot of β ratio against the logarithm 10 scaled total intensity fits well with polynomial regression model ( quadratic regression model ) . This model shows better fitting than linear regression model we employed earlier for normalization [26] , which works well in higher intensity part , but poor in lower intensity part in many samples . The normalization process can be briefly summarized to following steps: 1 ) The β ratio is calculated along with total intensity in log10 scale for all heterozygous SNPs . 2 ) All data points with greater than 3000 in total intensity are divided into 50 intensity bins . 3 ) A fitted curve from the median β ratio in each bin is computed using a polynomial regression model ( quadratic regression ) y = b1x+b2x2+a where y is expected β ratio from the curve and x is log10 scaled total intensity . 4 ) From the fitted curve , the expected β ratio based on total intensity calculated . 5 ) The final normalized β ratio equals ( βobs−βexpected+0 . 5 ) . Following normalization , all median β ratio values in all intensity bins should be close , if not equal , to 0 . 5 . Empirical probabilities for observing differences in AE for transcripts were assessed by first observing the genome-wide distribution of AE-magnitude at expressed heterozygous sites [Δhet ratio = XDNA/ ( XDNA+YDNA ) −XRNA/ ( XRNA+YRNA ) ] . The use of AE magnitude alone allows us to do the comparisons in unphased chromosomes , which we chose to use as a global test since statistical phasing introduces potential errors . However , in case of TNC ( Figure 6A ) , we used derived phased [46] data to show that significant biases at individual sites are observed for the same expressed allele . We defined three SNP expressed windows by requiring that at least three of four consecutive heterozygous sites showed signal above threshold . We note that in comparison across treatments , we used only multi-SNP windows that were above signal threshold in all treatments within an individual . This restricted the analysis to 6791/8097 informative RefSeq genes and captured all informative genes ( n = 2880 ) showing above median expression scores based on Illumina Ref-8 data used in eQTL analysis . Multiplicative likelihood of observing three consecutive SNPs with high Δhet ratio magnitude was compared to 5th percentile of multiplicative likelihood in randomly permuted data from same sample . The same process was used in assessing empirical probability of observing DEX-specific changes in allelic expression , except that direction of effect ( greater or lower ΔDexa het ratio ) was taken into account for the three consecutive SNPs and we applied the following formula to calculate ΔDex het ratio = XRNA_DEX/ ( XRNA_DEX+YRNA_DEX ) −XRNA_OTHER/ ( XRNA_OTHER+YRNA_OTHER ) . The gene-based false discovery rate ( FDR ) of DEX-specific AE was also empirically assessed for each sample using RefSeq annotated genes where multiple three SNP windows had been independently measured for same transcript and the empirical FDR represents the proportion of discordant calls among all genes from same sample . Association between TNC SNPs and clinical response to inhaled corticosteroids was performed using information from subjects participating in the Childhood Asthma Management Program ( CAMP ) . CAMP is a multicenter , randomized , double-blind , placebo-controlled trial to investigate the long-term effects of inhaled anti-inflammatory medication ( budesonide 200 µg twice daily or nedocromil 8 mg twice daily both versus placebo ) in children 5 to 12 years of age . 1 , 041 asthmatic children were followed for a mean 4 . 6 years . Trial design and primary outcomes have been published [47] . Individuals were randomized to budesonide , nedocromil , or placebo . Of the non-Hispanic white CAMP probands randomized to inhaled corticosteroids , 118 subjects and their parents were genotyped on the Illumina HumanHap550v3 BeadChip [48] , with an additional 52 trios genotyped on the Illumina Human 610-Quad BeadChip . All CAMP subjects provided assent and their legal guardians consent to study protocols and ancillary genetic testing . The two month change in FEV1 in response to inhaled corticosteroids was calculated as previously described and was shown to be normally distributed [49] . Association between TNC SNPs common to both genotyping platforms and inhaled steroid response was calculated using PLINK [41]under the assumption of an additive model and adjusted for non-genetic covariates including sex , height and age . | Population variation in normal gene expression has been convincingly shown to be under strong genetic control where the main genetic variants are located within close proximity to the gene itself ( so called cis-acting ) . However , the extent to which controlled , environmental stimuli influences cis-regulation of gene expression is unclear . Here , we combine different functional genomic approaches and examine the role of common genetic variants on induced gene expression in a population panel of primary human cells derived from ∼100 unrelated donors treated under multiple conditions . Using these approaches , we find that the interaction between cellular environment and cis-variants are relatively rare , with only a small proportion of the identified genetic variants being specific to treatment . However , although treatment-specific genetic regulation of gene expression seems to be infrequent , we prove its existence by thorough validation of treatment-specific effects of the glucocorticoid-specific regulation of TNC expression . Taken together , these findings indicate that the regulatory landscape within a cell is very stable but , by combining functional genomic tools gene-environmental interactions of clinical importance , can be detected and possibly used as biomarkers in future pharmacogenomic studies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
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... | 2011 | Global Analysis of the Impact of Environmental Perturbation on cis-Regulation of Gene Expression |
The ability to perceive noxious stimuli is critical for an animal's survival in the face of environmental danger , and thus pain perception is likely to be under stringent evolutionary pressure . Using a neuronal-specific RNAi knock-down strategy in adult Drosophila , we recently completed a genome-wide functional annotation of heat nociception that allowed us to identify α2δ3 as a novel pain gene . Here we report construction of an evolutionary-conserved , system-level , global molecular pain network map . Our systems map is markedly enriched for multiple genes associated with human pain and predicts a plethora of novel candidate pain pathways . One central node of this pain network is phospholipid signaling , which has been implicated before in pain processing . To further investigate the role of phospholipid signaling in mammalian heat pain perception , we analysed the phenotype of PIP5Kα and PI3Kγ mutant mice . Intriguingly , both of these mice exhibit pronounced hypersensitivity to noxious heat and capsaicin-induced pain , which directly mapped through PI3Kγ kinase-dead knock-in mice to PI3Kγ lipid kinase activity . Using single primary sensory neuron recording , PI3Kγ function was mechanistically linked to a negative regulation of TRPV1 channel transduction . Our data provide a systems map for heat nociception and reinforces the extraordinary conservation of molecular mechanisms of nociception across different species .
Although studies in inbred mouse strains and in human twin cohorts have indicated that pain has a strong genetic component [1]–[5] , with an estimated heritability of ∼50% , little is known about the specific genes involved in regulating pain sensitivity across phyla . Further , conservation of gene function between species and across evolutionary time acts as a useful tool to develop an understanding of core genetic mechanisms relative to more specialized programs and how they influence behavior [6] . Drosophila is an excellent model organism for characterizing genetic regulators of behavior such as nociception [7] . Use of Drosophila genetics has highlighted a conserved role for multiple genes in the detection and avoidance of noxious heat [8] , [9] , and recent work on mechanosensation suggests the genetics of this process is likely also highly conserved across phyla [10] . We have previously reported a global in vivo RNAi screen for avoidance of noxious heat in Drosophila , and identification of hundreds of novel genes required in the adult fly , for its manifestation [8] . To interrogate this resource , we have now constructed a global systems network of heat pain . Our goal was to identify potentially conserved genes and pathways involved in pain perception , to provide a tool for focusing research on key pain molecular pathways . One pathway highlighted in this global systems network was phosphatidylinositol signaling . Phosphatidylinositol signaling is a second messenger cascade involving the sequential phosphorylation of phosphatidylinositol 4-phosphate ( PIP ) to generate phosphatidylinositol 4 , 5-bisphosphate ( PIP2 ) via PIP5' kinases ( phosphatidylinositol-5-OH kinase; PIP5K ) , and then phosphorylation of PIP2 via PI3 kinases ( phosphatidylinositol-3-OH kinase; PI3K ) to generate phosphatidylinositol 3 , 4 , 5-trisphosphate ( PIP3 ) . In mammalian systems phospholipid signaling has been implicated in regulating pain perception [11]–[13] , TRPV1 function [14]–[21] , and itch [22] , but how phosphatidylinositol signaling is involved in mammalian nociception is controversial , with data suggesting for example that PIP2 may either increase or decrease TRPV1 function [23] , [24] . Based on the suggested involvement of phospholipid signaling in our conserved functional pain network , and in context of the controversial role for phosphatidylinositol signaling in pain perception , we employed genetic approaches to evaluate the role of phosphatidylinositol signaling in mammalian nociception .
To construct a global systems network of heat pain we first identified potential mouse and human orthologs of fly candidate pain genes ( Figure S1 ) . Of the 580 candidate fly thermal nociception genes we had previously identified [8] , 399 had human orthologs ( Table S1 ) , many of which are known mammalian pain genes ( Table S2 ) . Gene ontology ( GO ) analyses of the human and mouse orthologs of the fly thermal nociception hits showed a marked enrichment of genes involved in neurotransmission and secretion , housekeeping systems such as mitochondrial structure , ATP synthesis , metabolism , or calcium signaling ( Figure S2A–S2C and Table S3 ) . We next generated an interaction map based on first-degree binding partners for the fly thermal nociception hits ( Table S4 ) . All binding partners were identified in yeast-2-hybrid screens and reported in the biomolecular interaction network database BIND , i . e . binding partners experimentally confirmed to interact with the candidate genes . Among the first degree binding partners in this interaction network , we found fly allatostatin C receptor 1 , which has homology with mammalian opioid receptors , a fly homolog of Lmx1b , which regulates central serotonergic responses to opioids [25] , a fly gene related to the nuclear factor-erythroid 2-related factor 2 , which has anti-nociceptive effects [26] by inducing upregulation of heme oxygenase-1 , and tyrosine hydroxylase , the enzyme required for dopamine and catecholamine production [27] . Thus , our genome-wide functional screen for thermal nociception in flies has generated a human gene network that includes orthologs of several known mammalian pain genes , in addition to numerous uncharacterized genes and pathway not previously associated with pain perception . To construct a mammalian systems map of thermal nociception , we performed an enrichment analysis ( using KEGG pathways and Broad Institute C2 gene sets ) on the mouse and human orthologs of the fly pain genes and their first degree binding partners ( Figure S1; Table S5 and Table S6 ) . We found significant enrichment of genes ( hypergeometric enrichment >90% ) involved in mitochondria , metabolism , calcium signaling , inflammation , cell adhesion , RNA processing , and neurotransmission . Finally , to generate a comprehensive conserved network map of thermal nociception , the KEGG pathways from Drosophila , mouse and human were combined with relevant gene sets from the C2 annotations to create a global putative “nociception network” ( Figure 1; Table S7 ) . From this combined systems network , we identified gene sets or pathways known to play key roles in many major neural systems . The connectivity of the entire systems map remains intact even after omitting all binding partners ( Figure S3 ) , i . e . , expansion of the “pain” network map by including binding partners does not introduce a bias . Moreover , all except one pathway in the network map has >50% representation from the Drosophila heat pain hits alone . To test whether this approach can predict mammalian pain genes , we measured the overlap of the direct candidate pain hits and their binding partners with previous rat microarray expression profiling data generated from pain studies [28] and all “pain” annotated genes from OMIM ( Online Mendelian Inheritance in Man , NCBI ) . Intriguingly , we found a 38 . 55% overlap of our direct pain hits and a 42 . 33% overlap of their binding partners with the microarray and OMIM data for pain . In contrast , 100 random gene lists gave a maximum of 6% overlapping genes and a minimum of 0% ( Table 1; Table S8 ) . Thus , our hypothesis-free systems map is markedly enriched for genes known to be associated with rodent and human pain . Considering the complexity of the neuronal network involved in generating nociception in the periphery and CNS , these pathways may operate across several neurons; however , our genome-wide functional fly pain screen and in silico data mining provide a road map for conserved molecular components and pathways putatively involved in heat nociception globally across phyla . We next wanted to validate whether this “nociception network” has the power to identify conserved pathways involved in nociception , and if this pathway information can then help to pinpoint key mammalian pain genes . To this end we focused our first efforts on phosphatidylinositol signaling , one of the major nodes predicted from the pain systems map , and a heat pain precedented pathway . Phosphatidylinositol signaling has been implicated in heat nociception and regulation of TRPV1 by multiple groups [11] , [13] , [16] , [18] , however its precise role has been controversial [16] , [18] , [23] , and the specific participation of different phospholipid kinases has never been evaluated genetically , which we now decided to do . Phosphatidylinositol signaling involves the generation of PIP2 via PIP5K , and then phosphorylation of PIP2 via PI3K to generate PIP3 . PIP5Kα is highly expressed in the nervous system but no neuronal function for this kinase has been established [29] . PIP5Kα mutant mice are viable and exhibit an exaggerated anaphylactic immune reaction in response to Fc-receptor engagement [30] . We find that PIP5Kα mutant mice exhibit a significant hyper-responsiveness to radiant heat ( Figure 2A ) and contact heat , when compared to littermate controls ( Figure 2B ) . In mammals , TRPV1 is the prototypical noxious heat thermo-receptor , and is also the receptor for capsaicin , the active ingredient in chili peppers [31] . We therefore tested whether PIP5Kα mutant mice exhibit exaggerated TRPV1 agonist responses . Indeed , following capsaicin injection , PIP5Kα mutant mice display heightened reactivity compared to littermate controls ( Figure 2C ) but exhibited no difference in mechanical pain threshold using the von Frey test ( Figure 2D ) . PI3Kγ , the only G-protein coupled PI3K , is expressed in TRPV1-positive peripheral sensory neurons in both rats and mice [32] , [33] and has been implicated in morphine-induced peripheral analgesia [32] and morphine tolerance [33] . Use of non-specific inhibitors , like wortmannin , has suggested a role for PI3' kinases in producing NGF-mediated TRPV1 sensitization [21] , [34] . We therefore tested thermal nociception in PI3Kγ ( p110γ ) mutant mice [35] and found that these mice , like the PIP5Kα null mice , also exhibit an exaggerated behavioral response to radiant heat plantar stimulation using the Hargreaves test ( Figure 3A ) . This enhanced pain sensitivity was confirmed using the hot plate assay ( Figure 3B ) . PI3Kγ mutant mice also exhibit an enhanced pain response to a capsaicin challenge ( Figure 3C ) . Similar to PIP5Kα null mice , the mechanical pain threshold using the von Frey test ( Figure 3D ) , and the behavioral responses to acetone application ( a cooling sensation ) ( Figure 3E ) were comparable between control and PI3Kγ−/− littermates . Of note we found a similar thermal hyperalgesia phenotype in a second independent PI3Kγ−/− mutant mouse strain [36] ( not shown ) . Thus , genetic loss of PI3Kγ and PIP5Kα results in enhanced pain responses to heat and capsaicin , providing evidence that phosphatidylinositol signaling acts as a negative regulator of heat pain perception and TRPV1 reactivity in vivo . Since PIP5Kα and PI3Kγ cooperate to sequentially generate PIP3 , and both mutant mice exhibit a similar hypersensitivity phenotype , we focused further on the role of PI3Kγ and PIP3 generation in setting the threshold for heat pain perception . PI3Kγ is highly expressed in haematopoietic cells and functions as key mediator of inflammatory cell migration to the site of injury [35] , [37] . We therefore tested PI3Kγ mutant mice for potential defects in inflammation-induced pain sensitization , i . e . thermal hyperalgesia . PI3Kγ−/− and control mice developed comparable levels of thermal hyperalgesia ( Figure S4A and S4B ) following plantar CFA injection . CFA-induced inflammation , as determined by paw swelling , was also comparable between mutant and control mice ( Figure S4C ) . To further exclude a potential role of haematopoietic cells , we transplanted wild type bone marrow into PI3Kγ mutant mice ( WT→KO ) and PI3Kγ mutant bone marrow into wild type mice ( KO→WT ) . The presence of a wild type haematopoietic system did not rescue the enhanced sensitivity to thermal pain in the PI3Kγ mutant background ( Figure 3F ) , i . e . the requirement for PI3Kγ in thermal sensing maps to non-haematopoeitic cells . PI3Kγ has been shown to act both in a kinase-dependent fashion , through conversion of PIP2 to PIP3 , and in a kinase-independent manner [38] . We therefore tested the behavioral response of PI3Kγ kinase-dead ( KD ) knock-in mice . PI3Kγ KD mice exhibited a heightened reaction to noxious heat with a reduced thermal nociception latency ( Figure 3G ) , comparable to the enhanced heat pain responses observed in complete PI3Kγ mutant mice . Thus , the kinase activity generating PIP3 modulates heat pain . We also assessed the general neurological phenotypes of PI3Kγ−/− mice , all of which appeared normal ( Figure 3H; Figure S5A–S5G ) . Furthermore , the overall morphology and histology of the central nervous system appeared normal in PI3Kγ−/− mice . These data demonstrate that generation of PIP3 through PI3Kγ negatively regulates pain sensitivity in vivo . To test if the phosphatidylinositol signaling pathway acts in primary sensory nociceptors , we employed electrophysiology on isolated wild type and PI3Kγ−/− dorsal root ganglion ( DRG ) neurons . PI3Kγ−/− DRG neurons responded to a thermal ramp ( Figure 4A ) with a significantly increased steepness in the inward current response to increasing temperature when compared to control neurons . This translated into a substantial increase in the Q10 value ( Figure 4B ) , a measure of temperature-dependent rate change in channel conductivity , indicating that PI3Kγ−/− DRG cells exhibit massive hyper-activation in response to noxious heat , although initiation of this response occurs at a slightly elevated temperature ( 44 . 77°C for PI3Kγ−/− vs 42 . 22°C for wild type DRG neurons , Figure 4A ) . Since we also observed an enhanced response to capsaicin in PI3Kγ−/− mice in vivo , we directly tested TRPV1 reactivity to capsaicin in sensory neurons in vitro . In accordance with our behavioral data , isolated PI3Kγ−/− DRG neurons exhibited augmented sensitivity to capsaicin ( Figure 4C and 4D ) . Thus , PI3Kγ functions as a negative regulator of TRPV1 responses in nociceptive neurons . Our data provide a conserved functional systems network map for pain behaviour . This network map revealed many pathways and gene sets previously reported to be involved in mammalian nociception , including multiple genes annotated as candidate pain genes in the human OMIM database . Thus , our systems approach , starting from a functional whole genome fly screen and bioinformatic construction of a conserved pain network map , has the power to identify regulators of mammalian nociception . Our network pointed to a key role for phosphatidylinositol signaling in noxious heat nociception . Positive as well as negative regulatory functions of phosphatidylinositol signaling on the thermal nociceptive sensor TRPV1 have been reported [15] , [16] , [19] , [20] , [23] , [24] . Our results provide genetic data that the phosphatidylinositol signaling pathway is relevant to heat pain sensitivity in vivo . In particular , we find that the lipid kinases PIP5α and PI3Kγ are involved in regulating heat nociception responses by acting as negative modulators of thermal pain perception and TRPV1 activity . Our data reinforce the extraordinary evolutionary conservation of the neurobiological mechanisms of nociception , from its manifestation as an acute damage avoidance response in simple organisms like flies to the complex sensation of pain in mammals . When used in conjunction with additional complimentary approaches ( e . g . published literature , gene expression profiling , or genetic association studies ) , this systems network map should be a valuable tool to further pinpoint and prioritize novel candidate nociception genes in mammals .
Identification of fly orthologs in mouse and human was done using pre-computed orthology predictions [39] . Gene Ontology ( GO ) analysis was performed using GOstat . Binding partner identification was done using GeneSpring GX . Hypergeometric tests were used to identify over-represented gene lists ( BROAD Institute ) and pathways ( KEGG ) amongst the pain hits and to generate a conserved systems map . Pain genes and binding partners in the system map that have been annotated as pain genes in the Online Mendelian Inheritance in Man database or by our previous Microarray experiments [28] were also identified . PI3Kγ ( p110γ ) knock out [35] , [36] , kinase dead PI3Kγ knock-in [38] , and PIP5Kα mutant mice [30] and have been previously described . Thermal and mechanical sensitivities were assessed using the Hargreaves , hot plate , and von Frey tests . Capsaicin behavior was assessed over 5 minutes following intraplantar injection of capsaicin . Lumbar dorsal root ganglia ( DRG ) were harvested as previously reported [40] , [41] . Patch-clamp recordings were performed using the whole-cell voltage-clamp configuration of the patch-clamp technique as previously described [40] , [41] . All mice were bred and maintained according to an ethical animal license protocol complying with the current Austrian law . Detailed Materials and Methods are available in Text S1 . | Nociception is the perception of noxious , potentially damaging stimuli; and this pain or its equivalent behavioral readout is evolutionarily conserved from fruit flies to humans . Using genetic techniques in the fruit fly , we have been able to evaluate the potential functional contribution of every gene in the fruit fly genome for a role in avoidance of high noxious temperatures ( heat pain-like responses ) . Using this functional genomics data set , we have developed a conserved network map of heat pain/nociception that predicts numerous conserved genes and pathways as novel pain pathways , including phospholipid signaling . Studies in multiple mutant mice confirmed a role for lipid signaling in pain perception , and more specifically we identify the critical lipid kinase ( PI3Kγ ) as a negative regulator of TRPV1 ( receptor for noxious heat and capsaicin , the active component in chili peppers ) signaling . This finding shows that our fly-based genetic pain network map is a valuable tool for the discovery of novel “nociception genes” in mammals . | [
"Abstract",
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"p... | 2012 | Construction of a Global Pain Systems Network Highlights Phospholipid Signaling as a Regulator of Heat Nociception |
The influence of genetic interactions ( epistasis ) on the genetic variance of quantitative traits is a major unresolved problem relevant to medical , agricultural , and evolutionary genetics . The additive genetic component is typically a high proportion of the total genetic variance in quantitative traits , despite that underlying genes must interact to determine phenotype . This study estimates direct and interaction effects for 11 pairs of Quantitative Trait Loci ( QTLs ) affecting floral traits within a single population of Mimulus guttatus . With estimates of all 9 genotypes for each QTL pair , we are able to map from QTL effects to variance components as a function of population allele frequencies , and thus predict changes in variance components as allele frequencies change . This mapping requires an analytical framework that properly accounts for bias introduced by estimation errors . We find that even with abundant interactions between QTLs , most of the genetic variance is likely to be additive . However , the strong dependency of allelic average effects on genetic background implies that epistasis is a major determinant of the additive genetic variance , and thus , the population’s ability to respond to selection .
Epistasis , the interactions between genetic loci , is an important determinant of phenotypes across a large number of taxa [1–8] . Yet for quantitative ( complex ) traits , the net effect of epistasis on the components of variation , specifically the additive genetic variance ( VA ) that determines the response to natural or artificial selection , remains polemical . This is evidenced by a renewed debate over the evolutionary relevance of epistasis as exemplified by Crow [9] and Hansen [10] . An unfortunate source of confusion sustaining this debate is the simultaneous use of terms to describe both the effects of individual genes as well as the genetic variance components of populations ( additive , dominance , and epistatic ) . It has long been known that high additive genetic variance does not imply additive gene action [11] , a conclusion reiterated by the theoretical demonstration in Hill et al . [12] ( see also [13] ) . However , there is little empirical evidence regarding the extent to which gene interactions determine the additive genetic variance , [14–17] , which leaves several important questions unanswered . For instance , do interactions among genes tend to increase or decrease VA of traits , on average ? As allele frequencies change in response to selection , does epistasis accelerate or dampen the corresponding change in VA ? For a particular locus , the contribution to VA depends on the average effect of substitution at the locus and on the frequencies of the different alleles in the population [11] . The total VA is a simple sum over all loci affecting the trait . With epistasis , the average effect of a locus will change as the frequency of its epistatic partners in the population change [16] . Thus , the contribution of a locus to VA depends simultaneously on its own allele frequencies as well as the allele frequencies of all other segregating loci . Association mapping studies can estimate the VA contributed by a locus ( e . g [7] ) , but this estimate is an average over the genetic backgrounds in the population . The extent to which locus-specific VA is determined by interactions with other loci remains unknown . An alternative to association mapping is to estimate genetic effects from genotypes produced from experimental crosses , with the remainder of the genetic background held constant . These genetic effects , often termed functional effects [18] , can be defined as deviations from a reference genotype , and therefore , do not depend on an unknown distribution of genetic backgrounds . Given allele frequencies in a population , it is straightforward to calculate total and locus-specific variances based on these genetic effects . One can also calculate these variances under the assumption of additivity of loci ( i . e . no epistasis ) . Contrasting these variances to those calculated from genetic effects based on multi-locus measurements provides a simple , direct demonstration of the effect of epistasis on VA [15] . Further , one can observe how this contrast changes as population allele frequencies change . Standard equations used to calculate VA from genetic effects [11] assume that effects are estimated without error . Estimation error in genetic effects is often substantial even with large sample sizes , and failing to account for this error will result in an upward bias in variance predictions [19] . This is because genetic effects are squared and different effects are multiplied together when variances are calculated . As the true value for a genetic effect is the estimate minus a residual ( the estimation error ) , treating the genetic effect estimates as the truth results in the inclusion of squared residual terms thus biasing variance components upwards . Luo et al . [19] derived a correction that incorporates the variance-covariance matrix of the genetic effect estimates for the case of a single locus . Here , we extend the bias-correction to multiple loci , accommodating epistatic terms , and demonstrate its validity using parametric bootstrapping . Then , we use the corrected variances to explore the effects of epistasis on the total and additive genetic variances under different models of allele frequency . We consider loci affecting floral morphology and development rate that are polymorphic within a single population of Mimulus guttatus ( yellow monkeyflower ) . Mimulus guttatus is an emerging model in evolutionary genetics; notable for its high degree of phenotypic and genetic variation within and between populations as well as its ability to adapt to novel environments [20] . The species is broadly distributed across western North America and ranges from high alpine to low-elevation coastal environments . In Mimulus guttatus , multiple studies have shown that epistasis contributes to within-population variation in floral morphology , development time , and fitness components [3 , 21] . Interaction effects are routinely of the same magnitude as single-locus effects , although the magnitude and direction of epistasis between loci is highly variable . In this study , we maximize statistical power to estimate direct and epistatic effects between QTL using Double-NIL lines ( DNILs , hereafter ) , in which two loci segregate in an otherwise isogenic genetic background . We confirm the finding of important but highly variable epistasis between these QTL , but also quantify the contribution of epistasis to population genetic variance using the genetic effect estimates and a model of allele frequency . We find that the average effect , which determines the locus-specific response to selection , depends heavily on genetic background or rather the frequency of different genetic backgrounds in the population ( i . e . the genotypes at all other loci ) . For some traits , this leads to an average increase in genetic variance components , whereas in others , the effect is opposite albeit minimally . Overall , it is clear that epistasis is an important determinant of both individual phenotype as well as the genetic variance components , which govern the ability of a population to respond to natural or artificial selection .
The DNILs were derived from a previous study by Kelly and Mojica [3] . The process of mapping the original QTL began with a large-scale artificial selection experiment on a collection of lines derived from a single natural population at Iron Mountain in Central Oregon . This resulted in populations with highly divergent floral traits [22] . Individuals from the tails of the distribution were randomly selected and crossed to produce three F1 populations and each of these populations were backcrossed for six generations to IM767 , a commonly used inbred line with medium floral trait values derived from the same natural population [23] . This resulted in 3 panels of Nearly Isogenic Lines ( NILs; 493 NILs in total ) , with each NIL containing a random segment of donor genome from one of the three F1 populations . NILs were measured for corolla width , and selective genotyping of NILs from the tails of the distribution identified 7 QTLs affecting corolla width . Three rounds of background-cleaning were performed for each of the 7 NILs , using a combination of selfing and backcrossing to IM767 to eliminate segments of donor , non-IM767 genome from other parts of the genome . The seven NILs containing the QTL were crossed in each possible pairing to produce 21 F1’s each of which contained solely the double-heterozygote for the donor alleles present in their parents . A single individual from each F1 was self-fertilized , and the resulting F2’s were genotyped at the relevant loci ( see S1 Table for the list of diagnostic markers for the 7 QTL ) . The four true-breeding ( double-homozygous ) genotypes from each F2 were set aside and self-fertilized , in order to serve as the parents of a single Double-NIL line set . Each DNIL is essentially a collection of nine genotypes corresponding to two biallelic QTL segregating in an otherwise uniform genetic background . For a single DNIL , we create these nine genotypes by selfing the true-breeders as well as crossing them in all possible directions to produce the five heterozygous genotypes . With seven QTL , there are 21 possible pairs of loci; however , this study examines only 11 of the DNILs owing to loss of several lines used in Kelly and Mojica [3] . Plants were grown in the University of Kansas greenhouse in five large cohorts . All nine two-locus genotypes for a particular Double-NIL were included in a cohort , which meant that only a subset of Double-NILs could be included in any one cohort . Within a cohort , multiple seed families from each genotype were sprinkled into unique 2x2 in . pots and watered generously . After approximately 10 days , individual plants were transplanted to their own 2x2 in pot . The pot locations were randomized initially and rotated regularly to avoid effects of inconsistent conditions within the greenhouse . Plants were watered every other day following transplant and fertilized once a week . Upon flowering , plants were measured for several traits: corolla width ( CW ) , distance between stigma and anther ( SA separation ) , pistil length , and the number of days until first flower ( DTF ) . Measurements were taken on all open flowers present at time first flower , which was typically only one or two . The corolla width is the widest distance of the flattened width of the lower lip of the corolla , while the rest of the measurements are self-explanatory . Kelly and Mojica [3] measured the double-homozygotes for 17 of the 21 DNILs . For this study , we elaborated measurements to include all nine two-locus genotypes for 11 of the 21 DNILs . As there is significant overlap between individuals in this and the aforementioned study , we combined the relevant data from Kelly and Mojica [3] , which included plants grown in seven distinct cohorts . This resulted in a highly unbalanced dataset , but provided greater accuracy for estimating particular genotypic means . All individuals were grown at the KU greenhouse under the same watering and fertilizer regiment . Crossing two of the true-breeding genotypes within a DNIL will necessarily result in genetically identical heterozygous offspring; however , we genotyped a subset of individuals from each cohort via touch-down PCR at gene-based markers diagnostic of particular QTL ( S1 Table ) in order to confirm that progeny genotypes were as expected ( incorrect genotypes occasionally result from mislabeling or accidental pollen transfer during selfing/crossing ) . The few individuals with incorrect genotypes that were identified ( typically < 5% per cohort ) were removed from the analysis along with all of their siblings . PCR fragments were analyzed on ABI 3130 BioAnalyzer , and genotype calls were made using GeneMapper ( Applied Biosystems , Foster City , CA , USA ) . We performed a likelihood ratio test to compare a full and reduced model for each trait corresponding to models with and without epistatic parameters , respectively . In R , we fit each model using REML as implemented in the “lme4” package followed by a call to the “anova . merMod ( ) ” function [24] . This produces a likelihood for each model from which a likelihood ratio is calculated and compared to a Chi-squared distribution with degrees of freedom equal to the difference in the number of parameters between the full and reduced model . There were 60 degrees of freedom in the full model and 16 in the reduced model , which corresponds to the number of genetic effects plus the cohort and family effect . The difference , 44 , is the degrees of freedom for each of the likelihood ratio tests . We estimated the single-locus and epistatic genetic effects using the NOIA functional genetic effect model [18]: Zijklmn=μ+aiXai+diXdi+ajXaj+djXdj+aaijXaiXaj+adijXaiXdj+daijXdiXaj+ddijXdiXdj+Ck+Fl[Gm]+εijklmn ( 1 ) where Here , Z is the trait value , Ck is the random effect due to cohort , Fl is the effect of seed family ( environmental maternal effect ) , which is nested within genotype; a and d are the single-locus effects , and aa , ad , da , and dd are the epistatic effects . The residual variance applies to variance within families . The Xa and Xd variables ( corresponding to the design matrix in [18] ) are numerical values that , together , specify an individual’s diploid genotype at a locus . In this case , W is the donor allele and w is the reference IM767 allele at a QTL . For a pair of loci , the four pairwise products of these variables provide contrasts by which the four analogous epistatic parameters are estimated . For a completely homozygous IM767 individual ( ww at all loci ) , all Xa and Xd terms are 0 , and therefore , the standard inbred IM767 line serves as the reference point in the functional NOIA model by which all genetic effects are defined as deviations from . These genetic effects can then be used ( as described in the proceeding section ) to generate predicted genotypic values for multi-locus genotypes . To determine the predicted genotypic values in the absence of epistasis ( non-epistatic values ) , we fit separate models to estimate single-locus effects using only data corresponding to single-locus genotypes ( essentially , the NIL genotype data that forms a subset of the DNIL data ) . Again , we use the functional NOIA parameterization ( Eq 1 without epistatic terms ) , specifying the homozygous IM767 genotype as the reference point . By this method , we define the non-epistatic value as the predicted multi-locus genotypic value given only information from individual loci in an isogenic background . It should be noted that the NOIA model has both a statistical formulation and a functional formulation , which is used here . Effectively , Eq 1 is the traditional animal model of genetic effects , and the functional NOIA ( referred to as NOIA , hereafter ) simply refers to the index variables used to specify an individual’s genotype . Here , we require functional genetic effects , in order to predict variances for any set of population allele frequencies . We investigated alternative , functional parameterizations for the index variables including the F∞ model [25] and the unweighted-regression ( UWR ) of Cheverud and Routman [15] , but these models use a different reference point and the parameters have a different quantitative interpretation . F∞ and UWR yield the same predicted genotype values as NOIA ( the models are inter-convertible ) , but we prefer the NOIA because parameters are defined as deviations from a reference genotype and , therefore , are more clearly interpretable between the full model ( Eq 1 ) and the reduced ( non-epistatic ) model ( fit to the reduced dataset ) . As a result , we find that the non-epistatic coefficients of NOIA ( a and d terms of Eq 1 ) are “stable . ” If we fit the full NOIA model ( all terms ) to the full dataset ( all genotypic combinations included ) we get estimates for the a and d terms that are nearly equivalent to what we get when we fit the reduced NOIA model ( no interactions terms ) to the reduced dataset ( plants of the reference genotype plus those that differ from the reference genotype at only one QTL ) . This is not true of analogous estimates from the F∞ or UWR models , which do not use a common reference genotype as the reference point . While this consistency is convenient for the interpretation of genetic effects , it is not crucial to the results . It is the genotypic values predicted with and without epistasis that serve as the basis for determining the effect of epistasis on genetic variance , and as stated previously , the multiple parameterizations that we investigated all provide the same predicted values . We used REML ( implemented in JMP , Version 11 . SAS Institute Inc . , Cary , NC , 1989–2014 ) to estimate the fixed genetic effects as well as accommodate random effects ( cohort and family ) in the model . While Eq 1 is specified for only 2 loci , all relevant genetic effects were included in the linear model and fit to the entire DNIL ( full model ) or NIL data ( reduced model ) . Fitting this larger model accommodated the fact that many DNILs have overlapping genotypes . Models were fit separately for each trait . In total , there were 14 single-locus effects ( seven ‘a’ terms and seven ‘d’ terms ) as well as 44 epistatic terms ( four epistatic terms per DNIL x 11 DNILs ) . Model fits were based on 4263 measurements carried forward from Kelly and Mojica [3] plus 6234 measurements from the five additional grow-ups . Estimation error in genetic effect estimates must be properly accommodated because genetic effect estimates are squared and different effects are multiplied together , when calculating variances . This can introduce bias with or without epistasis . Consider the single locus , 2-allele model [11] , where the additive genetic variance ( VA ) is Here , a and d are the additive effect and dominance deviation , respectively , and p and q are the frequencies of alternative alleles . An experimental study will yield estimates for the genetic effects , a^ and d^ , but even if unbiased , these estimates will be encumbered with estimation error: Here , the γ are residuals; random variables with mean 0 and a variance contingent on experimental design ( e . g . sample sizes ) . If direct substitution is used to estimate VA , i . e . V^A=2pq[a^+d^ ( q−p ) ]2 , bias is introduced: The second term of the sum , involving the estimation variances ( Var[γa] , Var[γd] ) and the covariance of errors ( Cov[γa , γd] ) , is the bias . A bias corrected estimate , denoted VA* , can be derived using standard dispersion statistics: VA*=V^A−2pq ( sa2+2 ( q−p ) sad+ ( q−p ) 2sd2 ) ( 5 ) where sa2 is the estimated variance of γa ( the squared standard error of a^ ) , sd2 is the estimated variance of γd and sad is the sampling co-variance . This statistical issue has been addressed for a single locus [19] , and we here generalize bias-correction for genetic variance predictions when there are interactions among loci . We extend the logic of Eq 5 to all eight genetic effect estimates associated with each QTL pair ( see “Linear model for estimation of genetic effects” section above ) . The genetic variance of a trait for a particular population is a function of the individual genotypic values and their frequency in the population . For a particular multi-locus genotype ( u ) , we can calculate the predicted genotypic value using the genetic effect estimates from the linear model fit as follows: Z^G , u=∑i=1Bb^iXi , u ( 6 ) where b^i is the estimate for the i’th effect ( B is the total number of effects ) , and Xi , u is the relevant indicator variable from Eq 1 for genotype u . If we let ZG represent the genotypic value of an individual drawn randomly from a population , then the total genetic variance , VG , is simply the variance of ZG , which can be found by , These expected values are functions of the genotypic values and the multi-locus genotype frequencies . For example , E[Z^G2]=∑u∈ΩFuZ^G , u2 ( 8 ) where Ω is the set of all possible multi-locus genotypes and Fu is the population frequency of the multi-locus genotype , u . Expanding Z^G2 ( temporarily suppressing the u subscript ) , we see that We see that Z^G2 is biased because E[b^i2]>bi2 and E[b^ib^j]≠bibj , essentially the same reason evident in Eq 4 We correct for this upward bias by subtracting off the relevant sampling variance/covariance term , such that the corrected value is , More generally , ZG2*=∑i=1B∑k=1BXiXk ( b^ib^k−sb^ib^k ) ( 11 ) noting that sb^ib^k is equal to sb^i2 . Thus , the expected value for Eq 11 is Correcting the estimate for E[ZG]2 is slightly more involved because the full expansion ( substituting Eq 6 for ZG ) produces terms of squares and cross-products within and across multi-locus genotypes . The bias corrected estimator for E[ZG]2 is The additive genetic variance , VA , due to a set of L bi-allelic loci is VA=∑k=1L2pkqkαk2 ( 14 ) where αk is the average effect of the allele with frequency pk at locus k . The average effect is αk = αk , 1 – αk , 2 , where αk , 1=pkE[Z|WkWk]+qkE[Z|Wkwk] ( 15 ) and Here , E[Z | WkWk] is the mean phenotype across all multi-locus genotypes that are homozygous for allele 1 at locus k , E[Z | Wkwk] is the corresponding conditional mean for heterozygotes , and E[Z | wkwk] is the mean for allele 2 homozygotes . Eqs 14–16 assume Hardy-Weinberg genotype proportions . We focus on the Hardy-Weinberg case , because without random mating , the additive genetic variance is not a sufficient statistic to predict response to selection [26–28] . The average effect is a linear function of genetic effects , and as a consequence , direct substitution of effect estimates into Eqs 14 and 15 yields unbiased estimates . However , when αk is squared ( Eq 13 ) , upward bias is introduced by estimation error . Computation of bias-corrected estimates αk2 follows the same method of Eqs 7–13 , although the relevant sums are over all loci except k ( code to implement these calculations was written in C; available in supplemental information ) . Importantly , in these calculations , we assume that epistasis is absent for pairs of QTL corresponding to DNILs for which we have no measurements . We also assume no higher-order interactions . All parameter estimates from the linear model fit were incorporated regardless of their statistical significance . Our method accounts for uncertainty in parameter estimates by directly incorporating the sampling variance/covariance of estimates into the calculation of variance components . Predictions of VG , VA , and locus specific αk were generated for each set of simulated allele frequencies . We investigated the distributions of genetic variance components under two differing allele frequency models , a uniform distribution and a U-shaped distribution as in Hill et al . [12] . Allele frequencies were sampled independently for each locus to create a set of 7 frequencies per set . We drew 200 sets of frequencies from each distribution , and these have been included in the supplemental information . For each set of allele frequencies and for each trait , we calculated the corrected and uncorrected genetic variance components variance ( Eqs 6–16 ) . We performed this operation , first , using the entire suite of single-locus and epistatic genetic effects to predict genotypic values , and then a second time , using only the single-locus genetic effects estimated from the reduced data set consisting only of single-locus genotypic data . This allows us to observe the effect of epistasis on the total and locus-specific genetic variance components . The bias-correction procedure will produce unbiased estimates of variance components if the estimated sampling ( co ) variances ( sb^ib^j ) are unbiased estimates of the true sampling ( co ) variances . However , precision of bias-corrected estimates may be reduced , as including sb^ib^j in the calculations could increase sampling variance of bias-corrected estimates , particularly if sb^ib^j terms are large . We performed parametric bootstrapping to determine the effect of our procedure on the precision of estimates as well as to confirm its efficacy in eliminating bias . Using the estimated genetic effects as well as the within-group variance estimated from the linear model fits , we simulated trait values for individuals of particular genotypes producing 500 replicate data sets . Each simulated dataset for each trait was the same size and structure as the original dataset . We then estimated the genetic effects and sampling ( co ) variance matrices for each replicate dataset and calculated the corrected and uncorrected genetic variance components for two sets of allele frequencies . For the first set , the reference allele frequency was set equal to 0 . 5 for all loci . For the second set , the reference allele frequency was set equal to 0 . 05 , and thus , the donor allele frequency was set to 0 . 95 ( see “Linear model for estimation of genetic effects” section above for definition of reference vs . donor ) . To determine the bias exhibited by each variance calculation , we calculated the true variances given the genetic effects that we specified to simulate the data . We calculated the mean square error for each distribution of variance calculations by dividing the sum of squared deviations from the true value by the number of replicates ( 500 ) . To standardize the mean square error , we divided the sum by the square of the true variance . In addition , we calculated the standardized bias as x*−xx , where x* is the average of estimates and x is the true value .
There is strong evidence for epistasis for all of the traits ( Table 1 ) , consistent with the prior study of these loci based solely on homozygous genotypes [3] . Concerning the individual terms of the models , we find that epistatic genetic effects are occasionally significant and typically of the same order as single-locus effects ( S2 Table and S3 Table ) . There was no clear trend towards positive or negative epistasis , although additive-by-additive interactions are more frequently observed to be significant than other forms . Particular types of epistasis are illustrated by example in Fig 1 ( see S1 Fig for the full collection of graphs ) , which contrast the predicted genotypic value with epistasis ( the bars ) to the corresponding non-epistatic value ( the ‘X’ ) . The non-epistatic value is the genotypic value predicted using only the relevant single-locus effects ( a and d terms ) estimated from a linear model fit based on single-locus genotype data ( essentially , NILs ) . The epistatic genotypic value is based on all genetic effects ( single-locus and epistatic ) estimated from the full linear model fit to the DNIL data . The deviation between these values is the contribution of epistasis to the observed genotypic value . Fig 1E provides an example of sign epistasis , wherein the positive effect of the donor allele at QTL x8 in the QTL x10a AA background exhibits a negative effect in the Aa background . Fig 1A–1C demonstrate the potential for consistent epistatic deviations for certain genotypes across multiple traits; particularly , the AABb genotype exhibits striking positive epistasis for all morphological traits . Conversely , some genetic backgrounds have variable effects when combined with other loci . For instance , positive synergism is observed for QTL x1 in the QTL x9 AA background ( Fig 1D ) , whereas negative diminishing returns epistasis is observed for QTL x5a in the same AA background of QTL x9 . Lastly , Fig 1A–1C also provides evidence of the potential of epistasis to modify dominance relationships . In the AA background , overdominance emerges for all traits , despite single-locus predictions of partial dominance ( Fig 1A and 1B ) and underdominance ( Fig 1C ) . When considered together , the epistatic deviations ( deviation between the ‘X’s and bars in Fig 1 ) illustrate the pattern of epistasis . Focusing on only the genotypes unobserved in the single-locus NIL model fit ( AABB , AaBB , AABb , and AaBb ) , we find that the average epistatic deviation is near zero for corolla width , SA separation , and pistil length ( -0 . 04 , 0 . 01 , and 0 . 06 respectively ) , but is more appreciable for days to flower ( -0 . 49 ) . This indicates that plants tended to flower earlier than expected based on non-epistatic predictions . The standard deviations of the epistatic deviations speaks to the variability of epistatic effects and are 0 . 43 , 0 . 20 , 0 . 30 , and 0 . 68 for corolla width , SA separation , pistil length , and days to flower , respectively . Comparing the sum of absolute deviations from the reference genotype for non-epistatic versus epistatic values provides additional information on the pattern of epistasis . If we subtract the sum of epistasis values from the sum of non-epistatic values , a negative difference would indicate synergism ( sometimes called positive epistasis ) , wherein epistasis gives rise to greater deviations from the reference point , on average . The converse would indicate a diminishing effect of mutations under epistasis . For corolla width , days to flower , pistil length , and SA separation , the percent difference between the sum of absolute deviations for no-epistasis vs . epistasis was 0 . 04 , 0 . 02 , -0 . 11 , and 0 . 01 , respectively . Evidence for cumulative synergism or diminishing returns is rather weak for all cases . Pleiotropy is common for both single-locus and epistatic effects ( S2 Table and S3 Table ) . Effects were typically significant for between two and three traits . Pleiotropy seems to be modular in the sense that QTL/DNILs with a significant effect on one floral trait tends to also affect other floral traits , in contrast to the day of flowering . Effects were significantly correlated between pistil length and corolla width ( r = 0 . 47; p = 0 . 0002 ) , between pistil length and days to flower ( r = -0 . 28 , p = 0 . 0305 ) , and between pistil length and stigma-anther separation ( r = 0 . 29 , p = 0 . 0292; S4 Table for full list of values ) . The traits themselves were also strongly correlated ( S5 Table ) . While some of the effects are in line with the correlational structure of the data ( e . g . single-locus additive effect of QTL x10b ) , this was not a consistent pattern ( e . g . single-locus additive effect of QTL x5a ) . Density plots of VA across the 200 simulated allele frequency sets ( uniform distribution ) calculated from the single-locus ( termed “No Epistasis” ) and DNIL ( “Epistasis” ) data are depicted in Fig 2 ( the corresponding densities for the U-shaped allele frequency distribution are given as S4 Fig ) . With the exception of Corolla Width , epistasis produced an increase in the average of both VA and VG for all traits ( Table 2 ) regardless of the allele frequency distribution . As expected , average VA and VG are always larger for Uniform distribution compared to the U-shaped distribution , whereas the proportion of variance that is additive is greater for the U-shaped distribution . Notably , the genetic variance is mostly additive even in the presence of substantial epistatic interactions . Epistasis also significantly affected the shape ( variance ) of the distributions of genetic variance estimates in addition to the location ( mean ) . This is evident in Fig 2 , as well as S4–S10 Figs . Epistasis tended to increase the variance , oftentimes producing long tails representing the observance of more extreme values . The bias-correction procedure significantly reduced estimated VG values ( particularly for days to flower; DTF ) although there was considerable variation among sets ( Table 2 ) . The larger reduction due to bias-correction of DTF is expected given that estimation error is greatest for this trait and that this estimation error is directly related to the degree of bias in variance calculations . Bias-correction also reduced predicted VA , but to a lesser extent . As a consequence , the ratio of VA to VG is substantially greater for bias-corrected values ( 60–80% across the four traits ) than for uncorrected variance components ( 45–60% ) . This is true regardless of whether one calculates VA/VG for each simulation replicate and then averages , or takes the ratio of mean VA to mean VG ( as in Hill et al . [12] ) . Occasionally , unrealistic , negative values for VA result from the bias-correction procedure , and this tendency seems to be exacerbated by greater estimation error of the coefficient estimates . For example , effect estimates for Days to Flower routinely had the largest standard errors , and this is accompanied by many negative values ( Fig 2 , lower left panel ) . It should be noted that uncorrected variance estimates also produce negative values albeit to a lesser extent ( Figs S8 and S10 ) , which is true for any estimate whose standard error is large relative to its magnitude . The distributions resulting from the bias-correction simulations demonstrate that the correction procedure is effective ( Table 3 and Figs S11 and S12 ) . The mean of corrected estimates matches the truth more closely than uncorrected estimates ( Std . Bias in Table 3 ) . There is a slight negative bias to the bias corrected estimate ( typically 1–4% ) , perhaps because the sampling ( co ) variances of estimates are only approximate . The mean square error of corrected statistics is lower than for the uncorrected ( Table 3 ) . This is due entirely to the bias reductions given that the distributions of the corrected and uncorrected statistics have nearly identical variances . The VA for traits is a weighted sum of squared average effects ( Eqs 14–16 ) . When considering the effects of epistasis on individual loci , we see dependence of the average effect , α , on genetic background ( Fig 3 for selected examples , S2 Fig for full collection ) . The points in the figure are α values for a locus calculated from our set of 200 allele frequencies ( uniform distribution ) , and the dashed line represents the best-fit line through the points . The solid black line shows the values for α without epistasis , which depend only on the genetic effects and allele frequency at that locus . If the locus showed entirely additive gene action , this line would be perfectly horizontal , whereas dominance gives rise to a non-zero slope . Over- or under-dominance is implied when lines cross 0 on the y-axis . Deviations between the dashed and solid lines in Fig 3 demonstrate the effect of epistasis averaged over genetic backgrounds . A change in slope between solid and dashed lines indicates the statistical dominance effect depends on epistasis . In Fig 3A , we see that locus QTL x10a is predicted to contribute little to no VA without epistasis , but exhibits a substantial average effect when epistasis is considered . Fig 3C indicates a case in which epistasis does not affect dominance , but changes the sign of α . However , epistasis often affects the dominance properties of a locus as evidenced by differences in the slope of the dashed and solid lines . Epistasis is seen to make an additive locus exhibit dominance ( Fig 3A and 3D ) , reduce dominance to near additivity ( Fig 3B and 3E ) , and give rise to over or underdominance ( Fig 3A and 3F ) . Some loci are relatively less sensitive ( more robust ) to background than others: Note the small scatter and relatively shallow trajectory of x5a relative to x10a for Days to Flower ( Fig 3A and 3D ) .
The results of this study , documenting the role of variable epistasis in determining genetic variance components are timely given the renewed interest and debate on the subject [9 , 10] . Given that most genetic variance remains additive in the presence of epistasis and that additive variance is largely sufficient to predict the response to selection , it would seem , at first glance , as if epistasis is irrelevant . Upon further inspection , we find that epistasis contributes substantially to additive genetic variance , increasing it on average for most traits ( Table 2 and Fig 2 ) , which should accelerate the response to selection . We note however that epistasis reduces the additive variance for particular combinations of allele frequencies with all traits . Contrary to the perspective that epistasis will have only transient effects on selection dynamics due to allelic combinations held together by linkage disequilibrium [37] , our results suggest that the principal effect of epistasis may be as a major determinant of VA [10 , 14] , although empirical evidence supporting the generality of this conclusion is currently limited [15] . Semantics has been a major impediment to connecting epistasis and the additive variance; the terms additive , dominance , and epistatic effects are used in a broad range of genetic effect models , yet differ substantially both in their interpretation as well as their relationship to genetic variance . This has led several authors to make general statements regarding epistasis that may be valid in the context of their own study , but are incorrect generally . For instance , it is not uncommon for authors to simply claim that non-additive effects , such as dominance and epistasis , do not contribute to additive genetic variance [49] and , therefore , are unimportant for the evolution of polygenic traits [9] . While this is true of statistical models of genetic effects , it is not true of functional models , as this and several other articles have argued [10 , 14 , 15] . In addition to partially determining additive variance , epistasis implies that allelic dynamics will depend on initial frequencies , such that replicate selection events are expected to be largely idiosyncratic and perhaps unrepeatable in terms of changes at underlying loci . Therefore , understanding functional epistastic interactions are important for understanding the fate of individual genes as well as populations exposed to selection . | Complex traits are influenced not only by the effects of individual genes but also by the myriad ways that these genes interact with one another , commonly referred to as epistasis . Theory suggests that epistasis could have important population-level implications in terms of the genetic variance components that govern evolution in response to natural or artificial selection . Unfortunately , empirical examples extending from observed interactions between genes to genetic variances are scant , particularly for natural populations . Here , we characterize epistasis between naturally segregating polymorphisms in M . guttatus and determine the cumulative effect of epistasis on population genetic variance components . To do this , we first elaborate the necessary statistical theory to accommodate estimation error in genetic effects , as failing to do so will upwardly bias variance predictions . We find that gene interactions have a net positive effect on both the total and additive genetic variance for most traits; however , the contribution of individual loci to the additive variance depends heavily on the genotype frequencies at other loci . Therefore , the effect of epistasis extends beyond the individual’s phenotype to influence how both populations and their component alleles respond to selection . | [
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"Discussion"
] | [] | 2015 | Epistasis Is a Major Determinant of the Additive Genetic Variance in Mimulus guttatus |
Persistent infection by pathogenic organisms requires effective strategies for the defense of these organisms against the host immune response . A common strategy employed by many pathogens to escape immune recognition and clearance is to continually vary surface epitopes through recombinational shuffling of genetic information . Borrelia burgdorferi , a causative agent of Lyme borreliosis , encodes a surface-bound lipoprotein , VlsE . This protein is encoded by the vlsE locus carried at the right end of the linear plasmid lp28-1 . Adjacent to the expression locus are 15 silent cassettes carrying information that is moved into the vlsE locus through segmental gene conversion events . The protein players and molecular mechanism of recombinational switching at vlsE have not been characterized . In this study , we analyzed the effect of the independent disruption of 17 genes that encode factors involved in DNA recombination , repair or replication on recombinational switching at the vlsE locus during murine infection . In Neisseria gonorrhoeae , 10 such genes have been implicated in recombinational switching at the pilE locus . Eight of these genes , including recA , are either absent from B . burgdorferi , or do not show an obvious requirement for switching at vlsE . The only genes that are required in both organisms are ruvA and ruvB , which encode subunits of a Holliday junction branch migrase . Disruption of these genes results in a dramatic decrease in vlsE recombination with a phenotype similar to that observed for lp28-1 or vls-minus spirochetes: productive infection at week 1 with clearance by day 21 . In SCID mice , the persistence defect observed with ruvA and ruvB mutants was fully rescued as previously observed for vlsE-deficient B . burgdorferi . We report the requirement of the RuvAB branch migrase in recombinational switching at vlsE , the first essential factor to be identified in this process . These findings are supported by the independent work of Lin et al . in the accompanying article , who also found a requirement for the RuvAB branch migrase . Our results also indicate that the mechanism of switching at vlsE in B . burgdorferi is distinct from switching at pilE in N . gonorrhoeae , which is the only other organism analyzed genetically in detail . Finally , our findings suggest a unique mechanism for switching at vlsE and a role for currently unidentified B . burgdorferi proteins in this process .
Antigenic variation through targeted genome rearrangements is a common strategy for immune evasion and has been identified in many important pathogens including protozoa [1] , [2] , [3] , [4] , bacteria [5] , [6] , [7] , [8] , [9] , [10] , [11] and fungi [12] . In spite of the common occurrence of this strategy for immune evasion amongst pathogens , few molecular details of the recombinational switching processes that generate diversity in antigen-expressing genes have been reported for any organism . Lyme borreliosis is a world wide health problem . It is a multisystemic illness caused by the spirochete Borrelia burgdorferi , and related species . Disease progression occurs through three stages: early , disseminated and persistent and can result in various arthritic , cardiac and neurological concerns if left untreated [13] , [14] , [15] . Persistent infection by B . burgdorferi requires continual segmental gene conversion at the vlsE locus , which encodes a 35 kDa membrane lipoprotein [9] , [16] , [17] , [18] , [19] . The vlsE gene , or expression locus is carried at the right end of the linear plasmid lp28-1 . In the absence of lp28-1 or when the vls locus is deleted a productive murine infection ensues , but the spirochetes are cleared between days 8 and 21 post-infection [16] , [17] , [20] , [21] . Adjacent to vlsE ( also referred to as vls1 ) , is a contiguous upstream array of 15 silent cassettes separated from each other by 17 bp direct repeats , which also flank the vlsE variable region ( see Fig . 1C in [18] ) . During murine infection ( and probably in other mammals ) information is transferred unidirectionally from the silent cassettes into the expression site to generate diversity at six regions ( VR1–VR6 ) within the central region of the vlsE gene [17] , [18] , [19] . These regions correspond to highly exposed regions of the VlsE protein and are believed to be prominently displayed antigenic areas [22] . Generation of antigen diversity occurs through segmental gene conversion such that information from several silent cassettes can be transferred into the single vlsE locus to generate a mosaic gene with possibilities for the production of myriad unique VlsE proteins . All silent cassettes are utilized as sequence donors in the gene conversion events at vlsE and the majority of recombination events are short , ranging from 1–22 codon changes [17] . Similarly , the requirement for flanking sequence homology is also short , in the neighborhood of approximately 10 nucleotides . An interesting feature of switching at vlsE is that it does not occur when spirochetes are grown in culture or when they reside in the tick midgut . [18] , [23] . Moreover , the acquired immune response is not required , as switching occurs in SCID mice , which lack the ability to mount an acquired immune response to antigenic challenge [9] , [16] , [17] , [20] . The mammalian signal that triggers recombinational switching remains unknown at this time . These features make the study of antigenic variation in B . burgdorferi difficult and limit these studies to animal infection models . In the mouse , antigenic switching can be detected four days after infection and by 28 days no parental vlsE sequences remain in the population of spirochetes recovered from some tissues in infected animals [9] , [17] , [20] . Even though B . burgdorferi has a small genome [24] , [25] , genetic manipulation is time consuming , inefficient and sometimes difficult [26] . The protein machinery that promotes recombinational switching at vlsE is , therefore , unknown at this time . A single study towards this end has reported that the B . burgdorferi recA gene is not required for antigenic switching [27] . In this study we generated 17 mutants carrying disruptions in known DNA recombination , repair and replication genes in the hopes of identifying proteins involved in recombinational switching at vlsE . A single recombination function , the RuvAB Holliday junction branch migrase encoded by the ruvA and ruvB genes , was unambiguously identified as a requirement for switching at vlsE , a result also reported in the accompanying paper by Lin et al [28] . In contrast , 10 known recombination , repair , or replication genes are required in recombinational events underlying antigenic switching at pilE in N . gonorrhoeae [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] . Eight of those genes are either missing or not required for switching at vlsE in B . burgdorferi . Our results point towards a unique mechanism for switching at vlsE in and suggest that it may involve specialized proteins that help to mediate the process .
A systematic approach was undertaken to disrupt 21 different genes in order to investigate their role in vlsE recombination in B . burgdorferi . Knockout plasmids were constructed ( Figure S1 ) and used to transform the infectious B . burgdorferi B31 clone 5A4 [40] . Following transformation , allelic exchange results in successful gene disruption ( Fig . 1A ) . However two other transformation outcomes can arise: integretative recombination , which results in merodiploid formation , and cases where no recombinants are recovered [26] . To investigate the structure of the B . burgdorferi transformants , they were screened using PCR with various primer combinations ( Fig . 1B ) . The presence of the gentamicin resistance cassette ( Panel 1 ) and the absence of the expected deleted sequences ( ∼500 bp ) from the disrupted target gene ( Panel 2 ) were first confirmed using the indicated primer sets . The target gene was also amplified ( Panel 3 ) to confirm the approximate 0 . 7kb size increase relative to wild-type DNA due to the insertion of the gentamicin resistance cassette . Finally , the correct insertion site was verified using combinations of the target and knockout primers to amplify the insertion boundaries ( Panel 4 ) . In addition to the PCR analyses , gene disruptions were independently confirmed by Southern hybridizations using probes specific to the gentamicin resistance cassette and the deleted portion of the target gene ( see Fig . S2 and Table S2 ) . Of the 21 DNA replication , repair and recombination gene knockouts attempted , 17 were successful ( Table 1 ) . When a disruption attempt was unsuccessful , the knockout plasmid was re-constructed in an effort to minimize possible effects on adjacent gene expression from read-through of transcription from the gent cassette . This was accomplished by either changing the polarity of the gentamicin resistance cassette relative to the gene target , or by adding ( or removing ) a T7 transcriptional terminator . Three gene targets required reconstruction of the knockout plasmid in order to successfully obtain B . burgdorferi gene disruptions . recJ was first attempted without the T7 terminator in the reverse orientation and resulted only in merodiploids . The gentamicin resistance cassette in the forward orientation with the T7 terminator did result in knockouts and further attempts were halted . The sbcD knockout was first attempted with a construct containing the T7 terminator and the gentamicin resistance cassette in the reverse orientation . This attempt resulted only in merodiploids; however , when the polarity was changed to the forward orientation , allelic exchange was successful . The recA disruption was also difficult to obtain . Unsuccessful attempts were first made with the gentamicin resistance cassette in the forward orientation with and without the T7 terminator . When the T7 terminator was removed and the gent gene was in the reverse polarity , true knockouts of the recA gene were obtained . Difficulty in obtaining a recA gene disruption has also been previously reported [41] , however , a single recA null mutant has been previously constructed with the insertion of a kanamycin resistance cassette in the forward orientation [27] . Finally , dnaB , hbb , recB and recC knockouts were not obtained despite changing the polarity of the gentamicin resistance cassette and adding or removing a T7 transcriptional terminator . For each gene disruption two clones were chosen which contained the full plasmid complement required for infectivity , as determined by PCR screening with primers specific to the plasmids . Most mutant constructs contained the full complement of plasmids found in the parental clone B31 5A4 [40] . Some of the mutant B . burgdorferi clones lacked plasmids that are not required for infection or persistence as follows: recJ1 lacks lp28-2 , mag2 lacks lp28-4 , ruvA1 is missing cp9 , mutL2 lacks cp9 and cp32-3 , nucA1 lacks lp21 and cp32-3 , ruvB5 lacks lp28-4 and cp9 and priA3 , recA2 and recA3 are missing cp9 . The possible effect of the mutations on growth of B . burgdorferi in culture was assessed by performing growth comparisons of each of the mutants with the wild-type clone 5A4 . All mutants displayed growth curves that were indistinguishable from the parent strain ( data not shown ) . Two independent knockout clones for each mutation were used to infect C3H/HeN immunocompetent mice ( at least two mice for each clone , four mice for each mutated gene ) as described in Materials and Methods . Cultures were grown from blood samples at day 7 to monitor infectivity . At days 14 and 21 , by which time spirochetes are largely cleared from the blood , ear biopsies were used to monitor infection and switching at vlsE . The upper portion of Table 2 shows mutant strains that displayed a productive infection ( ≥75% of mice infected at day 7 ) that did not decline at day 21 , the point post-infection when strains unable to switch at vlsE have been cleared [16] , [17] , [20] , [42] . The variable region of vlsE was amplified from DNA isolated from day 21 ear cultures and analyzed using RFLP assays ( Fig . 2 ) , which detect new restriction sites resulting from switching at the vlsE locus [16] . As noted in the upper portion of Table 2 , mutS2 , recA , recG , rep , nucA , mag , mfd and nth mutants all displayed switching at the vlsE locus . The lower portion of Table 2 shows mutant strains that displayed <75% positive cultures from ear biopsies at day 21 . When spirochetes could be cultivated at day 21 ( sbcD , sbcC and BBG32 ) switching was monitored and shown to occur using the RFLP assay . For the remainder of the mutant strains , infections were allowed to continue until day 35 . At this time the mice were euthanized and spirochetes cultivated from heart , bladder , joint and ear . RFLP switching assays indicated that switching at vlsE had occurred in all mutant strains from tissues where spirochetes could be recovered at day 35 , with the exception of those carrying the ruvA or ruvB mutations , whose functional genes encode the two subunits for a Holliday junction branch migrase [43] , [44] , [45] . Although the ruvA and ruvB mutant strains recovered from organ harvest at day 35 were negative for switching by RFLP , DNA sequencing analysis revealed that switching had occurred at low efficiency ( data not shown ) . Mutants of B . burgdorferi strains that did not show switching in wild-type C3H/HeN mice using the RFLP assay ( ruvA and ruvB ) and five other mutants that displayed a decreased persistence at day 21 ( recJ , mutL , sbcC , sbcD and BBG32 ) were used to infect SCID C3H/HeN mice , which lack an acquired immune response . This effectively removes the selective pressure on antigenic variation and allows B . burgdorferi mutants with defective switching at vlsE to persist in the host . Direct analysis of vlsE switching beyond 21 days post-infection can , therefore , be performed in SCID mice , whereas by this time non-switching spirochetes would be cleared in a wild-type mouse [16] , [17] , [20] , [42] . All the mutant strains tested displayed wild-type levels of infectivity and persistence throughout the 35 day course of infection in SCID mice ( Table 3 ) . This indicated that the ruvA and ruvB mutant strains , which did not switch in wild-type mice using the RFLP assay and which showed greatly reduced levels of persistence at day 21 ( Table 2 , bottom ) , were fully competent for the infection process in mice lacking an acquired immune response . The other mutant B . burgdorferi strains that displayed reduced infectivity and persistence were also fully rescued in mice lacking an acquired immune response . The RFLP assay used here provides a quick and convenient assay method to detect switching at vlsE [16] . The incorporation of new restriction endonuclease sites from the silent cassettes into the variable region of vlsE is a clear indicator of the switching process . However , the fact that the assay is not quantitative , coupled with the observation that switching is apparently less frequent in SCID mice [17] , led us to further analyze switching in SCID mice in a limited set of mutants by DNA sequencing . We chose the two mutants that were negative for switching in wild-type mice by RFLP analysis ( ruvA and ruvB ) as well as two mutants that were shown to switch by RFLP at 35 days in wild-type mice , but that displayed no spirochetes in ear cultures at 21 days ( recJ and mutL ) . For DNA sequencing studies the same PCR product used for the RFLP assay ( a 776 bp fragment containing the vlsE variable region ) was amplified from the spirochetes recovered from four different tissue types at day 35 for each SCID mouse and gel purified ( see Material and Methods ) . Equimolar amounts of the vlsE PCR product from each tissue from the 4 mice used in the infections were combined , providing four pools for each disrupted gene: heart , bladder , joint and ear . The pools were cloned and 10 E . coli clones were chosen for each tissue type for a total of 40 vlsE sequences examined for each of the recJ , mutL , ruvA , ruvB and wild-type genotypes . Using a primer specific to the cloning vector , the plasmid DNA was sequenced and compared to the B . burgdorferi 5A4 parental vlsE sequence for both templated ( present in a silent cassette ) and non-templated nucleotide changes [17] , [19] . Each sequenced clone where switching at vlsE had occurred displayed a unique sequence; hence , all switch variants from each mouse represented independent switching outcomes . Sequencing revealed that 10 out of 10 wild-type clones contained nucleotide changes corresponding to sequences found in the silent cassettes in the heart and bladder tissue cultures while 5/10 and 8/10 clones had switched in the joint and ear tissues , respectively ( see Fig . 3 ) . These results are similar to previously reported data which indicated a greater proportion of switched clones in heart , bladder and skin tissues than in joint and ear tissues [17] . The overall switching frequency that we observed ( 82 . 5% at day 35 post-infection in SCID C3H/HeN mice ) also correlates closely with the value of 85% at 28 days post-infection recently observed [17] . For proper analysis , a tissue-specific comparison between mutants and wild-type spirochetes was undertaken as shown in Fig . 3 . The most significant reduction in switching occurred with ruvA and ruvB mutants with only one of 40 clones ( 2 . 5% ) differing from the wild-type vlsE sequence in ruvA and no changes observed in any of the 40 ruvB clones . The single clone demonstrating switching at vlsE in the ruvA mutant was from a clone cultivated from joint that displayed at least four exchanges with silent cassettes and did not show any features with obvious differences from switching in wild-type B . burgdorferi . The P-values indicated a significant difference ( <0 . 05 ) in the incidence of switching for all tissues , with the exception of the ruvA mutant in joint . These results corroborated the negative switching phenotype of the ruvA and ruvB mutants observed in the RFLP switching assay after infection of wild-type and SCID mice ( Tables 2 and 3 ) . The results are further strengthened by the fact that ruvA and ruvB encode the two subunits of an enzyme known to promote branch migration of Holliday junction recombination intermediates . Clones carrying a mutL mutation displayed an intermediate phenotype with a decrease in switching resulting in a total of only 27 . 5% of the clones exhibiting nucleotide changes , versus 82 . 5% for wild-type . Significant tissue-specific differences were observed in the bladder and heart but not the joint and ear ( Fig . 3 ) . recJ showed a slight change in the level of switching with 57 . 5% of the clones displaying changes in the vlsE variable region compared to 85% for wild-type . A significant difference in tissue-specific switching was only observed in the heart . Switching sequencing data were also analyzed by counting the number of nucleotide changes in each clone ( Table S3 ) and gave similar results ( see also Discussion ) . DNA sequencing was also performed on the vlsE variable region from infections with sbcC , sbcD and BBG32 B . burgdorferi mutants , which displayed wild-type switching levels in all four ( sbcC ) tissues or in three of the four four ( sbcD and BBG32 ) tissue types ( data not shown ) . In addition to the apparent templated nucleotide changes observed in switches at vlsE , some non-templated changes ( NTCs ) , where new sequence at vlsE did not correspond to the sequence found in any of the silent cassettes , were also observed [17] , [46] . There were no NTCs in the total of 80 vlsE sequences analyzed for wild-type and ruvB clones . In ruvA , four NTCs were observed in non-switching clones and three in the single clone that switched . In the mutL mutant there were five NTCs and all of these occurred in clones that did not switch . Finally the recJ heart sample had six NTCs in three clones , two of which switched . Taken together there were a total of 18 NTCs in the ruvA , mutL and recJ mutants .
Homologous recombination in bacteria is typically initiated by RecA-mediated pairing and strand invasion [51] , [52] , [53] . It has been previously reported that a recA gene disruption in B . burgdorferi did not affect switching at vlsE [27] . Because the recA gene in B . burgdorferi is not easily disrupted [41] and because a single clone with the disrupted gene was used to assess the role of recA in recombinational switching at vlsE [27] , we constructed several recA knockouts and tested two of them for switching at the vlsE locus . Our results confirm the previous findings that recA is not required [27] . This raises the interesting question of how pairing and strand invasion is initiated for recombinational switching at the vlsE locus . The lack of a requirement for the RecA protein , and the unidirectional segmental gene conversion events that characterize switching at the vls locus in B . burgdorferi argue for the need of a specialized protein ( s ) to help mediate the process . The expendability of RecA for antigenic variation in B . burgdorferi is also a stark difference from antigenic variation systems in other organisms , as will be discussed below . The RuvAB complex is required for homologous recombination and facilitates ATP-dependent branch migration of heteroduplex DNA in Holliday junctions [43] , [44] , [45] . RuvA tetramers bind and unfold the heteroduplex DNA and recruit RuvB hexamers , which function as a helicase to move DNA through the RuvAB complex . B . burgdorferi RuvA and RuvB share 32% and 48% identity with their E . coli orthologues , respectively . ruvAB mutants in E . coli have only modest defects in homologous recombination . However , these defects become significant when there are also mutations in other recombination proteins , such as recBC , recG and sbcBC [54] , [55] . The role of RuvAB in DNA repair and recombination has not been previously investigated in B . burgdorferi . The observed infection phenotype of both ruvA and both ruvB mutant strains reported here was as previously observed for strains lacking either lp28-1 [16] , [17] , [20] , [21] or the vls locus [16] , [17] , [20] , [21] , where switching cannot occur . Infection of wild-type mice was 100% at day 7 with apparent complete clearance at day 21 ( Table 2 ) . Complete rescue of the persistence defect for ruv mutant strains was observed in all cases in SCID mice ( Table 3 ) . A difference in phenotype between strains lacking either lp28-1 or the vls locus , with those carrying a ruv mutation is that at 35 days post-infection spirochetes could be recovered in some organ harvest cultures from wild-type mice infected with the ruv mutants . DNA sequence analysis revealed that a single switch variant was present in spirochetes from a given tissue , or from both tissues in the two mice where positive cultures were recovered from two sites . These results are indicative of low frequency switching in the mutants , resulting in occasional survival and selection of a single switch variant . This phenotype has also been observed by [28] . The phenotype and the dramatic inhibition of switching at vlsE ( Fig . 3 ) of the strains carrying the ruvA and ruvB gene disruptions in this study identify the first protein factors involved in switching and support a mechanism involving branch migration of a recombination intermediate for antigenic variation in B . burgdorferi . Although we were unable to complement our ruv mutations ( data not shown ) , genetic complementation in B . burgdorferi is frequently difficult to achieve , for reasons not currently understood . We nonetheless argue for the absence of secondary mutations in the mutant B . burgdorferi strains based upon the following arguments: 1 ) Our studies were performed with two independent mutations in both the ruvA and ruvB genes . The four independent mutant strains demonstrated the same phenotype , making the existence of secondary mutations exceedingly remote . 2 ) Although not a strict genetic complementation , the rescue of persistent infectivity in all four ruv mutants following the infection of SCID mice is compelling evidence for the absence of any secondary mutations affecting infectivity . 3 ) Similar results and conclusions with ruv mutants made by transposon mutagenesis in the accompanying idependent study from the Norris lab [28] corroborate the findings presented here and our combined results provide compelling evidence for a RuvAB role in switching at vlsE . A companion protein to RuvAB in most bacteria is the Holliday junction resolving enzyme RuvC . B . burgdorferi does not encode a RuvC orthologue and has no characterized junction resolving enzyme . This leaves unanswered the question of how recombination intermediates involved in homologous recombination or switching at vlsE are processed . A series of putative LE family exonucleases encoded by the cp32 family of circular plasmids has been proposed as possible substitutes for RuvC in B . burgdorferi [56] . The actual function of these λ exonuclease-type proteins remains to be established and simultaneous disruption of all of them ( ∼9 ) is outside the realm of possibility with current genetic methods available for B . burgdorferi . mutL and recJ are both players in bacterial mismatch repair [57] , [58] , [59] . In E . coli MutL acts as a liaison between MutS , which recognizes the mismatch , and MutH which is responsible for introducing a nick on either side of the mismatch . Both mutS1 and mutS2 are present in the B . burgdorferi genome , but disruption of either gene did not affect the infectivity phenotype in wild-type mice or switching as assayed by RFLP . There is no identifiable mutH orthologue in B . burgdorferi [25] . Disruption of mutL resulted in a modest decrease in switching at vlsE that was significant ( Fig . 3 ) or near significant ( Table S3 ) in heart and bladder but not in ear and joint . The results did not allow a clear-cut conclusion on the involvement of MutL in switching as demonstrated for RuvA and RuvB . Further analyses will be required to derive an unambiguous answer to this question . It is possible that MutL plays a role in recombinational switching at vlsE , but that another B . burgdorferi protein can substitute for MutL because of functional redundancy . In such a case a double knockout will be required for further investigation; however , a functional paralogue of MutL has not been identified in B . burgdorferi at this time . It is also possible that the reduction in switching in mutL mutants results from a decreased level of fitness and a slower growth rate of the mutant in the mouse ( Table 2 ) . RecJ is a 5′ to 3′ exonuclease that in E . coli can promote mismatch excision and prepares DNA for strand invasion by creating the single-stranded 3′-overhang [53] . Disruption of recJ resulted in a decrease in infectivity in wild-type mice at 21 days and a modest decrease in switching at vlsE that was significant only in the heart for both methods of analysis ( Fig . 3 and Table S3 ) . Again , the results did not permit an unambiguous conclusion as to the possible involvement of RecJ in switching at vlsE . The slightly decreased vlsE switching phenotype observed in B . burgdorferi could be a result of redundancy of function for recJ and recD as has been previously reported in E . coli [60] , [61] . A recD recJ double mutant , if viable , might provide further information regarding the role of recJ in vlsE recombination . Alternatively , the decrease in switching might simply reflect a decreased level of fitness and infectivity of the mutant spirochete in the mouse and there may be no direct role of RecJ in switching at vlsE . Finally , it is noteworthy that mutL and recJ were the only genotypes sequenced that also contained non-templated nucleotide changes ( NTCs ) . mutL had five NTCs while recJ had six for a total of eleven across 7 clones . The NTCs in this study occurred predominantly in invariable regions where nucleotide changes are not normally observed . An explanation for why NTCs were only observed in mutL and recJ could be due to their involvement in the mismatch repair pathway . These data suggest that these repair proteins are normally operative at vlsE to correct mismatches and would , therefore , normally be temporally and spatially positioned to play a role in the switching process as well . Recent work on switching at vlsE has reported that approximately 15% of wild-type vlsE variants carry NTCs [17] . This was not observed in our sequencing data with wild-type , ruvA or ruvB mutant clones . We have no explanation for this discrepancy . While a wide variety of bacterial and protozoan pathogens employ antigenic variation systems driven by gene conversion [5] , [6] , the molecular details of the recombinational events underlying the process remain largely obscure . Information about some of the protein factors required for gene conversion events are available only from studies on the bacterial pathogen N . gonorrhoeae and the protozoan parasite Trypanosoma brucei . Both of these organisms require either RecA [37] or paralogues of its eukaryotic RecA counterpart , Rad51 [62] , [63] . In contrast , both this study and a previous one [27] have shown that RecA is not necessary for switching at vlsE in B . burgdorferi . In N . gonorrhoeae the recFOR pathway is also involved and disruptions in recQ , recO , recR or recJ result in elimination or fairly dramatic reductions in antigenic variation [30] , [32] , [33] . In contrast , B . burgdorferi does not carry recO , recR or recQ orthologues and disruption of recJ did not demonstrate a clear role for its encoded protein in switching at vlsE . The Holliday junction resolution pathway ( ruvA , ruvB , ruvC and recG ) was also found to be important in N . gonorrhoeae , with disruptions in these genes resulting in a dramatic decrease in antigenic variation [31] , [32] . In B . burgdorferi we found the RuvAB branch migrase to be required for switching at vlsE , however , there is no known ruvC orthologue and disruption of recG , which encodes a helicase that can function in Holliday junction migration , does not affect switching at vlsE . In summary , the process of recombinational switching at the vlsE locus shows very dramatic differences in protein requirements compared to the antigenic variation process in N . gonorrhoeae , with only the RuvAB branch migrase in common . Further studies on the recombinational switching underlying antigenic variation will be required to unravel the elusive molecular details of this fascinating process .
Infectious Borrelia burgdorferi 5A4 , derived from the type strain B31 [40] , was cultivated in BSK-II medium prepared in-house [64] , supplemented with 6% rabbit serum ( Cedarlane Laboratories , Burlington , ON , CA ) ) and incubated at 35°C ( with a 1 . 5% CO2 environment for plating ) . Bacterial density was determined using a Petroff- Hausser Chamber ( Hausser Scientific Partnership ) and dark-field phase contrast microscopy with a Nikon Eclipse E400 microscope . E . coli DH5α was used for all knockout plasmid construction and maintenance . B . burgdorferi 5A4 were transformed as previously described with 25–50 µg of knockout plasmid DNA [65] , [66] . Following transformation , the cell suspensions were immediately added to 10ml of pre-warmed BSK II supplemented with 6% rabbit serum . The transformations were allowed to recover for 24 hours at 35°C with 1 . 5% CO2 . Recovery cultures were added to BSK II with 6% rabbit serum to a final volume of 50–100 ml and supplemented with 200 µg/ml gentamicin after which 250 µl aliquots were distributed into 96-well plates and incubated at 35°C and 1 . 5% CO2 until some wells with a visible color change from red to yellow were observed , usually between 8–12 days . Yellow wells were chosen for PCR analysis . Primers for amplifying a centrally located ∼1500 bp portion in the gene of interest were designed according to the published sequence information ( accession numbers NC_001318 and NC_001852 ) [25] . The target was amplified by PCR using Phusion High-Fidelity DNA Polymerase ( Finnzymes ) , used for all subsequent PCRs unless otherwise noted using 80ng of genomic DNA template from B . burgdorferi B31 5A4 [40] . In early experiments the PCR product was cloned into the pJET1 . 2/blunt vector ( Fermentas ) and subsequently into the pCR BluntII-TOPO vector ( Invitrogen ) . Plasmid DNA was isolated using the GeneJet Plasmid Miniprep Kit ( Fermentas ) . Inverse PCR was employed to generate the knockout plasmid backbone [26] . Outward-oriented primers ( see Table S1 ) with 5′ NheI restriction sites were designed within the target gene such that the amplicon produced did not contain approximately 500bp from the center of the target gene ( see Fig . S1 ) . The inverse PCR product was purified using the QIAquick PCR Purification Kit ( Qiagen ) . The flgB promoter-driven gentamicin resistance cassette ( aacC1 ) was used to disrupt the B . burgdorferi target gene . This cassette was incorporated into the knockout plasmid by amplifying the flgB promoter-driven gentamicin resistance gene from the plasmid shuttle vector pBSV2G [67] using primers B415 and B416 containing 5′ Nhe I sites . A gentamicin resistance cassette with a T7 transcriptional terminator ( 5′ CTG CTA ACA AAG CCC GAA AGG AAG CTG AGT TGG CTG CTG CCA CCG CTG AGC AAT AAC TAG CA TAA CCC CTT GGG GCC TCT AAA CGG GTC TTG AGG GGT TTT TTG 3′ ) was also used . This cassette was constructed using overlap extension PCR . The gentamicin resistance gene was amplified from pBSV2G using primers B820 and B1350 , and the T7 terminator , originally from the pGEM-T easy vector ( Promega ) , was amplified from a plasmid construct ( pTAKanT7t ) generously provided by Scott Samuels using primers B1349 and B1345 . Following NheI ( New England BioLabs ) digestion the gentamicin resistance cassette and knockout plasmid backbone were ligated and used to transform DH5α , with selection using 10µg/ml gentamicin , with the addition of 50µg/ml kanamycin when the pCR BluntII-TOPO vector was used . Transformants were analyzed by PCR to identify clones with bona fide gene disruptions and to distinguish them from merodiploids . PCR was performed using Taq polymerase ( New England BioLabs ) and a combination of primers to confirm legitimate allelic exchange ( see Fig . 1 ) . Primers B348 and B349 were used to amplify the gentamicin resistance cassette , the knockout primers ( KO/f and KO/r ) for each mutant were used to confirm gene disruption . The target gene primers ( target/f and target/r ) for each mutant were used to confirm the correct insertion size upon recombination . Finally , primers B349 and B1281 were used in conjunction with the target primers to verify the correct insertion site and integrity of the recombination boundaries . Southern hybridization analysis was used for verification of legitimate allelic exchange in the mutants selected for further study . Approximately 600ng of genomic DNA , prepared using the Wizard Genomic DNA Purification Kit ( Promega ) or mini-genomic DNA preps [68] , was digested with HindIII ( New England BioLabs ) and separated on a 1 . 2% agarose gel run at 75V for 1 . 5 hours . After staining with 0 . 5µg/ml ethidium bromide to confirm complete enzymatic digestion the DNA was depurinated , denatured and neutralized as previously noted [69] . DNA was transferred to membranes ( Hybond-N+ Amersham ) and cross-linked using the UV Stratalinker 1800 ( Stratagene ) . The gent probes were prepared from the PCR product of pBSV2G using primers B348 and B349 . The KO/f and KO/r primers specific to each mutant were used to generate the probes using PCR from the genomic DNA of B . burgdorferi 5A4 . The probes were labeled with [α-32P] dCTP by random primer labeling with a Random Primers DNA Labeling System Kit ( Invitrogen ) . Standard procedures were used to pre-hybridize , hybridize and wash the blots [69] , after which they were exposed to phosphor screens and analyzed with a Cyclone Phosphoimager ( Packard ) . PCR analysis of plasmid content was performed as previously described to ensure the mutant clones contained the full plasmid complement required for infectivity [40] , [70] . All animal infections were carried out in accordance with approved protocols from the University of Calgary Animal Research Centre and were approved by the University of Calgary Animal Care Committee . Three to four week old male C3H/HeN ( wild-type ) or three to five week old male C3H . C-PrkdcSCID/IcrSmnHsd ( SCID ) mice ( Harlan , Indianapolis , IN ) were inoculated with 200 µl of 1×104 spirochetes/ml , in two 100 µl doses via dorsal subcutaneous and intraperitoneal injection . At seven days post-infection , 50 µl of blood was taken from the saphenous vein on the hind leg of the mouse under aseptic conditions . The exposed vein was opened using a needle prick and the pooled blood was drawn with a pipette . The pipette tip used to draw the blood was first coated with 0 . 5M EDTA to prevent clotting . The blood sample was suspended in 1 . 7ml BSK II supplemented with 6% rabbit serum and 1× Borrelia antibiotic cocktail ( 20 µg/ml phosphomycin , 50 µg/ml rifampicin and 2 . 5 µg/ml amphotericin B ) and cultivated as described above . Ear biopsies were performed at days 14 and 21 and the recovered material was cultivated in 1 . 5ml of BSK II supplemented with 6% rabbit serum and 1× B . burgdorferi antibiotic cocktail for one to five weeks . The presence or absence of spirochetes was periodically monitored by dark-field microscopy . When necessary , a 35 day organ harvest was performed and the heart , bladder , joint and ear biopsy samples were removed aseptically and cultured in 1 . 7ml of BSK II supplemented with 6% rabbit serum as noted above . Switching at the vlsE locus was determined by a restriction fragment length polymorphism ( RFLP ) assay using the 775 bp product of PCR amplification of the vlsE expression site using primers B248 and B249 [16] . This crude switching assay was performed on week three ear biopsy cultures when available , or on week 5 organ harvest cultures . Phusion polymerase ( Finnzymes ) was used for PCR and the product was purified using the QIAquick PCR Purification Kit ( Qiagen ) . Approximately 200ng of PCR product was digested with 2 units of HphI ( New England BioLabs ) for 1 . 5 hours . Reaction products were analyzed on a 1 . 2% agarose gel run at 75 V for 1 . 5 hours in Tris-acetate buffer , stained with 0 . 5 µg/ml ethidium bromide and images acquired using a FluorChem 8900 imaging system . For detailed analysis of switching at vlsE , mutant B . burgdorferi strains were used to infect C3H/HeN SCID mice . Switching in SCID mice was characterized through sequencing of the variable regions of the vlsE expression site . PCR amplification was performed using primers B248 and B249 on 1µl of BSK-II cultures , grown to a density of 1×106 spirochetes/ml , taken from glycerol stocks of the heart , bladder , joint and ear organ harvests for each mouse . Reaction conditions were as follows: 98°C for 2 minutes , 28 cycles of 98°C for 10 seconds and 72°C for 30 seconds , followed by a final extension of 72°C for 5 minutes . PCR products were visualized and quantified on a 1% agarose gel run in TAE buffer at 75V for 1 . 5 hours , stained with 0 . 5 µg/ml ethidium bromide . Equimolar portions of the PCR product from each of the four mice ( two mice for each clone ) and each tissue type were combined to give a total of four samples: heart , bladder , joint and ear for each genotype investigated . These PCR products were run on a 1% agarose gel and the 775 bp PCR product was excised and gel purified using the Qiagen Gel Extraction Kit ( Quiagen ) . The PCR fragments were cloned into the pJET1 . 2/blunt vector ( CloneJet , Fermentas ) and used to transform E . coli DH5α . The transformations were plated on LB agar plates containing 100 µg/ml carbenicillin at 37°C . In preparation for sequencing , 10 colonies of each tissue type for each mutant were picked and grown in five ml LB supplemented with100 µg/ml carbenicillin for a total of 40 samples from each genotype . These cultures were grown overnight at 37°C and plasmid DNA was isolated using the Qiagen 96 Turbo miniprep kit ( Qiagen ) . The University of Calgary Core DNA Services sequenced 500 ng of the plasmid DNA with the pJET1 . 2forward sequencing primer ( CloneJet , Fermentas ) in a 96 well format using an Applied Biosystems 3730XL 96 Capillary Sequencer ( http://www . ucalgary . ca/dnalab/ ) . Alignments comparing the cloned vlsE sequencing results for each tissue type in each mutant to the parental vlsE sequence of B . burgdorferi 5A4 were performed using the Seqman DNASTAR-Lasergene v6 Software . Templated nucleotide changes , those corresponding to the sequence of at least one silent casette , were counted in each variable region as well as the invariable regions and noted ( Table S3 ) . Additionally , non-templated changes were documented ( data not shown ) . It is important to note that each of the 10 sequences obtained for each tissue type for each mutant were different and , therefore , all sequenced clones represented completely independent switching outcomes . Results were analyzed on a tissue-specific basis via two methods ( Fig . 3 ) . The first method took into account how many clones from each tissue type switched and how many retained the parental vlsE sequence . The two-tailed Fisher's Exact test was used to determine the P-values of the mutant switch events ( GraphPad Prism ) . The second method used to analyze switching was a comparison of data based on the number of nucleotide changes in each tissue for each mutant ( Table S3 ) . A two-tailed non-parametric Mann-Whitney student t-test was used to determine the P-values of these data ( Graph Pad Prism ) . | A common strategy for evasion of the host immune system is the continuous variation of a major surface protein that elicits a dominant immune response ( antigenic variation ) . Many pathogens accomplish this goal by unidirectional movement of DNA sequence information from silent or archival gene copies into an expression site . The molecular details of how this gene shuffling is accomplished are not understood for any organism . In the flat-wave shaped bacterium causing Lyme disease , information is moved from 15 silent cassettes into the vlsE gene to promote antigenic variation . In this work we have investigated the effect of independent mutation of 17 DNA replication , recombination and repair genes on the movement of genetic information into vlsE . We found that mutation of either of the genes encoding the two subunits of the RuvAB branch migrase blocked transfer of genetic information into vlsE during mouse infections , identifying the first required function for antigenic variation in the Lyme disease spirochete . | [
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] | 2009 | Investigation of the Genes Involved in Antigenic Switching at the vlsE Locus in Borrelia burgdorferi: An Essential Role for the RuvAB Branch Migrase |
Deep convolutional networks ( DCNNs ) are achieving previously unseen performance in object classification , raising questions about whether DCNNs operate similarly to human vision . In biological vision , shape is arguably the most important cue for recognition . We tested the role of shape information in DCNNs trained to recognize objects . In Experiment 1 , we presented a trained DCNN with object silhouettes that preserved overall shape but were filled with surface texture taken from other objects . Shape cues appeared to play some role in the classification of artifacts , but little or none for animals . In Experiments 2–4 , DCNNs showed no ability to classify glass figurines or outlines but correctly classified some silhouettes . Aspects of these results led us to hypothesize that DCNNs do not distinguish object’s bounding contours from other edges , and that DCNNs access some local shape features , but not global shape . In Experiment 5 , we tested this hypothesis with displays that preserved local features but disrupted global shape , and vice versa . With disrupted global shape , which reduced human accuracy to 28% , DCNNs gave the same classification labels as with ordinary shapes . Conversely , local contour changes eliminated accurate DCNN classification but caused no difficulty for human observers . These results provide evidence that DCNNs have access to some local shape information in the form of local edge relations , but they have no access to global object shapes .
DCNNs have attracted considerable attention , and several different approaches have been used to compare their performance to human object processing . Some of the similarities begin with the basic architecture . Deep convolutional neural networks perform a series of nonlinear transformations on input data such as an image in the case of object recognition . The final transformation outputs a vector of category probability values , one for each object category . Critically , early layers of these networks are not fully connected as in classical neural networks . Instead , they have convolutional windows that preserve spatial information in the image [6] . In modern DCNNs , early layers tend to operate on very local regions of the image , while deeper into the network , each node receives input from filters over a larger area of the image , allowing the network to access relations between more distant regions [4] . This network architecture has some obvious similarities with biological vision . Convolutional layers are analogous to receptive fields in visual cortex , which likewise consider more disparate regions together at higher levels of extrastriate cortex [7] . Do the similarities between DCNNs and biological vision go deeper than this basic architectural feature ? One way to evaluate this is by comparing physiological activity of neural units in biological systems with the activity of certain nodes in an artificial network . Pospisil , Pasupathy , and Bair presented AlexNet , a groundbreaking DCNN , with shape stimuli to which V4 cells are optimally tuned [8] and found that there is some resemblance between node responses in intermediate layers of AlexNet and cell responses in V4 , although the network response was quite sparse compared to biological systems , with many units responding to none or very few of the shape stimuli [9] . Other studies have looked at the correlation between a network’s classification accuracy and the similarity between network representations and representations in the inferotemporal gyrus ( IT ) . Randomly varying parameters across several networks , they found that the activity of nodes in networks that perform better on the object classification task give better predictions about the activity of clusters of neurons in IT when primates are presented with the same image [10 , 11] . Comparisons have also been made between a network’s performance and human behavior in similar tasks . In a sense , all performance measures on image classification are a comparison to human vision , as accuracy is being measured based on labels assigned by humans [12] . However , to evaluate similarities and differences between DCNNs and human vision , it can be instructive to examine network performance on tasks for which they were not explicitly trained . Several experiments have found similarities between convolutional networks and humans in such tasks . One study used features from a convolutional network to predict the memorability of certain object segments . Features extracted from the DCNN were predictive of objects’ memorability for human subjects , suggesting that humans and networks might be attending to similar features when viewing an object [13] . In another study , Peterson , Abbott , and Griffiths found a strong correlation between similarity judgments made by DCNNs with human similarity ratings [14] . Although it is interesting to observe similar performance level for object recognition between DCNNs and biological vision , it is unclear whether the systems process information for object recognition in a similar manner . The present paper focuses on the perception of shapes , the most important cue for object recognition [15] . Objects can be recognized accurately despite impoverishments across every other visual dimension provided that global shape information is preserved [16 , 17] . For example , consider the image pair presented in Fig 1 . In Fig 1A , the information available for recognition has been significantly reduced across several feature dimensions . The object has no texture or background context , and the information along its contour has been simplified . Still , it is far more easily recognized as a bear than the object in Fig 1B , where cues like texture and context are preserved , but object shape is interrupted . Similarly , an ordinary line drawing , or even a few well-chosen lines as in a Picasso sketch , readily allows object recognition via shape perception processes in humans . If deep networks are to be taken as models of human perception , we would expect object shape to be a critical component of their classification decisions . Currently , it is unclear to what extent shape representations play a role in object recognition in DCNNs . Kubilius , Bracci , and Op de Beeck conducted several intriguing experiments that suggest deep networks do have shape representations that are reasonably similar to human shape representations [18] . Networks were able to classify object silhouettes with ~40% accuracy and had some sensitivity to non-accidental features of an object , which are thought to be important for recognition in human observers [16] . They also compared the impact of shape cues on recognition performance for the networks with different architectures ( e . g . , different number of layers ) and found some evidence that deeper networks did better on tasks where object shape was important to performance . On the other hand , some research on DCNNs is difficult to reconcile with the claim that they utilize global shape of objects in detecting and recognizing objects . Szegedy , Zaremba , Sutskever , Bruna , Erhan , Goodfellow , and Fergus found that perturbation of a small subset of image pixels could result in consistent misclassification of the image , across multiple DCNNs , despite the changes being undetectable to human observers [19] . In the perturbed images , global shape of the object is unchanged , so the change in classification revealed that the global shape information of objects is likely not used for recognizing objects in the DCNNs . Another study used evolutionary algorithms to develop images that networks classified as certain objects with a high degree of confidence , despite a total absence of object shape in the images [20] . Zhu , Xie , and Yuille tested DCNN classification accuracy for images in which the object to be classified was removed [21] . They found that despite the object not actually being present in the image , networks performed reliably better than chance on the classification task , based purely on contextual information . These examples suggest that shape might be neither sufficient nor necessary for recognition in DCNNs . More systematic tests are needed to understand whether or not , or in what ways , DCNNs process object shape . The fact that a network’s responses are sensitive to variables other than shape ( such as texture or color ) may reflect a valid use of information in its training history . The supervised learning method with which DCNNs are trained is agnostic about what information to consider when classifying an image . In natural images , texture and shape information are often highly correlated , so we can learn little about what cue is most relevant to the network’s classification without disentangling them . Reduction of performance by disruption of other variables may indicate that shape representations do not predominate , but such outcomes do not necessarily imply that shape information is not captured or potentially usable within the network . We term this “the latency problem” . To show that shape information is not implicitly captured or usable in a system , more systematic tests are required than simply showing that variables other than shape can be decisive in classification performance . Shape information may nevertheless be something DCNNs can in principle capture , but it may be latent in the system , overshadowed by other informational variables relevant for classification . Conversely , tests that have suggested that DCNN’s do use shape information have not distinguished local shape features from more global shape characteristics , nor have they disentangled responses to local orientation relations in visible contours from the more shape-defining contributions of the bounding contours of objects . Questions about shape processing in DCNNs can be considered at three different levels: First , do deep networks trained to recognize objects use shape features in their classification decision ? Second , can deep networks be trained to use shape information in their classification decisions ? Third , can formal analysis of deep networks’ computational processes tell us what kinds of shape features they can and cannot extract from an image ? In the current study , we focus on the first question , aiming to understand the capabilities that deep networks automatically learn through training on natural images . We consider the study of trained networks to be particularly important for two reasons . First , the attention garnered by DCNNs relates to the success of trained networks in object classification tasks . Both for theoretical understanding of these achievements and for practical applications , understanding how DCNNs achieve their high classification is important . Second , comparisons between biological vision systems and DCNNs require understanding how trained networks are operating , and require an understanding of the role of shape processing in particular . A sizable literature has recently emerged finding similarity between DCNNs trained on object recognition tasks and human perception [9–11 , 13 , 14 , 22–26] . These efforts highlight the importance of understanding the functioning of trained DCNNs that are successful in object recognition . In the experiments reported here , we presented DCNNs trained for object recognition ( and , where appropriate , human observers ) with stimuli intended to reveal information about the usability of global shape and bounding contours . The series of experiments was designed to provide multiple sources of information with regard to the latency problem in characterizing shape capabilities , in the process clarifying the roles of texture information and object shape in deep convolutional networks’ classification decisions . To carry out these studies , we tested two commonly used deep convolutional networks: AlexNet [4] and VGG-19 [27] . AlexNet has eight layers and is the deep network that started the DCNN revolution for object recognition . VGG-19 is deeper , 19 layers , and approaches the state of the art in object classification . Our approach was to use a variety of systematically modified stimuli to reveal the contribution of shape information to network responses . In Experiment 1 , we examined the relative importance of overall shape and texture information by using objects in which the overall shape was preserved , but different texture , from another object , was superimposed on the object’s silhouette . In Experiments 2–4 , we tested the networks on images with impoverished or altered texture and context information by using glass figurines , object outlines , and silhouettes . Following up the results and hypotheses emerging from these experiments , we tested the networks on images with manipulations that altered shape at a global level , while largely preserving local shape features , and vice versa ( Experiment 5 ) .
As noted earlier , shape is of great importance in human perception of objects , and shape information predominates in human object recognition . Prior tests of DCNNs have yielded some evidence that they classify by means of shape , whereas other work revealed examples in which images with textural similarity , but no shape in common with an object , were classified as that object with a high degree of confidence [20] . In Experiment 1 , we directly compared convolutional networks’ use of shape and texture information in their classification decision . Using object silhouettes with no surface information , we overlaid a texture from a different object on top of the black figural region . We then compared the networks’ preference for both the object whose shape is displayed and the object whose texture is displayed . Experiment 1 showed that in displays that preserved overall object shape but altered their texture , shape was a poor predictor of network classifications . There was some indication , however , of sensitivity to shape information in certain cases . The network made several accurate classifications of objects with non-canonical surface texture . This success appeared to be largely confined to artifacts , although even artifact classifications included many implausible top selections . On the other hand , shape information appeared to be largely irrelevant for classification responses generated for animal displays . In Experiments 2–4 , we developed more detailed tests to examine whether networks could classify objects only based on shape with changed or absent surface texture and context information . It is a remarkable fact , one attesting to the primacy of shape processing in human perception , that human observers readily recognize shapes in arbitrary materials ( and construct and display them , etc . ) . In Experiment 2 , we presented two deep networks with glass figurines . All figurines were pictures of real glass objects . Since glass figurines lack the natural surface colors and textures of the objects represented , we expected that accurate classification would be difficult without a representation of the object’s bounding shape . We expected that if the networks did have access to object shape , they might be able to accurately classify the glass figurines even in the absence of other , usually accompanying , cues for recognition . In other words , DCNN classifications that would resemble even a child’s intuitive response the first time they see a glass elephant would furnish evidence that shape information plays a role in DCNN classification . A remarkable fact about human object perception is that we readily extract shape from outline drawings . This ability clearly depends on shape , as outlines omit surface information completely . Object outlines have the same texture within the bounding contour as outside it , and there is no variation in texture between the outlines of two different objects . We tested outlines in Experiment 3 to extend the earlier results and specifically to remove competing texture or surface information as much as possible . If , for example , the pictures of glass figurines somehow distracted deep networks from utilizing some encoded shape information due to competing surface texture , we expected that the problem would be substantially mitigated by using outlines . On the other hand , if deep networks cannot access shape from outline drawings of objects , we expected poor performance . Experiments 2 and 3 found little evidence that deep convolutional networks access global shape in object recognition tasks . These results may seem surprising , as some recent reports have suggested that DCNNs do possess some shape classification abilities . Kubilius et al . [18] found that removing surface features from the Snodgrass and Vanderwart dataset of colored-in line drawings [37] did not totally destroy networks’ classification performance . With the regular line-drawing images , classification performance was 80–90% . Removal of color information reduced performance to around 70% , and removal of all inner surface gradients ( black silhouettes ) brought performance down to about 40% . It is arguable whether 40% classification accuracy represents a success or failure in shape-based classification for DCNNs . On the one hand , this marks a divergence from human performance , which is largely unaffected by the removal of color and inner surface gradient information from most objects . On the other hand , it seems almost impossible that the network would reach even 40% accuracy without some information about object shape . In contrast , our findings about recognition for glass objects and line drawings provided almost no evidence that shape representations are used for classification in DCNNs . In Experiment 4 , we tried to replicate Kubilius et al . ’s findings as a first step in clarifying this apparent discrepancy . In Experiments 2–4 , only the silhouette condition provides some supportive evidence that DCNNs use shape . However , further analysis of the networks’ classification of individual items in Experiments 2–4 suggest that some accurate classification decision is likely based on local contour features , not global object shape . We tested this hypothesis in Experiment 5 , by comparing the effects of changing local and global features on network classification performance . In Experiment 5a , we explicitly tested the hypothesis that deep networks use local shape features , such as the curvature of contour segments , but not global shape , in their classification decisions . We found new examples of shape silhouettes that could be correctly classified and tested to see if the network can still classify them despite changes to their global contour . Preserving most local curvatures , we scrambled the shapes so that the overall shape was radically changed . If the network was robust to these alterations , that would provide evidence that the network is using local shape primitives as cues , rather than the shape’s global contour . If the network utilizes global shape , these disruptions should impede accurate classification . Experiment 5a tested deep networks’ ability to classify objects whose global shape was destroyed , with local curvature largely preserved . We hypothesized that if DCNNs did not classify based on global shape , performance would remain good , provided that enough local contour features were preserved in the scrambled objects . In Experiment 5b , we conducted a complementary study to test the hypothesis that if local contour features are disrupted , but global shape is preserved , network classification accuracy will suffer .
In human vision , abstract representations of shape describe how the various parts of an object spatially relate to one another . They are critical for recognition across a variety of viewing conditions , and are robust to perturbations of local contour features . Deep networks have impressive capabilities for object recognition , but they do not appear to handle the problem of recognition the same way humans do . Unlike humans , surface texture appears to be an equally strong cue for recognition as shape . Moreover , the shape information used by by deep networks is highly limited: DCNNs appear to be capable of encoding local shape features including local edge segments and relations . Sensitivity to how these local features fit together as a whole is lacking; DCNNs trained for object recognition do not appear to represent global shape at all .
All research on human subjects in this and subsequent experiments was under IRB approval ( IRB#11-002079-CR-00001 ) . Tests were conducted on VGG-19 [27] and AlexNet [4] . For most experiments , the results of the better-performing VGG-19 are reported . Both networks were trained on the ImageNet database prior to testing . Tests were conducted using the Neural Networks Toolbox from Matlab 2017b . Testing images are described in the Methods description for individual experiments . In all experiments , the correct classification for the testing images was among the 1000 object categories that the networks had been trained to classify . | “Deep learning” systems–specifically , deep convolutional neural networks ( DCNNs ) –have recently achieved near human levels of performance in object recognition tasks . It has been suggested that the processing in these systems may model or explain object perception abilities in biological vision . For humans , shape is the most important cue for recognizing objects . We tested whether deep convolutional neural networks trained to recognize objects make use of object shape . Our findings indicate that other cues , such as surface texture , play a larger role in deep network classification than in human recognition . Most crucially , we show that deep learning systems have no sensitivity to the overall shape of an object . Whereas deep learning systems can access some local shape features , such as local orientation relations , they are not sensitive to the arrangement of these edge features or global shape in general , and they do not appear to distinguish bounding contours of objects from other edge information . These findings show a crucial divergence between artificial visual systems and biological visual processes . | [
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"p... | 2018 | Deep convolutional networks do not classify based on global object shape |
Organisms maintain competitive fitness in the face of environmental challenges through molecular evolution . However , it remains largely unknown how different biophysical factors constrain molecular evolution in a given environment . Here , using deep mutational scanning , we quantified empirical fitness of >2000 single site mutants of the Gentamicin-resistant gene ( GmR ) in Escherichia coli , in a representative set of physical ( non-native temperatures ) and chemical ( small molecule supplements ) environments . From this , we could infer how different biophysical parameters of the mutations constrain molecular function in different environments . We find ligand binding , and protein stability to be the best predictors of mutants’ fitness , but their relative predictive power differs across environments . While protein folding emerges as the strongest predictor at minimal antibiotic concentration , ligand binding becomes a stronger predictor of mutant fitness at higher concentration . Remarkably , strengths of environment-specific selection pressures were largely predictable from the degree of mutational perturbation of protein folding and ligand binding . By identifying structural constraints that act as determinants of fitness , our study thus provides coarse mechanistic insights into the environment specific accessibility of mutational fates .
Environmental conditions shape natural selection and drive rates of organismal adaptation through Genotype-by-Environment Interactions ( GEI ) and alterations of the genotype-phenotype map linking DNA sequence variation to the expression of quantitative traits [1] . Depending on the environment , such interactions can thus predispose a particular genotype to alternative fates and divergent evolutionary trajectories [2–7] . While the roles of standing variation and de novo mutation in adaptation to new environments have received much theoretical and empirical consideration [8–11] , these sources of genetic variation are also likely to differ in fundamental ways . In particular , GEI based on standing variation may differ from GEI from de novo mutation as the former are shaped by selection [12] while the latter will be so to a much lesser extent [13] . Indeed , while the Distribution of Fitness Effects ( DFE ) of mutations has fundamental consequences for rates of evolution , little is known generally about their environmental specificity [9 , 14–16] . Chemical and physical properties exercise fundamental constraints on enzymatic reactions and protein function , and in extension , organismal fitness [17] . Thus , in-depth knowledge about environmental influences on biochemical properties and molecular features underpinning phenotypic traits may bring considerable insights and predictive power of organismal adaptation and evolutionary trajectories in heterogeneous and complex environments . Indeed , maintenance of proteostasis is key to survival in stressful environments [18 , 19] and many diseases are associated with dysfunctional proteostasis machinery [20] . Hence , investigating whether and to what extent proteostasis in terms of intracellular protein folding and stability play a role in determining GEI and environment-specific DFE may be a key step in predicting mutational fates and thereby understanding molecular basis of environmental influences on the genotype-phenotype map . Monitoring of environment-specific DFEs is greatly enhanced by prospective mutational scanning of single mutants which provide a rapid means to study single steps of molecular evolution , as compared to spontaneous mutations which occur at a very low rate [21] . Deep sequencing based high throughput approaches such as deep mutational scanning [22 , 23] have now rendered large scale assessment of mutational effects on gene function possible [24]; allowing comprehensive analysis of the sequence-space of a gene . Resultant DFEs of the mutations provide a continuous series of fitness effects ranging from strongly deleterious to beneficial , and represent a valuable resource for quantitative genetic research [25] . In recent years , exploration of environmental influence on the DFE of mutations with large-scale genotype to phenotype data has resulted in the identification of environment-specific mutational effects [16 , 26] . However , qualitative and quantitative identification of determinants of these mutational fitness effects has been challenging [27 , 28] . Therefore , mechanistic understanding of GEI and environment-specific DFE is much needed in order to increase the robustness in current approaches of predicting genotype-phenotype relationships [29 , 30] . In this study , we monitored the fitness landscape of the Gentamicin ( Gm ) resistant gene—GmR ( aminoglycoside 3-N-acetyltransferase ( aacC1 ) ) under different sets of physical and chemical environments . We utilized a single site mutation ( SSM ) library ( >2000 mutants ) of the gene , heterologously expressed in E . coli . We acquired relative fitness of single site mutants of GmR , by carrying out co-culture bulk competition assays that select for the gene’s function , under predominantly purifying selection in different environmental conditions . Adopting a deep mutational scanning approach , preferential enrichments of the mutants were monitored via deep sequencing . The physical environments investigated in this study include growth temperatures; lower ( 30°C ) or higher ( 42°C ) than the optimal growth temperature ( 37°C ) of E . coli . High temperature is known to severely impair protein folding of temperature-sensitive mutants [31] , while low temperature has been shown to induce reversible effects on protein folding [32] . Hence , the influence of temperature on the fitness landscape of GmR may allow us to understand how the requirement of proteostasis limits the gene sequence space available for evolution . Among chemical environments , we studied effects of TMAO ( Trimethylamine N-oxide ) and glycerol , which are known to act as chemical chaperones that may buffer mutational effects by assisting protein folding via alternative mechanisms [33] [34] . Assessment of the role of such solvent-protein interactions in guiding mutational fates is of particular importance , considering that the solvent accessible surface area of proteins are strong predictors of protein evolution rate [35–37] . The assessed mutational effects depended strongly on the acting environmental conditions , a hallmark of mutational GEI . Moreover , molecular constraints such as protein stability and ligand binding were identified to be common across all test environments . The selection pressures imposed by physical and chemical environments , at minimal concentration of antibiotic , were largely mediated via folding constraints , and hence , could be predicted . For instance , elevated temperature imposed stronger purifying selection against mutants whereas chemical chaperones were found to increase mutational robustness , alleviating deleterious fitness effects ( buffering effect ) . Collectively , through mutational scanning of a conditionally essential gene , this study uncovers how environments guide molecular evolution and assigns a central role to underlying molecular constraints in form of protein folding and ligand binding in determining mutational fates in different environments .
In order to assess survival and competitive fitness of individual single site mutants , we carried out deep mutational scanning [22 , 23] of GmR , by carrying out co-culture bulk competitions of a single site mutants ( SSM ) library ( see Materials & Methods ) . Since antibiotic resistance of GmR is dosage dependent ( S1A Fig ) , the strength of purifying selection ( i . e . the concentration of Gentamicin ( Gm ) ) in competition assays was optimized at ~4 fold lower than the inhibitory concentration for wild type GmR while still being higher than the inhibitory concentration for the host ( E . coli K-12 ) alone ( S1B Fig ) . This moderate purifying selection allows detection of a diverse set of mutants rather than only 'quick fix' outcomes that would be detected at stringent purifying selection [38] . If not mentioned otherwise , 12 . 5 μg/mL of Gm is therefore used in subsequent deep mutational scanning experiments . For obtaining relative fitness , which would be a proxy for the catalytic activity of the mutants , two parallel co-culture bulk competitions were carried out—one in presence of Gm ( selected pool ) and another in absence of Gm ( unselected pool ) ( Fig 1A ) . Optimal growth temperature of E . coli i . e . 37°C was designated as a reference environment ( if not otherwise stated ) . At the end of bulk competitions , ultra-deep sequencing provided counts of mutants ( see Materials & Methods ) –that correlated strongly among independent biological replicates ( S2 Fig ) ; signifying low inherent noise in the measurements and absence of emergent mutations during the selection process . Next , relative fitness scores of mutants were calculated by preferential enrichments , i . e . log fold differences between counts of the mutants in the selected pool versus the unselected pool–generating a mutational matrix of fitness effects for each environment ( S3 Fig ) . Note that catalytic fitness scores obtained by this strategy represent maximum asymptotes of mutants’ growth which are different from ‘canonical’ relative fitness estimated from growth rates . Also , completely eliminated highly deleterious mutants were assigned a null fitness . Therefore , unless otherwise mentioned , subsequent analysis of fitness scores is carried out with surviving mutants alone . Upon estimating thresholds for statistically neutral fitness effects ( See Materials & Methods , S4 Fig ) , it was evident that fitness effects of synonymous mutants across all the environmental conditions studied in this work were mostly neutral ( S5 Fig ) . Therefore , subsequent analysis is mainly focused on the fitness effects of non-synonymous mutants . In order to test whether our experimental system is able to capture the catalytic activities of mutants , we first assessed dosage dependent survival of the mutants . Expectedly , bulk competitions carried out at high dosage of the antibiotic ( 25 μg/mL Gm ) indeed showed a skew towards lower fitness scores ( Fig 1B ) . The fitness effects of mutants in a given environment were captured through following 4 parameters ( S1 Table ) . ( 1 ) Mean viability selection coefficient ( s ) against non-synonymous mutations: s = 1 –[vnon/vsyn] , where , vnon and vsyn are mean viabilities of the non-synonymous and synonymous mutants respectively . A higher value of s thus indicates decreased relative survival of all non-synonymous mutants in the given environment . ( 2 ) Change in average fitness ( ΔF ) equals Ftest- Fref , where , Ftest and Fref are average fitness of all mutants of a given test environment and that of the corresponding reference environment respectively . A lower value of ΔF would indicate a relative decrease in average fitness . ( 3 ) In order to capture mutational robustness in a given environment , a rank correlation coefficient ( ρ ) between fitness scores of all mutants in a given environment and that in the corresponding reference environment was determined . A high value of ρ indicates higher mutational robustness . Lastly , ( 4 ) the ratio of the number of mutants with positive and negative fitness effects ( npos/nneg ) relative to the reference environment is estimated ( see Materials & Methods ) . Among these 4 parameters , the mean viability selection coefficient ( s ) is a direct estimate of the mean strength of selection against non-synonymous mutations for a given environmental condition , while the remaining 3 parameters are estimated relative to the reference environment . Deleterious fitness effect of high Gm-dosage was well captured through the set of 4 parameters . Firstly , selection coefficient ( s ) showed an increase ( s = 0 . 164 ) compared to the reference concentration of 12 . 5 μg/mL Gm ( s = 0 . 048 ) . In terms of relative parameters , average fitness decreases ( ΔF = -0 . 380 ) , mutational robustness is compromised ( ρ = 0 . 869 ) and a greater number of mutants cause deleterious fitness effects ( npos/nneg = 0 . 035; See Materials & Methods ) . This dosage dependent deleterious fitness effect is consistent with previous reports from mutational scanning of other antibiotic resistant genes [39–41] . This dosage dependence taken together with a positive correlation between fitness scores and predicted evolutionary rates per site ( S6 Fig ) signify that the empirical fitness scores indeed capture catalytic activities of GmR mutants . Next , we tested the two sets of environmental conditions using our experimental system . Firstly , among physical environments , lower ( 30°C ) and higher ( 42°C ) temperature were found to confer moderate ( s = 0 . 103 ) and considerable ( s = 0 . 338 ) increase in mean viability selection respectively , compared to the reference environment of 37°C ( s = 0 . 048 ) . For surviving non-synonymous mutants , strong negative effects at 42°C ( npos/nneg = 0 . 14 ) can be explained by potentially pronounced protein misfolding at high temperature [42] . Note that here the increase in average fitness of surviving mutants ( ΔF = 0 . 19 ) at 42°C is due to the complete elimination of highly deleterious mutants . Chemical chaperones–TMAO and glycerol–comprising a set of chemical environments , have relatively weak effects on mean viability selection ( s = 0 . 066 and s = 0 . 023 respectively ) relative to the reference environment ( s = 0 . 048 ) , with positive fitness effects on growth ( npos/nneg = 2 . 00 and npos/nneg = 33 . 60 respectively ) ( Fig 2 ) . Additionally , mutational robustness scores were higher in both the environments ( ρ = 0 . 961 for TMAO and ρ = 0 . 900 for glycerol ) than in the absence of these chemical chaperones . To examine the extent of these positive effects , we analyzed the bulk competitions at high Gm dosage ( 25 μg/mL ) too . There we find that , unlike TMAO ( s = 0 . 219 ) , glycerol is still able to provide mutational robustness ( s = 0 . 036 ) ( S7 Fig ) . Collectively , therefore , among the two chemical environments , glycerol seems to exert more pronounced positive effects than TMAO . A possible explanation for this difference may lie in the two chemical chaperones’ alternative mechanisms of aiding protein folding [33] . Having characterized effects of individual environments , we next explored how combinations of environments ( complex environments ) influenced mutational fitness . Environments with significant and opposing effects on mutational fitness i . e . high temperature in combination with one of the two chemical chaperones–were simultaneously applied in the bulk competitions . There were evident increases in selection relative to the reference environment ( 37°C: s = 0 . 048 ) in both cases ( s = 0 . 270 and s = 0 . 142 for 42°C + TMAO and 42°C + glycerol , respectively ) , demonstrating a major effect contributed by high temperature . However , selection was alleviated , and mutational robustness increased , as compared to when high temperature was applied alone ( s = 0 . 338 ) . This demonstrates mutational buffering conferred by the chemical chaperones , which is consistent with an earlier finding [34] . Noticeably , TMAO went from causing a slight increase in the strength of purifying selection at 37°C , to having a buffering effect at 42°C , demonstrating environmental-specificity in the fitness consequences of this chemical chaperone . In order to gain insights into the mechanistic basis underlying the environmental influence on mutational fitness effects , we scanned a comprehensive set of molecular features of the single site mutations ( see Materials & Methods and S2 Data ) and correlated these features with the mutants’ fitness score in each of the test environments ( Fig 3 and S2 Table ) . From the Euclidean clustering of these correlation coefficients , it is apparent that the correlations roughly separate the environments with high selection pressure ( s ) from the ones with low selection pressure . This thus suggests that information encoded in the molecular features , to some extent , can predict the selection pressures imposed by each environment . Among the set of molecular features , evolutionary rate per site ( predicted from ConSurf [43] ) was found to most strongly correlate with the fitness scores; indicating that even in different environmental conditions , inherent mutational tolerance of a gene is still conserved . However , this feature summarizes individual contributions of various interrelated features . Therefore , in order to gain finer mechanistic understanding , correlations with nearly independent individual structural features are required . Among folding related features– ΔΔG ( perturbation of protein stability , predicted from PoPMusic [44] ) and residue depth ( distance of a residue from the surface of the protein , calculated using MSMS libraries [45] ) were negatively correlated with the fitness scores ( P<0 . 0001 ) . Here , residue depth can be considered as a folding feature because mutations at buried sites are known to cause more stability perturbation than mutations at the surface [46] . Effectively , mutations at buried sites of the protein ( high ΔΔG ) are more likely to be associated with decreased fitness compared to mutations at the surface of the protein ( low ΔΔG ) . The Distance of mutated residues from active sites of the protein , serving as proxies for potential perturbation of ligand binding , show positive correlations ( P<0 . 05 ) with fitness scores of surviving mutants across all environments . This suggests that mutations near active sites are more likely to bear fitness costs . Other molecular features were more weakly related to fitness of the surviving mutants in the different environments . Distances of mutation sites from the dimer interface also show positive correlations with fitness scores across all environments , suggesting that dimer formation is an essential condition for proper functionality of the enzyme . In addition , residue flexibility , Δ ( logP ) per substitution and Δ ( Solvent Accessible Surface Area ) per substitution were mostly negatively and relatively weakly correlated with the fitness scores . Note that the relatively weak correlations may arise from the combination of uncertainty in estimations of structural and predicted features and also possible interactions among structural features . Therefore , in the subsequent analysis , we focus mainly on the prominent folding and binding constraints that are likely to suffer the least from these potential uncertainties . Protein folding and ligand binding are known to act as spandrels underlying mutational fitness effects [47 , 48] . Here we demonstrate that the two factors act as strong constraints on fitness of GmR mutants . In order to further understand the influence of these two coupled constraints , we created four subsets of mutants with unique combinations of protein folding and ligand binding states: ( 1 ) both proper ( i . e . non-compromised ) folding and binding ( FB ) , ( 2 ) compromised folding and proper binding ( cFB ) , ( 3 ) proper folding and compromised binding ( FcB ) and ( 4 ) both compromised folding and binding ( cFcB ) . Here , F and B denote proper folding ( low ΔΔG ) and proper binding ( high distance from active site ) respectively , whereas cF and cB denote compromised folding ( high ΔΔG ) and compromised binding ( low distance from active site ) respectively . Median values of ΔΔG and distance from active site for all mutants are used as cut-offs in assigning the subsets . Additionally , in order to reduce influence of the uncertainties involved in the estimations of the structural features , mutants whose values lie within 10 percentiles around the median cut-off were excluded . In order to understand how environmental sensitivity of folding and binding perturbations affect mutational GEIs , cross-environment correlations of fitness scores were carried out through Bayesian resampling for each of the four mutant subsets separately ( S8A Fig , S3 Table and S1 Text ) . The correlations between 30°C and 37°C were strong and close to unity and did not differ between the four subsets ( all PMCMC > 0 . 2 , S8A Fig ) , recapitulating the similarity in selection pressures across these temperatures . However , the correlations between 42°C and the other two test temperatures were significantly lower for the subsets of mutants with compromised folding or binding ( cFB and FcB compared to FB; all PMCMC < 0 . 001 , S8B & S8C Fig ) , again pinpointing folding and binding constraints as central in determining environmental specificity of mutational fitness effects . Next , subset wise mean viability selection coefficients were determined for all the environments ( S4 Table ) . Across all the environments , a pronounced trend of increased mean viability selection with compromised folding and binding is evident: ‘FB < FcB < cFB < cFcB’ . Folding constraints in particular impose the largest and statistically significant ( P<0 . 05 ) increase in mean viability selection coefficients; implying that it may act as a stronger constraint among the two ( Fig 4 ) . Further , utilizing the predictability of folding and binding constraints in determining mutational fitness , we visualized the environmental effects in the form of low-dimensional fitness landscapes ( Fig 5 ) . Outlined by the constraints , regimes at the corners of the landscapes represent the four subsets of mutants i . e . FB , FcB , cFB and cFcB . The fitness landscape in the reference environment seems to be shaped by folding constraint , producing a pronounced fitness cliff at ΔΔG~2 kcal/mol separating high and low fitness mutants ( Fig 5A ) . In contrast , at the stringent Gm concentration , mutations close to the active site ( i . e . cB subsets ) show a prominent decrease in fitness ( Fig 5B ) , corroborating the observed dosage dependent effects reported above . Indeed , the imposed higher load of Gm seems to generate an additional pronounced fitness cliff along the binding axis–at a ~15Å distance from the active site . Among the physical environments , the fitness landscape at the low temperature condition ( Fig 5C ) show no clear difference from that of the reference environment; echoing the earlier noted weak environmental effect on selection . Contrastingly , elevated temperature conditions show reduced survival of mutants , especially at cFB and cFcB regimes ( Fig 5D ) , signifying a strong influence of folding constraints . Among chemical environments , the mutational robustness conferred by TMAO and glycerol at 37°C is evident from the close similarity of these fitness landscapes and that of the reference environment ( Fig 5E and 5G ) . At 42°C though , partial assistance is evident in FB subset ( Fig 5F and 5H ) . Notably , across all the fitness landscapes , the common existence of fitness cliffs along the folding axis suggests that folding constraint is universally strong among all the environments . This in turn also explains the conformity between the anticipated alteration of protein folding by each environment and corresponding selection pressures . Overall , visualizing the complex environmental effects on fitness of GmR mutants through the perspective of molecular constraints reveals a shaping of mutational fates that is closely dependent on the inherent strengths of the molecular constraints .
Large-scale elucidations of genotype-by-environment interactions ( GEI ) and the environmental specificity of mutational fitness effects enabled by high throughput mutational scanning [22] have opened up new possibilities to comprehensively assess fundamental questions in molecular evolution . Here , we linked environment-specific competitive fitness of mutants to the underlying molecular basis of GEI , by deep mutational scanning of the antibiotic resistant gene GmR . Upon monitoring empirical fitness of a library of single site mutants of the gene , under sets of physical and chemical environments , we characterized corresponding selection pressures . In line with earlier findings [16 , 26 , 49] , we demonstrate that the environment can significantly change selection and the fitness consequences of de novo mutations ( Fig 2 ) . Among physical environments , elevated temperature ( 42°C ) exerts strong selection against non-synonymous mutations , underscoring overall temperature sensitivity [31] upon protein misfolding [42] . Low temperature ( 30°C ) , on the other hand , imposes comparatively weaker selection , conforming to known non-deleterious effects on protein folding at low temperature [50] . Among chemical environments , chemical chaperones too exert weaker selection , while when applied in combination with high temperature , they even alleviate selection pressure imposed by high temperature; underscoring earlier results identifying mutational buffering properties [34] . The alleviation of deleterious effects of elevated temperature by chemical chaperones also indicates a partial additivity and therefore a degree of predictability in the action of complex environments . The reason for this degree of predictability can likely be attributed to the heterologous expression of GmR that made mutant fitness directly dependent on the properties of a single gene . This is in contrast with a previous study in which GEIs of an endogenous gene–Hsp90 were found to be largely unpredictable [16] . Participation of Hsp90 in dense signaling networks of stress response pathways [51] may have potentially obscured the predictability in that case . For example , a candidate mutation that rendered Hsp90 inactive at high temperature while maintaining activity at high salinity can be equally well explained by two alternative hypotheses: either the Hsp90 mutant misfolded specifically at high temperature , or the temperature-specific signaling through Hsp90 was abrogated . By contrast , our work pinpoints the former factor as the main contributor mutational effects and illustrates the utility of the used experimental system for the study of evolution of structure and function in the context of environmental change . The next step will be to integrate protein-protein interactions and signaling networks to define environmental effects on higher levels of GEIs . The correlative analysis ( Fig 3 ) identified protein stability perturbations ( ΔΔG ) and perturbations of ligand binding ( distance from active site ) as strong molecular constraints on fitness , and hence determinants of environment-specific mutational fitness effects . This finding is in line with the proposed spandrel-like properties of these two constraints [47 , 48] . Our measures of fitness scores were highly repeatable ( S2 Fig ) . Assuming the same accuracy in estimating the structural features of the mutations , the generally weak ( rs<0 . 5 ) correlations between these two estimates indicate that fitness scores of only a fraction of mutants were explainable by any individual structural feature . This may suggest that some of these molecular features ( e . g . folding and binding ) have interactive effects on fitness , necessitating accounting for this dependence to better predict mutants’ fitness . Additionally , potential non-monotonic relationships would also contribute in weakening the strengths of the correlations [27] . In this study , we extended the results of previous studies [7 , 39 , 52] to understand how the effective contribution to molecular constraints change in different environments . The central role of both constraints in shaping fitness effects in different environments was evident from the subset-wise mean viability selection coefficients , where environmental effects are more pronounced in subsets of mutants with compromised folding and binding ( Fig 4 ) . Among the two constraints , however , folding seem to introduce a prominent limiting fitness cliff ( at ΔΔG = ~2 kcal/mol on the folding axis of Fig 5 ) across most of the environments . However , the relative strengths of the constraints were context dependent . For example , we observed that the binding constraint emerges to be stronger as the antibiotic concentration is elevated . These results conform to other studies ( e . g . [52] ) showing that biophysical constraints dictate mutational tolerance . Overall , our findings thus suggest that GEI associated with de novo mutations can be understood in terms of environmental alteration of protein folding and binding constraints , which is in alignment with their central role in molecular evolution [18 , 19] . Collectively , from a simple experimental system consisting of a conditionally essential gene , we identify that environment-specific mutational fitness effects are dependent on the relative strengths of underlying molecular constraints . The heterologous gene expression produced relatively predictable GEIs that opened up possibilities to contextualize fairly complex GEIs of endogeneous genes , as well as to forecast molecular evolution in complex environments , premises that only recently would have seemed a daunting and perhaps unrealistic task . A mechanistic understanding of GEIs is arguably one of the most important challenges when predicting evolution of complex traits [1] and innovations [53] . Information such as we present here may considerably advance our understanding of the molecular underpinnings of the genotype-phenotype map and how the materialization of molecular constraints shape phenotypic evolution in complex environments [49 , 54] . Moreover , including knowledge about how the environment may induce phenotypic variability , or alter the fitness consequences of allelic variants , can potentially increase the robustness and accuracy of predictions of phenotypic outcomes of genomic variants [29 , 30] . In the future , the comprehensive approach utilized here to elucidate environment-specific fitness landscapes can be extended to monitor intragenic and intergenic epistasis .
The primary culture was prepared by inoculating ( 1% v/v ) E . coli ( K-12 ) in culture media ( Luria-Bertani ( LB ) broth ( HiMedia ) containing 100μg/mL , ampicilin ( Sigma ) and 0 . 1% Arabinose ( Sigma ) ) and incubating at 37°C for 18 hrs . The primary culture was inoculated at OD600 of 0 . 025 in culture media containing a range of Gm ( Sigma ) concentrations from 6 . 25 to 400 μg/mL with 2 fold increase at each increment ( in 96-well storage plates ) . The assay plates were incubated at 37°C for 18 hrs before measuring growth ( OD600 ) in Tecan microwell plate reader . E . coli ( K-12 ) harboring pBAD-GmR is grown in culture media ( LB media containing 100μg/mL and ampicilin 0 . 1% Arabinose ) for ~18 hr . The primary culture was used as an inoculum ( ~0 . 01 OD ) for the growth assays . Growth assays in different environments were carried out using Bioscreen C kinetic growth reader . The growth parameters were obtained by fitting absorbance data to a five parameter Logistic equation . An SSM library of GmR was constructed by PCR based site directed mutagenesis , using primers with degenerate codons ( NNK ) . For detailed information regarding the mutagenesis , please refer to Supporting methods described in Bandyopadhyay et al . [34] . For co-culture bulk competition assays , the mutation library cloned in pBAD vector was transformed into E . coli ( K-12 ) . Primary culture was prepared by inoculating pool of SSM library ( 1% v/v ) in culture media ( LB media containing 100μg/mL ampicilin and 0 . 1% Arabinose ) at 37°C for 18 hrs . A competition was carried out at the secondary culture where primary culture in inoculated at OD600 of 0 . 025 and incubated for 18 hrs . Physical environmental conditions were created by carrying out the bulk competitions at 30°C ( low temperature ) or 42°C ( elevated temperature ) . Chemical environmental conditions were created by supplementing either TMAO ( 250mM ) or glycerol ( 250mM ) in the culture media of competition assay . Biological replicates were made by carrying out independent co-culture bulk competitions of the mutant libraries . For measuring fitness of mutants in a particular environmental condition , bulk competition under Gm selection ( selected pool ) ( as shown in Fig 1A ) was carried out . An independent bulk competition was carried out at 37°C in the absence of Gm ( unselected pool ) which serves as a reference for calculating preferential enrichments . At the end of bulk competition assays , cells are pelleted and plasmid is purified . Amplicons were generated by a short PCR ( initial denaturation: 95°C for 3 min , denaturation: 95°C for 1 min , annealing: 60°C for 15 sec , extension: 72°C for 1 min , final extension: 72°C for 10 min ) using high fidelity KAPA HiFi DNA polymerase ( cat . no . KK2601 ) . High template concentration ( 1 ng/μl ) and 20 cycles were used to reduce potential PCR bias . Multiplexing was carried out using flanking barcoded primers ( 4 forward , 4 reverse , sequences in S5 Table ) . Amplicons of barcoded samples were grouped in equimolar concentration and gel purified . A dual index library for each such set was prepared using Truseq PCR-free DNA HT kit ( Illumina Inc . Cat no . F-121-3003 ) and sequenced using paired end ( 300 X 2 ) chemistry on Illumina Miseq platform . Raw sequencing data is available at Sequence Read Archive ( SRA ) as a BioProject: PRJNA384918 . Analysis of sequencing data was carried out by using dms2dfe [55]—a comprehensive analysis pipeline exclusively designed for analysis of deep mutational scanning data . Through dms2dfe workflow , output files from the sequencer ( . fastq ) were demultiplexed using ana0_fastq2dplx module of dms2dfe . Average read depth of each demultiplexed sample was ~1X105 . Next , though dms2dfe's modules namely ana0_fastq2sbam , sequence alignment was carried out using Bowtie2 [56] , followed by variant calling through ana1_sam2mutmat module which utilizes pysam libraries [57] . A variant is called only if average Q-score of the read and that of the mutated codon is more than 30 . Additionally a cut off of 3 reads per variants is used to filter out anomalous low counts . As a result a codon level mutation matrix of counts of mutations is generated . Codon level mutation matrix is then translated to amino acid level ( based on the codon usage bias of the E . coli ) . For each experimental condition , counts of ~2000 individual mutants were quantified ( S1 Data ) . Raw sequencing data is available at Sequence Read Archive ( SRA ) as a BioProject: PRJNA384918 . Through ana2_mutmat2fit module of dms2dfe , counts of mutants are first normalized by the depth of sequencing at each position of the gene . Then preferential enrichments which are log ( base 2 ) fold change of counts of the mutants in pool selected in presence of Gm against unselected ( 0 μg/mL Gm ) reference pool were estimated . Here , preferential enrichment of a mutant serves as a proxy for its relative fitness and hence we simply refer it as ‘fitness’ ( S1 Data ) . Upper and lower thresholds for statistically neutral fitness effects were defined by adopting a strategy from a similar previous study [41] . As shown in S4 Fig , the thresholds were obtained as mean ± two SD from a distribution of Fi obtained from unselected condition . We analyzed the survival of all 2104 non-synonymous mutants ( S1 Data ) , in each of the seven environments , as a binomial response ( presence/absence ) in logistic regression using Bayesian Markov Chain Monte Carlo simulations in the MCMCglmm package [58] for R [59] . Temperature ( 30 , 37 or 42°C ) , treatment ( reference , glycerol or TMAO ) and their interactions were included as fixed effects . We ran the model with residual variance fixed to 1 and a flat prior on the probability scale for the fixed effects , recommended when the number of observations in some cells are low [60] ( as for mutant absence in some environments; S1 Data ) and the data show near complete separation [61] . The model ran for 2000000 simulations preceded by 200000 burn-in simulations that were discarded . We stored every 2000th simulation , resulting in 1000 uncorrelated posterior estimates of mean mutant survival in each environment . As 30°C was only applied using reference media , we analyzed differences between 30 and 37°C separately . Similarly , as the Gm25 treatment only was applied across the different media at 37°C , the comparison between Gm 25μg/mL and 12 . 5μg/mL was analyzed in a separate model . To formally estimate the influence of environment on the magnitude of fitness effects and the strength of selection on de novo mutation we compared the viability of the non-synonymous mutants to that of the 157 synonymous mutants . Hence , mean viability selection coefficients ( s ) against the non-synonymous mutations in each environment ( i ) was estimated as: s=1–[vinon/visyn] where vinon and visyn is the mean survival of the non-synonymous and synonymous mutants , respectively , in environment i . We utilized the 1000 stored Bayesian posterior estimates of mean viability of the non-synonymous mutants ( vinon ) ( S6 Table ) , and then generated 1000 matching estimates of visyn by applying the equivalent Bayesian analysis described above to the synonymous mutant data . We then used these two posterior distributions to calculate s per environment and tested if the generated posterior distributions of s differed significantly across environments at an alpha level of 0 . 05 . In addition to these selection coefficients , we provide three relative measures of fitness effects for comparison with reference environment . Values of all four parameters for all the environments are included in S1 Table . Mutant stability perturbations ( ΔG ) are predicted by PoPMusic [44] server . Evolutionary rate per site ( conservation score ) is acquired from ConSurf ( 7 ) server . MSMS libraries [45] were used for calculations of residue depth from surface of protein . Distances between atoms of GmR are measured using various modules of Biopython package [62] . Distances of residues ( mutation sites ) from active site residue D147 are estimated . Here , minimum distance between the atoms of the D147 and C-alpha atom of a given residue is used to ensure maximum sensitivity . Physico-chemical properties of the amino acids such as logP and pI were retrieved from PubChem [63] and ChemAxon ( http://www . chemaxon . com ) . Structural features of mutations used in the study are included in S2 Data . | Environmental conditions are known to shape natural selection . However , their influence on molecular evolution is still largely unclear . Here , we use a high throughput mutational scanning approach to investigate how representative physical and chemical environments alter mutational fates of an antibiotic resistant gene . From co-culture bulk competitions with purifying selection carried out under different test environments , we obtained empirical fitness of individual single site mutants of the gene . Mutant fitness was found to differ across environments . In order to gain mechanistic insights into the observed environmental influence on mutational effects , we analyzed relative strengths of protein level structural constraints in determining the fitness effects . Remarkably , this analysis revealed a high degree of predictability: overall strengths of environment-specific selection pressures were determined by the degree of mutational perturbation of protein folding and ligand binding . Overall , our results show that these structural constraints act as determinants of environment specific mutational fates . | [
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... | 2018 | Differential strengths of molecular determinants guide environment specific mutational fates |
In Drosophila melanogaster , recognition of an invading pathogen activates the Toll or Imd signaling pathway , triggering robust upregulation of innate immune effectors . Although the mechanisms of pathogen recognition and signaling are now well understood , the functions of the immune-induced transcriptome and proteome remain much less well characterized . Through bioinformatic analysis of effector gene sequences , we have defined a family of twelve genes – the Bomanins ( Boms ) – that are specifically induced by Toll and that encode small , secreted peptides of unknown biochemical activity . Using targeted genome engineering , we have deleted ten of the twelve Bom genes . Remarkably , inactivating these ten genes decreases survival upon microbial infection to the same extent , and with the same specificity , as does eliminating Toll pathway function . Toll signaling , however , appears unaffected . Assaying bacterial load post-infection in wild-type and mutant flies , we provide evidence that the Boms are required for resistance to , rather than tolerance of , infection . In addition , by generating and assaying a deletion of a smaller subset of the Bom genes , we find that there is overlap in Bom activity toward particular pathogens . Together , these studies deepen our understanding of Toll-mediated immunity and provide a new in vivo model for exploration of the innate immune effector repertoire .
Constant interaction with microbes is a fact of life , and sometimes death , for animals . Many microbes are neutral or beneficial to the host’s health . Some , however , are pathogenic and threaten the host’s viability . In vertebrates and invertebrates alike , immune responses are initiated by recognition of pathogen associated molecular patterns ( PAMPs ) following invasion of host tissues [1 , 2] . This recognition of conserved microbial products triggers innate immune signaling pathways that are closely related in species as divergent as flies and humans [3–5] . In each case , pathway activation initiates a transcriptional program encoding an array of effector peptides and proteins . In the fruit fly Drosophila melanogaster , Toll and Imd proteins define the two major immune signaling pathways [6–11] . Fragments of fungal cell walls and bacterial peptidoglycan serve as PAMPs for these pathways . Toll signaling is triggered by the β-1 , 3-glucans of fungal cell walls or by Lys-type peptidoglycan [12–16] . In contrast , the Imd pathway is activated by DAP-type peptidoglycan [17–21] . Upon activation , Toll and Imd direct expression of distinct but overlapping effector gene repertoires . These effector genes bring about the humoral immune response via factors , including antimicrobial peptides ( AMPs ) , that circulate throughout the fly hemolymph . Effector genes also support other immune processes by , for example , upregulating genes promoting melanization and wound healing [22] . The Drosophila immune effector repertoire has been characterized by microarray , RNA-seq , and mass spectrometry experiments [23–27] . The most highly upregulated genes include most known AMPs , but also many as yet uncharacterized effector peptides . For both the characterized and novel effectors , delineation of in vivo requirements based on loss-of-function phenotypes is largely lacking . Here , we describe the application of recent advances in genome engineering technology to the genetic dissection of innate immune effector function . Generating a designer deletion of multiple members of an effector gene family , we demonstrate an essential role for these genes in Toll-mediated defense against microbial pathogens .
Carrying out sequence comparisons among Drosophila melanogaster loci induced by the Toll pathway [24] , we identified a family of twelve genes encoding secreted peptides lacking similarity to known AMPs . Each of the twelve peptides contains one or two copies of a 16 amino acid-long motif that includes a CXXC bend surrounded by a region of high sequence conservation ( Fig 1A ) . All orthologs identified to date are from members of the Drosophila genus . We propose naming this family of genes the Bomanins ( Boms ) , after Hans Boman , who carried out pioneering work in peptide-mediated innate immunity [28–31] . Several Bom peptides belong to the set of Immune-induced Molecules ( IMs ) first identified in mass spectrometry studies carried out by Bulet , Hoffmann , and colleagues [23 , 25] . Combining those findings with detailed sequence comparisons reveals post-translational processing events: signal peptide cleavage and , often , removal of additional residues at the amino-terminal end as well as carboxyl-terminal amidation ( Fig 1B ) . The Bom peptides fall into three distinct groups ( Fig 1B and S1 Table ) . For six of the twelve , the mature peptide is just 16 or 17 amino acids long . The sequences of these six short-form peptides are highly similar and correspond to the conserved , CXXC-containing region that we have defined as the Bom motif ( see Fig 1A and 1B ) . Three other Bom peptides have a tailed form—a Bom motif followed by a C-terminal extension or tail , 15 to 82 amino acids in length . The remaining three peptides have a Bom motif at each end , connected by a linker region of 43 to 103 amino acids . We refer to these peptides as two-headed or bicipital . Sequence identity and similarity within the Bom motif is reduced , but still significant , in the tailed and bicipital forms ( see Fig 1A ) . In contrast , the tail and linker regions in these two classes are rich in homopolymeric stretches and contain no appreciable sequence conservation either with each other or with other proteins in available databases . Published microarray , RNA-seq , and mass spectrometry experiments document robust expression of the Bom transcripts and peptides after bacterial or fungal infection [22–27] . Indeed , induced expression of many Boms is at levels equal to or greater than those of AMP loci . Furthermore , Bom peptides , like AMPs , are abundant in the hemolymph of infected flies [23 , 32] . Ten of the twelve D . melanogaster Bom genes are clustered on chromosome 2 at cytogenetic position 55C ( henceforth 55C Bom cluster , Fig 1C ) . The two remaining Bom genes , CG5791 and CG5778 , reside in a mini-cluster on chromosome 3 and encode a bicipital and a tailed Bom peptide , respectively . Because the predicted mature Bom peptides are highly similar and hence potentially overlapping in function , we began our investigation of the Boms by precisely deleting the ten genes of the 55C Bom cluster using a TALEN-based approach . The deletion , henceforth BomΔ55C , is 9 kb long and removes no annotated loci other than the Bom genes . Because Toll signaling induces Bom expression , we challenged BomΔ55C adults with Enterococcus faecalis , a bacterium that has Lys-type peptidoglycan and therefore specifically induces the Toll pathway . Using septic wounding , we systemically infected adult flies and then monitored survival . In control experiments , we found that flies lacking a functional Toll pathway ( MyD88- ) were much more susceptible to E . faecalis infection than were flies with wild-type immune competence ( w1118 ) , as reported previously [7 , 33] . Following infection , more than 50% of MyD88- flies died within one day and nearly all ( >90% ) were dead within two days ( Fig 2A ) . In contrast , more than 95% of wild-type adults were alive one day post-infection and more than 50% survived two days or longer . Strikingly , BomΔ55C flies were as susceptible to E . faecalis infection as MyD88- flies . Indeed , the survival curves of BomΔ55C and MyD88- flies were almost indistinguishable , suggesting that loss of the 55C Bom cluster is as detrimental to defense against this bacterial pathogen as is loss of Toll signaling entirely . Having observed that BomΔ55C flies rapidly succumb to septic wounding with E . faecalis ( see Fig 2A ) , we wondered if this phenotype reflected a defective response to wounding or stress rather than infection per se . To test this idea , we wounded wild-type and mutant flies with a clean needle or with one dipped in a suspension of heat-killed E . faecalis . The survival of BomΔ55C flies was markedly better for either challenge compared to septic wounding over the same time period . Specifically , upon either clean wounding or wounding with heat-killed bacteria , more than 75% of BomΔ55C flies survived for four or more days , comparable to the wild type ( Fig 2B and S1 Fig ) . We conclude that active infection , rather than wounding itself or response to PAMP recognition , causes the rapid death of BomΔ55C flies challenged with live E . faecalis . Toll mediates resistance not only to a number of bacteria , but also to fungi , including yeast [6 , 34] . To determine if this Toll activity is also Bom-dependent , we assayed the effect of deleting the 55C Bom genes on survival after infection with the yeast Candida glabrata . Wild-type flies exhibit significant resistance to C . glabrata , with over 80% of wild-type flies surviving five days after infection ( Fig 2C ) . In contrast , 50% of MyD88- flies succumbed just two days after being infected . BomΔ55C flies were similarly affected . Although it took BomΔ55C flies slightly longer than MyD88- flies to drop to 50% survival ( three days ) , survival rates were nearly coincident at later time points . We next tested the survival of BomΔ55C flies after infection with a filamentous fungus , Fusarium oxysporum , that also triggers a Toll-dependent immune response . Among wild-type flies , roughly 80% survived for four or more days ( Fig 2D ) . In contrast , both MyD88- and BomΔ55C flies succumbed much more quickly . Specifically , BomΔ55C flies had a median survival of just over two days post-infection , nearly identical to MyD88- flies . We conclude that the 55C Boms are also essential for Toll-mediated defense against both a unicellular and a filamentous fungus . Because the Imd pathway does not appear to regulate Bom expression [24] , we predicted that the Bom genes would be dispensable for Imd-mediated defenses . We could test this hypothesis with Enterobacter cloacae , a bacterium that has a DAP-type peptidoglycan and therefore triggers Imd signaling . We used E . cloacae to infect BomΔ55C flies , as well as control flies lacking imd function . Whereas more than 90% of imd- flies died within 24 hours of septic wounding with E . cloacae , greater than 80% of BomΔ55C , MyD88- , and wild-type flies survived for four or more days ( Fig 2E ) . Taken together , these studies indicate that the 55C Bom peptides are specifically required in the Toll-mediated , acute phase defense against systemic infection . Given the similarity in phenotypes between BomΔ55C and MyD88- flies , we wondered if loss of the 55C Boms disrupts Toll signaling . These Boms might , for example , be required to counteract pathogen virulence factors that target Toll signaling . They might also provide positive feedback , spreading and amplifying Toll signaling after initial pathogen detection . According to such models , induction of Toll-responsive genes should be reduced in BomΔ55C flies relative to the wild type . To test this idea , we infected flies with E . faecalis and used qRT-PCR to measure induction of marker loci . For this purpose , we chose two genes that are strongly expressed upon Toll activation but that lie outside of the 55C cluster: IM4 and Drosomycin ( Drs ) . Six hours after E . faecalis infection , we detected robust expression of IM4 and Drs in the wild type but , as expected , negligible induction in MyD88- ( Fig 3A and 3B ) . In BomΔ55C flies , induction of both Toll-responsive genes was comparable to that in the wild type . In fact , expression of IM4 was greater in BomΔ55C flies than wild-type flies , perhaps reflecting the enhanced induction of Toll by an unchecked infection . These experiments reveal that the susceptibility of BomΔ55C flies to microbial infection does not reflect a general block in Toll signaling . Further , they strongly suggest that flies lacking Bom gene function have increased susceptibility to E . faecalis , C . glabrata , and F . oxysporum despite normal , Toll-mediated induction of AMP genes . Infection resistance is defined as the ability to clear microbes , while infection tolerance is the ability to endure the presence of microbes [35] . Expression of Toll-responsive genes appears unaffected in BomΔ55C flies . Is it the case that AMPs and other Toll-induced effectors kill pathogens in BomΔ55C flies , but the flies nevertheless die due to an inability to tolerate the infection ? Alternatively , do BomΔ55C flies succumb because Bom peptides are in fact required to control and clear infections ? We set out to distinguish between these hypotheses . To assess resistance and tolerance , we assayed bacterial load over the course of an E . faecalis infection , using wild-type , MyD88- , and BomΔ55C flies in parallel . Because BomΔ55C and MyD88- flies have a median survival after E . faecalis infection of about 23 hours , time points were taken at intervals up to 18 hours . At two hours post-infection , all flies had similar bacterial loads ( Fig 4A ) . At later time points , however , differences emerged . At six hours , the bacterial load was on average 3-fold greater in BomΔ55C than in the wild type . The bacterial load of MyD88- flies was similarly elevated relative to wild-type flies . At 18 hours , both BomΔ55C and MyD88- flies had a bacterial load at least 20-fold greater than did wild-type flies . This elevation in bacterial load in MyD88- and BomΔ55C flies over the course of infection suggests that Toll signaling in general , and Bom peptides specifically , contribute to resistance . To further explore the questions of resistance and tolerance , we measured bacterial load in wild-type flies at 44 hours post-infection , an interval slightly shorter than their median survival time ( 48 hours ) . If Boms contribute to resistance rather than tolerance , the bacterial load in wild-type flies at 44 hours post-infection should be similar to the bacterial load of BomΔ55C and MyD88- flies at 18 hours post-infection . The response of the wild type to E . faecalis infection necessitated a minor modification in our protocol for assaying bacterial load . In particular , some wild-type flies appear to clear E . faecalis infection , as evident in survival curves that do not reach 0% survival , but instead level out at an intermediate value ( see , for example , Fig 2A ) . Foreseeing a bimodal distribution of bacterial loads among wild-type flies at 44 hours—some clearing infection and others not—we measured bacterial load for this time point in individual flies , rather than in groups . Measured in this way , there were indeed two groups with quite distinct bacterial loads . Of 32 wild-type flies still alive at this time point , 23 had a bacterial load less than 4 , 000 colony forming units ( CFU ) ( Fig 4B , “low” ) . These low CFU flies presumably represent the fraction of the population that survives infection . The other nine wild-type flies had bacterial loads at 44 hours ranging from 60 , 000 to 36 , 000 , 000 CFU , comparable to those of BomΔ55C and MyD88- flies at 18 hours ( compare Fig 4B , “high” to Fig 4A , 18h ) . Thus wild-type flies that succumb to infection do so at a bacterial load comparable to that in the mutants . We conclude that the BomΔ55C flies succumb to infection more quickly than the wild type due to a defect in resistance . Our experiments with BomΔ55C flies demonstrate that the 55C Bom cluster is required to provide Toll-mediated resistance to our test set of pathogens . Is the entire gene cluster required ? If not , to what extent do the 55C genes overlap in function ? To address these questions , we set out to assay how flies expressing a subset of the 55C Bom genes fare when infected with the same test set . In the course of investigating the 55C cluster , we came across a publically available stock carrying an insertion in the 3’ UTR of IM2 of a MiMIC ( Minos-mediated integration cassette ) transposon [36] . By inducing the excision of this MiMIC element , we obtained two chromosomes that had lost the insertion . In one case the excision was imprecise . The resulting chromosome , hereafter BomΔleft , lacks IM2 and the five Bom genes to the left ( proximal ) of IM2 , but retains the four Bom genes to the right ( distal ) of IM2 ( see Fig 1C ) . The other chromosome , hereafter IM2ΔMi , had undergone a precise excision and thus provided a valuable control for subsequent studies . To assay the activity of the four 55C Bom genes present in the imprecise excisant , we challenged BomΔleft , IM2ΔMi , and BomΔ55C flies by infection and monitored survival . For all pathogens tested , we defined the phenotype of BomΔ55C flies as lacking resistance and that of IM2ΔMi flies as having full resistance over a four to five day post-infection interval . Based on this scale , BomΔleft flies lacked resistance to E . faecalis , exhibited partial resistance to F . oxysporum , and had full resistance to C . glabrata ( Fig 5A–5C ) . The BomΔleft chromosome thus provided a subset of the 55C Bom cluster immune activity . We draw three conclusions from the experiments shown in Fig 5 . First , the wild-type resistance of BomΔleft flies to challenge with C . glabrata demonstrates that the complete 55C Bom gene set is not a prerequisite for Bom function . Second , the fact that BomΔleft flies have partial resistance to F . oxysporum indicates that at least some Bom genes overlap in specificity . Third , resistance to some pathogens requires more than one Bom peptide . In particular , wild-type resistance to F . oxysporum must require at least one of the genes present in BomΔleft but deleted in BomΔ55C , as well as one or more of the genes deleted in in BomΔleft . In and of themselves , these studies do not reveal whether Bom peptides have a narrow- or broad-spectrum of activity , but do provide clues in this regard , as addressed in the discussion .
We report here that Toll-mediated defenses against a bacterium , yeast , or filamentous fungus require Bom gene function . Having reached this conclusion based on loss-of-function phenotypes , we note that such an approach has only rarely been applied to the role of innate immune effectors [37–40] . The paucity of such studies has several likely causes . First , many effector genes , such as the Boms and the known AMP genes , encode peptides that are sufficiently small as to be relatively refractory to random mutagenesis . Second , large-scale screens that rely on reporter genes are useful for identifying lesions that block pathogen recognition or response pathway signaling , but opaque to disruptions in more downstream processes . Perhaps the biggest obstacle , real or imagined , to loss-of-function studies of immune effectors has been the existence of families of closely related genes . One might reasonably expect significant overlap in gene function , meaning that multiple family members would need to be inactivated to uncover reliable phenotypes . Instead , researchers interested in knockout phenotypes have typically focused on those examples where paralogs are absent . Thus , for example , the loss-of-function study demonstrating that disruption of a mouse cathelicidin gene promoted invasive skin infection with Group A Streptococcus [40] relied on the fact that mice , unlike some other mammals , encode only one member of this gene family . How do Bom peptides promote infection resistance ? One possibility is that the Bom peptides support cellular immune function . To explore this question , we infected BomΔ55C flies with Staphylococcus aureus . Defense against S . aureus has been shown to involve cellular immune activities to a greater extent than for some other Toll-activating bacteria , including E . faecalis [41–46] . Although BomΔ55C flies succumbed to S . aureus infection more quickly than did the wild type , BomΔ55C survival was indistinguishable from that of MyD88- and the genetic background control , IM2ΔMi ( S2A Fig ) . We thus found no evidence that the 55C Bom gene cluster contributes to Toll-independent cellular mechanisms of resistance . In additional studies of cellular immune functions , we found neither defects in wound site melanization in BomΔ55C adults nor any deficiency in hemocyte number in BomΔ55C larvae ( S2B and S2C Fig ) . These experiments do not , however , preclude a role for for the Boms in Toll-dependent cellular immunity . A likely alternative is that the Bom genes encode antimicrobial peptides ( AMPs ) . Like many AMPs , Bom peptides are short , secreted , have intramolecular disulfide bonds , and undergo post-translational processing . Although short-form Bom peptides would be the shortest characterized Drosophila AMP , the mature form of Drosocin is just three amino acids longer [47] . Furthermore , both the Boms and the known AMPs populate the upper echelons of the sets of genes most highly upregulated upon activation of the Toll pathway . Specifically , at 12 , 24 , and 96 hours after natural infection by the fungus Beauveria bassiana , the 30 most highly upregulated genes include five or more Bom family members and five or more known AMP genes [24] . Additionally , mass spectrometry data indicate that a number of Bom peptides are as abundant as known AMPs , which after infection reach concentrations of 10–100 μM in the hemolymph [23 , 32] . The structure and sequence of the 55C Bom cluster suggests that the Bom family arose by multiple gene duplications . Such events are enriched among loci involved in pathogen resistance and provide the opportunity for divergence in gene function driven by positive selection [48 , 49] . One example is the Peptidoglycan Recognition Protein ( PGRP ) gene family , which consists of 13 genes , some clustered , encoding 19 proteins . While all PGRPs share a peptidoglycan-recognition domain , the functions of the proteins vary considerably [50] . Some activate the Toll or Imd signaling pathways and promote phagocytosis , autophagy , and melanization . Others suppress the Imd pathway , protecting commensal gut bacteria . A third class has direct bactericidal activity . Gene duplications need not , however , result in functional divergence . Rather , there are circumstances in which gene duplications instead lead to changes in the level , location , or timing of expression of what is essentially the same gene product . Minor sequence variation will arise , but in general the coding regions will not bear the hallmarks of positive selection [51–53] . Comparing the effects of eliminating some or all Bom genes in the Bom 55C cluster , we observe differential effects on resistance to particular pathogens . How does this observation fit with the alternative potential outcomes for gene duplication ? If functional divergence has occurred , we would expect that at least some Bom peptides have a narrow-spectrum effector activity , i . e . , are specific for a particular pathogen or set of pathogens . One or more of the six Bom genes deleted in the BomΔleft chromosome would be specific for E . faecalis , while one or more of the four 55C Bom genes remaining in BomΔleft would protect specifically against C . glabrata . There would need to be at least two peptides specific for F . oxysporum: one or more of the six genes deleted in the BomΔleft chromosome , as well as one or more of the four remaining 55C Bom genes . Although our data can accommodate a narrow-spectrum activity model , we favor the idea that Bom peptides have a common , broad-spectrum activity . In this scenario , resistance to different pathogens would require different total Bom peptide levels , as could be produced by variation in the numbers of Bom genes . In particular , defense against E . faecalis would require the greatest level of Bom activity , while C . glabrata would require the least . Defense against F . oxysporum would require a level intermediate to that required for E . faecalis and C . glabrata . When considered in toto , our survival data support this broad-spectrum effector model: the predicted hierarchy of Bom activity levels required for particular pathogens parallels the overall virulence levels for these pathogens ( E . faecalis > F . oxysporum > C . glabrata ) . This holds true when virulence is measured either by the proportion of wild-type flies that succumb to infection or by the rates at which the mutants succumb after infection ( compare Fig 2A , 2C and 2D ) . The parallel between required Bom activity level and pathogen virulence makes sense if we make the reasonable assumption that the broad-spectrum activity of Bom peptides is more efficient and rapid at higher concentrations . Future comprehensive consideration of the Bom genes will necessitate taking into account four additional loci . Two of these , CG5778 and CG5791 , are Bom genes located outside the 55C cluster . The remaining two , IM4 and IM14 , encode peptides that lack a CXXC motif , but nevertheless exhibit sequence similarity with Bom family members . Furthermore , the IM4 and IM14 peptides , like the Bomanins listed in Fig 1A , are small , secreted , specific to the Drosophila genus , and robustly induced by Toll . Lemaitre and colleagues have reported that overexpression of a UAS-Drs construct using a ubiquitous GAL4 driver restores F . oxysporum resistance to flies lacking both Toll and Imd pathway function [54] . In our studies , however , we found that BomΔ55C flies induce Drs expression upon infection ( see Fig 3B ) , but succumb as rapidly as flies lacking Toll signaling ( see Fig 2 ) . Why was a requirement for Bom gene function not apparent in the Lemaitre study ? One possibility is that loading the flies with high levels of Drosomycin prior to infection obviates the need for additional Toll-induced loci , including the Bom genes . An alternative explanation lies in the fact only inducible Bom expression was blocked in the Lemaitre study , whereas our study eliminated all Bom gene function . It might be that the synergistic activity of both Drosomycin and Boms is required to defend against F . oxysporum , but that a basal , Toll-independent level of Bom expression is sufficient for this synergy . In support of this idea , RNA-seq data from modENCODE demonstrate that expression of many Bom genes is robust even in the absence of infection [55] . Given that innate immune signaling pathways direct expression of large batteries of effector genes upon infection , including many AMPs , one might have expected that disabling a small subset of that repertoire would have only minor effects on the overall immune response . That is not what we observe . Instead , elimination of Bom activity is indistinguishable in phenotype from loss of the entire Toll-mediated immune defense for the pathogens tested , although Toll signaling is intact . We envision at least four explanations for the essential role of the Bom gene family: The Boms have a unique and central role in Toll-mediated defense . This might seem unlikely , given that the Bom family is apparently specific to the Drosophila genus and thus represents a relatively young family of effectors . However , such a model is , in fact , in keeping with recent studies on genes that are essential in the sense of being required for viability . In particular , we now know that essentiality is found in equal proportions among old and young Drosophila genes , where age is measured on the scale of divergence time between species [56] . It could therefore be that a gene family and associated function that arose fairly recently has become a dominant and essential feature of the innate immune response . The Boms are essential for Toll-mediated responses specific to certain microbial pathogens . We know from a variety of expression studies that the entire Toll effector repertoire is upregulated regardless of the source of the activating signal , PAMP or otherwise . By this model , Toll activates a large number of effectors , each attacking a subset of pathogens , rather than collectively fighting a common target pathogen . If so , there should be additional pathogens for which Toll is required and the Bom peptides are not . The Bom genes and other effectors synergize , such that loss of just a single factor disrupts defense as strongly as loss of all components . By this model , innate immunity involves a network of effector functions that comprise multiple hubs , each making a vital contribution to defense . The Bom family and other components of the Toll repertoire are each expressed at the minimal level required for resistance . There is good evidence that immune activation requires an energy tradeoff with metabolic processes [57 , 58] . As a result , limiting the resources used by an immune response can be beneficial to overall health . By either of the last two models , knocking out other effector families should result in the same phenotype observed with the BomΔ55C deletion , i . e . , inactivation of Toll defenses . Testing this prediction thus holds promise for a broader understanding of innate immune effector function in vivo .
Flies were raised at 25°C on standard cornmeal agar media . The w1118 strain was used as the wild type . MyD88- flies were MyD88kra1 , and imd- flies were imdshadok . All flies were homozygous for the listed mutations . TALEN mutagenesis was conducted as previously described [59] . In vitro transcription was conducted using the Ambion Megascript kit with a Promega 5’-cap analog . TALEN transcripts were injected into a fly line containing a MiMIC element in the IM2 gene ( y1w*; Mi[MIC]IM2MI01019 , Bloomington stock center , #32727 ) . The MiMIC element carries the mini-yellow marker . F0 flies were crossed to yw; Sco/CyO and the resulting y- F1 flies were collected and crossed to yw; Sco/CyO . Stocks were established and genotyped using Phusion polymerase and primers flanking the predicted deletion end points . We confirmed the exact endpoints of the deletions by sequencing the PCR product . Excision of the MiMIC element from Mi[MIC]IM2MI01019 was conducted as described previously [60] with the transposase source coming from stock y1 w*; snaSco/SM6a , P{hsILMiT}2 . 4 ( obtained from Bloomington stock center , stock #36311 ) . Microbes were prepared for infection experiments as follows . Enterococcus faecalis strain NCTC 775 ( ATCC 19433 ) and Enterobacter cloacae were cultured in LB media at 37°C and concentrated to OD600 = 10 in 20% glycerol . For heat-killed E . faecalis challenges , cultures were concentrated to OD600 = 10 in 20% glycerol and then boiled for 30 minutes . Staphylococcus aureus subsp . aureus Rosenbach ( ATCC 29213 ) was cultured in LB media at 37°C and concentrated to OD600 = 0 . 5 in 20% glycerol . Candida glabrata strain CBS 138 ( ATCC 2001 ) was cultured in YPD media at 30°C and concentrated to OD600 = 50 in 20% glycerol for infection . Fusarium oxysporum f . sp . lycopersici ( obtained from the Fungal Genetics Stock Center ) was cultured on oatmeal agar plates for 7–10 days at 29°C before being strained through steel wool to isolate spores . Purified spores were resuspended in 20% glycerol and stored at -80°C until infection . For infection , at least 20 2–7 day old , male flies per genotype were anaesthetized and septically wounded in the anterior lateral thorax with a size 000 insect pin dipped in a suspension of pathogen . Survival analysis was conducted essentially as described previously [61] . After infection , flies were incubated at 25°C ( live or heat-killed E . faecalis or live S . aureus ) or 29°C ( clean wounding , F . oxysporum , C . glabrata , and E . cloacae ) , and the number of dead flies were counted at least once per day for the given time interval . Flies that died within 6 hours of infection were excluded from analysis , except in challenges with a clean needle or heat-killed E . faecalis . Colony forming units ( CFUs ) were assayed as described previously [62] . For the wound site melanization assay , 2–7 day old males were wounded as described above with a clean insect pin , incubated for three days at 25°C , and visually inspected for melanization at the wound site . Hemocytes from five wandering third instar larvae per genotype per experiment were obtained and counted as previously described [63] . RNA was prepared using Trizol ( Ambion ) from 2–7 day old males , and first-strand cDNA was synthesized with the SuperScript II kit ( Invitrogen ) . Quantitative RT-PCR was performed on an iQ5 cycler ( BioRad ) using iQ SYBR Green Supermix ( BioRad ) . GraphPad Prism was used to run statistical analyses . Survival data were plotted on a Kaplan-Meier curve and the Gehan-Breslow-Wilcoxon test was used to determine significance . The Gehan-Breslow-Wilcoxon test is recommended for analysis of infection with sublethal pathogen doses; where a sublethal dose is defined as a dose where some proportion of wild-type flies survive . Hemocyte count data were analyzed by one-way ANOVA using the Bonferroni post method . Quantitative RT-PCR and bacterial load data were analyzed by two-way ANOVA using the Bonferroni post method . | Dedicated defense systems in the bodies of humans and other animals protect against dangerous microbes , such as bacteria and fungi . We study these processes in the fruit fly Drosophila , which can be readily grown and manipulated in the laboratory . In this animal , as in humans , protective activities are triggered when fragments of bacteria or fungi activate a system for defense gene regulation known as the Toll signaling pathway . The result is the large-scale production of defense molecules and , in many cases , clearance of the infection and survival of the animal . Although the systems for recognizing and initiating responses are well described , the role of many defense molecules is not understood . We have identified a group of closely related defense molecules in flies and used state-of-the-art genomic engineering to simultaneously eliminate most of the genes in the group . By comparing the effect of fungal or bacterial infection on the genetically altered flies and normal siblings , we find that this group of defense molecules is essential for disease resistance . | [
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] | [] | 2015 | An Effector Peptide Family Required for Drosophila Toll-Mediated Immunity |
Bacteria induce stress responses that protect the cell from lethal factors such as DNA-damaging agents . Bacterial populations also form persisters , dormant cells that are highly tolerant to antibiotics and play an important role in recalcitrance of biofilm infections . Stress response and dormancy appear to represent alternative strategies of cell survival . The mechanism of persister formation is unknown , but isolated persisters show increased levels of toxin/antitoxin ( TA ) transcripts . We have found previously that one or more components of the SOS response induce persister formation after exposure to a DNA-damaging antibiotic . The SOS response induces several TA genes in Escherichia coli . Here , we show that a knockout of a particular SOS-TA locus , tisAB/istR , had a sharply decreased level of persisters tolerant to ciprofloxacin , an antibiotic that causes DNA damage . Step-wise administration of ciprofloxacin induced persister formation in a tisAB-dependent manner , and cells producing TisB toxin were tolerant to multiple antibiotics . TisB is a membrane peptide that was shown to decrease proton motive force and ATP levels , consistent with its role in forming dormant cells . These results suggest that a DNA damage–induced toxin controls production of multidrug tolerant cells and thus provide a model of persister formation .
Bacterial populations form persisters , dormant cells that are highly tolerant to antibiotics and play an important role in recalcitrance of biofilm infections [1] , [2] . Time-dependent or dose-dependent killing by antibiotics is distinctly biphasic , revealing a surviving subpopulation of persister cells . Reinoculation of surviving cells produces a culture with a new subpopulation of persisters , showing that these cells are not mutants , but rather phenotypic variants of the wild type [3] , [4] . Re-exposure of persisters to a different bactericidal antibiotic resulted in little or no additional killing , showing that persisters are multidrug-tolerant cells [5] . Gain-of-function mutants in the E . coli hipA toxin gene lead to an increase in the frequency of ampicillin- and fluoroquinolone-tolerant persisters in a growing population from 1 in 10 , 000 cells or less ( wild-type levels ) to 1 in 100 cells [6]–[10] , and this hipA7 mutant was shown to form persisters prior to addition of antibiotic [11] . These persisters were slow- or nongrowing cells . Wild-type persisters have been isolated from an exponential culture of E . coli untreated with antibiotic , by sorting out dim cells of a strain expressing a degradable GFP that is transcriptionally fused to a ribosomal RNA promoter [12] . This indicated that persisters are cells that have diminished protein synthesis and are dormant . The apparent dormancy of persisters accounts for their tolerance to bactericidal antibiotics whose action requires an active , functional target [13]–[16] . The mechanism of persister formation is currently unknown . Isolated persisters show increased expression levels of chromosomal toxin/antitoxin ( TA ) genes [9] , [12] . Ectopic overproduction of RelE , an mRNA endonuclease [17] , inhibits protein synthesis and creates dormant , multidrug-tolerant cells [9] . The HipA protein is an Ef-Tu kinase [18] , [19] , which also inhibits protein synthesis and produces multidrug-tolerant cells upon overproduction . However , strains deleted in individual TA loci do not have a phenotype [9] , [12] , possibly due to their functional redundancy [20]–[22] . In E . coli , there are at least 15 TA modules [20] , [22] , [23] . Importantly , a screen of an ordered 3 , 985 open reading frame ( out of a total of 4 , 288 ) knockout library of E . coli [24] for mutants lacking persisters in stationary phase produced a largely negative result—not a single strain lacking persister formation was identified [25] . Similar negative findings were reported with screens of E . coli transposon insertion ( Tn ) libraries [26] , [27] and a Pseudomonas aeruginosa Tn library [28] . Only mutants with modest reduction in persister levels were identified , and in the case of E . coli , these were primarily in global regulators [25] . This strongly suggests that there are multiple , redundant mechanisms of persister formation . Persisters were originally described by Bigger in 1944 [3] , but functional redundancy has made it very challenging to elucidate the mechanism by which they form . A useful clue to a possible mechanism of persister formation comes from the analysis of the SOS response . Interestingly , SOS induces several TA genes in E . coli , whose promoters contain a Lex box: symER , hokE , yafN/yafO , and tisAB/istR [23] , [29]–[35] Another locus , dinJ/yafQ , contains a Lex box but is not believed to be under SOS control [29] , [30] . Importantly , only the toxin gene is predicted to be up-regulated in the three type 1 TA modules ( symER , hokE , and tisAB/istR ) following SOS induction , whereas in the type 2 TA modules , toxin and antitoxin form an operon and are therefore both expected to be induced . Fluoroquinolones such as ciprofloxacin induce the SOS response [36] by blocking the ligase activity of DNA gyrase and topoisomerase , converting them into endonucleases [14] , [37] . In a separate study , we have shown that the SOS response is also necessary for persister formation in response to the fluoroquinolone antibiotic ciprofloxacin [38] . In the present study , we examine the mechanism of this ciprofloxacin-induced persister formation and find that it is governed by the TisB toxin .
Ciprofloxacin rapidly killed the bulk of E . coli cells , leaving surviving persisters ( Figure 1 ) . Strains deleted in one of the five SOS-TA loci were examined for time-dependent killing by ciprofloxacin , and one of them , ΔtisAB ( GenBank accession number NC_000913 ) , had a sharply decreased level of persisters ( Figure 1A ) . This suggests that the majority of persisters , ≥90% , were formed in response to ciprofloxacin treatment , and their production is dependent on tisAB . Introduction of tisAB in single copy into the lambda attachment site of the ΔtisAB strain complemented the low persister phenotype of the knockout strain ( Figure 1B ) . Persister levels observed in time-dependent killing experiments with ampicillin or streptomycin that do not cause DNA damage were unchanged in the ΔtisAB strain ( unpublished data ) . Ampicillin has been reported to induce the SOS response [39] , but apparently the level of induction is insufficient to influence TisB-dependent persister formation . IstR-1 is an antisense RNA antitoxin that is expressed constitutively from its own , LexA-independent promoter and controls the production of the TisB toxin [28] . IstR-2 is a longer small RNA transcript that is LexA controlled and contains the entire IstR-1 RNA sequence . IstR-2 , however , has been suggested not to be involved in the control of TisB production [40] . tisA is an untranslated open reading frame that contains the antisense RNA binding site as well as the ribosome binding site for tisB [32] . A schematic of the tisAB/istR locus based on [40] is shown in Figure 2 . A strain deleted in istR-1 caused a marked , 10- to 100-fold increase in the level of persisters ( Figure 1A ) . This is consistent with increased levels of TisB leading to persister formation . This result is also in apparent contradiction to a published study showing that ectopic expression of tisB kills cells [41] . It seems likely that the high levels of expression from the multicopy plasmid used in the above-cited study were responsible for cell death . Importantly , the minimal inhibitory concentration ( MIC ) of ciprofloxacin for tisAB and istR-1 knockouts was the same as in the wild type , showing that these genes do not affect resistance to this antibiotic , but rather control drug tolerance by modulating persister production . To test whether IstR-2 was also involved in tisB regulation in persisters , we produced a knockout of the istR-2 promoter region and tested it for ciprofloxacin-induced persister formation . Unexpectedly , the ΔPistR-2 strain had reduced persister levels similar to the tisAB knockout ( Figure S1 ) . It is possible that the istR-2 promoter region contains a binding region of a positive regulator that is essential for tisB expression . Using a plasmid-borne promoter-gfp fusion , we measured induction of tisAB in response to ciprofloxacin , and compared this to the expression of other SOS-TA genes ( Figure 3 ) . The tisAB promoter was the most active after 6 h of exposure to ciprofloxacin and showed a 1 , 000-fold induction , followed by the symE promoter , which showed a 100-fold induction . tisAB promoter activity was even higher than that of the sulA promoter , a standard readout of the SOS response . The dinJ/yafQ promoter was not significantly activated by ciprofloxacin . This is in agreement with a previous report showing that despite the presence of a putative LexA binding box , the dinJ/yafQ locus may not be under control of the SOS response [29] . The results of the induction experiment are consistent with the prominent role of TisB in persister formation in response to ciprofloxacin . A common feature of inducible responses is an increase in tolerance upon repeated exposure to a noxious factor . In a separate study [38] , we showed that ciprofloxacin induces persister formation in a typical step-wise induction experiment ( exposure to a low dose of an antibiotic followed by a higher dose ) . Here , we wanted to test whether tisB was responsible for this phenotype . Wild-type E . coli cells were pre-exposed to low levels of ciprofloxacin ( 0 . 1 µg/ml , 5×MIC ) followed by a higher dose ( 1 µg/ml ) of the same antibiotic ( Figure 4 ) . In a control experiment , the population was exposed to the high dose from the beginning . Step-wise exposure resulted in a 10- to 100-fold higher persister level as compared to a population that was immediately exposed to a high dose of the antibiotic . This pattern is typical of an adaptive response . In contrast to the wild type , pretreatment with a low dose of antibiotic did not induce a higher level of surviving persisters in the ΔtisAB mutant . This shows that this adaptive response to ciprofloxacin depends on tisAB . Next , we tested the ability of persisters formed in response to tisB expression to tolerate multiple antibiotics . For this purpose , tisB was cloned into a low-copy-number vector pZS*24 with an IPTG inducible promoter , and the toxin gene was expressed in exponentially growing cells . Growth leveled off approximately 1 h after the addition of IPTG ( unpublished data ) . Cells overproducing TisB were exposed to antibiotics from four unrelated classes , and survival was measured after a 3-h incubation ( Figure 5 ) . As expected of nongrowing cells , the strain overproducing TisB was completely tolerant to ampicillin , a cell wall synthesis inhibitor that only kills growing cells . Interestingly , cells overproducing TisB were completely tolerant to ciprofloxacin as well . In contrast to ampicillin , ciprofloxacin is very effective in killing regular nongrowing cells , even those without ongoing replication [4] , [9] , [42] . It appears that TisB produces persisters highly tolerant to this DNA-damaging agent . TisB-producing cells also survived exposure to streptomycin , a protein synthesis inhibitor , 100-fold better than the control strain . This shows that TisB-dependent persisters exhibit multidrug tolerance . Antibiotics tested in these experiments act against defined targets . Decreased activity of the target functions in persisters would lead to drug tolerance . Persisters formed by TisB overproduction were susceptible to colistin , a polypeptide antibiotic permeabilizing the outer membrane [43] . This is expected , since an intact outer membrane is essential for cell survival . Further , TisB overproduction protected a ΔrecA mutant against bactericidal antibiotics from three different classes ( Figure 5B ) . The SOS response is initiated when RecA senses damaged DNA and activates cleavage of the global repressor LexA . It was important to establish whether TisB-dependent formation of persisters was controlled by this well-studied SOS response pathway . The persister level of a ΔrecA strain treated with ciprofloxacin was lower as compared to the wild type , and similar to that of a ΔrecA ΔtisB double mutant ( Figure 6A ) . E . coli can also constitutively express SOS-controlled genes if the LexA repressor is deleted . The level of surviving persisters in E . coli ΔrecA lexA300 ( Def ) treated with ciprofloxacin was dramatically increased as compared to the wild type ( Figure 6A ) . Importantly , the MIC of the E . coli ΔrecA lexA300 ( Def ) to ciprofloxacin is 0 . 002 , which is 8-fold lower than in the wild type . RecA is the main recombinase participating in DNA repair , which explains the increased susceptibility of the mutant to fluoroquinolones that cause double-strand breaks . This experiment clearly distinguishes between the decreased resistance of the regular cells , and increased levels of drug-tolerant persisters in the E . coli ΔrecA lexA300 ( Def ) population . Finally , we deleted the tisAB locus in ΔrecA lexA300 ( Def ) and measured survival in response to ciprofloxacin ( Figure 6A ) and tobramycin ( Figure 6B ) . Persister levels in the ΔtisAB ΔrecA lexA300 ( Def ) triple mutant were drastically reduced as compared to the ΔrecA lexA300 ( Def ) strain and were similar to that of the ΔrecA single deletion after exposure to either antibiotic . Taken together , these experiments show that the SOS response triggers induction of TisB , causing formation of multidrug-tolerant persisters ( Figure 7 ) .
Previous research clearly indicated redundancy in persister formation mechanisms , suggesting a unique design of this cell-surviving function [2] . Indeed , all other complex systems of bacteria are made of components usually linked into a single linear pathway , and a screen of a knockout library readily identifies the genes . By contrast , a screen of a knockout library did not result in discovery of strains lacking persisters , and the only genes that were identified as contributing to the persister phenotype were global regulators ( hnr , dksA , fis , hns ) and genes involved in nucleotide metabolism ( apaH , yigB ) [25] . The screen was done in stationary phase , and the library did not contain a tisAB knockout strain . TisB-dependent persister formation is observed under conditions of maximal expression of the SOS response , which is in exponentially growing cells . Consistent with this , we did not observe a phenotype for the ΔtisAB strain in stationary phase ( unpublished data ) , suggesting that under these conditions , persisters form through other mechanisms . The screen [25] did identify the upstream elements of tisB induction , recA and recB . These knockout strains have increased susceptibility to fluoroquinolones and were therefore initially not considered as valid candidates for persister genes . Another persister component , the glpR regulon , was identified in a selection of an expression library of E . coli for increased drug tolerance [27] . Perhaps this redundancy of mechanisms evolved in response to antibiotics in the natural environment . If persisters are specialized survivors , then having multiple mechanisms of formation would ensure that no single compound will lead to their elimination . This underscores the challenges in finding approaches to persister eradication . Redundancy of mechanisms is also challenging for identifying these mechanisms . Given that persisters are dormant , the search narrows for determinants that can reversibly block cellular functions . TA loci contain attractive candidates for persister genes . HipA encoded by the hipBA locus was the first candidate persister gene identified by a targeted selection for high-persister mutants [6] , [7] . The hipA7 allele carries a gain-of-function mutation that causes an increase in persister formation [4] , [8] . Our recent studies showed that HipA is a protein kinase that phosphorylates EF-Tu , rendering it nonfunctional [18] , [19] . Inhibition of protein synthesis leads to multidrug tolerance and presents a compelling scenario for persister formation . However , deletion of hipBA has no phenotype ( [25]; an earlier report of a phenotype [9] was due to deleting a flanking region ) . Expression of other toxins ( RelE; MazF [9] , [44] ) similarly leads to multidrug tolerance , but deletions do not have a phenotype . Extreme redundancy of TA genes would explain the lack of a phenotype , and therefore it seemed useful to search for conditions where a particular toxin would be expressed in a wild-type strain , and then examine a possible link to persister formation . Several TA genes are expressed under conditions of the SOS response , which is induced by fluoroquinolone antibiotics . Examination of deletion strains showed that the level of persisters dropped dramatically in a ΔtisAB mutant and increased equally in a ΔistR-1 mutant overproducing TisB . During steady-state growth , a fraction of cells induces the SOS response stochastically , which could have resulted in production of TisB-dependent persisters [45] . However , the level of persisters surviving treatment with streptomycin or ampicillin was not affected by the absence of tisB . This suggests that spontaneous SOS expression is insufficient to produce cells expressing enough TisB to cause dormancy . This is consistent with our findings that a strain unable to induce the SOS response exhibits reduced persistence in response to ciprofloxacin , but not ampicillin or streptomycin [38] . SOS caused by endogenous DNA damage during normal growth has been shown to induce a “viable but not culturable” state in a subpopulation of cells [45] . It is possible that this is the consequence of induction of SOS TA modules as well . Ectopic overexpression of tisB sharply increased the level of persisters . Drug tolerance following artificial overexpression of a protein , however , may not be a good indicator of a bona fide persister gene . Ectopic overproduction of misfolded toxic proteins causing stasis produces an artificial state of drug tolerance in E . coli [44] . At the same time , overexpression experiments are necessary: if induction of a gene does not lead to an increase in drug tolerance , it can be safely eliminated as a candidate . Drop in persisters in a deletion strain and increase upon overexpression gives reasonable confidence in functionality of a persister gene . The dependence of TisB-induced persisters on a particular regulatory pathway , the SOS response , further strengthens the case for TisB as a specialized persister protein . The long and unsuccessful search for a mechanism of persister formation has lead to the provocative hypothesis of dormant cells being formed by random fluctuations in any protein whose overproduction produces a toxic effect [44] . We previously showed that persisters are not formed in an early-exponential culture of E . coli , suggesting the presence of specific persister proteins , rather than random noise in expression of nonspecific genes [4] . However , this debate could only be settled with the identification of a persister protein . Our finding of an SOS-dependent induction of TisB resulting in multidrug tolerance suggests that there is in fact a specific mechanism of persister formation . The role of TisB in persister formation is unexpected based on what we know about this type of proteins . TisB is a small , 29 amino acid hydrophobic peptide that binds to the membrane and disrupts the proton motive force ( pmf ) , which leads to a drop in ATP levels [41] . Bacteria , plants , and animals all produce antimicrobial membrane-acting peptides [46]–[48] . Toxins of many TA loci found on plasmids belong to this type as well , and represent the plasmid maintenance mechanism . If a daughter cell does not inherit a plasmid , the concentration of a labile antitoxin decreases , and the toxin such as the membrane-acting hok kills the cell [49] . High-level artificial overexpression of tisB also causes cell death [41] . It is remarkable from this perspective that the membrane-acting TisB under conditions of natural expression has the exact opposite effect of protecting the cell from antibiotics . Cells expressing tisB stop growing , and the drop in pmf and ATP levels will shut down the targets of bactericidal antibiotics . Ciprofloxacin kills cells primarily by converting its target proteins , DNA topoisomerases , into DNA endonucleases [14] , [50] . A drop in ATP will then prevent topoisomerases from damaging the DNA . β-lactams such as ampicillin kill by activating the autolysins [15] , [51] , and this requires active peptidoglycan synthesis by the target penicillin-binding proteins . Peptidoglycan synthesis ceases in nongrowing cells . Similarly , the aminoglycoside streptomycin requires an active ribosome for its killing action . Aminoglycosides kill primarily by interrupting translation , which creates toxic , misfolded peptides [13] , [52] . Antibiotics also induce the formation of reactive oxygen species , which contributes to killing [16] , and this requires an active target as well . By creating a dormant state , TisB causes a shutdown of antibiotic targets and multidrug tolerance . Fluoroquinolones such as ciprofloxacin are widely used broad-spectrum antibiotics , and their ability to induce multidrug-tolerant cells is unexpected and a cause of considerable concern . Induction of persister formation by fluoroquinolones may contribute to the ineffectiveness of antibiotics in eradicating biofilm infections . Indeed , pre-exposure with a low dose of ciprofloxacin drastically increases tolerance to subsequent exposure with a high dose [38] . Induction of persisters by the SOS-induced TisB toxin links together two seemingly opposite strategies of survival: active repair , and entry into a dormant state . It seems that in the presence of DNA-damaging factors , the optimal strategy is to both induce repair and increase the number of dormant cells , which will survive when everything else fails . Indeed , a progressive increase in the concentration of fluoroquinolones rapidly kills regular cells but has little effect on the survival of persisters ( [53]; this study ) . This means that it is the dormant persisters rather than regular cells with induced repair that will ultimately survive the DNA-damaging antibiotic . Apart from describing a key element of persister formation , this study also provides a precedent for a physiological function for a chromosomal TA gene pair . Although the role of TAs in plasmid maintenance is well established , the function of chromosomal TAs remains largely unknown . In a recent study , Van Melderen and coauthors produced a knockout of E . coli lacking five toxins , including the well-studied RelE and MazF ( mRNA endonucleases ) ( Tsilibaris et al . [21] ) . The deletion strain had no apparent phenotype and showed normal growth , susceptibility to antibiotics , and stringent response . In Erwinia chrysanthemi , the chromosomal ccdAB TA module prevented postsegregational killing of cells that lost an F plasmid , which contains a homologous ccdAB locus [54] . Prevention of postsegregational killing may be a function of some TA genes but would not explain the presence of >80 TAs in the chromosome of Mycobacterium tuberculosis [55] , [56] , for example , which is not known to harbor plasmids . Induction of TA genes under specific conditions such as described in this study may shed some light on their function . This study opens an intriguing possibility of a wider link between other stress responses and persister formation . Pathogens are exposed to many stress factors in the host environment apart from DNA-damaging agents , including oxidants , high temperature , low pH , membrane-acting agents . It is possible that all stress responses induce the formation of a small but resilient subpopulation of surviving persisters .
Experiments were conducted in 0 . 1 M HEPES-buffered ( pH 7 . 2 ) Mueller Hinton Broth ( MHB ) enriched with 10 mg/l MgSO4 and 20 mg/l CaCl2 according to NCCLS guidelines for susceptibility testing . Killing experiments were conducted by diluting overnight cultures 1∶100 in 3 ml of fresh medium in culture tubes , growing to approximately 2×108 colony forming units ( CFU ) /ml and challenging with 0 . 1 or 1 µg/ml ciprofloxacin . For CFU counts , cells were plated on LB agar plates containing 20 mM MgSO4 to minimize carryover effects of ciprofloxacin . Strains MG1655 ΔtisAB::FRT , ΔIstR-1::FRT , and ΔPistR-2::cat are precise deletions constructed using the method of Datsenko and Wanner [57] and cured of their chloramphenicol resistance cassette with pCP20 where applicable . P1 transduction was used to move the delta recA::Kan , delta sulA::Kan alleles ( from the MORI KEIO collection [24] ) and lexA300 ( Def ) ( kindly provided by G . Walker ) into the MG1655 background . Strain MG1655 pZS*24tisB was constructed by cloning the tisB ORF into the Kpn1/Cla1 sites of pZS*24 [58] using primers tisBfwKpn1 ( 5′-GTAGTAGGTACCATGAACCTGGTGGATATCGCCA-3′ , Kpn1 site in bold ) and tisBrevCla1 ( 5′ GTAGTAATCGATACTTCAGGTATTTCAGAACAGCAT-3′ , Cla1 site in bold ) . MG1655 pUA66PtisB-gfp was constructed by cloning the tisAB promoter region into the XhoI/BamHI sites of vector pUA66gfp using primers PromTisFwXho1 ( 5′-GTAGTACTCGAGGCCGGAGCGAGGTTTCGT-3′ , Xho1 site in bold ) and PromTisRevBamH1 ( 5′-GTAGTAGGATCCAACACAGTGTGCTCACGCGG-3′ , BamH1 site in bold ) . The other promoter-gfp fusions were taken from a commercial library [59] . For complementation experiments , the tisAB locus was cloned into the CRIM vector pCAH63 using primers RegiontisBAfwKpn1 ( 5′-GTCGTCGGTACCTTGAGTATCGATCACAGTTTGCGT-3′ , Kpn1 site in bold ) and RegiontisBArevKpn1 ( 5′-GTCGTCGGTACCCCTTTGGTGCGACTTGAATCTG-3′ , Kpn1 site in bold ) and inserted into the lambda attachment site of strain MG1655 ΔtisAB::FRT as described by Haldimann and Wanner [60] . Cells carrying pUA66-promoter-gfp fusions were grown in MHB to exponential phase as stated before and exposed to ciprofloxacin . At each time point , aliquots were removed , washed 2×in 1% NaCl , and then transferred to a 96-well plate . GFP fluorescence was measured with Ex/Em 485/515 on a Gemini XS spectrophotometer ( Molecular Devices ) . Induction was normalized to background ( pUA66gfp ) , CFU/ml , and initial fluorescence . MG1655 carrying either pZS*24 or pZS*tisB was grown to exponential phase in 12 ml of MHB in 125-ml baffled flasks containing 20 µg/ml kanamycin . TisB expression was induced for 2 h in mid-exponential phase by addition of 500 µM IPTG . The culture was then split and exposed to either ciprofloxacin ( 1 µg/ml ) , ampicillin ( 50 µg/ml ) , streptomycin ( 25 µg/ml ) , or colistin methane sulfonate ( 10 µg/ml ) for 3 h . | Bacterial populations contain a small number of dormant cells ( persisters ) that are tolerant to antibiotics . Persisters are not mutants , but rather phenotypic variants of regular cells . Persisters play a major role in resistance of bacterial biofilms to death , and are likely to be responsible for recalcitrance of chronic infections to antibiotics . A lead into the mechanism by which these specialized survivor cells arise comes from the fact that DNA damage induces the SOS response in bacteria , a signaling pathway that up-regulates DNA repair functions . SOS response induction also leads to expression in E . coli of a tisB “toxin” gene encoding a small membrane-acting peptide that leads to a decrease in ATP and can kill cells if artificially overexpressed . We reasoned that tisB may actually be a persister gene and its product induces reversible dormancy by shutting down cell metabolism . We show that a knockout of tisB resulted in a sharply decreased frequency of persisters tolerant to ciprofloxacin , an antibiotic that causes DNA damage , whereas mild overproduction of the peptide induced persister formation . TisB-dependent persisters also were highly tolerant to unrelated antibiotics . It appears that production of persisters tolerant to all antimicrobials is a “side-effect” of fluoroquinolone antibiotics . Our results suggest that induction of TisB by the SOS response controls production of multidrug-tolerant cells and represents , to our knowledge , the first mechanism of persister formation . | [
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] | 2010 | Ciprofloxacin Causes Persister Formation by Inducing the TisB toxin in Escherichia coli |
High levels of serum IgE are considered markers of parasite and helminth exposure . In addition , they are associated with allergic disorders , play a key role in anti-tumoral defence , and are crucial mediators of autoimmune diseases . Total IgE is a strongly heritable trait . In a genome-wide association study ( GWAS ) , we tested 353 , 569 SNPs for association with serum IgE levels in 1 , 530 individuals from the population-based KORA S3/F3 study . Replication was performed in four independent population-based study samples ( total n = 9 , 769 individuals ) . Functional variants in the gene encoding the alpha chain of the high affinity receptor for IgE ( FCER1A ) on chromosome 1q23 ( rs2251746 and rs2427837 ) were strongly associated with total IgE levels in all cohorts with P values of 1 . 85×10−20 and 7 . 08×10−19 in a combined analysis , and in a post-hoc analysis showed additional associations with allergic sensitization ( P = 7 . 78×10−4 and P = 1 . 95×10−3 ) . The “top” SNP significantly influenced the cell surface expression of FCER1A on basophils , and genome-wide expression profiles indicated an interesting novel regulatory mechanism of FCER1A expression via GATA-2 . Polymorphisms within the RAD50 gene on chromosome 5q31 were consistently associated with IgE levels ( P values 6 . 28×10−7−4 . 46×10−8 ) and increased the risk for atopic eczema and asthma . Furthermore , STAT6 was confirmed as susceptibility locus modulating IgE levels . In this first GWAS on total IgE FCER1A was identified and replicated as new susceptibility locus at which common genetic variation influences serum IgE levels . In addition , variants within the RAD50 gene might represent additional factors within cytokine gene cluster on chromosome 5q31 , emphasizing the need for further investigations in this intriguing region . Our data furthermore confirm association of STAT6 variation with serum IgE levels .
High levels of IgE have been considered for many years as markers of parasite and helminth exposure to which they confer resistance [1] . In Western lifestyle countries with less contact , however , elevated IgE levels are associated with allergic disorders [2] . Only recently , it has been established that IgE antibodies also play a key role in anti-tumoral defence [3] and are crucial mediators of autoimmune diseases [4] , thus challenging the traditional Th1/Th2 dogma . High total serum IgE levels are closely correlated with the clinical expression and severity of asthma and allergy [5] , [6] . The regulation of serum IgE production is largely influenced by familial determinants , and both pedigree- and twin-based studies provided evidence of a strong genetic contribution to the variability of total IgE levels [7] , [8] . Genetic susceptibility of IgE-responsiveness is likely to be caused by a pattern of polymorphisms in multiple genes regulating immunologic responses[9] , but so far only very few loci could be established consistently and robustly , most notable FCER1B , IL-13 and STAT6 [10] , [11] . Family and case-control studies indicated that total serum IgE levels are largely determined by genetic factors that are independent of specific IgE responses and that total serum IgE levels are under stronger genetic control than atopic disease [8] , [12] , [13] , [14] . An understanding of the genetic mechanisms regulating total serum IgE levels might also aid in the dissection of the genetic basis of atopic diseases . In an attempt to identify novel genetic variants that affect total IgE levels , we conducted a genome-wide association study ( GWAS ) in 1 , 530 German adults and replicated the top signals in altogether 9 , 769 samples of four independent study populations .
For the GWAS 1 , 530 individuals from the population-based KORA S3/F3 500 K study with available total IgE levels were typed with the Affymetrix 500 K Array Set . For statistical analysis , we selected SNPs by including only high-quality genotypes to reduce the number of false positive signals . A total of 353 , 569 SNPs passed all quality control measures and were tested for associations with IgE levels . Figure 1 summarizes the results of the KORA S3/F3 500 K analysis . No single SNPs reached genome-wide significance , but the scan pointed to the gene encoding the alpha chain of the high affinity receptor for IgE ( FCER1A ) on chromosome 1 ( Figure 1A ) . Particularly the quantile-quantile-plot of the P values illustrates observed significant associations beyond those expected by chance ( Figure 1B ) . For replication in the independent population-based KORA S4 cohort ( N = 3 , 890 ) , we used the following inclusion criteria: ( i ) P<10−4 in the genome wide analysis ( 39 SNPs , 35 expected ) ; ( ii ) P<10−3 with at least one neighboring SNPs ( ±100 kb ) with P<10−3 ( 45 SNPs ) . The specific results for all SNPs in the GWAS and KORA S4 are given in supplementary table S3 . Six SNPs were significantly associated with total IgE levels in KORA S4 with P values ranging from 2 . 47×10−4 to 3 . 23×10−9 ( given a Bonferroni-corrected significance level of 5 . 10×10−4 ) . The strongest associations were observed for rs2427837 ( P = 3 . 23×10−9 ) , which is located in the 5′ region of FCER1A , and rs12368672 ( P = 2 . 03×10−6 ) , which is located in the 5′ region of STAT6 . In addition , all 4 RAD50 SNPs which had been selected in the GWAS could be replicated . Effect estimates of the SNPs in FCER1A and STAT6 were only slightly lower compared to those in the KORA S3/F3 500 K sample whereas clearly lower effects were observed for the SNPs in RAD50 . The rare allele “G” of the top ranked SNP rs2427837 in FCER1A had an estimated effect per copy of −0 . 212 based on the logarithm of total IgE . This translates into an estimated decrease of 19 . 1% in total serum IgE level for the heterozygote genotype and 34 . 6% for the rare homozygote genotype , which was significantly associated with an increased FCER1A expression on IgE-stripped basophils ( Figure 2 ) . The estimated effect of the STAT6 SNP rs12368672 was 0 . 156 resulting in an increase of total IgE of 16 . 9% and 36 . 6% for the heterozygote and rare homozygote genotype , respectively . The most significant SNP in the RAD50 gene ( rs2706347 ) had an effect estimate of 0 . 143 ( P = 2 . 26×10−4 ) with an associated increase in total IgE of 15 . 4% and 33 . 1% . Altogether the variance of total IgE level explained by genotypes of the three replicated regions was about 1 . 9% . To fine-map the regions of strong association in greater detail , we selected additional SNPs covering the FCER1A and RAD50 gene region based on HapMap data from individuals of European ancestry . In addition , two previously described promoter SNPs of FCER1A ( rs2251746 , rs2427827 ) [15] , [16] , as well as 2 SNPs in the RAD50 hypersensitive site 7 ( RHS7 ) in intron 24 ( rs2240032 , rs2214370 ) [17] were included . In total , 14 SNPs were genotyped in KORA S4 . We found the strongest association in the proximal promoter region of the FCER1A gene , at rs2251746 , which was in strong LD ( r2 = 0 . 96 ) with rs2427837 ( Table 1 and Figure 3 ) . The contribution of the two alleles of rs2251746 in homozygotes and heterozygotes is given in Figure S1 . Their effect is observed across the full range of IgE values . The strongest observed association of SNP rs2251746 and the distribution of the SNPs in the region are shown in Figure 3A . None of the RAD50 SNPs in the fine-mapping showed distinctly stronger association with total IgE ( Figure 3B ) . We additionally sequenced all FCER1A exons with adjacent intronic sequences in 48 male and 48 female samples selected equally from the extremes of the serum IgE distribution in 3 , 890 individuals from the KORA S4 cohort . We identified two new mutations , each present in one individual only , and concurrently confirmed three SNPs already annotated in public databases ( dbSNP ) with validated minor allele frequencies in Europeans . None of the novel mutations were predicted to have functional consequences ( for details see Text S1 and Tables S5 and S6 ) . Haplotype analysis for the FCER1A gene showed lower total IgE levels with effect estimates ranging from −0 . 18 to −0 . 32 for a haplotype described by the rare “G” allele of rs2427837 and the rare “C” allele of rs2251746 ( haplotype frequency 26 . 4% ) in comparison to all other common haplotypes carrying both major alleles ( Table S7 ) . For further replication of the KORA S4 results in the population-based children cohorts GINI ( n = 1 , 839 ) , LISA ( n = 1 , 042 ) and ISAAC ( n = 2 , 998 ) the top 6 SNPs: rs2251746 , rs2427837 , rs2040704 , rs2706347 , rs3798135 , rs7737470 and rs12368672 were tested for association with total serum IgE levels . In GINI , all SNPs except rs12368672 yielded significant P values ranging from 0 . 029 to 8 . 14×10−6 . After correction for multiple testing SNP rs2706347 is slightly above the significance level . In LISA , the two FCER1A polymorphisms rs2251746 and rs2427837 were strongly associated ( P = 4 . 18×10−5 and 6 . 58×10−5 ) , while the RAD50 SNPs showed consistent trends , but no statistical significance . In ISAAC , the effect estimates of the two FCER1A SNPs were distinctly smaller than in the other replication samples but in the same direction and significantly associated with P values of 2 . 11×10−4 for rs2251746 and of 4 . 27×10−4 for rs2427837 . The RAD50 SNPs showed effect estimates in concordance with the other replication samples but were only borderline significant . Additional analysis of markers in the RAD50-IL13 region in a subset of 526 children from the ISAAC replication cohort ( for details see Table S9 ) indicated presence of one linkage disequilibrium ( LD ) block , which encompasses the entire RAD50 gene and extends into the promoter region of the IL13 gene , whereas rs20541 showed low levels of LD with RAD50 variants ( r2<0 . 3 ) ( Figure S2 ) In the combined analysis of all replication samples both selected FCER1A SNPs ( P = 1 . 85×10−20 and 7 . 08×10−19 for rs2251746 and rs2427837 , respectively ) and RAD50 SNPs ( P = 6 . 28×10−7−4 . 46×10−8 ) were significantly associated with IgE levels . Effect estimates were consistent throughout all replication cohorts . In a post hoc analysis of the KORA S4 and ISAAC replication cohorts , FCER1A polymorphisms rs2251746 and rs2427837 showed association with allergic sensitization ( P = 7 . 78×10−4 and 1 . 95×10−3 in KORA , P = 0 . 025 and 0 . 032 in ISAAC ) , while there were no significant associations for the dichotomous traits asthma , rhinitis and atopic eczema ( AE ) . However , the number of cases for these traits was relatively low . We therefore additionally typed a cohort of 562 parent-offspring trios for AE from Germany and a population of 638 asthma cases and 633 controls from UK . In these cohorts we observed weak associations of RAD50 variants with eczema ( P = 0 . 007–0 . 01 ) and with asthma ( P = 0 . 017–0 . 002 , Table S8 ) .
In this large-scale population-based GWAS with follow-up investigations in 9 , 769 individuals from 4 independent population-based study samples we show that functional variants of the gene encoding the alpha chain of the high affinity receptor for IgE ( FCER1A ) are of major importance for the regulation of IgE levels . The high affinity receptor for IgE represents the central receptor of IgE-induced type I hypersensitivity reactions such as the liberation of vasoactive mediators including serotonin and histamine , but also for the induction of profound immune responses through the activation of NFkappa B and downstream genes [18] . It is usually expressed as a αβγ2 complex on mast cells and basophils , but additionally as a αγ2 complex on antigen-presenting cells ( APCs ) as shown for dendritic cells and monocytes [18] . Interestingly , in APCs , IgE-recognition of allergens also leads to facilitated allergen uptake via FCER1 and thereby contributes to a preferential activation of Th2-subsets of T-cells . Its expression is substantially influenced by the binding of IgE to either form of the receptor as bound IgE apparently protects the receptor from degradation and thus enhances surface expression without de novo protein synthesis . Of note , binding of IgE in the two different complexes only uses the alpha subunit of the receptor lacking contact sites with the beta or gamma subunits . Consequently , the expression level of the alpha subunit is crucial for IgE levels on immune cells [18] . Previous studies suggested linkage of atopy to the gene encoding the β chain of the high-affinity IgE receptor ( FCRER1B ) [19] . FCER1B plays a critical role in regulating the cellular response to IgE and antigen through its capacity to amplify FCER1 signalling and regulate cell-surface expression [18] , and there have been several studies which reported an association of FCER1B variants and atopy-related traits but conflicting results for total IgE [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] . In a more recent study , no association between FCER1B tagSNPs and IgE levels was observed [22] . The 500 k random SNP array contained only one SNP within as well as 31 SNPs within a 100-kb region around this gene , which were not significantly associated with total IgE . However , we cannot rule out that we missed relevant variants in this gene . In the present study we identified FCER1A as susceptibility locus in a genome-wide association scan and replicated association of the FCER1A polymorphism rs2427837 with serum IgE levels in a total of 9 , 769 individuals from 4 independent population-based cohorts with a combined P value of 7 . 08×10−19 . This SNP is in complete LD with the FCER1A polymorphism rs2251746 , for which we observed a combined P value of 1 . 85×10−20 . Besides the continuous cycling of the IgE receptor subunits from intracellular storage pools to the surface , there is also a substantial expression of the alpha subunit after stimulation with IL-4 which requires de novo protein synthesis [18] . This induction is stimulated by the transcription factor GATA-1 , which has a binding site in the putative promoter region of the FCER1A gene . Notably , in a previous study with Japanese individuals it could be shown that the minor allele of the polymorphism rs2251746 is associated with higher FCER1A expression through enhanced GATA-1 binding [15] . In line with this we observed an increased cell surface expression of FCER1A on IgE-stripped basophils from individuals homozygous for the “G” allele at rs2427837 ( Figure 2 ) . Analysis of the correlation of FCER1A expression with IgE levels in 320 KORA samples where whole genome blood expression profiles were available revealed no significant effect . However , FCER1A expression showed a significant dependency on IL-4 ( P = 0 . 0087 ) and GATA-1 expression ( P = 1 . 4×10−4 ) , confirming the known stimulation pathway . Interestingly , we found a highly significant dependency of FCER1A expression on GATA-2 transcript levels ( p = 7 . 8×10−27 ) . While whole blood expression levels could easily obscure the situation in basophils , this finding might indicate a novel regulatory mechanisms of FCER1A expression via GATA-2 [18] . The large ( >50 kb ) RAD50 gene , which encodes an ubiquitously expressed DNA repair protein , is located within the Th2-cytokine locus on chromosome 5q31 , which has been linked with total IgE [29] . It contains multiple conserved non-coding sequences with presumed regulatory function [30] . Remarkably , evidence has been provided for the presence of a locus control region ( LCR ) within a 25 kb segment of the 3′ region of this gene , which plays an important role in the regulation of Th2 cytokine gene transcription [31] . The core of this LCR is constituted by four RAD50 hypersensitive sites ( RHS ) in intron 21 ( RHS4-6 ) and 24 ( RHS7 ) [17] , [32] , [33] . The finding of an association between RAD50 variants and IgE levels is new and biologically compelling . However , it has to be considered that so far RAD50 has not emerged as candidate , but that several known candidate genes for atopy-related traits map to this region with strong linkage disequlibrium , especially IL13 , which is one of the strongest and widely replicated candidate genes [10] , [11] . Notably , two functional IL13 polymorphisms , IL13-1112CT ( rs1800925 ) in the promoter region and IL13+2044GA ( IL13 Arg130Gln , rs20541 ) in Exon 4 , have been shown to be associated with a range of atopy-related disorders . IL13+2044GA ( rs20541 ) did not pass our selection criteria , and IL13-1112CT ( rs1800925 ) is not contained in the Affymetrix 500 K Array Set . Additional analysis of markers in this region including these two SNPs showed one LD block encompassing the entire RAD50 gene and extending into the IL13 promoter region , whereas rs20541 showed low levels of LD with RAD50 SNPs ( Figure S2 ) . Thus , we cannot reliably differentiate the specific source of the signal between RAD50 and IL13 in our data . Functional studies are needed to assess whether RAD50 is a true causal gene and to identify the causal genetic variants modulating IgE levels in this region . The identification and positive replication of the STAT6 locus , which is located in one of the most frequently identified genomic regions linked to atopy-related phenotypes [34] , serves as positive control for the experiment . Our results confirm previous candidate studies which showed that genetic variants in the gene encoding STAT6 , a key regulatory element of the TH2 immune response , contribute to the regulation of total serum IgE [35] , [36] . Other previously reported candidate genes for total IgE showed no or only weak signals in our genome-wide scan ( Tables S10 and S11 ) . However , it has to be considered that there are only very few genes that have been associated in the first place to IgE such as STAT6 , whereas most reported candidate genes for total IgE were investigated in asthma or eczema cohorts [10] , [11] . In addition , there have been queries with regard to replication for many of the genes reported . Thus , our data obtained in a population-based and ethnically homogeneous sample ( South German Caucasians ) are not readily comparable with previous candidate gene studies . Furthermore some previously implicated variants were covered insufficiently by the 500 k random SNP array ( Table S10 ) . In summary , in this first GWAS on total IgE FCER1A was identified and replicated as new susceptibility locus at which common genetic variation influences serum IgE levels . In addition , our data suggest that variants within the RAD50 gene might represent additional factors within cytokine gene cluster on chromosome 5q31 , emphasizing the need for further investigations in this intriguing region .
A detailed description of the GWAS population and the replication samples is given in Text S1 and Table S1 . In all studies informed consent has been given , and all studies have been approved by the local ethical committees . The participants were of European origin . The study population for the GWAS ( KORA S3/F3 500 K ) and the first replication cohort were recruited from the KORA S3 and S4 surveys . Both are independent population-based samples from the general population living in the region of Augsburg , Southern Germany , and were examined in 1994/95 ( KORA S3 ) and 1999/2001 ( KORA S4 ) . The standardized examinations applied in both surveys have been described in detail elsewhere [37] . In the KORA S3 study 4 , 856 subjects ( participation rate 75% ) , and in KORA S4 in total 4 , 261 subjects have been examined ( participation rate 67% ) . 3 , 006 subjects participated in a follow-up examination of S3 in 2004/05 ( KORA F3 ) . For KORA S3/F3 500 K we selected 1 , 644 subjects of these participants in the age range 25 to 69 years including 1 , 530 individuals with total IgE level available . From KORA S4 , DNA samples from 3 , 890 individuals with total IgE level were available . Total and specific IgE antibodies to aeroallergens ( S×1 ) were measured using RAST FEIA CAP system ( Pharmacia , Freiburg , Germany ) . Specific sensitization was defined as specific IgE levels ≥0 . 35KU/l ( CAP class > = 1 ) . GINI ( German Infant Nutritional Intervention Program ) and LISA ( Influences of lifestyle-related factors on the immune system and the development of allergies in childhood study ) are two ongoing population-based birth cohorts conducted in Germany . A detailed description of screening and recruitment has been provided elsewhere [38] . Briefly , the GINI birth cohort comprises 5 , 991 newborns , who were recruited between January 1996 and June 1998 in 16 maternity wards in Wesel and Munich , Germany . Children with a positive medical history of atopic disease were invited to a randomized clinical trial with hydrolyzed formulae [39] . The LISA birth cohort study includes 3 , 097 neonates who were recruited between December 1997 and January 1999 in Munich , Leipzig and Wesel , Germany . Blood samples were collected from 1 , 962 ( 51% ) and 1 , 193 ( 50% ) children from the GINI and LISA study , respectively , at age 6 . Total IgE was determined by standardized methods with CAP-RAST FEIA ( Pharmacia Diagnostics , Freiburg , Germany ) . Between 1995 and 1996 , a cross sectional study was performed in Munich and in Dresden , Germany as part of the International Study of Asthma and Allergy in Childhood phase II ( ISAAC II ) to assess the prevalence of asthma and allergies in all schoolchildren attending 4th class in both cities ( age 9 to 11 years ) [40] . Serum measurements for total and specific IgE were performed according to standardized procedures as previously described [40] . Allergic sensitization was defined as positive prick test reaction to at least one out of six common aeroallergens . Within the study population of 5 , 629 children , all children of German origin with DNA and total IgE level available were included in this analysis ( n = 2 , 998 ) . Genotyping for KORA S3/F3 500 K was performed using Affymetrix Gene Chip Human Mapping 500 K Array Set consisting of two chips ( Sty I and Nsp I ) . Genomic DNA was hybridized in accordance with the manufacturer's standard recommendations . Genotypes were determined using BRLMM clustering algorithm . We performed filtering of both conspicuous individuals and single nucleotide polymorphisms ( SNPs ) to ensure robustness of association analysis . Details on quality criteria are described in Text S1 and Table S2 . The power of the replication was estimated for a difference in log total IgE per allele of 0 . 2 and a nominal significance level of 0 . 05 . The power to detect a true association was above 85% in KORA S4 , GINI and ISAAC; whereas in LISA it was about 55% . No single SNPs in the GWAS reached genome-wide significance using a Bonferroni threshold of 1 . 4×10−7 . To fine map the replicated loci in KORA S4 we selected tagging SNPs and used the pairwise tagging algorithm ( r2>0 . 8 ) implemented in HAPLOVIEW 3 . 3 ( HapMap data release #22 , March 2007 , on NCBI B36 assembly , dbSNP b126 ) and additionally selected putative functional SNPs in FCER1A and RAD50 . In all replication samples genotyping of SNPs was realized with the iPLEX ( Sequenom San Diego , CA , USA ) method by means of matrix assisted laser desorption ionisation-time of flight mass spectrometry method ( MALDI-TOF MS , Mass Arraay , Sequenom , San Diego , CA , USA ) according to the manufacturers instructions . In KORA S4 for 7 of 84 replicated SNPs a deviation from Hardy-Weinberg-Equilibrium was observed ( P value<0 . 01 ) . In LISA , GINI and ISAAC all replicated SNPs were in HWE . Details on genotyping are described in Text S1 and Table S4 . FCER1A exons were amplified with intronic primers ( Tables S5 and S6 ) and were directly sequenced using a BigDye Cycle sequencing kit ( Applied Biosystems ) . Genomic DNA ( ∼30 ng ) was subjected to PCR amplification carried out in a 15 µl volume containing 1× PCR Master Mix ( Promega ) , 0 . 25 µM of each forward and reverse primer under the following cycle conditions: initial step at 95°C for 5 min , for 30 cycles at 95°C for 30 s , 58°C ( exon 1 62°C ) for 30 s , and 72°C for 30 s; and final extension at 72°C for 5 min . In the KORA S3/F3 500 K sample possible population sub-structures were analyzed ( Text S1 ) . Additive genetic models assuming a trend per copy of the minor allele were used to specify the dependency of logarithmic values of total IgE levels on genotype categories . The result is a multiplicative model on the original scale of total IgE with effects interpreted in percental changes . All models were adjusted for gender and in the adult cohorts we adjusted additionally for age . We used a linear regression algorithm implemented in the statistical analysis system R ( http://www . r-project . org/ ) and SAS ( Version 9 . 1 . ) . To select significant SNPs in the genome-wide screening and the replications we used conservative Bonferroni thresholds which corresponded to a nominal level of 0 . 05 . Haplotype reconstruction and haplotype association analysis was performed in the KORA S4 replication sample using the R-library HaploStats that allows including all common haplotypes in the linear regression and incorporating age and gender as covariates . The most common haplotype served as reference . Details on haplotype analysis are described in Text S1 . Peripheral blood ( 2 . 5 ml ) was drawn from individuals participating in the KORA study under fasting conditions . The blood samples were collected between 10–12am directly in PAXgene ( TM ) Blood RNA tubes ( PreAnalytiX ) . The RNA extraction was performed using the PAXgene Blood RNA Kit ( Qiagen ) . RNA and cRNA quality control was carried out using the Bioanalyzer ( Agilent ) and quantification was done using Ribogreen ( Invitrogen ) . 300–500 ng of RNA was reverse transcribed into cRNA and biotin-UTP labeled using the Illumina TotalPrep RNA Amplification Kit ( Ambion ) . 1 , 500 ng of cRNA was hybridized to the Illumina Human-6 v2 Expression BeadChip . Washing steps were carried out in accordance with the Illumina technical note # 11226030 Rev . B . The raw data were exported from the Illumina “Beadstudio” Software to R . The data were converted into logarithmic scores and normalized using the LOWESS method [41] . The association between FCER1A gene expression ( independent variable ) and IgE level ( dependent variable ) was computed using the linear regression model adjusted for gender . | High levels of serum IgE are considered markers of parasite and helminth exposure . In addition , they are associated with allergic disorders , play a key role in anti-tumoral defence , and are crucial mediators of autoimmune diseases . There is strong evidence that the regulation of serum IgE levels is under a strong genetic control . However , despite numerous loci and candidate genes linked and associated with atopy-related traits , very few have been associated consistently with total IgE . This study describes the first large-scale , genome-wide scan on total IgE . By examining >11 , 000 German individuals from four independent population-based cohorts , we show that functional variants in the gene encoding the alpha chain of the high affinity receptor for IgE ( FCER1A ) on chromosome 1q23 are strongly associated with total IgE levels . In addition , our data confirm association of STAT6 variation with serum IgE levels , and suggest that variants within the RAD50 gene might represent additional factors within cytokine gene cluster on chromosome 5q31 , emphasizing the need for further investigations in this intriguing region . | [
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] | 2008 | Genome-Wide Scan on Total Serum IgE Levels Identifies FCER1A as Novel Susceptibility Locus |
Lantibiotic synthetases are remarkable biocatalysts generating conformationally constrained peptides with a variety of biological activities by repeatedly utilizing two simple posttranslational modification reactions: dehydration of Ser/Thr residues and intramolecular addition of Cys thiols to the resulting dehydro amino acids . Since previously reported lantibiotic synthetases show no apparent homology with any other known protein families , the molecular mechanisms and evolutionary origin of these enzymes are unknown . In this study , we present a novel class of lanthionine synthetases , termed LanL , that consist of three distinct catalytic domains and demonstrate in vitro enzyme activity of a family member from Streptomyces venezuelae . Analysis of individually expressed and purified domains shows that LanL enzymes install dehydroamino acids via phosphorylation of Ser/Thr residues by a protein kinase domain and subsequent elimination of the phosphate by a phosphoSer/Thr lyase domain . The latter has sequence homology with the phosphothreonine lyases found in various pathogenic bacteria that inactivate host mitogen activated protein kinases . A LanC-like cyclase domain then catalyzes the addition of Cys residues to the dehydro amino acids to form the characteristic thioether rings . We propose that LanL enzymes have evolved from stand-alone protein Ser/Thr kinases , phosphoSer/Thr lyases , and enzymes catalyzing thiol alkylation . We also demonstrate that the genes for all three pathways to lanthionine-containing peptides are widespread in Nature . Given the remarkable efficiency of formation of lanthionine-containing polycyclic peptides and the latter's high degree of specificity for their cognate cellular targets , it is perhaps not surprising that ( at least ) three distinct families of polypeptide sequences have evolved to access this structurally and functionally diverse class of compounds .
Macrocyclization is a common strategy to constrain the conformational flexibility of natural peptides of both ribosomal and nonribosomal origin [1] , thereby conferring increased proteolytic stability and improved affinity for their targets . Lantibiotic synthetases are remarkable catalysts that achieve macrocyclization by utilizing two simple posttranslational modification reactions , dehydration of Ser/Thr residues and subsequent intramolecular addition of Cys thiols to the dehydro amino acids to generate thioether crosslinks called ( methyl ) lanthionines [2] . The resulting polycyclic products have high affinity for their various targets , which to date all consist of small molecules [3] . For instance , nisin binds with high affinity to the bacterial cell wall precursor lipid II [4] , and cinnamycin specifically recognizes phosphatidyl ethanolamine [5] . Nisin is the most studied lantibiotic [6] and has been used commercially to combat food-borne pathogens for 40 years in more than 80 countries without widespread development of resistance . A remarkable feature of lantibiotic biosynthesis is the extraordinary efficiency by which one or two enzymes typically generate 3–5 rings from a linear precursor peptide . How these exceptional catalysts carry out their reactions with apparently high promiscuity and yet a high degree of control and what their evolutionary origin is has not been clear since they have no obvious homology with other protein families in the databases . In this study , we report the discovery of a new class of lanthionine synthetases that provides important new insights into both their mechanisms of catalysis as well as their likely evolutionary origin . Lantibiotics have been categorized into two classes based on their biosynthetic pathways [7] . For class I lantibiotics , LanB dehydratases convert Ser and Thr present in precursor peptides to dehydroalanine ( Dha ) and Z-dehydrobutyrine ( Dhb ) , respectively . Subsequent intramolecular Michael addition of Cys thiols to Dha/Dhb catalyzed by LanC cyclases form the characteristic lanthionine ( Lan , from Ser ) and methyllanthionine ( MeLan , from Thr ) thioether crosslinks ( Figure 1A ) . Class II lantibiotics are produced by bi-functional LanM modifying enzymes , which are responsible for both dehydration and cyclization [8] . The C-terminal cyclase domain of LanM proteins has sequence homology with the LanC enzymes , but the N-terminal dehydratase domain of LanM proteins has no homology with LanB enzymes [9] . Recent X-ray structure analysis and mutagenesis studies of LanC enzymes and LanC-like domains in LanM proteins have provided structural and mechanistic insights into the cyclization steps in lantibiotic biosynthesis [10]–[13] . In contrast , the molecular mechanism of the dehydration reaction by lantibiotic synthetases remains an open question . In this paper , we conducted a search of genome databases for putative alternative lantibiotic synthetases and report the discovery of a new biosynthetic route to lantibiotic-like peptides . We demonstrate in vitro activity for one member from a cryptic gene cluster in the differentiating mycelial soil bacterium Streptomyces venezuelae . This new class of enzymes provides a rare glimpse into an evolutionary path leading to lanthionine-containing peptides . Moreover , we show that the genes for all three pathways to lanthionine-containing peptides are widespread in nature and that they are not restricted to Gram-positive bacteria as long believed .
Analysis of the draft genome sequence of S . venezuelae revealed a lantibiotic-like gene cluster ( Figure 1B ) with a gene encoding a putative and unusual bifunctional lantibiotic synthetase with an N-terminal region ( residues 225–480 ) resembling a serine/threonine kinase instead of the dehydratase domain found in LanM enzymes . At its C-terminus , the protein contains a LanC-like cyclization domain ( residues 540–930 ) ( Figure 1C ) . A nearby small open reading frame has all the hallmarks of a putative lantibiotic precursor gene including a series of Cys , Ser , and Thr residues that are localized in the C-terminal part of the gene product ( Figure 1D ) . A search of the publicly available databases uncovered at least nine other gene clusters that encode proteins with an N-terminal serine/threonine kinase-like domain and a C-terminal LanC-like domain ( Table S1 ) . We hypothesized that these proteins would be novel bifunctional lanthionine synthetases in which the N-terminal region containing the kinase-like domain and the C-terminal LanC-like domain would be responsible for dehydration and cyclization , respectively . Continuing the common nomenclature for lantibiotic biosynthetic genes , we refer to them as LanL proteins with the enzyme from S . venezuelae given the annotation VenL and its putative substrate VenA . Located immediately downstream of venL and venA ( Figure 1B ) are two genes , venT and venH , that appear to encode the ATP-binding and membrane permease subunits , respectively , of an ABC transporter that may be involved in export of the modified peptide out of the cell as occurs for many lantibiotics . Interestingly , the ven cluster does not contain apparent immunity genes unless that role is fulfilled entirely by the putative transport genes venTH , which is not common for lantibiotics . Consistent with other sequenced lantibiotic gene clusters of actinomycete origin [14] , [15] , no genes are present encoding a protease or protease domain that might be involved in cleavage of the leader peptide . Nor does the cluster contain any regulatory genes , although the latter is not unprecedented in actinomycete antibiotic gene clusters ( e . g . , the erythromycin biosynthetic gene cluster of Saccharopolyspora erythraea ) [16] . To verify the hypothesized function , venL and venA were cloned and heterologously expressed in Escherichia coli . VenA was produced as a fusion protein with an N-terminally located maltose-binding protein ( MBP ) and hexahistidine tag ( His6 ) to improve its solubility and provide ease of purification , respectively . The fusion protein was purified by immobilized metal affinity chromatography ( IMAC ) and subsequently treated with tobacco etch virus protease to obtain His6-VenA with a predicted mass of 7 , 552 . 5 ( Figure 2A ) . VenL was also produced with an N-terminal His6-tag ( His6-VenL ) and purified by IMAC and gel filtration chromatography ( Figure S1 ) . His6-VenA was incubated with His6-VenL in the presence of adenosine triphosphate ( ATP ) , MgCl2 , and tris ( 2-carboxyethyl ) phosphine ( TCEP ) and after 3 h subjected to matrix-assisted laser desorption/ionization time-of-flight mass spectrometry ( MALDI-ToF MS ) . After incubation with His6-VenL , His6-VenA was converted into a product with a mass corresponding to the loss of four molecules of water ( −72 Da ) ( Figure 2B ) . This result clearly demonstrated that His6-VenL carried out the dehydration of all four Ser/Thr residues present in the putative core peptide of VenA , the region of the precursor peptide that undergoes the posttranslational modifications ( underlined sequence , Figure 1C ) [17] . The molecular weight of a cyclized or uncyclized product is identical , and therefore we determined the presence of any free Cys thiols in VenA after VenL treatment by reaction with iodoacetamide ( IAA ) , a well-known thiol-selective alkylation agent . As expected , when intact His6-VenA was treated with IAA , its mass increased by 228 Da , corresponding to alkylation of the four Cys in the core peptide ( Figure S2B ) . In contrast , IAA treatment of His6-VenA after incubation with His6-VenL did not result in detectable alkylation products , indicating that the great majority of free thiols were converted to thioether rings ( Figure S2C ) . Taken together , these results demonstrate that VenL functions as a bifunctional enzyme that catalyzes both dehydration and cyclization reactions . To address the hypothesis that the N-terminal region of VenL containing the kinase-like domain would be involved in dehydration of the Ser/Thr residues in VenA , a gene encoding a truncated protein lacking the C-terminal cyclase domain of VenL ( VenL-ΔC ) was constructed . VenL-ΔC was expressed and purified with an N-terminal His6-tag and shown to catalyze the 4-fold dehydration of His6-VenA ( Figure 2C ) . IAA treatment resulted in four alkylations ( Figure S2D ) and no detectable cyclized product lacking any of the alkylations , showing that the N-terminal region of VenL is responsible for the dehydration activity of VenL but has no or greatly reduced cyclase activity . Moreover , these findings strongly suggest that non-enzymatic cyclization is not responsible for the lack of free thiols after incubation of His6-VenA with full length VenL . VenL-ΔC is the first example of an in vitro reconstituted active , monofunctional peptide dehydratase . To provide insights into the mechanism whereby VenL dehydrates rather than phosphorylates Ser and Thr residues , the N-terminal region of LanL proteins was aligned with RamC protein family members and known protein kinases . RamC is involved in the biosynthesis of SapB [18] , a lantibiotic-like morphogenetic peptide that plays a role in sporulation in streptomycetes . RamC possesses an N-terminal kinase-like domain similar to LanL [19] but lacks a prototypical C-terminal cyclase-like domain with its characteristic zinc binding site and active site residues [2] , [11] , [12] . Figure 3A shows the result of sequence alignment analysis of the N-terminal regions of five LanL proteins with two RamC family proteins and three bona fide protein kinases . The alignment clearly indicates that the C-terminal part of the analyzed region of LanL ( residues 230–487 in VenL ) contains conserved kinase-like sequence motifs ( blue stars , Figure 3A ) . On the other hand , its N-terminal part ( residues 1–229 in VenL ) showed no sequence homology to kinases , whereas it shares some homology with members of the RamC protein family . Based on this result , we postulated that the N-terminal part of LanL might be important for the β-elimination reaction of the phosphate group of phosphoserine/threonine to afford Dha and Dhb residues . To test this model , additional sequence alignments were carried out of the N-terminal part of LanL and RamC with the OspF protein family ( Figure 3B ) . Members of this family are phosphothreonine lyase effector proteins that catalyze the irreversible β-elimination reaction of a phosphate group from a phosphothreonine to produce a Dhb residue in their substrate proteins , mitogen-activated protein kinases ( MAPKs ) [20]–[23] . Several pathogenic bacteria use this strategy to modulate host signaling pathways . The alignment revealed that the N-terminal region of VenL possesses conserved motifs characteristic of the OspF family . In particular , four of six residues proposed as catalytic site residues in phosphothreonine lyases based on a crystal structure [21] , [22] are conserved in LanL enzymes ( red stars , Figure 3B ) . Taken together , the sequence alignment analysis suggests that LanL enzymes consist of three catalytic domains: a phosphoserine/threonine lyase domain , a Ser/Thr protein kinase domain , and a cyclase domain that act together to form Lan and MeLan . To verify the hypothesis that LanL enzymes consist of three catalytic modules , we constructed two His6-tagged VenL truncation mutants consisting of only the kinase domain ( residues 201–513 , VenL-ΔLC ) or only the putative phosphoserine/threonine lyase domain ( residues 1–212 , VenL-ΔKC ) . Incubation of His6-VenA with His6-VenL-ΔLC gave a series of new peaks in the mass spectrum corresponding to two , three , and four phosphorylations of VenA ( Figure 2D ) , demonstrating the kinase activity of VenL-ΔLC . When His6-VenA was incubated in the presence of both His6-VenL-ΔLC and His6-VenL-ΔKC , dehydration of VenA was observed albeit with incomplete conversion ( Figure 2E ) , showing that VenL-ΔKC can catalyze the β-elimination of phosphate groups present in phosphorylated VenA to afford Dha/Dhb residues . Collectively , these results are consistent with dehydratase activity of VenL being divided into two distinct catalytic modules , a central kinase domain and an N-terminal lyase domain . To determine the possible physiological function of VenA , both venA and venL were deleted individually from the S . venezuelae genome . Neither deletion had an effect on cell growth or morphological differentiation , and no difference was observed between the mutants and the wild type strain when screened against Micrococcus luteus ATCC4698 for antibiotic activity on 10 different solid growth media . An attempt to enhance expression of the ven gene cluster by introducing the strong constitutive ermE* promoter upstream of venL in S . venezuelae did not yield any phenotypic differences using the same screening conditions . MALDI-ToF analyses of culture supernatants of the wild type and mutant strains grown in different liquid media gave identical spectra , and the wild type culture failed to reveal a peptide corresponding in mass to the in vitro produced compound or to any analogs differing in the site of leader peptide cleavage , number of dehydrations , or that had retained the leader peptide . Apparently the ven gene cluster is not expressed in its natural host under the growth conditions used . Consequently , cosmids containing the wild type ven gene cluster , as well as the venL- and venA-deleted versions , were introduced into Streptomyces lividans by conjugation in an attempt to obtain heterologous expression . No phenotypic differences were observed between the three ex-conjugants , and MALDI-ToF analyses of culture supernatants again failed to identify a peptide with any predicted masses . It is conceivable that some unknown environmental signal is required to activate ven gene expression . The absence of genes with putative regulatory and proteolytic functions and potentially self-resistance mechanisms , and the possibility that they lie elsewhere in the genome of S . venezuelae , might also explain the failure to detect heterologous expression of the ven gene cluster in S . lividans . With few exceptions [24] , [25] , all lantibiotics known to date were first isolated and purified from natural sources . On the other hand , these investigations into VenA originated from a bioinformatic approach , and to date we have not succeeded in detecting production of the peptide by S . venezuelae or after cloning the gene cluster in S . lividans , which is often used to express heterologous gene clusters of actinomycete origin . Therefore , the structure of the mature peptide , which we have termed venezuelin , is unknown . To gain insight into the topology of the lanthionine rings in VenA after processing by VenL , a series of VenA analogs were made in which each cysteine residue was replaced with an alanine residue ( VenA-C32A , VenA-C34A , VenA-C45A , and VenA-C50A ) . Ala29 in the leader peptide region was also replaced with a Lys in these VenA analogs to improve their solubility . Each VenA analog was incubated with His6-VenL followed by endoproteinase Glu-C treatment to yield the C-terminal cyclized peptide spanning Thr18-Ala51 , and subsequently subjected to electron spray ionization-quadrupole/ToF ( ESI-Q/ToF ) MS ( Figure S3 ) . In the tandem mass spectra of the VenA , VenA-C34A , and VenA-C45A peptides , no fragmentation was observed in the C-terminal region ( Dhb31–Ala51 ) except for a fragment ion resulting from cleavage between Cys50 and Ala51 ( Figure 4A–C ) . This observation suggests that the processed VenA peptide contains overlapping cyclic structures between Dhb31 and Cys50; such cyclic structures are less susceptible to fragmentation than linear regions [8] . Additional ions were observed in the C-terminal region in the spectrum of VenL-processed VenA-C32A ( Figure 4D ) , providing more insights into the ring pattern . Two segments were still protected from fragmentation ( Cys34–Dhb39 and Cys45–Dha49 ) , implying the formation of a MeLan between Cys34 and Dhb39 and a Lan between Cys45 and Dha49 . The fragmentations that are observed around Dhb32 and Dhb43 in this mutant suggest that these two Dhb residues , whose cyclization partners would be Cys32 and Cys50 , are not cyclized . Hence , disruption of a MeLan involving Cys32 , which is mutated in VenA-C32A , appears to interfere with formation of another ring . Nonetheless , the fragmentation pattern limits the ring topology to two possibilities: Cys32–Dhb43 and Cys50–Dhb31 , or Cys32–Dhb31 and Cys50–Dhb43 , in addition to the two assigned rings ( Cys34–Dhb39 and Cys45–Dha49 ) . VenA-C50A was also treated with VenL and the product analyzed by ESI-Q/ToF MS ( Figure 4E ) . The large number of fragmentations in the C-terminal region suggests that Cys45 is not cyclized by VenL when Cys50 is mutated . On the other hand , protection from fragmentation in the N-terminal region strongly supports ring formation between Cys32 and Dhb43 . With three rings assigned , the final ring would have to form between Cys50 and Dhb31 . Collectively , the only structure that can account for the regions that are protected from fragmentation in all VenA peptides tested is the ring topology shown in Figure 4F . Future NMR studies will be needed to further confirm this structure , but these will require much larger amounts of material than can currently be obtained . The proposed ring pattern is not found in known lantibiotics and most closely resembles the globular structures of cinnamycin and the duramycins , also products of streptomycetes [14] , [26] . Both cinnamycin/duramycin and the structure proposed here for venezuelin contain four overlapping rings with one particularly large ring structure encompassing 18 amino acids in cinnamycin/duramycin and 20 amino acids in venezuelin . Given the difficulties encountered in detecting venezuelin production in both S . venezuelae and S . lividans , we sought to apply in vitro techniques . All lantibiotics characterized thus far require the removal of the N-terminal leader sequence of the modified precursor peptide to attain their active forms . Since the cluster does not contain an identifiable protease or protease domain that might provide insights , the site of protease cleavage is unknown . However , a peptide with sequence homology to VenA encoded in the genome of Streptomyces clavuligerus has an AlaPheAla sequence that is identical to the cleavage site found in the CinA precursor to the lantibiotic cinnamycin ( Figure S4A ) . For cinnamycin , this cleavage site is thought to be recognized by a protease of the secretory machinery [14] . In VenA , this AlaPheAla-like sequence is a ProPheAla sequence spanning positions 27–29 . Thus , several mutant VenA peptides were generated in which cleavage sites for commercial proteases were engineered such that after proteolysis the C-terminal product would be that predicted by a ProPheAla cleavage site ( Figure S5A ) . Alternatively , GlyAla and AlaAla sequences are found in VenA that could be potential cleavage sites for a protease of the double-Gly type that are found in class II lantibiotics [27] . These sequences are located at positions 25–26 and 29–30 of VenA , respectively . To investigate these potential cleavage sites , a series of VenA analogues were generated in which either a Lys-C/trypsin or Factor Xa cleavage site was introduced ( Figure S5A ) . All five mutant peptides were expressed and purified as His6-tagged peptides and incubated with His6-VenL resulting in the anticipated four dehydrations . Furthermore the products were devoid of free thiols as determined by IAA alkylation as described above . All VenL-processed peptides were then treated with the corresponding protease and the products were tested against Micrococcus luteus ATCC4698 , Lactococcus lactis HP , and Bacillus subtilis LH45 using well-diffusion assays . These three strains are highly susceptible to a wide range of lantibiotics . However , no antimicrobial activity was detected ( Figure S5B ) under the conditions used .
We describe a novel class of lanthionine synthetases consisting of three catalytic modules ( Figure 5A ) that install the thioether rings via phosphorylation of Ser/Thr residues by a kinase-like domain , elimination of the phosphate by a lyase domain , and cyclization by a LanC-like cyclase domain ( Figure 5B ) . These enzymes appear to possess the same cyclization strategy as the previously discovered LanM and LanC proteins , retaining the conserved zinc binding site that is believed to activate the Cys residues for nucleophilic attack onto the dehydro amino acids [10]–[12] . However , the dehydration reaction is carried out in two separate domains that have no significant sequence homology with either the LanB or the LanM proteins ( Figures S7 , S8 , and S9 ) . The central part of LanL proteins ( residues 230–487 in VenL ) contains all 12 conserved subdomains of protein serine/threonine kinases [28] . Importantly , the highly conserved residues in these kinases are also well conserved in LanL proteins . On the other hand , the characteristic kinase motifs found in VenL are not conserved in either LanB or LanM enzymes ( Figures S8 and S9B ) . The N-terminal part ( residues 1–163 ) of VenL has homology to members of the OspF protein family . Most importantly , Lys51 , His53 , Arg83 , and Tyr108 in VenL align well with the residues that play essential roles in catalysis by OspF enzymes ( Lys104 , His106 , Lys136 , and Tyr158 in SpvC , an OspF family member from Salmonella ) . In the proposed catalytic mechanism of SpvC based on an X-ray structure [21] , Lys104 and Tyr158 donate hydrogen bonds to the carbonyl oxygen of the target phosphothreonine to decrease the pKa of its α proton . Lys136 is the catalytic base that abstracts the α proton triggering the β-elimination of phosphate , with His106 protonating the oxygen of the phosphate leaving group . An important difference between the OspF and LanL proteins is that the latter process several phosphorylated Ser/Thr residues present in one peptide , unlike the OspF family , which recognizes one specific phosphoThr in host MAPKs . The essential residues of the lyase domain are not found in LanB proteins ( Figure S9 ) , but sequence alignments with a series of LanM enzymes suggested that possibly some of the conserved residues in OspF might also be present at the N-termini of LanM enzymes ( Figure S7A ) . To test the functional importance of these residues , mutants were generated in a representative LanM enzyme ( lacticin 481 synthetase; LctM ) in which the conserved residues that may be part of a putative lyase active site were mutated ( Lys227Met , Tyr225Ala , and the double mutation Arg226Met/Lys227Met ) . When incubated with the LctA substrate , all three mutants had full dehydration activity ( Figure S7B ) , strongly suggesting that these residues are not part of a lyase domain in LctM . The discovery of a third route for the synthesis of lanthionine-containing peptides illustrates the biological importance of these conformationally constrained peptides . Given the remarkable efficiency of forming polycyclic peptides from linear ribosomally produced peptides and their proven capability to recognize molecular targets with high affinities , it is perhaps not surprising that nature has evolved three distinct families of polypeptide sequences in order to access the reactive dehydro amino acids Dha and Dhb . A search of the available protein databases shows that the genes responsible for the three strategies for lanthionine biosynthesis are widespread , with some species containing two or all three pathways; e . g . , LanL and LanB in Streptomyces clavuligerus and Streptomyces griseus , LanM and LanB in Streptococcus pyogenes and Lactococcus lactis , and all three pathways in Streptococcus pneumoniae ( Figure 6 ) . The wealth of genomic information that is now available shows that putative lantibiotic biosynthetic gene clusters are not restricted to Gram-positive organisms as long believed ( see also [29] ) . Such gene clusters are also found in Gram-negative bacteria such as the proteobacterium Myxococcus xanthus and in cyanobacteria such as Nostoc punctiforme . We propose that the VenL proteins evolved from stand alone protein Ser/Thr kinases and phosphoSer/Thr lyases . The OspF proteins likely evolved high substrate specificity from a promiscuous ancestor , whereas the LanL proteins maintained low substrate specificity . Interestingly , although LanM enzymes do not contain the characteristic motifs of Ser/Thr kinases or phosphoThr lyases ( Figures S7 and S8 ) , recent in vitro studies of a LanM demonstrated that the Ser/Thr residues in the precursor peptide are phosphorylated followed by phosphate elimination to generate the dehydro amino acids [30] , [31] . Thus , while on the sequence level the dehydratases in the three classes of peptide dehydratases have no obvious homology , the chemical logic to carry out the dehydration reaction may well be similar . Interestingly , all three classes of putative lantibiotic synthetases utilize the same cyclization strategy . As mentioned previously [3] , the cyclization reaction is chemically not very demanding and takes place readily non-enzymatically . However , control over chemo- and regio-selectivity requires enzyme catalysis [32] , suggesting that primordial lantibiotics may have been mixtures of non-enzymatically cyclized compounds that became enzyme-guided to enrich the biologically active isomers by acquisition of zinc-containing proteins that are used widespread in nature for thiol alkylation [33] , [34] . Historically , lantibiotics have been discovered from antimicrobial screens , but as more and more genomes are sequenced , bioinformatic analyses are likely to become a major route to the discovery of new compounds and one that is not limited by the biological activity defined by the screen . Many of the lantibiotic-like gene clusters discovered by genome scanning may direct the production of peptides that do not have antibiotic activity , but that may act , for example , as signaling molecules . In fact , two lanthionine-containing peptides from streptomycetes with morphogenetic activities have already been reported [18] , [35] . Moreover , given the extraordinary efficiency of producing conformationally constrained peptides by the dehydration/cyclization strategy , it is likely that compounds generated by genetic diversification will have a variety of biological activities . The name lantibiotics was introduced in 1988 as a shortcut for lanthionine-containing antibiotics [36] . We would like to suggest the name lantipeptides for compounds that by structure and biosynthetic strategy are clearly related to lantibiotics but that are not known to possess antimicrobial activity . In summary , a new family of lanthionine synthetases termed LanL was discovered in S . venezuelae that contain a phosphoSer/Thr lyase domain , a kinase domain , and a cyclase domain . These novel enzymes provide unique new insights into the potential evolutionary origin and the mechanism of these remarkable catalysts . The approach taken here demonstrates that genome scanning , combined with in vitro enzymology , can be a potential strategy to reveal novel mechanistic and evolutionary insights . The genes encoding LanL proteins are widespread in nature , as are lantibiotic/lantipeptide biosynthetic gene clusters in general .
S . venezuelae ATCC10712 was maintained as previously described [37] . Methods for cloning and protein purification are provided in the Supporting Information ( Text S1 and Table S2 ) . His6-VenA was incubated with His6-VenL , His6-VenL-ΔC , His6-VenL-ΔLC , or both His6-VenL-ΔLC and His6-VenL-ΔKC ( final concentration: 2 µM each for His6-VenL and His6-VenL-ΔC , 4 µM each for His6-VenL-ΔLC and His6-VenL-ΔKC ) in a reaction buffer that contained ( final concentrations ) 50 mM HEPES·Na buffer ( pH 7 . 5 ) , 10 mM MgCl2 , 2 . 5 mM ATP , 1 mM TCEP , 25 µM His6-VenA , and 5% DMSO . The reactions with His6-VenL and His6-VenL-ΔC were incubated at 25°C for 3 h and the assays with His6-VenL-ΔLC or His6-VenL-ΔKC were incubated for 15 h . For mass spectrometric analysis , 10 µL of each reaction mixture was desalted using ZipTipC18 ( Millipore ) , eluted with 1 µL of a 75% MeCN/25% water solution containing 0 . 1% TFA and saturated sinapinic acid , and spotted onto the target plate for analysis by using a Voyager-DE-STR mass spectrometer ( Applied Biosystems ) . After incubation of His6-VenA with His6-VenL in 20 µL of modification assay buffer , 6 µL of IAA reaction buffer ( 330 mM Tris-HCl buffer pH 8 . 5 , 33 mM IAA , 6 . 7 mM TCEP ) was added . The solution was incubated at 25°C for 15 h , desalted using ZipTipC18 , and subjected to MALDI-ToF MS . After treatment of each His6-VenA analog with His6-VenL , 0 . 3 U of endoproteinase Glu-C ( Fluka ) was added to 20 µL of assay solution . The solution was further incubated at 25°C for 18 h . An aliquot of 10 µL of the resulting sample was fractionated on an Acquity UPLC ( Waters ) equipped with a C8 column ( 100 mm×1 mm ) using a gradient of 3%–97% B over 12 min ( A = water containing 0 . 1% formic acid , B = methanol containing 0 . 1% formic acid ) , and directly subjected to ESI-Q/ToF MS ( Synapt MS system , Waters ) . Nitrogen was used as cone gas ( 150 L/min ) and desolvation gas ( 600 L/min ) . The capillary voltage was set to 3 . 5 kV . The ionization source and desolvation gas were heated to 120°C and 300°C , respectively . The ions having m/z = 1 , 104 . 34 ( trivalent ion of VenA ) or 1 , 093 . 65 ( trivalent ions for the VenA mutants ) were fragmented with a trap collision energy of 20–40 V . The acquired spectrum was converted to a deconvoluted spectrum by using the MaxEnt3 program ( Waters ) . | Many bacteria generate cyclic peptides , which have improved biological activities compared to linear peptides , including higher stability . Lanthionine-containing peptides are one such group , and different members of this group have antibiotic , anti-inflammatory , and anti-viral activities . For example , one lanthionine-containing peptide called nisin has been used to protect food items from harmful bacteria . Two different pathways for the biosynthesis of lanthionine-containing peptides have been described previously . By comparing the DNA sequences of bacterial genomes we reveal a third biosynthetic route that provides further insight into how the biosynthetic pathways for these cyclic peptides have evolved . We characterized the novel lanthionine synthetase utilized in this third pathway in the soil bacterium Streptomyces venezuelae and show that the purified enzyme catalyzes the chemical reactions necessary to turn a linear peptide into a peptide with multiple rings . The discovery of this third biosynthetic pathway widens the scope for the engineering of new lanthionine-containing peptides for potential use in human therapeutics . | [
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] | 2010 | Discovery of Unique Lanthionine Synthetases Reveals New Mechanistic and Evolutionary Insights |
Clostridium perfringens type B or D isolates , which cause enterotoxemias or enteritis in livestock , produce epsilon toxin ( ETX ) . ETX is exceptionally potent , earning it a listing as a CDC class B select toxin . Most C . perfringens strains also express up to three different sialidases , although the possible contributions of those enzymes to type B or D pathogenesis remain unclear . Type D isolate CN3718 was found to carry two genes ( nanI and nanJ ) encoding secreted sialidases and one gene ( nanH ) encoding a cytoplasmic sialidase . Construction in CN3718 of single nanI , nanJ and nanH null mutants , as well as a nanI/nanJ double null mutant and a triple sialidase null mutant , identified NanI as the major secreted sialidase of this strain . Pretreating MDCK cells with NanI sialidase , or with culture supernatants of BMC206 ( an isogenic CN3718 etx null mutant that still produces sialidases ) enhanced the subsequent binding and cytotoxic effects of purified ETX . Complementation of BMC207 ( an etx/nanH/nanI/nanJ null mutant ) showed this effect is mainly attributable to NanI production . Contact between BMC206 and certain mammalian cells ( e . g . , enterocyte-like Caco-2 cells ) resulted in more rapid sialidase production and this effect involved increased transcription of BMC206 nanI gene . BMC206 was shown to adhere to some ( e . g . Caco-2 cells ) , but not all mammalian cells , and this effect was dependent upon sialidase , particularly NanI , expression . Finally , the sialidase activity of NanI ( but not NanJ or NanH ) could be enhanced by trypsin . Collectively these in vitro findings suggest that , during type D disease originating in the intestines , trypsin may activate NanI , which ( in turn ) could contribute to intestinal colonization by C . perfringens type D isolates and also increase ETX action .
Clostridium perfringens , a Gram-positive , spore-forming anaerobe , is an important pathogen of both humans ( causing , for example , gas gangrene and type A human food poisoning ) and livestock ( causing severe enterotoxemias and enteritis ) [1] . The virulence of this bacterium is largely attributable to its ability to express a plethora of potent toxins . However , while C . perfringens can produce >15 different toxins , individual strains express only portions of this toxin arsenal [1]-[3] . Therefore , based upon production of four typing toxins ( α , β , ε , and ι ) , isolates of this organism are commonly classified into five toxinotypes ( type A through E ) [4] . By definition , C . perfringens type D isolates must produce alpha and epsilon toxins , while type B isolates must express alpha , beta and epsilon toxin [4] . Beyond those typing toxins , type D and type B isolates commonly produce additional toxins , e . g . , perfringolysin O , enterotoxin , TpeL or beta2 toxin [5]-[7] . Type B and D isolates cause enterotoxemias in livestock that initiate with toxin production in the intestines , followed by absorption of those toxins into the circulation to affect other internal organs , such as the brain and kidneys . Type D isolates can also cause acute or chronic enteritis in goats [1] , [8] , [9] . Epsilon toxin ( ETX ) is considered important for the virulence of both type B and type D isolates [5] , [6] . This CDC class B select toxin , which ranks as the third most-potent clostridial toxin after the botulinum toxins and tetanus toxin , belongs to the aerolysin family of pore-forming toxins [9] , [10] . ETX is synthesized and secreted as an inactive prototoxin of 311 amino acids ( 32 . 7 kDa ) . In the animal intestines , the prototoxin can be proteolytically activated to the fully-active toxin ( 274 amino acids ) by trypsin and chymotrypsin [11] . To date , only a few ETX-sensitive cultured cell lines have been identified , including MDCK cells , mpkCCDc14 cells , and human leiomyoblastoma ( G402 ) cells [11] . The mechanism of ETX action on MDCK cells is still under active study but first involves the binding of this toxin to unidentified protein receptors on the MDCK cell membrane surface . The bound toxin then uses lipid rafts to form a heptameric prepore complex on the membrane surface [12] , [13] . When this prepore complex inserts into the cell membrane , an active pore is created that causes , or strongly contributes to , MDCK cell death [9] . Genome sequencing has revealed that C . perfringens strains typically possess three sialidase-encoding genes , named nanH , nanI and nanJ , which are located on a conserved region of the chromosome [14]-[16] . The nanH gene product , which is not secreted , is the ∼43 kDa NanH sialidase . The nanI gene product is the secreted ∼77 kDa NanI sialidase , while the nanJ gene product is the secreted ∼129 kDa NanJ sialidase . The nanH ORF shares only 19% nucleotide sequence identity with nanI and nanJ , but the ORFs encoding the two larger exoenzymes are more closely related , sharing 57% nucleotide sequence identity [17] . Sialidase ( neuraminidases ) production by some pathogenic bacteria has been implicated in their virulence . For example , Vibrio cholerae sialidase enhances the activity of cholera toxin by modifying surface gangliosides to create additional toxin receptors and thereby increase toxin binding levels [18] . Sialidases can also apparently contribute to virulence in other ways besides enhancing toxin binding . For example , neuramindases are thought to assist Streptococcus pneumoniae pathogenesis by providing nutrients for growth , assisting in biofilm formation , and enhancing colonization by exposing adhesion sites for this bacterium in the airways [19] . Possible sialidase contributions to C . perfringens virulence have received only limited attention . A recent study [20] used mutants to evaluate the potential pathogencity contributions of NanI and NanJ sialidase when C . perfringens type A strain 13 causes clostridial myonecrosis ( gas gangrene ) . That study [20] found sialidases can enhance alpha-toxin-mediated cytotoxic effects in vitro , but also reported that sialidase production is not necessary for strain 13 to cause gas gangrene in a mouse model . Possible virulence contributions of sialidases when other C . perfringens strains , such as type D strains , cause disease originating in the intestines has received even less study . It was reported that sialidases can enhance ETX cytotoxicity towards MDCK cells [21] , but an apparently contradictory conclusion was reached by another study determining that treatment of synaptosomal membrane fractions with sialidases lowered ETX binding levels [22] . The current study has used both biochemical and isogenic mutant approaches to better evaluate the possible sialidase enhancement of in vitro ETX action . In addition , a possible role for sialidases in facilitating C . perfringens cell adhesion to host cells was examined for the first time . Results of these in vitro studies suggest that sialidases could contribute to type D pathogenesis via at least two distinct mechanisms .
To clarify whether C . perfringens sialidases can enhance ETX cytotoxicity in sensitive mammalian cells , MDCK cells were pretreated with purified C . perfringens NanI sialidase and , after washing , those cells were challenged with purified ETX . The results showed that sialidase pretreatment substantially increased ETX-induced cytotoxicity ( Figure 1A ) . This enhancement was stronger using a 0 . 005 U/ml vs . a 0 . 001 U/ml dose of sialidase for the pretreatment . However , while enhancement of ETX cytotoxicity increased up to a 90 min sialidase pretreatment using the 0 . 001 U/ml NanI dose , the higher 0 . 005 U/ml NanI dose caused no further increase in cytotoxicity beyond a 60 min pretreatment ( Figure 1A ) . Sialidase treatment alone , or treatment with prototoxin , did not increase cytotoxicity above background levels ( data not shown ) . Experiments were then performed to explore the mechanistic basis behind the sialidase-induced enhancement of ETX cytotoxicity for MDCK cells that was shown in Figure 1A . First , MDCK cells were pretreated with purified C . perfringens NanI sialidase and , after washing , those cells were incubated with Alexa Fluor 488-labeled epsilon prototoxin ( AF488-pETX ) , which was used since it binds similarly as active ETX to MDCK cells , yet causes no cytotoxicity [23] . Results from this experiment ( Figure 1B ) showed that pretreating MDCK cells with either 0 . 001 U/ml or 0 . 005 U/ml of purified NanI sialidase substantially increased subsequent AF488-pETX binding levels . This enhancement of toxin binding increased with longer sialidase pretreatment time , although no further increase in AF488-pETX binding was noted after a 60 min sialidase pretreatment beyond the 0 . 005 U/ml sialidase dose ( Figure 1B ) . An experiment then assessed the ability of sialidase pretreatment to increase formation of the ETX oligomeric complex that is considered responsible for pore formation-induced ETX cytotoxicity [9] . SDS-PAGE analyses of MDCK cell lysates treated with 5 µg/ml of Alexa Fluor 488-labeled ETX ( AF488-ETX ) detected an increased formation of the ETX oligomeric complex after NanI pretreatment ( Figure 1C and 1D ) . This effect increased up to a 60 min pretreatment with 0 . 005 U/ml of NanI . Lastly , similar NanI pretreatment also increased MDCK cell binding and complex formation using a 10 µg/ml dose of AF488-pETX or AF488-ETX , respectively ( data not shown ) . The Figure 1 results indicated that pretreating MDCK cells with purified C . perfringens NanI sialidase can enhance ETX cytotoxicity by increasing toxin binding and thus , complex formation . Therefore , we next sought to assess whether sialidases also contribute to ETX-induced cytotoxicity when these glycoside hydrolyases are expressed at natural levels and in the natural presence of other C . perfringens exoproducts , including other toxins . To initiate this work , we first surveyed sialidase activity in 6 h and overnight supernatants from cultures of various C . perfringens isolates ( Figure 2 ) . This survey detected considerable strain-to-strain variations in supernatant sialidase activity and also identified type D strain CN3718 as a moderately high sialidase producer . Since CN3718 is also transformable and produces ETX ( see below ) , it was chosen for construction of sialidase mutants . PCR analyses first determined that CN3718 carries all three identified C . perfringens sialidase genes ( data not shown ) , including nanI and nanJ , which encode the secreted NanI and NanJ sialidases , and nanH , which encodes the NanH sialidase that lacks a signal peptide and thus localizes in the cytoplasm . Therefore , the current study used a Clostridium-modified Targetron insertional mutagenesis method [24] to construct isogenic single nanH , nanI , and nanJ null mutants , a nanI/nanJ double null mutant , and a nanH/nanI/nanJ triple null mutant in a CN3718 background . The identity of the CN3718 nanJ , nanI and nanH single null mutants ( named BMC201 , BMC202 and BMC203 , respectively ) was first demonstrated by PCR using primers specific for internal nanJ ORF , nanI ORF or nanH ORF sequences . Using DNA from wild-type CN3718 , these internal PCR primers specifically amplified the expected PCR products of 306 bp for nanJ , 467 bp for nanI , and 285 bp for nanH ( data not shown ) . However , consistent with the insertion of an ∼900 bp intron into the target ORF , the same primers amplified PCR products of ∼1200 bp , ∼1400 bp , and ∼1200 bp for the nanJ , nanI and nanH null mutants , respectively ( data not shown ) . Those PCR assays also confirmed the identity of a nanJ/I double null mutant ( BMC204 ) and a nanJ/I/H triple null mutant ( BMC205 ) constructed by Targetron technology ( data not shown ) . The nanJ/I double null mutant PCR supported amplification of two larger bands , which matched the product sizes of a disrupted nanJ gene and nanI gene , along with a small band matching the ∼285 bp product size for the wild-type nanH gene ( data not shown ) . Using BMC205 DNA , PCR amplified larger bands for all three sialidase ORFs ( data not shown ) . After curing the intron delivery plasmids from the BMC201 , BMC202 , BMC203 , BMC204 or BMC205 sialidase null mutants , DNA from the wild-type strain and each null mutant was subjected to Southern blot analysis ( Figure 3A ) using an intron-specific probe [25] . No probe hybridization to wild-type DNA was detected , as expected . In contrast , the presence of a single intron insertion was visible on this Southern blot using DNA from each single sialidase null mutant . In addition , two intron insertions were detected using DNA from the BMC204 nanI/J double null mutant and three intron insertions were noted using DNA from the BMC205 nanI/J/H triple null mutant . To initiate a phenotypic characterization , vegetative growth of the isogenic mutants was compared in Todd Hewitt ( TH ) medium . After similar inoculation into TH medium , the vegetative growth rate of the isogenic mutants was nearly identical to the CN3718 parent ( data not shown ) . Western blots then compared sialidase expression between CN3718 and the isogenic single , double and triple sialidase null mutants . These analyses demonstrated that sonicated 8 h TH cultures of the wild-type strain contain the 129 kDa NanJ , the 77 kDa NanI and the 43 kDa NanH ( Figure 3B ) . In contrast , the BMC201 null mutant only produced NanI and NanH , the BMC202 null mutant only produced NanJ and NanH , and the BMC203 null mutant produced only NanI and NanJ ( Figure 3B ) . Similar Western blotting of sonicated cultures ( Figure 3C ) showed that the BMC204 nanI/J double null mutant only produced the 43 kDa NanH , while the BMC205 nanJ/I/H triple null mutant did not produce any sialidase protein . To evaluate the effects of mutating each sialidase gene on the total sialidase activity of CN3718 , sialidase activity assays were carried out using either 8 h TH culture supernatants ( to measure exosialidase activity ) or sonicated 8 h TH culture supernatants ( to measure total sialidase activity ) from the wild-type parent and each null mutant strain . Collectively , the results ( Figure 3D ) indicated that the exosialidase activity measured in supernatants from the wild-type strain is mainly attributable to NanI . The Figure 3D results also confirmed that NanH predominantly accumulates in the cytoplasm since 8 h supernatants of the BMC204 nanI/J double mutant exhibited almost no sialidase activity , while the sonicated culture supernatant of this double mutant possessed significant sialidase activity . Lastly , this analysis demonstrated that CN3718 produces only the three recognized sialidases since the supernatant and sonicated culture of the BMC205 nanI/J/H triple null mutant lacked sialidase activity . Since a major goal of the current study was to evaluate a possible relationship between sialidases and ETX action , ETX expression was measured for wild-type CN3718 and the isogenic sialidase null mutants . Surprisingly , each single sialidase mutant , but especially the BMC202 nanI null mutant , showed decreased ETX expression compared to the wild-type parent ( Figure 3E and 3F ) . ETX expression levels by the BMC204 nanI/J double null mutant and the BMC205 nanI/J/H triple mutant decreased further compared against ETX production levels by wild-type CN3718 ( Figure 3E and 3F ) . The considerable ETX expression differences between wild-type strain CN3718 and the sialidase mutants shown in Figure 3E and 3F precluded using those strains for comparative ETX cytotoxicity experiments aimed at evaluating sialidase contributions to ETX action under natural sialidase expression levels and in the presence of natural levels of other secreted C . perfringens products . To overcome this complication , etx null mutations were engineered into the CN3718 wild-type strain and the BMC205 triple sialidase null mutant strain to create BMC206 and BMC207 , respectively . The availability of these two etx mutants would allow later comparative cytotoxicity experiments using equivalent amounts of ETX added to supernatants of these strains . These etx mutants were constructed by a Targetron insertional mutagenesis approach . PCR confirmed the presence of an intron insertion into the etx gene ( Figure 4A ) . Using DNA from wild-type CN3718 , internal etx PCR primers specifically amplified a PCR product of 117 bp . However , consistent with a 900 bp intron insertion into the etx ORF , the same primers amplified a PCR product of ∼1000 bp using DNA from the putative etx mutants of both BMC206 and BMC207 . PCR approaches also confirmed that BMC207 maintains intron insertions in its nanI , nanJ and nanH ORFs ( Figure 4B ) . After curing the intron delivery plasmids from these mutants , DNA was isolated from the two putative etx null mutants and subjected to Southern blotting using an intron-specific probe ( Figure 4C ) . This analysis detected no probe hybridization with wild-type CN3718 DNA , as expected . As no suitable enzyme was identified that could separate the four intron copies present in BMC207 , EcoRI was used to distinguish the presence of the etx intron from the other three sialidase introns , whose presence in the BMC205 mutant had already been demonstrated ( Figure 3A ) . By this approach , the presence of the etx intron was clearly shown in BMC206 and BMC207 by Southern blotting ( Figure 4C ) . To initiate phenotypic comparisons , the growth rates of the BMC206 and BMC207 etx null mutants were first determined to be similar to wild-type CN3718 in TH medium ( data not shown ) . Western blotting then confirmed the lack of ETX expression by these etx null mutants ( Figure 4D ) . For later experiments it was important to demonstrate that culture supernatants could be supplemented with purified ETX to achieve equivalent ETX levels . This was confirmed by adding 1 , 5 or 10 µg/ml of ETX to supernatants of the two etx null mutants ( Figure 4D ) . Sialidase Western blotting and sialidase activity analyses ( Figure 4E and 4F ) were next performed using cultures or sonicated cultures ( to release NanH ) of wild-type CN3718 , BMC206 and BMC207 to evaluate whether introducing an etx gene mutation had affected sialidase expression . Western blot results demonstrated comparable levels of NanJ , NanI and NanH production by wild-type CN3718 and BMC206 when grown for 8 h in TH . However , as expected , production of these three sialidases was absent from similar cultures of BMC207 ( Figure 4E ) . Measurement of sialidase activity in sonicated cultures ( Figure 4F ) further confirmed those Western blotting results . The sialidase activity detected in TH sonicated lysates or TH culture supernatants of CN3718 vs . BMC206 was very similar . However , no sialidase activity was measured in similarly prepared BMC207 sonicated lysates or culture supernatants . For preparing a nanI complementing strain of BMC207 , we first constructed pJIR750Icomp , which consists of the CN3718 nanI ORF , 500 bp of upstream sequence and 300 bp of downstream sequence cloned into the C . perfringens/E . coli shuttle plasmid pJIR750 . A nanJ complementing plasmid , named pJIR751Jcomp , was similarly prepared by PCR-amplifying a product corresponding to the nanJ ORF , 1000 bp of upstream sequence and 400 bp of downstream sequence , and then cloning that PCR product into the C . perfringens/E . coli shuttle plasmid pJIR751 . Finally , by the same method , a nanH complementing plasmid named pJIR751Hcomp was prepared that contains 500 bp of upstream sequence , the nanH ORF and 300 bp of downstream sequence cloned into pJIR751 . The three sialidase complementing plasmids were then individually transformed into BMC207 by electroporation . PCR confirmed the resultant transformants contained nanI , nanJ or nanH wild-type genes ( Figure 5A ) . Using primers to internal sequences of each sialidase gene and BMC207 DNA , the nanI , nanJ or nanH PCR products amplified large bands indicating an intron insertion . In contrast , using DNA from the three complementing strains , the PCR products amplified were of smaller size , consistent with their containing a wild-type sialidase gene , which is preferentially amplified over the larger intron-disrupted sialidase gene also present in these complementing strains . Western blot analyses then assessed sialidase expression by the complementing strains ( Figure 5B ) . As expected , BMC206 produced the ∼43 kDa NanH , the ∼77 kDa NanI and the ∼130 kDa NanJ . In contrast , the BMC207 null mutant expressed no sialidase proteins . These Western blots also showed that the nanH complementing strain BMC2073 only produced NanH , the nanI complementing strain BMC2072 only made NanI , and the nanJ complementing strain BMC2071 only expressed NanJ ( Figure 5B ) . The sialidase activity of the complementing strains was also assessed ( Figure 5C ) . When sialidase activity was measured in 2 h , 4 h , 6 h , 8 h , 10 h or overnight TH cultures , the nanI complementing strain BMC2072 started expressing NanI as early as 2 h in TH culture . In contrast , BMC206 and the nanJ complementing strain began producing sialidase at 4 h . Wild-type CN3718 also expressed sialidase starting at 4 h ( data not shown ) . The NanI complementing strain BMC2072 produced more sialidase activity than other complementing strains . Since the BMC2072 nanI single complementing strain already expressed more sialidase activity than either CN3718 or BMC206 , and also due to technical limitations , no double or triple sialidase complementing strains were prepared . The availability of sialidase mutants and complementing strains allowed us to evaluate whether specific sialidases can enhance ETX binding to MDCK cells at natural sialidase expression levels and in the background of other secreted C . perfringens products . The same 5 µg/ml amount of AF488-pETX was added to concentrated supernatants from BMC206 , the BMC207 triple sialidase null mutant strain , or the three BMC207 sialidase complementing strains ( i . e . BMC2071 , BMC2072 and BMC2073 ) . Note that , i ) a natural 1× concentration of C . perfringens culture supernatant was finally present in these MDCK cell cultures , ii ) a 5 µg/ml ETX concentration corresponds to a typical natural supernatant concentration of this toxin for ETX-producing C . perfringens strains [5] , [6] and iii ) these binding experiments were performed at 37°C to obtain the enzymatic effects of sialidases ( when present ) , but previous studies [13] have shown that prototoxin binding to MDCK cells is equivalent at 4°C or 37°C . When these mixtures were added to MDCK cells , the fluorescent readings showed that the BMC207 supernatant sample supported less AF488-pETX binding to MDCK cells compared against the BMC206 supernatant ( Figure 6 ) . Supernatant from the BMC2072 nanI complementing strain exhibited even greater levels of AF488-pETX binding than did BMC206 supernatant after a similar supplementation with the labeled prototoxin . In contrast , a smaller increase in AF488-pETX binding was noted using supernatants of the nanJ or nanH complementing strains supplemented with labeled prototoxin . Figure 1 results demonstrated that pretreating MDCK cells with purified C . perfringens NanI sialidase increases subsequent ETX cytotoxicity . Results shown in Figure 7A confirmed this conclusion and demonstrated that this effect is not due to direct sialidase-induced cytotoxicity . The Figure 6 binding results suggested that , compared against supernatants from BMC207 , increased MDCK cell cytotoxicity should also be observed using supernatants from BMC206 or the nanI complementing strain BMC2072 when those supernatants were supplemented with ETX . Verifying this suggestion , the ETX-supplemented culture supernatants of BMC206 strain caused about 30% more MDCK cell cytotoxicity than did similarly ETX-supplemented BMC207 supernatants ( Figure 7B ) . Furthermore , complementation with the wild-type nanI gene substantially increased the cytotoxic effects of the sialidase-deficient BMC207 mutant , while complementation of the BMC207 mutant with the wild-type nanJ or nanH genes increased cytotoxicity to a lesser extent . During natural disease , ETX is secreted into the animal intestines as an inactive prototoxin of ∼33 kDa , which can then be activated by intestinal proteases such as trypsin or chymotrypsin [11] . Interestingly , when CN3718 wild-type supernatants were trypsin-treated to activate prototoxin ( data not shown ) , overall sialidase activity in these supernatants also significantly increased . This result was confirmed by treating purified C . perfringens NanI with trypsin . As shown in Figure 8A , the trypsin-treated NanI possessed significantly increased sialidase activity compared against the same amount of non-trypsin-treated NanI . When these samples were subjected to Western blotting , the results indicated that most of the trypsin-treated NanI ran as a slightly smaller protein than native NanI , supporting proteolytic processing ( Figure 8B ) . In addition , chymotrypsin also activated NanI sialidase activity ( data not shown ) . A further experiment then compared ETX cytotoxicity in the presence of purified NanI sialidase that had , or had not , been trypsin-activated prior to mixing with purified active ETX . After blocking trypsin activity from the sialidase sample with trypsin inhibitor , an enhancement of ETX cytotoxicity was observed using the trypsin-activated sialidase compared against the same amount of sialidase that had not been trypsin-activated ( Figure 9A ) . Trypsin-activated sialidase alone caused no increase in cytotoxicity above background . Similar experiments using concentrated C . perfringens culture supernatants showed that trypsin activation of these supernatants enhanced MDCK cell cytotoxicity by about 25% for BMC206 ( Figure 9B ) . Further supporting the involvement of sialidases in this trypsin-induced increase in the cytotoxic properties of BMC206 , similar trypsin treatment of BMC207 supernatants did not increase ETX-induced cytotoxicity ( Figure 9B ) . The Figure 8A results indicated that NanI can be activated by trypsin . To assess whether the other two C . perfringens sialidase ( NanJ and NanH ) can also be trypsin-activated , the supernatants of nanI , nanJ and nanH complementing strains were similarly treated with trypsin and their sialidase activity was measured . The results obtained indicated that only the sialidase activity of NanI is enhanced by trypsin ( Figure 10A ) . In contrast , NanJ and NanH sialidase activity slightly decreased after similar trypsin treatment ( Figure 10A ) . Consistent with this result , trypsin increased the MDCK cell cytotoxicity of supernatants containing only NanI , but not supernatants containing only NanJ or NanH ( Figure 10B ) . During natural enterotoxemias , type D strains remain present in the intestines , where they contact host enterocytes [8] . Therefore , an experiment examined whether contact with cultured mammalian cells for 6 hr ( including intestinal Caco-2 and HT-29 cells as well as MDCK , Vero and NT6 fibroblasts ) might affect the exosialidase activity of BMC206 . This study revealed that contact between BMC206 and Caco-2 , HT-29 , or MDCK cells resulted in increased culture supernatant sialidase activity , although this effect was weaker using MDCK cells . In the absence of BMC206 infection , supernatant sialidase activity for Caco-2 , HT-29 and MDCK cell cultures did not increase above background ( Figure 11A ) , suggesting that the increased culture supernatant sialidase activity measured in the BMC206-infected cultures was not from secreted mammalian sialidase activity . The results also detected no culture supernatant sialidase activity upon infection of these mammalian cells with the triple sialidase null mutant BMC207 . In contrast to the stimulation of sialidase activity observed using Caco-2 , HT-29 or MDCK cells , exosialidase activity did not increase after BMC206 infection of Vero cell or NT6 fibroblast cultures ( Figure 11A ) . The nature of this host cell-induced increase in sialidase activity was further investigated using a Caco-2 cell infection model ( Figure 11B ) . This study detected no culture supernatant sialidase activity upon infection of the triple null mutant BMC207 with Caco-2 cells up to 6 h , consistent with the increased sialidase activity detected upon BMC206 infection of Caco-2 cells involving upregulated sialidase expression by BMC206 upon Caco-2 cell contact . Results obtained using the BMC2072 NanI complementing strain suggested this upregulation of sialidase activity upon contact of BMC206 with Caco-2 cells primarily involves increased NanI production ( Figure 11B ) . To definitively resolve whether contact with Caco-2 cells upregulates NanI expression by BMC206 , quantitative RT-PCR studies were performed ( Figure 11C ) . Those studies clearly demonstrated a substantial increase in nanI transcription in the presence of Caco-2 cells . When causing disease originating in the intestines , C . perfringens type D vegetative cells likely adhere to intestinal tissue to colonize and sustain toxin production [8] . Relatively little is known about C . perfringens adherence to host cells , particularly enterocytes during enteritis or enterotoxemia . However , since enterocytes are coated with a variety of sialic acid-containing glycoconjugates , sialic acid residues could conceivably modulate attachment of these bacteria to the intestines . Therefore , experiments tested whether BMC206 or BMC207 can attach to cultured mammalian cells , including Caco-2 cells , HT-29 cells , MDCK cells , Vero cells and NT6 fibroblasts . As shown in Figure 12A and 12B , BMC206 attached well to Caco-2 and HT-29 cells , but substantially less well to the other surveyed host cells . BMC206 attachment to Caco-2 and HT-29 cells involved sialidase production since BMC207 failed to attach to those two mammalian cell lines ( Figure 12A and 12B ) . Additional studies were performed with the human enterocyte-like Caco-2 cell line as an in vitro model to examine whether sialidases play a role in CN3718 adherence to intestinal cells ( Figure 13A and 13B ) . As already established ( Figure 12A ) , BMC206 attached well to Caco-2 cells . In contrast , there was little or no attachment of BMC207 to these host cells under the same experimental conditions . However , the BMC2072 nanI complementing strain exhibited Caco-2 cell adhesion levels equal to , or exceeding those of , BMC206 ( Figure 13A and 13B ) . In contrast , the Caco-2 adhesion ability of the nanJ and nanH complementing strains was not substantially increased over the attachment of BMC207 ( Figure 13A and 13B ) . A role for NanI sialidase in modifying the Caco-2 cell surface to increase the adherence of CN3718 vegetative cells to these host cells was further supported by results of an experiment where Caco-2 cells were pretreated with purified NanI . This pretreatment substantially increased the Caco-2 cell adherence of the BMC207 mutant ( data not shown ) . Phase-contrast and immunofluorescence microscopy analyses ( Figure 13C ) confirmed the strong adhesion of BMC206 or BMC2072 vegetative cells to Caco-2 cells . This microscopy detected many adherent bacteria attached to Caco-2 cells , often along their edges . In contrast , few ( if any ) BMC207 cells were adherent to Caco-2 cells .
A recent study [20] showed that a nanI mutation nearly completely abolished the sialidase activity of C . perfringens type A strain 13 , which produces NanI and NanJ . However , unlike strain 13 , many C . perfringens strains can also produce NanH [14] . Using a combination of nanI , nanJ or nanH single null mutants , a nanI/nanJ double null mutant , and a sialidases triple null mutant , the current study evaluated the relative contribution of each sialidase to the exosialidase activity and total sialidase activity of C . perfringens type D strain CN3718 . These analyses revealed that , using the sialidase assay conditions employed in this study , i ) CN3718 produces all three recognized C . perfringens sialidases but no additional unknown sialidases , ii ) NanI is responsible for most exosialidase activity of this strain and iii ) NanH is also a major contributor to the total sialidase activity possessed by CN3718 . Furthermore , by demonstrating that CN3718 produces all three sialidases , the current results argue against proposals [17] that only myonecrosis strains of C . perfringens produce all three sialidases . Bioinformatics analyses of database sequences support carriage of three sialidase genes as the norm for C . perfringens strains , regardless of origin . In addition to our current CN3718 findings , analysis of the 8 completed or partially-completed C . perfringens genomes revealed that all three sialidase genes are present in 6 of those 8 isolates , which include several isolates from humans or livestock suffering from C . perfringens disease originating in the intestines . The two exceptions are strain 13 and SM101 that , respectively , lack nanH or both nanI and nanJ [14] , [16] . Previous studies with other bacterial pathogens showed that sialidases can contribute to virulence in several ways , e . g . , by exposing sites to facilitate more bacterial adhesion or toxin binding to host cells [19] , [26] . Our results indicated that NanI sialidase increases the ETX sensitivity of MDCK cells . The mechanism of this enhancement was shown , for the first time , to involve an increase in toxin binding , suggesting NanI exposes additional ETX receptors on the host cell surface . Alternatively , NanI could modify host cell surface nonreceptors so they acquire ETX binding ability , similar to the situation in Vibrio cholerae , where a sialidase modifies glycolipids to increase cholera toxin binding [18] . We also demonstrated that increased ETX binding to NanI-exposed MDCK cells leads to more ETX complex formation , which translates to greater pore formation and host cell death [23] . Overall , these conclusions are consistent with , and explain for the first time , observations from a previous study reporting that C . perfringens sialidases can increase ETX cytotoxicity towards MDCK cells [21] . They are also consistent with previous results showing that soluble sialic acid cannot inhibit ETX binding to , or action on , MDCK cells [21] . However , the current results apparently contrast with another study [22] reporting that sialidases lower ETX binding to brain synaptosomal membrane vesicles . It is not immediately clear whether the varying conclusions amongst these studies are attributable to using MDCK cells vs . synaptosomal membranes or involve some other explanation . Sialidases assist the ability of some pathogens to adhere to host cells or tissues . For example , sialidase contributions to adherence and colonization are well-documented for S . pneumoniae [19] . However , prior to the current work , only one study had addressed C . perfringens vegetative cell adherence to the mammalian intestinal epithelium , despite the likely importance of this process for pathogenesis; that earlier study suggested a putative C . perfringens collagen adhesion protein ( CNA ) might contribute to porcine enteritis by promoting adhesion of this bacterium to damaged intestinal tissue [27] . Our study now reports the first evidence that adherence of a C . perfringens strain to certain host cells , including enterocyte-like Caco-2 cells , is facilitated by sialidases , especially NanI . Inactivation of NanI sialidase production decreased adhesion of CN3718 strain by at least 20-fold and this effect was reversible by complementation . Furthermore , pretreating Caco-2 cells with purified NanI allowed adherence of the BMC207 mutant unable to produce any sialidase , indicating that NanI modifies the Caco-2 cell surface to render it favorable for CN3718 adherence . NanH and NanJ appeared to play a lesser role in facilitating C . perfringens adherence to Caco-2 cells based upon complementation results using the triple sialidase mutant . The varying ability of the three sialidases to enhance ETX binding or C . perfringens adherence likely reflects the reported enzymatic difference in sialidase kinetic properties and substrate specificities [17] , although further comparative study of the properties of C . perfringens sialidases is warranted . These in vitro findings open the possibility that sialidases , particularly NanI , contribute to virulence by facilitating C . perfringens adherence in the intestines , a hypothesis that should be tested in animals . In addition , future studies should identify the adhesins mediating CN3718 binding to certain host cells . Beyond the CNA study [27] mentioned in the preceding paragraph , the only other available information regarding molecular mechanisms of C . perfringens adhesion to host cells is a report implicating type IV pilin in type A strain 13 attachment to myoblasts [28] . Future studies will examine whether the type IV pilus or CNA protein contribute to CN3718 adherence to host cells . A notable finding of the current work was that CN3718 adhesion is specific for certain mammalian cells . Notably , this strain adheres well to Caco-2 and HT-29 cells , both of which are intestinal cell lines . Much lower attachment of this strain was detected using cell lines of nonintestinal origin , including MDCK cells ( canine kidney cells ) , Vero cells ( African green monkey kidney cells ) or NT6 rat fibroblasts cells . Furthermore , contact of CN3718 with the intestinal cells lines , and MDCK cells to a lesser degree , also stimulated nanI transcription . Collectively , these results suggest that CN3718 , a type D disease strain , is well-adapted for sensing the presence of enterocytes and then responding by producing more sialidase to facilitate its specific attachment to these cells . However , a survey including many additional cell lines and C . perfringens strains are needed to test this hypothesis . The current study also suggests that trypsin could be a greater contributor to type D disease than previously appreciated [29] . It is well established that , following secretion , the inactive ETX prototoxin must be activated by protease-induced removal of residues from the N- and C-terminus [29] . This prototoxin activation normally occurs in the gut of infected animals , where it likely involves intestinal proteases , including trypsin . Interestingly , the current study found that trypsin proteolytically activates NanI as well as ETX , thereby increasing sialidase activity . Since we also showed that trypsin activation of NanI enhances ETX binding and cytotoxicity towards MDCK cells , trypsin-activated NanI could similarly increase ETX action in vivo . Furthermore , since NanI was also determined to be important for CN3718 adherence to Caco-2 cells , trypsin activation of NanI could also contribute to type D disease by promoting C . perfringens colonization in the intestines . Chymotrypsin also activated NanI sialidase activity ( data not shown ) , so that intestinal protease could further enhance disease . Another possible virulence role for sialidases is suggested by our observation that various CN3718 sialidase mutants , particularly nanI mutants , exhibited significantly decreased ETX production levels despite wild-type growth properties ( data not shown ) . This result might indicate that sialic acid signals ETX production , but initial attempts to prove this hypothesis have yielded inconclusive results ( data not shown ) . Interestingly , previous studies showed that inactivating the nanI gene in strain 13 also affects toxin production levels [20] . However , for that strain , the nanI mutant showed increased production levels of alpha-toxin and perfringolysin O . Whether the increased production of alpha toxin and perfringolysin O by strain 13 involves a sialic acid signal should also be evaluated in the future . While our findings suggest NanI plays , at minimum , two virulence roles for CN3718 , i . e . , enhancing both ETX binding and attachment of this strain to enterocytes , it remains unclear why this strain also produces NanJ and NanH sialidases . As mentioned , NanH and NanI have different temperature optimums , kinetic properties and substrate specificities [17]; the enzymatic characteristics of NanJ , which was only discovered during genome sequencing studies [14] remains uncharacterized . Possibly NanH and NanJ are important for growth or survival in specific environments such as soil or sewage or in other infections . Furthermore , NanJ possesses additional domains of unknown function that are missing from NanI [17]; those additional NanJ domains might contribute to growth or virulence under conditions that remain to be identified . In summary , our in vitro results suggest that , while NanI sialidase does not appear to be involved in the pathogenesis of type A isolates during myonecrosis [20] , it could contribute to type D enterotoxaemia and enteritis in at least two ways . First , NanI ( particularly after trypsin activation in the intestines ) could increase ETX binding and complex formation , thereby potentiating ETX cytotoxicity . The potential relevance of this effect for damage to non-intestinal target organs , such as brain and kidneys , is unclear since it has not yet been evaluated whether NanI sialidase can be absorbed into the circulation during type D enterotoxemias . Similarly , ETX causes limited damage in the intestines of sheep [8] , so sialidase potentiation of ETX may have less importance during ovine type D disease . However , sialidases could possibly increase ETX binding to the intestines of sheep or goats and thus facilitate absorption of this toxin into the circulation . Moreover , ETX does substantially damage the caprine gastrointestinal tract [8] , [30] , so , by enhancing ETX intestinal toxicity , trypsin-activated NanI could directly contribute to caprine type D enteritis . A second possible virulence contribution of NanI may be to increase C . perfringens vegetative cell adherence to the intestinal epithelium , thereby contributing to type D infections by facilitating colonization of ETX-producing bacteria in the intestines . Lastly , our results suggest that contact with intestinal cells may stimulate C . perfringens to produce more NanI , thereby further potentiating ( particularly after trypsin and chymotrypsin activation ) ETX effects and bacterial adherence during disease . The relevance of these in vitro findings for possible sialidase virulence contributions should now be tested experimentally in animal models .
CN3718 , an ETX-positive C . perfringens type D animal disease strain ( Table 1 ) , originated from the Burroughs-Wellcome collection and was obtained via Dr . R . G . Wilkinson [5] . NCTC8346 , another type D animal disease isolate , was used for ETX toxin purification [5] . E . coli Top10 cells ( Invitrogen ) were used as the cloning host . Mutant , complementing strains , and plasmids used in this study are listed in Table 1 . Media used in this study for culturing C . perfringens included FTG medium ( fluid thioglycolate medium; Difco Laboratories ) ; TH medium ( Bacto Todd Hewitt Broth [Becton-Dickinson] , with 0 . 1% sodium thioglycolate [Sigma Aldrich] ) ; TGY medium ( 3% tryptic soy broth [Becton-Dickinson] , 2% glucose [Fisher scientific] , 1% yeast extract [Becton-Dickinson] , and 0 . 1% sodium thioglycolate [Sigma Aldrich] ) and BHI agar plates ( brain heart infusion , Becton-Dickinson ) . For culturing E . coli , Luria-Bertani ( LB ) broth ( 1% tryptone [Becton-Dickinson] , 0 . 5% yeast extract [Becton-Dickinson] , 1% NaCl [Fisher scientific] and LB agar ( 1 . 5% agar [Becton-Dickinson] ) were used . All antibiotics used in this study were purchased from the Sigma-Aldrich Chemical Company or Fisher Scientific Company . Purified C . perfringens neuraminidase ( NanI ) was purchased from Roche Applied Science . ETX prototoxin was purified to homogeneity using a previously described method [5] . Following the method described by the manufacturer , the purified ETX prototoxin was fluorescently-labeled using an Alexa Fluor 488 protein labeling kit ( Invitrogen ) , creating AF488-pETX . For ETX detection by Western blots , an ETX-specific monoclonal antibody ( 5B7; kindly provided by Paul Hauer , Center for Veterinary Biologics , Ames , Iowa ) was used as primary antibody , followed by rabbit anti-mouse immunoglobulin G ( IgG ) -peroxidase conjugate ( Sigma ) as a secondary antibody . A polyclonal rabbit antibody against C . perfringens neuraminidases was purchased from Thermo Scientific . The nanJ , nanI and nanH genes of CN3718 were each inactivated by insertion of a group II intron via the Clostridium-modified TargeTron gene knock-out system [24] . Using intron insertion sites identified by the Sigma TargeTron algorithm , an ∼900 bp intron was targeted into the nanJ , nanI and nanH ORFs in a sense orientation . The intron insertion was targeted between nucleotides 657 and 658 of the nanJ ORF . The primers used for PCR targeting the nanJ intron were 657/658-IBS , 657/658-EBS1d and 657/658-EBS2 ( Table 2 ) . The intron insertion was targeted between nucleotides 730 and 731 of the nanI ORF using primers 730/731-IBS , 730/731-EBS1d and 730/731-EBS2 ( Table 2 ) . The intron insertion was targeted between nucleotides 707 and 708 of the nanH ORF . The primers used for PCR targeting the intron into nanH were 707/708-IBS , 707/708-EBS1d and 707/708-EBS2 ( Table 2 ) . The 350 bp intron PCR products were inserted into pJIR750ai between the HindIII and BsrGI enzyme sites in order to construct nanJ , nanI and nanH-specific TargeTron mutagenesis plasmids . The resultant plasmids , named pJIR750nanJi , pJIR750nanIi and pJIR750nanHi respectively ( Table 1 ) , were individually electroporated into wild-type CN3718 . This produced nanJ , nanI and nanH single null mutants named , respectively , BMC201 , BMC202 and BMC203 ( Table 1 ) . The CN3718 transformation efficiency was ∼240 transformants/µg plasmid DNA . Transformants were selected on BHI agar plates containing 15 µg/ml of chloramphenicol . Transformant colonies were then identified by colony PCR using primers nanJKOF and nanJKOR ( Table 2 ) for screening nanJ-null mutants ( BMC201 ) , primers nanIKOF and nanIKOR ( Table 2 ) for screening nanI-null mutants ( BMC202 ) , and primers nanHKOF and nanHKOR ( Table 2 ) for screening nanH-null mutants ( BMC203 ) . Each reaction mixture was subjected to the following PCR amplification conditions: cycle 1 , 95°C for 5 min; cycles 2 through 35 , 95°C for 30 s , 55°C for 40 s , and 68°C for 90 s; and a final extension for 5 min at 68°C . An aliquot ( 20 µl ) of each PCR sample was electrophoresed on a 1 . 5% agarose gel and then visualized by staining with ethidium bromide . The mutants were cured of the intron-carrying donor plasmid as described [25] . To prepare a nanI/nanJ double null mutant , pJIR750nanJi was electroporated into the BMC202 nanI null mutant strain , which had been cured of the pJIR750nanIi plasmid . Transformants were grown on BHI agar plates containing 15 µg/ml chloramphenicol and putative nanJ/nanI double null mutants were then identified by demonstrating the presence of introns in both the nanI and nanJ ORFs by PCR . The confirmed nanI/nanJ double null mutant ( named BMC204 , Table 1 ) was cured the intron-carrying donor plasmid pJIR750nanJi . The identity of this BMC204 nanI/nanJ double null mutant was then confirmed by Southern blotting , which demonstrated the presence of two introns in the mutant . To prepare a nanI/nanJ/nanH triple null mutant , pJIR750nanHi was electroporated into the BMC205 double nanI/nanJ null mutant strain and transformants were then grown on BHI agar plates containing 15 µg/ml of chloramphenicol . Putative nanH null mutants were screened by PCR , as described above . The presence of introns in the nanI , nanJ and nanH genes of this mutant ( named BMC205 , Table 1 ) was confirmed by PCR and Southern blotting , following curing of the intron-carrying donor plasmid pJIR750nanHi . An intron insertion , with a sense orientation , was targeted between nucleotides 330 and 331 of the etx ORF . The primers used for targeting this intron were 330/331-IBS , 330/331-EBS1d and 330/331-EBS2 ( Table 2 ) . The 350-bp PCR products were inserted into pJIR750ai between the HindIII and BsrGI enzyme sites in order to construct an etx-specific TargeTron plasmid . The resultant plasmid , named pJIR750etxi ( Table 1 ) , was electroporated into either wild-type CN3718 or BMC205 to inactivate their etx genes , which produced ( respectively ) etx null mutant strains named BMC206 and BMC207 ( Table 1 ) . The primers used for etx null mutant scanning were etxkoF and etxkoR ( Table 2 ) . DNA was isolated from wild-type CN3718 , the five sialidase null mutants ( including the three single null mutants BMC201 , BMC202 and BMC203 , the double nanJ/nanI null mutant strain BMC204 , and the triple sialidase null mutant strain BMC205 ) and the two etx null mutants of CN3718 and BMC205 ( including the BMC206 null mutant that produces all three sialidases and the BMC207 null mutant unable to produce any sialidase ) using the MasterPureTM Gram-Positive DNA Purification Kit ( Epicenter ) . Each DNA was then digested with BsrGI or EcoRI overnight at 37°C and run on a 1% agarose gel . After the alkali transfer to a nylon membrane ( Roche ) , the blot was hybridized with a digoxigenin-labeled , intron-specific probe as described previously [25] . This intron-specific probe was prepared using the primers KO-IBS and KO-EBS1d [25] and a PCR DIG Labeling Kit ( Roche Applied Science ) according to the manufacturer’s instructions . DNA was isolated from CN3718 using the Master PureTM Gram-positive DNA purification kit . The primers nanJcomF and nanJcomR ( Table 2 ) were used for nanJ complementing strain construction . The primers nanIcomF and nanIcomR ( Table 2 ) were used for nanI complementing strain construction . The primers nanHcomF and nanHcomR ( Table 2 ) were used for construction of a nanH complementing strain . The PCR reactions were set up as: 1 µl of each pair of primers ( at a 0 . 5 µM final concentration ) , 1 µl of purified DNA template and 25 µl 2×Taq Long Range Mixture ( NEB ) were mixed together and ddH2O was added to reach a total volume of 50 µl . The reaction mixtures were then placed in a thermal cycler ( Techne ) and subjected to the following amplification conditions: 1 cycle of 95°C for 2 min , 35 cycles of 95°C for 30s , 55°C for 40s , and 65°C for 5 min , and a single extension of 65°C for 5 min . The resultant 4 . 9kb nanJ PCR product , 2 . 8 kb nanI PCR product , or 2 . 3 kb nanH PCR product were each separately cloned into the Invitrogen pCR2 . 1 TOPO vector according to the manufacturer’s instruction and inserts were then sequenced at the University of Pittsburgh Core Sequencing Facility . Using BamHI and SalI , the nanJ insert was removed from the TOPO vector and ligated into the pJIR751 C . perfringens/E . coli shuttle plasmid , forming a plasmid named pJIR751nanJcomp ( Table 1 ) . Using the same method , nanI and nanH inserts were separately cloned into pJIR750 and pJIR751 C . perfringens/E . coli shuttle plasmids , forming plasmids named pJIR750nanIcomp and pJIR751nanHcomp , respectively ( Table 1 ) . These plasmids were individually introduced , by standard electroporation techniques , into the BMC207 null mutant strain to create nanJ , nanI and nanH complementation strains named , respectively , BMC2071 , BMC2072 and BMC2073 ( Table 1 ) . A 0 . 2 ml aliquot of an overnight FTG culture of the wild-type , null mutants or complementing strains was inoculated into 10 ml of TH medium . To perform ETX and sialidase Western blots , supernatants of 8 h or overnight TH cultures were used . Samples were collected and each supernatant ( or the sonicated whole culture ) was mixed with SDS loading buffer and boiled for 5 min . Those mixtures were electrophoresed on a 12% polyacrylamide gel containing SDS for analyzing ETX or an 8% polyacrylamide gel containing SDS for analyzing sialidase proteins . The gels were then subjected to Western blotting using appropriate antibodies , as previously described [31] . To assay sialidase enzyme activity , a previously described protocol was used [20] Briefly , 20 µl of TH culture supernatant was added to 60 µl of 100 mM sodium acetate buffer ( pH 7 . 2 ) in a microtiter tray . A 20 µl of aliquot 4 mM 5-bromo-4-chloro-3-indolyl-α-D-N-acetylneuraminic acid ( Sigma ) was then added , and the tray was incubated at 37°C for 30 min . The absorbance at 595 nm was then determined using a microplate reader ( Bio-Rad ) . A 0 . 1 ml aliquot of an FTG overnight culture of the wild-type strain , null mutants or complementing strains was transferred to TH medium , which was grown at 37°C overnight . A 0 . 2 ml aliquot of each TH overnight culture was then inoculated into 10 ml of pre-warmed TH medium and the OD600 of those cultures was measured at 37°C over time to determine vegetative growth , as described previously [32] . Madin-Darby Canine Kidney ( MDCK ) epithelial cells were cultured in a 1∶1 ( v/v ) mix of Dulbecco’s Modified Eagle’s Medium ( DMEM , Sigma ) and Nutrient Mixture F12 HAM ( Sigma ) , supplemented with 3% fetal bovine serum ( Mediatech ) , 100 µg/ml penicillin/streptomycin ( Sigma ) and 1% glutamine ( Sigma ) . Caco-2 cells were maintained in Eagle minimal essential medium ( Sigma ) supplemented with 10% fetal bovine serum ( Mediatech ) , 1% nonessential amino acids ( Sigma ) , and 100 µg/ml of penicillin/streptomycin . Vero cells were cultured in M199 medium ( Sigma ) supplemented with 5% fetal bovine serum and 100 µg/ml of penicillin/streptomycin . HT-29 cells were cultured in RPM1-164 medium ( Sigma ) supplemented with 10% fetal bovine serum and 100 µg/ml of penicillin/streptomycin . NT6 fibroblast cells were culture in Alpha-MEM ( Modified Eagle Medium ) supplemented with 7 . 5% fetal bovine serum , 2 mM L-glutamine , 1× Non Essential Amino Acids , 1 mM sodium pyruvate , and 100 µg/ml of penicillin/streptomycin . All cells were grown at 37°C with 5% atmospheric CO2 . Prior to use in ETX cytotoxicity assays , purified ETX prototoxin was activated by trypsin . For this purpose , 50 µl aliquots of labeled or unlabeled prototoxin ( 2 mg/ml ) were incubated with 12 . 5 µg of trypsin ( Sigma ) /µg prototoxin for 1 h at 37°C . After that incubation , trypsin inhibitor ( Sigma ) ( 1∶1 v/v ) was added to remove trypsin activity . The same trypsin/trypsin inhibitor mix ( no ETX ) was used as a negative control for cytotoxicity assays . Confluent monolayer MDCK cells were incubated in the presence or absence of 5 µg/ml of activated ETX; in some experiments , that same amount of activated ETX was added to a 10 × concentrated TH supernatant culture for 1 hr at 37°C . After this incubation , cytotoxicity was measured using the LDH Cytotoxicity Detection Kit ( Roche ) . An aliquot of 0 . 005 U/ml NanI ( Roche ) or 8 h TH culture supernatants of BMC206 , BMC207 , BMC2071 , BMC2072 or BMC2073 were incubated with 12 . 5 µg of trypsin ( Sigma ) or chymotrypsin ( Sigma ) for 1 h at 37°C . After that incubation with trypsin , trypsin inhibitor ( Sigma ) ( 1∶1 v/v ) was added to remove trypsin activity from those samples . Sialidase activities were detected by the method described before [20] . Confluent monolayers of MDCK cells grown in 6 well cluster plates ( Corning ) were or were not pretreated with 0 . 005 U/ml or 0 . 001 U/ml of C . perfringens sialidase ( Roche ) for 30 , 60 , or 90 min . After this treatment , 5 µg/ml of AF488-ETX was added and the cells were then further incubated for 60 min at 37°C . Following this toxin challenge , the cells were harvested and washed twice with PBS buffer and the pellets were then resuspended in 50 µl of PBS . After treatment of those asample with 1 µl of Benzonase nuclease ( Novagen ) for 5 min at room temperature , 12 µl of 5×SDS loading buffer was added . These samples were then electrophoresed on a SDS-containing 8% polyacrylamide gel , in the dark . The resultant gel was imaged using a Typhoon 9400 variable mode imager ( Amersham Biosciences ) , with fluorescence emission set to detect the Alexa Fluor 488 label using the green laser with wavelength 532 nm . For detection of the molecular weight markers , the red laser was used with a wavelength of 633 nm . Complex formation was then quantified using Imagequant version 5 . 2 ( Molecular Dynamics ) . To evaluate ETX binding , confluent monolayers of MDCK cells grown in 24 well cluster plates ( Corning ) were pretreated with a 0 . 005 U/ml or 0 . 001 U/ml of NanI sialidase at 37°C , and then challenged with 5 µg/ml of AF488-pETX suspended in 250 µl of buffer or culture supernatants ( from BMC206 , BMC207 , BMC2071 , BMC2072 , BMC2073 ) that had been concentrated 10× using an Ultrafiltration centrifuge tube ( Millipore ) . After 60 min at 37°C , the cells were harvested and washed twice in PBS . The washed pellets were then resuspended in 50 µl of RIPA buffer ( Invitrogen ) and added to 96-well plates , where fluorescence was quantified using a Multi-mode Microplate Reader ( SynergyTM4 , BioTek ) . A 0 . 1 ml aliquot of an FTG overnight culture of BMC206 , BMC207 , BMC2071 or BMC2072 was transferred to TH medium , which was grown at 37°C overnight . A 0 . 1 ml aliquot of each TH overnight culture was then inoculated into fresh 10 ml TH medium . These overnight cultures were centrifuged and the pelleted cells were washed three times with HBSS buffer and then resuspended in HBSS buffer at 107/ml . Confluent monolayers of Caco-2 cells , MDCK cells , Vero cells , HT-29 cells , and NT-6 cells grown in 6 well cluster plates were washed three times with HBSS and then challenged with a 1 ml aliquot of the washed bacterial cell suspension . Following anaerobic incubation at 37°C , for 2 , 4 , or 6 h , the supernatants were removed , centrifuged and 60 µl supernatant aliquot were used to measure sialidase activity by the method described above , except the sample was incubated at room temperature overnight instead of 37°C for 30 min before the absorbance was read in order to increase assay sensitive . Control washed bacterial cell suspensions were treated similarly except for the absence of cells . As described previously [25] , total RNA was extracted from 1 ml of a 2 , 4 , or 6 h culture of BMC206 that had or had not been in contact with Caco2 cells . All RNA samples were treated with DNAaseI ( Promega ) . The purified RNA was quantified by absorbance at 260 nm and stored in a -80°C freezer . qRT-PCR reactions were performed on the purified RNA samples using the iScriptTM one-step RT-PCR kit with SYBR green ( Bio-Rad ) . Briefly , 100 ng of each RNA sample was reverse-transcribed to cDNA at 50°C for 10 min . That cDNA was then used as template for PCR reactions with primers 16sF and 16sR ( Table 2 ) targeting the 16sRNA as a housekeeping gene; primers nanIKOF and nanIKOR targeting the nanI gene , primers nanJKOF and nanJKOR targeting the nanJ gene , or primers nanHKOF and nanHKOR targeting the nanH gene ( Table 2 ) . All reactions were performed at AB Appled Biosystems Step One plus Real-Time PCR system . A 1 . 5 ml aliquot of a TH overnight culture of BMC206 , BMC207 , BMC2071 , BMC2072 or BMC2073 was centrifuged and the bacterial pellet was then washed three times with HBSS buffer . After resuspension of the washed pellet in 1 . 5 ml of HBSS buffer , these suspensions were serially diluted from 10-2 to 10-7 with sterile water and aliquots were spread onto BHI agar plates . Following an overnight anaerobic incubation at 37°C , the colonies arising on these plates were counted to determine the number of CFU added to the mammalian cell cultures . Monolayers of Caco-2 , HT-29 , Vero , MDCK or NT6 fibroblasts were then incubated under anaerobic conditions with a 100 µl aliquot of these washed bacteria for 2 h at 37°C . After this incubation , the monolayers were washed with HBSS three times and host cell-associated bacteria were retrieved by lysing the monolayers in distilled water . Aliquots of these suspensions were plated onto BHI agar plates . After overnight anaerobic incubation at 37°C , the colonies arising on the plates were used to calculate the number of adherent CFU . Caco-2 cells were grown to confluency in an 8-well chamber slide ( Fisher ) . A 1 µl aliquot of a BMC206 , BMC207 and BMC2072 overnight TH culture was added to each chamber of the slide , and the slide was then incubated at 37°C for 2 h . Cells were washed three times with HBSS buffer . The slides were fixed in 4% formaldehyde in HBSS for 30 min at room temperature . The fixed cells were incubated with a 1∶500 dilution of anti-C . perfringens rabbit polyclonal antibody ( Genway ) in HBSS with 10% BSA for 2 h at room temperature . Those cells were then washed three times with HBSS buffer and incubated with 1∶500 Alexa Fluor-488 goat anti-rabbit IgG ( Invitrogen ) in HBSS with 10% BSA for 1 h at room temperature . After 3 more washes with HBSS buffer , the cells were incubated with 1∶200 VybrantTM Cell-labeling solutions ( Molecular Probes ) for 30 min at room temperature . Following a final three washes with HBSS buffer , the chambers were removed , and a coverslip was mounted with Fluoro-Gel ( Electron Microscopy Sciences ) . Imaging was performed on a Zeiss Axioskop 40 immunofluorescence microscope . | Clostridium perfringens type D strains cause enteritis and enterotoxemias in livestock after colonizing the intestines and then producing toxins , notably epsilon toxin ( ETX ) . Initially produced and secreted in an inactive form , ETX can be rapidly proteolytically-activated by trypsin and other intestinal proteases . While most C . perfringens strains produce three sialidases , no pathogenic role has yet been identified for these enzymes that remove terminal sialic acid residues from glycoproteins and glycolipids . Our current study found that trypsin increases the activity of the NanI sialidase made by type D strain CN3718 . This effect enhanced the ability of NanI to modify the surface of MDCK cells , leading to increased ETX binding and cytotoxicity . We also found that modification of the host cell surface by NanI sialidase allows efficient attachment of CN3718 cells to Caco-2 cells . These results identify interactions between intestinal proteases , ETX , sialidases , and ETX-producing bacteria , whereby trypsin activates not only ETX but also NanI sialidase . If similar effects occur in the intestines , the activated NanI sialidase may modify the host cell surface to facilitate bacterial attachment and thereby worsen disease by facilitating intestinal colonization by type D strains to prolong toxin delivery and , in some species , increase ETX binding . | [
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] | 2011 | Sialidases Affect the Host Cell Adherence and Epsilon Toxin-Induced Cytotoxicity of Clostridium perfringens Type D Strain CN3718 |
In some regions in Africa , elimination of onchocerciasis may be possible with mass drug administration , although there is concern based on several factors that onchocerciasis cannot be eliminated solely through this approach . A vaccine against Onchocerca volvulus would provide a critical tool for the ultimate elimination of this infection . Previous studies have demonstrated that immunization of mice with Ov-103 and Ov-RAL-2 , when formulated with alum , induced protective immunity . It was hypothesized that the levels of protective immunity induced with the two recombinant antigens formulated with alum would be improved by formulation with other adjuvants known to enhance different types of antigen-specific immune responses . Immunizing mice with Ov-103 and Ov-RAL-2 in conjunction with alum , Advax 2 and MF59 induced significant levels of larval killing and host protection . The immune response was biased towards Th2 with all three of the adjuvants , with IgG1 the dominant antibody . Improved larval killing and host protection was observed in mice immunized with co-administered Ov-103 and Ov-RAL-2 in conjunction with each of the three adjuvants as compared to single immunizations . Antigen–specific antibody titers were significantly increased in mice immunized concurrently with the two antigens . Based on chemokine levels , it appears that neutrophils and eosinophils participate in the protective immune response induced by Ov-103 , and macrophages and neutrophils participate in immunity induced by Ov-RAL-2 . The mechanism of protective immunity induced by Ov-103 and Ov-RAL-2 , with the adjuvants alum , Advax 2 and MF59 , appears to be multifactorial with roles for cytokines , chemokines , antibody and specific effector cells . The vaccines developed in this study have the potential of reducing the morbidity associated with onchocerciasis in humans .
Onchocerciasis , caused by the filarial worm Onchocerca volvulus , is a neglected tropical disease ( NTD ) endemic predominantly in Africa . The Global Burden of Disease Study 2013 estimate indicates that 17 million people are currently infected with O . volvulus [1] . The disease , also referred to as river blindness , is an important cause of blindness , skin disease and chronic disability . Moreover , in children from Uganda and South Sudan , there are links between O . volvulus infection and a serious neurological disorder known as “nodding syndrome” [2 , 3] . In some endemic regions evidence suggests that elimination of onchocerciasis may be possible with mass drug administration ( MDA ) of ivermectin [4] . Several significant obstacles must be overcome before complete elimination in Africa can be achieved . First , it has been estimated that elimination will require 14–35 years of continuous treatment [5 , 6] . Furthermore , based on animal and human studies , susceptibility to reinfection increases after treatment [7–9] . In addition , there have been several reports which suggest that O . volvulus in some regions in Africa may have developed resistance to ivermectin [10–18] . Finally , MDA of ivermectin is not possible in large areas of central Africa where loiasis is co-endemic , because of the risk of developing severe adverse reactions to the treatment including encephalopathy in individuals with high level of Loa loa microfilaremia [19] . Therefore , there is a growing consensus supported by mathematical modeling , that onchocerciasis in Africa will not be eliminated within the original proposed timeframes using MDA alone . It has been estimated now that elimination would require 1 . 15 billion treatments up until 2045 , while other estimates suggest that onchocerciasis cannot be eliminated solely through MDA with ivermectin [20 , 21] . A vaccine against onchocerciasis , to complement the present control measures , would therefore provide a critical tool for the ultimate elimination of this infection from humans [22 , 23] . Mathematical modeling of the impact of vaccination against O . volvulus suggests that a prophylactic vaccine would reduce disease burden related to onchocerciasis in regions where ivermectin cannot be administered safely and would decrease the chance of re-emergence of the parasite after mass drug administration has been stopped [24] . A mouse model was developed for studying immunity to O . volvulus in which larvae are implanted subcutaneously in mice within diffusion chambers [25] . Protective immunity was demonstrated in this model following immunization of mice with irradiated third-stage infective larvae of O . volvulus [26–30] . The model was also used to identify recombinant antigens that could be used in a vaccine against infection with larval O . volvulus [31 , 32] . When some of these recombinant antigens were produced under standardized conditions , two antigens emerged as lead vaccine candidates , Ov-103 and Ov-RAL-2 , based on their ability to induce significant levels of protective immunity after immunization using alum as the adjuvant [33] . This observation was confirmed in gerbils immunized with the Brugia malayi proteins BM-103 and Bm-RAL-2 , which are orthologous to the O . volvulus proteins . Vaccination with BM-103 and Bm-RAL-2 , with alum as the adjuvant , induced protective immunity to infection with B . malayi in gerbils [34] . Both proteins are highly conserved within nematodes and homologs of these antigens have been shown to induce protective immunity to other nematodes [35–41] . The functional properties of Ov-103 and Ov-RAL-2 are currently unknown , however , both proteins are localized on the surface and glandular esophagus of third-stage larvae ( L3 ) as well as in the hypodermis and cuticle of adult worms and on the surface of microfilariae [34 , 42 , 43] . The primary objective of the present study was to test the hypothesis that the levels of protective immunity induced with Ov-103 and Ov-RAL-2 formulated with alum could be increased by formulating these antigens with immune enhancing adjuvants . Five adjuvants ( alum , Advax 1 , Advax 2 , CpG oligonucleotide ( CpG ) , and MF59 ) were selected for comparative analysis based on their ability to induce different types of immune responses . Alum , the most commonly used adjuvant in human vaccines , elicits strong humoral immune responses , which are mediated primarily by IgG1 [44 , 45] . This adjuvant stimulates strong Th2 responses but does not induce cell-mediated responses [46–50] . Injection of alum into mice increased the expression of the neutrophil-specific chemokines CXCL1 ( KC ) and CXCL2 , the monocyte-specific chemokines CCL2 ( MCP-1 ) and CCL4 ( MIP-1β ) and the eosinophil chemokines CCL11 ( eotaxin-1 ) and CCL24 ( eotaxin-2 ) [51 , 52] . Alum appears to act mainly on macrophages and monocytes to induce secretion of chemokines involved with cell recruitment from the blood into peripheral tissues [53] . Advax 1 is a novel polysaccharide adjuvant derived from delta inulin [54] that is under development for use in humans [55 , 56] . It is successful at inducing a mixed Th1/Th2 associated IgG1 and IgG2a antibody response [57] , as well as Th1 , Th2 and Th17 cytokine responses [58 , 59] . Advax 2 is comprised of delta inulin formulated with a small amount of CpG , a TLR9 agonist which shifts some of the responses to Th1 while retaining the Th2 response . It also potently induces CD8+ CTL and generally also gives the highest overall IgG response due to induction of a broad combination of IgG1 , IgG2 and IgG3 antibodies . CpG is a strongly Th-1 biased adjuvant that typically gives an IgG response comprised predominantly of IgG2 antibodies [60 , 61] . MF59 , an oil-in-water emulsion adjuvant , has been established as safe and potent adjuvant for use in human vaccines [62 , 63] . This adjuvant induces a mixed Th1/Th2 response in humans and animals [64 , 65] with both antigen-specific IgG1 and IgG2a antibodies produced [66] , and has been shown to be more potent than alum for the induction of both antibody and CD4+ responses [67 , 68] . MF59 appears to act on macrophages , monocytes and granulocytes to induce secretion of MCP-1 , CCL3 ( MIP-1α ) , MIP-1β , all involved with cell recruitment from blood into peripheral tissue [53] . Two criteria were used for measuring protective immunity in the present studies . The first criterion was killing of parasites as represented by the comparison between the mean numbers of larvae surviving in control vs . immunized mice . This metric assesses the ability of the induced effector responses to kill worms and the capacity of all or part of the worm population to evade the killing response . Parasite reduction is of particular importance in the case of onchocerciasis since reducing worm burden would have a beneficial effect on health status , without a requirement for achieving sterile immunity . The second measure was host protection , where the objective was to determine the number of immunized mice that had parasite recovery below the 95% confidence interval seen in the control mice . This metric describes the efficacy of the vaccine , by estimating what percent of vaccinees benefited from the prophylactic vaccine . Reduced levels of infection within a population will likely enhance control of new infections and thus disease within the endemic region . A reductionist experimental approach was used in this study , with the initial screening of all five adjuvants performed using Ov-103 as the antigen . Adjuvants that were successful at inducing immunity with Ov-103 were then tested with Ov-RAL-2 and finally in a vaccine consisting of co-administered Ov-103 and Ov-RAL-2 . Analyses were performed to identify immune correlates and potential mechanisms of protective immunity induced by the antigens , individually or when co-administered , with the selected adjuvants . Once again , a reductionist experimental approach was undertaken; all classes and sub-classes of antibody responses were initially tested in mice immunized with Ov-103 . Analyses of antibody response to Ov-RAL-2 and the Ov-103/Ov-RAL-2 co-administered vaccine were then limited to the antibody subclasses that yielded positive responses to Ov-103 . Likewise , cytokine and chemokine responses were measured in mice immunized with Ov-103 and the positive sub-sets measured in subsequent experiments . Three adjuvants were identified that induced protective immunity with Ov-103 and Ov-RAL-2 and with the co-administered vaccine . Immunological correlates of protective immunity were also observed based on unique antibody , cytokine and chemokine signatures .
O . volvulus L3 were collected from black flies ( Simulium damnosum ) that were fed on consenting infected donors ( Protocol 320 was approved by the New York Blood Center and the Medical Research Station , Kumba , Cameroon IRBs ) . After seven days the flies were dissected to collect the developed L3 , which were cleaned and cryopreserved as previously described [69] . Male BALB/cByJ mice , 6–8 weeks of age , were purchased from The Jackson Laboratory ( Bar Harbor Maine ) . All mice were housed in micro-isolator boxes in rooms that were pathogen free and under temperature , humidity and light cycle controlled conditions in the Laboratory Animal Sciences Facility at Thomas Jefferson University . Mice were fed autoclavable rodent chow and given water ad libitum . All experimental procedures were performed in compliance with the ethical and regulatory standards set by the NIH for animal experimentation . The animal use protocol ( 00136 ) was approved by the Thomas Jefferson University Institutional Animal Care and Use Committee . The animal care and use protocol adhered to the “Guide for the Care and Use of Laboratory Animals” published by the National Research Council , USA . Based on previous studies , Ov-103 was expressed in PichiaPink yeast and Ov-RAL-2 in Escherichia coli . Antigens were prepared and analyzed as previously described [33] . In addition , Ov-RAL-2 with His-tag at C-terminus was expressed in E . coli BL21 , purified with nickel column and endotoxin removed with a Q anion exchange column . The level of endotoxin in the final products was less than 20EU/mg ( 13 . 2–19 . 3 EU/mg ) . For experiments testing individual antigens , mice were immunized with 25 μg of the produced vaccine antigen formulated with each of the five different adjuvants in a 100 μl total volume preparation as per the manufacturer's directions . The alum immunization consisted of 50% v/v of vaccine antigen in TBS and 1:5 Rehydragel LV ( alum ) in TBS ( General Chemical , Parsippany , NJ ) . Advax 1 , Advax 2 , and CpG ( Vaxine Pty Ltd , Adelaide , South Australia ) were used at 1 mg of Advax 1 or Advax 2 or 10 μg of CpG mixed with vaccine antigen in TBS immediately prior to injection . For vaccines formulated with MF59 ( Novartis Vaccines , Cambridge , MA ) , 50 μl vaccine antigen in TBS was mixed 1:1 v/v with the adjuvant . Mice were immunized intramuscularly with 50 μl of the formulated vaccines in each caudal thigh . The Ov-103/Ov-RAL-2 co-administered vaccine consisted of 25 μg of each vaccine antigen , formulated with adjuvant for a total of 50 μl; Ov-103 was injected in the left caudle muscle and Ov-RAL-2 in the right caudal muscle . Immunization was followed by two booster injections 14 and 28 days later . Cryopreserved L3 were defrosted in a two-step process , 15 minutes on dry ice followed immediately by a 37° water bath . The thawed L3 were then washed 5 times in a 1:1 mixture of NCTC-135 and Iscove's modified Dulbecco's medium supplemented with 100 U penicillin , 100 μg streptomycin , 100 μg gentamicin and 30 μg of chloramphenicol per ml . Diffusion chambers were constructed from 14 mm Lucite rings covered with 5 . 0 μM pore-size Durapore membranes ( EMDMiIIipore , Billerca , CA ) and fused together using an adhesive containing a 1:1 mixture of 1 , 2-dichloroethane ( Fisher Scientific , Pittsburg , PA ) and acryloid resin ( Rohm and Haas , Philadelphia , PA ) . The constructed diffusion chambers were then sterilized via 100% ethylene oxide followed by 12 hr aeration . Fourteen days after the final booster , mice were challenged using a diffusion chamber containing 25 L3 . The diffusion chambers were implanted in a subcutaneous pocket on the rear flank of the mice . The diffusion chambers were recovered 21 days later and larval survival was calculated based on the mobility and morphology of the remaining larvae . Protective immunity was evaluated by two different methods: ( 1 ) Percent reduction of larvae , calculated by: ( ( Average worm survival in control mice—Average worm survival in immunized mice ) ÷ Average worm survival in control mice ) x 100 . ( 2 ) Host protection , calculated by: ( Number of immunized mice with parasite recovery levels below the 95 confidence interval of parasite recovery in control mice ÷ total number of immunized mice ) x 100 . Cells within the diffusion chamber were collected , placed onto slides by centrifugation using a Cytospin 3 ( Shandon Inc , Pittsburgh , PA ) , and then stained and analyzed for differential cell counts using Hemastain 3 ( Fisher Scientific ) . Serum for antigen-specific antibody analyses was collected when mice received challenge infections within diffusion chambers and at the conclusion of the experiment . lgG1 , lgG2a , lgG2b , lgG3 , IgM and IgE were measured in mice immunized with Ov-103 with each of the five adjuvants . Antigen-specific lgG1 , lgG2a and lgG2b responses were measured in mice immunized with Ov-RAL-2 and co-administered Ov-103/Ov-RAL-2 formulated alone or with either alum , Advax 2 or MF59 . Maxisorp 96-well plates ( Nunc Nalgene International , Rochester , NY ) were coated with 2 μg /ml of Ov-103 or Ov-RAL-2 in 50 mM Tris-CI coating buffer pH 8 . 8 overnight 4°C . Plates were washed with deionized water between each step . Borate buffer solution ( BBS ) ( 0 . 17 M boric acid , 0 . 12 M NaCl , 0 . 5% Tween-20 , 0 . 025% bovine serum albumin , I mM EDTA , pH 8 . 2 ) was used to block the plates for 30 min at room temperature . Individual sera were diluted to an appropriate starting concentration with BBS and serially diluted; plates were sealed and incubated at 4°C overnight . Biotinylated anti-lgG1 , -lgG2a , -IgG2b , -IgG3 and -IgE ( BD Biosciences , San Jose , CA ) and -IgM ( Vector Labs , Burlingame , CA ) antibodies were diluted 1:250 in BBS and incubated for 1 hr at room temperature . ExtrAvidin PX ( Sigma , St . Louis , MO ) was diluted 1:1000 in BBS and added for 30 min at room temperature . After the final wash , one component ABTS peroxidase substrate ( KPL , Gaithersburg , MD ) was added and optical densities were read after 30 min at 405 nm in an iMark Microplate reader ( Bio-Rad , Hercules , CA ) . Endpoint titers were calculated as the lowest serum dilution from experimental animals that had an optical density reading three times higher than the lowest optical density recorded for control serum . One week after the diffusion chamber recovery , spleens from control and immunized mice were aseptically removed and made into single cell suspensions . Cells were cultured in a 96-well plate at a concentration of 2x106/well . The cells were stimulated with either 10 μg of Ov-103 , Ov-RAL-2 , media control or with anti-CD3 mAb ( BD Biosciences ) which was pre-coated at 0 . 5 μg/ml for 2h at 37°C . Each well also received 0 . 5 μl of anti-IL-4r ( BD Biosciences ) [70] . Cells were incubated at 37°C for 3 days , after which supernatants were collected and frozen at -20°C . All experiments consisted of 5–6 mice per group with the experiments performed at least twice with consistent results between experiments . Data presented are cumulative from all experiments . Data were analyzed for parasite killing by multifactorial analysis of variance ANOVA in Systat v . ll ( Systat Inc . , Evanstown , IL ) . Probability values less than 0 . 05 were considered statistically significant . Bootstrap statistical analysis of host protection was performed using R package "boot" . Bootstrap sample means were estimated from the control groups and the lower bound of the 95% confidence interval reported . A kernel density estimate of the vaccine group was calculated and the percentage below the bootstrap 95% confidence interval calculated .
BALB/cByJ mice were immunized intramuscularly three times with Ov-103 or Ov-RAL-2 without adjuvant . Control and immunized mice received challenge infections within diffusion chambers , and there was no evidence of protective immunity in the immunized mice ( Fig 1 ) . Cell migration into the diffusion chambers was equivalent between control and immunized groups with 5 x 105 ± 8 x 105 cells found within the parasite microenvironment . The differential distribution of cells found within the diffusion chamber was neutrophils ( 48 ± 18% ) , macrophages ( 48 ± 19% ) , lymphocytes ( 0 ± 0% ) and eosinophils ( 5 ± 5% ) in all groups of mice regardless of treatment status . Mice were immunized with Ov-103 formulated with one of the following five adjuvants: alum , Advax 1 , Advax 2 , CpG or MF59 . Immunization with Ov-103 in combination with alum , Advax 2 and MF59 induced statistically significant reductions of larval survival ( Fig 2 ) . Mice immunized with Ov-103 formulated with alum had reductions in mean larval survival of 30% and host protection levels of 80% , with Advax 2 they had a 39% reduction in larval survival and 90% host protection and with MF59 they had a 32% reduction in larval survival and 75% host protection . Vaccination of mice with Ov-103 formulated with Advax 1 or CpG as adjuvants did not result in significant reductions in parasite survival yet they were associated with 35% and 68% host protection , respectively ( Fig 2 ) . Cell recruitment into diffusion chambers in control and immunized mice were comparable between all adjuvants with 1 . 8 x 106 ± 1 . 6 x 106 total cells and differential distribution of cells of neutrophils ( 56 ± 17% ) , macrophages ( 39 ± 16% ) , lymphocytes ( 1 ± 1% ) and eosinophils ( 4 ± 4% ) . Ov-RAL-2 was tested as a vaccine in combination with the three adjuvants that induced protective immunity with Ov-103 , specifically alum , Advax 2 and MF59 . Immunization of mice with Ov-RAL-2 formulated with each of these three adjuvants induced statistically significant reductions in larval survival ( Fig 3 ) . Mice immunized with Ov-RAL-2 formulated with alum had reductions in mean larval survival of 27% and 68% host protection , with Advax 2 mice had a 35% reduction in larval survival and 85% host protection , and with MF59 mice had a 28% reduction in larval survival and 87% host protection ( Fig 3 ) . Cell recruitment into the diffusion chamber was comparable between all adjuvants with 1 . 4 x 106 ± 1 . 7 x 106 total cells and differential distribution of cells neutrophils ( 46 ± 17% ) , macrophages ( 49 ± 17% ) , lymphocytes ( 0 ± 1% ) and eosinophils ( 5 ± 4% ) . Mice were immunized with co-administered Ov-103/Ov-RAL-2 formulated with alum , Advax 2 and MF59 . Immunization of mice with Ov-103 and Ov-RAL-2 using all three adjuvants induced statistically significant reductions in larval survival ( Fig 4 ) . Mice immunized with the two antigen co-administered vaccine formulated with alum , had reductions in mean larval survival of 38% and 100% host protection , with Advax 2 mice had a 47% reduction in larval survival and 80% host protection and with MF59 mice had a 29% reduction in larval survival and 67% host protection ( Fig 4 ) . Cell recruitment into the diffusion chamber was comparable between all adjuvants with 1 . 0 x 106 ± 1 . 1 x 106 total cells and differential distribution of cells neutrophils ( 40 ± 14% ) , macrophages ( 56 ± 15% ) , lymphocytes ( 0 ± 0% ) and eosinophils ( 4 ± 5% ) . Immunization of mice with Ov-103 or Ov-RAL-2 without adjuvant did not induce a significant IgG antibody response to either of the antigens . Antibody responses in mice immunized with Ov-103 formulated with each of the five adjuvants , were measured in serum recovered from mice at study termination . Mice immunized with Ov-103 formulated with each of the five adjuvants had positive IgG1 responses , with CpG inducing the lowest endpoint titer . Ov-103-specific IgG2a responses were only discernible in mice immunized with Advax 2 as the adjuvant ( Table 2A ) . All immunized mice were negative for antigen-specific IgG2b , IgG3 and IgE . Control mice and mice immunized with Ov-103 formulated with any of the five adjuvants had equivalent antigen-specific IgM responses . Analysis of serum , recovered at the same time point , from mice immunized with Ov-RAL-2 was limited to IgG1 , IgG2a and IgG2b . Mice immunized with Ov-RAL-2 formulated with the adjuvants alum , Advax 2 or MF59 developed positive IgG1 , IgG2a and IgG2b antigen-specific responses . Enhanced IgG2a and IgG2b responses were observed in mice immunized with Ov-RAL-2 formulated with Advax-2 ( Table 2B ) . When antigen-specific antibody responses were measured in mice immunized with the co-administered vaccines formulated with each of the three adjuvants , the Ov-103 and Ov-RAL-2 antigen-specific IgG1 responses developed were significantly elevated as compared to those seen in mice immunized with the corresponding single antigen vaccine . Antigen-specific IgG2a endpoint titers to Ov-103 and Ov-RAL-2 were only elevated in mice immunized with Advax 2 as the adjuvant in the co-administered vaccines , as compared to mice immunized with the single vaccines . Similarly , the IgG2b endpoint titers to Ov-RAL-2 were elevated in mice immunized with the co-administered vaccines with Advax 2 as the adjuvant , as compared to mice immunized with the Ov-RAL-2 formulated with Advax 2 ( Table 2C ) . Pre-challenge serum was collected from all mice and antibody class and sub-class responses were measured . It was determined that the type and magnitude of the responses mirrored those measured at study termination . Correlation analyses comparing parasite recovery numbers and antibody endpoint titers for both pre- and post-challenge serum did not reveal consistent significant levels of statistical correlation . Twenty eight analytes were measured in both ex-vivo spleen cell stimulation supernatants and fluid from diffusion chambers , collected at the time of parasite recovery , from mice immunized with Ov-103 and formulated with each of the five adjuvants . Twenty-two cytokines were analyzed in the spleen cell supernatants and 9 were detected regardless of adjuvant used . All six of the chemokines were negative in the spleen cell supernatants . The diffusion chamber fluids were negative for all of the cytokines but had detectable levels of 5 chemokines ( Table 1 , S1A and S2A Tables ) . Based on these observations , 9 cytokines were selected for further analysis in the spleen cell supernatants and 5 chemokines were selected for analysis in the diffusion chamber fluid collected from mice immunized with Ov-RAL-2 or the co-administered vaccines . In the absence of adjuvant , immunization with either Ov-103 or Ov-RAL-2 induced elevated antigen-stimulated IL-5 and IL-10 responses in the spleen cells ( Table 3 , S1B Table ) . All other cytokines measured in the spleen cell supernatants , and chemokines measured in the diffusion chamber fluid , were either not detected or responses were not different between control and immunized mice . This observation suggests that both antigens predispose towards a Th2 immune response . This was confirmed when cytokines were measured in spleen cell supernatants from mice immunized with Ov-103 and Ov-RAL-2 formulated with each of the tested adjuvants . A consistent observation was the increased production of IL-5 and IL-10 , although the levels varied based on vaccine antigen and adjuvant combination . Antigen-stimulated IL-4 and IL-13 levels were elevated in most of the groups , confirming the development of Th2 responses . In addition , there was an increase in IL-6 for both antigens when formulated with all adjuvants except Advax 2 . There was also an increase in IL-17A and IL-17F in mice immunized with Ov-103 formulated with alum or MF59 and mice immunized with RAL-2 formulated with MF59 ( Table 3 , S1A and S1C Table ) . Mice immunized with Ov-103/Ov-RAL-2 co-administered vaccines formulated with each of the three adjuvants also preferentially induced Th2 immune responses , based on the elevated levels of IL-5 , IL-10 and IL-13 in the supernatants from ex vivo stimulated spleen cells ( Table 4 , S1D Table ) . Mice immunized with Ov-103 or Ov-RAL-2 without adjuvants had equivalent but low levels of the five measured chemokines in the diffusion chamber fluid , as compared to controls . Interestingly , mice immunized with Ov-103 formulated with each of the three adjuvants that induced protective immunity , shared the phenotype of having elevated KC and eotaxin in the parasite microenvironment , in distinction to mice immunized with Ov-103 and Advax 1 or CpG as adjuvants . Mice immunized with Ov-103 and MF59 as the adjuvant also had elevated MCP-1 , MIP1α and MIP1β ( Table 5 , S2B Table ) . In comparison , mice immunized with Ov-RAL-2 with alum or Advax 2 as adjuvants had elevated MCP-1 and MIP1α , but did not have increased KC or eotaxin ( Table 5 , S2C Table ) . Mice immunized with the co-administered vaccine formulated with alum had elevated response to all five measured chemokines . Mice immunized with the co-administered vaccine formulated with Advax 2 had elevated levels of KC and mice immunized with MF59 as the adjuvant had elevated MCP-1 and MIP1β ( Table 6 , S2D Table ) . Correlation analyses comparing mean-parasite-recovery numbers and cytokine or chemokine levels did not reveal significant correlations .
Mice immunized with Ov-103 , Ov-RAL-2 or with co-administered Ov-103/Ov-RAL-2 formulated with alum , Advax 2 or MF59 as the adjuvants consistently developed significant levels of both larval killing and host protection . Immunization of mice with Ov-103 or Ov-RAL-2 without adjuvant did not induce protective immunity , although immunization with the antigens stimulated recall IL-5 and IL-10 responses by spleen cells . The induction of a Th2 response by the antigens was anticipated , as evidence from both animal [27 , 29] and human studies [71 , 72] demonstrate that O . volvulus infection typically induces Th2-type immunity . Mice immunized with the two antigens without adjuvant did not develop antigen-specific antibody responses and there was an absence of elevated host-chemokines within the parasite microenvironment . In the absence of antibody and chemokine responses , the antigen-specific Th2 cytokine response in the spleen was insufficient to induce protective immunity to the infection . Previous studies with Ov-103 and Ov-RAL-2 demonstrated that immunization with alum as the adjuvant induced statistically significant levels of protective immunity [33] . The goal of this study was to determine if altering the adjuvant could further enhance the induced protective immune response . Initial trials with Ov-103 compared five adjuvant formulations , of which three , alum , Advax 2 and MF59 , induced equivalent levels of larval killing and host protection . Subsequent studies with Ov-RAL-2 confirmed that alum , Advax 2 and MF59 were effective adjuvants to induce equivalent levels of protective immunity . Co-administration of Ov-103 and Ov-RAL-2 with the three adjuvants induced significant larval killing and host protection , in most cases equivalent to those seen in mice receiving single antigen immunizations , as has been previously reported [31 , 33] . However , in some instances there was a trend to higher levels of protective immunity in mice receiving the co-administered vaccine . The highest level of larval killing was 47% achieved in mice immunized with the two antigens with Advax 2 as the adjuvant , with some individual animals in this group achieving levels of larval killing of ~90% , which is higher than the maximal levels of larval killing achieved in any of the other single or double antigen vaccine groups . The highest level of host protection of 100% was seen in mice immunized with co-administered vaccine plus alum . Similarly , BM-103 and Bm-RAL-2 , injected as a fusion protein or concurrently , induced more consistent and enhanced levels of protective immunity in gerbils to B . malayi , as compared to levels achieved with individual antigens [34] . Both of the metrics used in this study , larval killing and host protection , are integral in the evaluation of a vaccine . A reduction in worm burden of approximately 50% would translate into a significant decrease in disease in the vaccinated individual and a reduction in potential transmission of infection . Host protection of 100% indicates that all vaccinated individuals responded in an efficacious manner to the vaccine and reduced infection burden , which is an important indicator of the robustness of the vaccine . The dominant antibody isotype that was produced after immunization with Ov-103 and the five adjuvants and Ov-RAL-2 with the three adjuvants was IgG1 . Only vaccines with Advax-2 induced significant IgG2a/b responses , consistent with the mixed Th1/Th2 response previously reported for this adjuvant [57–59] . The IgG1-dominated response could be predicted based on the Th2 nature of the response induced by Ov-103 and Ov-RAL-2 antigens [73] . Antigen-specific IgE was not measureable in any of the immunized mice in this study . However , IgE was shown to be a component of the protective immune response to O . volvulus induced by irradiated larvae [26] . The protective immune response induced with the recombinant antigens therefore differs from the mechanism induced by irradiated larvae . The absence of an IgE response induced by Ov-103 and Ov-RAL-2 is a significant benefit , as it reduces the possibility of adverse allergic responses when the vaccine is used clinically [74] . Finally , antigen-specific IgM levels at the terminal bleed were equivalent in control and immunized mice . This suggests that parasites within the diffusion chambers implanted in control mice released the antigens that induced an IgM response rather than this reflecting a response primed by the vaccine . Immunization with Ov-RAL-2 stimulated much higher antibody endpoint titers than Ov-103 , but this did not translate to higher levels of protective immunity . Likewise , immunization with co-administered Ov-103/Ov-RAL-2 vaccines further increased the antibody endpoint titers but with inconsistent increases in protective immunity . Correlation analysis using both pre-challenge and study termination sera was performed in an attempt to identify potential components of the killing mechanism . A clear relationship between antibody titer and protective immunity was not observed . Antibody is required for killing larval O . volvulus after immunization with irradiated larvae [26] . The quantity of antibody may not be a limiting factor to kill the larvae , with only a low titer of antibody required to effect larvae killing and hence any potential correlations were possibly obscured in the present study . In previous studies mice were immunized with Ov-103 and Ov-RAL-2 as fusion proteins with alum as the adjuvant . The levels of larval killing ( 11–21% ) and host protection ( 45–58% ) [33] were significantly less than observed in the present study , where the two antigens were co-administered in separate sites . Immunization of mice with both antigens , either as a fusion protein [33] or as co-administration , resulted in significantly higher antibody endpoint titers as compared to mice immunized with the antigens individually . Apparently , the two antigens act synergistically to boost the antibody response to the reciprocal antigen . This is in distinction to other O . volvulus antigens that were found to compete with each other in vaccines resulting in reduced antibody titers [31] . Changing the route of immunization from subcutaneous , used in the previous studies [31 , 33] , to intramuscular , used in the present study , may have enhanced the protective immune response . Immunization of mice with the Ov-103 or Ov-RAL-2 without adjuvant induced spleen cells to produce Th2 cytokines . A consistent observation regarding Ov-103 and/or Ov-RAL-2 in combination with the different adjuvants , was the development of Th2 immune responses based on the presence of the cytokines IL-4 , IL-5 , IL-10 and IL-13 in supernatants from re-stimulated spleen cells . It was predicted that the adjuvants would govern the immune response with alum stimulating a restricted Th2 response [46–50] , Advax 1 stimulating mixed Th1 and Th2 responses [58 , 59] , Advax 2 stimulating an increased Th1 response while retaining the Th2 response , CpG stimulating a Th1 response [60 , 61] , and MF59 stimulating a mixed Th1/Th2 response . Apparently , the Th2 nature of the antigens and the larval challenge was sufficient to dominate the immune response even under the pressure produced by the more Th1 biased adjuvants . The cytokine recall response in mice was limited to Th2 cytokines , with the exception of mice immunized with Advax 2 . Two to three fold increases in the IFNγ recall responses were seen in mice immunized with Advax 2 plus either Ov-103 or the co-administered vaccine . Antibody responses in mice immunized with Ov-103 , Ov-RAL-2 or the co-administration with Advax 2 resulted in a combined IgG1 and IgG2a/b response , consistent with a mixed Th1/Th2 response . Immunizing mice with inulin as the adjuvant with other filarial antigens derived from B . malayi , demonstrated that the adjuvant induced a balanced Th1/Th2 response [75] . In the present study , a limited Th2 cytokine response and positive IgG1 titers was seen in mice immunized with Ov-103 and Advax 1 or CpG , yet parasite killing was absent . Surprisingly , the CpG adjuvant was unable , despite its normal Th1 bias , to induce an IFNγ response to Ov-103 or IgG2 isotype switching , consistent with Ov-103 antigen imparting an overwhelming Th2 bias to the adaptive immune response . Examination of the diffusion chamber contents allowed analysis of the immune response in the parasite microenvironment . Differential cell analyses were performed and relationships were not seen between specific cell types and the presence of protective immunity in mice . In addition , differences were not seen between the numbers of cells that migrated into the diffusion chambers implanted in control and immunized mice . As an alternative approach to determine the effector cells involved with parasite killing , chemokine levels were measured in the fluid found in the diffusion chambers in which the parasites were implanted . A similar approach has been utilized in studying serum from patients with occult infections with O . volvulus . With expiring microfilariae infections , MIP-1 α and MIP-1β levels increased while after treatment with ivermectin , eotaxin and MCP-1 increased , which may have attracted effector monocytes and eosinophils to clear the microfilariae from the skin of the patients [76] . Mice immunized with Ov-103 formulated with alum , Advax 2 or MF59 , in which there was protective immunity , had increased levels of the chemokines KC and eotaxin as compared to controls . These increases were not seen in mice immunized with Ov-103 alone or when formulated with Advax 1 and CpG , which suggests that either these chemokines were critical for the killing response induced by Ov-103 or were produced as a secondary response to larval killing . KC is involved in the activation and chemotaxis of neutrophils [77 , 78] . Eotaxin is a potent chemoattractant of eosinophils and basophils by binding CCR3 [79 , 80] . Based on the chemokine observations , we hypothesize that protective immunity induced by Ov-103 formulated with alum , Advax 2 or MF59 requires neutrophils and/or eosinophils as effector cells that collaborate with antibody . Neutrophils with antibody have been shown to be effective at killing larval O . volvulus in vitro [81] and in vivo studies have shown that eosinophils with antibody are capable of killing O . volvulus larvae [26] . Elevated levels of the chemokines MIP-1α and MCP-1 were found in diffusion chambers recovered from mice with protective immunity induced by Ov-RAL-2 , but not Ov-103 , when formulated with alum or Advax 2 . This observation suggests that the mechanism of protective immunity induced by Ov-RAL-2 differs from the mechanism induced by Ov-103 . MIP-1α is involved in the recruitment and activation of granulocytes including neutrophils during the acute inflammatory response [82–84] . MCP-1 exhibits a chemotactic activity for monocytes and basophils but not for neutrophils or eosinophils [80 , 85 , 86] . Chemokine results from protected mice vaccinated with Ov-RAL-2 formulated with alum or Advax 2 suggest that the effector cells required for protective immunity induced by Ov-RAL-2 might be macrophages and/or neutrophils . Macrophages from mice and humans have been shown to kill nematode larvae in both the innate and adaptive immune response . Furthermore , optimal killing required both neutrophils and macrophages to be active [87] . Chemokine levels in diffusion chambers from mice immunized with the co-administered vaccine displayed disparate responses . In mice immunized with the co-administered vaccine formulated with Advax 2 and MF59 , chemokine levels were similar to those seen in mice immunized with Ov-103 but different from that seen in mice immunized with Ov-RAL-2 , suggesting that with these adjuvants Ov-103 is the dominant antigen . Chemokines in mice immunized with the co-administered vaccine formulated with alum , had chemokines found to be associated with both of the individual antigen vaccines . The combined chemokine response might explain the development of 100% host protection in mice immunized with Ov-103 and Ov-RAL-2 co-administered vaccines formulated with alum . Cytokines found in the spleen-cell supernatants also support a role for eosinophils , neutrophils and macrophages in the protective immune responses . All of the immunized mice had elevated levels of IL-5 , which has been shown to be required for eosinophil differentiation , maturation and survival [88] . Mice immunized with Ov-103 formulated with alum or MF59 and mice immunized with Ov-RAL-2 formulated with MF59 also had increases in IL17A/F , consistent with a Th17 response . IL-17 has been shown to promote the production of IL-6 , IL-8 , G-CSF , and GM-CSF [89–91] , and was demonstrated to induce numerous proinflammatory chemokines including MCP-1 and GRO-α that lead to monocyte and neutrophil recruitment [92–94] . However , the fact that mice immunized with vaccines formulated with Advax 2 induced robust larval killing despite not inducing IL17 suggests IL17 is not critical for vaccine protection . Immunization of mice with Ov-103 or Ov-RAL-2 formulated with alum or MF59 also resulted in production of IL-6 from stimulated spleen cells . IL-6 has been shown to play a crucial role in both innate and adaptive immune response , and along with IL-1β and TNFα attracts neutrophils during the initial phase of the immune response . Following the initial response , IL-6 trans-signaling leads to a switch from neutrophil recruitment to monocyte recruitment by suppressing many cytokines involved in the recruitment of neutrophils . Further , it upregulates a number of monocyte-attracting chemokines such as MCP-1 [95–98] . However , the protection in the Advax 2 group in the absence of a significant IL-6 response again argues against a key role of IL-6 in larval killing . In conclusion , immunizing mice with the recombinant antigens Ov-103 and Ov-RAL-2 formulated with alum , Advax 2 or MF59 induced significant levels of larval killing and host protection . The immune response was biased towards Th2 with all three adjuvants , with IgG1 the dominant antibody induced in response to both antigens . Only Advax 2 induced high levels of IgG1 and IgG2a/b antibodies to both antigens . Co-administration of Ov-103/Ov-RAL-2 formulated with each of the three adjuvants induced larval killing , improved host protection and significantly increased antibody titers . Based on chemokine results , it appears that neutrophils and eosinophils may play a role in protective immunity induced by Ov-103 and macrophages and neutrophils in protective immunity induced by Ov-RAL-2 . The co-administered vaccines , comprised of immunizations with both Ov-103 and Ov-RAL-2 antigens , had enhanced efficacy in controlling infections with O . volvulus , potentially based on the collaboration of two unique but synergistic killing mechanisms . Therefore , the mechanism of protective immunity induced by Ov-103 and Ov-RAL-2 formulated with alum , Advax 2 or MF59 appears to be multifactorial , with roles for antibody , cytokines , chemokines and specific effector cells . Further improving these vaccines will require strategies to optimize levels of all these protective mechanisms that contribute to larval killing . | In some regions in Africa , elimination of onchocerciasis may be possible with mass drug administration , although there is concern based on several factors that onchocerciasis cannot be eliminated solely through this approach . A vaccine against Onchocerca volvulus would provide a critical tool for the ultimate elimination of this infection . Previous studies have demonstrated that immunization of mice with two antigens induced protective immunity and it was hypothesized in the present study that the levels of protective immunity would be improved by formulation with other agents known to enhance immune responses . Protective immunity was observed in mice immunized with the two antigens using three different adjuvants . The vaccines developed in this study have the potential of reducing the morbidity associated with onchocerciasis in humans . | [
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... | 2016 | The Immunomodulatory Role of Adjuvants in Vaccines Formulated with the Recombinant Antigens Ov-103 and Ov-RAL-2 against Onchocerca volvulus in Mice |
Emergency hematopoiesis facilitates the rapid expansion of inflammatory immune cells in response to infections by pathogens , a process that must be carefully regulated to prevent potentially life threatening inflammatory responses . Here , we describe a novel regulatory role for the cytokine IFNγ that is critical for preventing fatal encephalitis after viral infection . HSV1 encephalitis ( HSE ) is triggered by the invasion of the brainstem by inflammatory monocytes and neutrophils . In mice lacking IFNγ ( GKO ) , we observed unrestrained increases in G-CSF levels but not in GM-CSF or IL-17 . This resulted in uncontrolled expansion and infiltration of apoptosis-resistant , degranulating neutrophils into the brainstem , causing fatal HSE in GKO but not WT mice . Excessive G-CSF in GKO mice also induced granulocyte derived suppressor cells , which inhibited T-cell proliferation and function , including production of the anti-inflammatory cytokine IL-10 . Unexpectedly , we found that IFNγ suppressed G-CSF signaling by increasing SOCS3 expression in neutrophils , resulting in apoptosis . Depletion of G-CSF , but not GM-CSF , in GKO mice induced neutrophil apoptosis and reinstated IL-10 secretion by T cells , which restored their ability to limit innate inflammatory responses resulting in protection from HSE . Our studies reveals a novel , complex interplay among IFNγ , G-CSF and IL-10 , which highlights the opposing roles of G-CSF and IFNγ in regulation of innate inflammatory responses in a murine viral encephalitis model and reveals G-CSF as a potential therapeutic target . Thus , the antagonistic G-CSF-IFNγ interactions emerge as a key regulatory node in control of CNS inflammatory responses to virus infection .
Early detection and eradication of invading pathogens by the immune response , without excessive bystander damage to the inflamed organ , is the most desirable outcome for the host [1] . Efficient pathogen control requires activation of innate immune cells , including BM derived neutrophils and monocytes , and their recruitment to the infected tissue to initiate pathogen clearance and shape the ensuing immune response [2–4] . Although the recruitment of inflammatory cells to the target organ is directed by the establishment of a chemokine gradient [5] , the generation of inflammatory cells is facilitated by a hematopoietic response program , termed emergency hematopoiesis , which is characterized by greatly increased de novo neutrophil and monocyte production from BM progenitor cells [6–9] . Under steady state conditions , hematopoiesis is a tightly regulated process; but , to satisfy the enormous demand for leukocytes after infection , a complete overhaul of the program occurs . This overhaul results in emergency hematopoiesis and the subsequent production of substantial numbers of the desired cell type ( s ) [9] . However , under some conditions , impaired regulation of emergency hematopoiesis results in unrestrained inflammation and toxicity to the host [2 , 10–14] . Several intrinsic and extrinsic factors influence emergency hematopoiesis [7 , 15–21] . In the last decade , various reports have elucidated the key role played by cytokines in the regulation of both steady state and emergency hematopoiesis [7 , 15–17] . Cytokines are master regulators of the immune response , and they are produced during immunological stress , such as infections [15 , 22] . The evolution of an immune response can be predicated by the amount and location of cytokine production , cytokine access to other cells , and the stage of infection during which the cytokine is produced . Several cytokines , including IL-6 , TNF-α , GM-CSF , G-CSF , IL-1α , and the IFNs , control leukocyte production and emigration during steady state and emergency hematopoiesis [7 , 8 , 17 , 23 , 24] . G-CSF and GM-CSF influence myeloid homeostasis and GM-CSF is thought to be a key mediator of Th17-driven neutrophil-mediated inflammatory diseases [25–28] . Some cytokines , such as IFN , play prominent dual roles in both development and homeostasis of the immune system and immunity to pathogens [23 , 24 , 29–31] . Type I IFN subtypes ( α/β ) are considered indispensable for antiviral protection while IFNγ , a type II IFN , is essential for bacterial eradication and also for controlling replication of some viruses [31–34] . Because we found increased IFNγ , rather than IFNα/β , following HSV infection in mice , we investigated the role of IFNγ in HSV-induced inflammation of the CNS . IFNγ has long been considered a prototypical pro-inflammatory Th1 cytokine that drives cell-mediated immunity . However , its role in inflammation remains controversial , even though evidence for its immuno-regulatory role has gradually accumulated over the last decade [30 , 35–37] . Several organ-specific autoimmune and chronic inflammatory diseases have been associated with dysregulated Th1 responses , but unexpectedly , these diseases progress at accelerated rates in mice deficient in IFNγ or its receptor due to increased Th17 responses [30 , 36–40] . These observations have challenged the dogmatic pro-inflammatory role of IFNγ , as well as the Th1-Th2-Th17 paradigm , as a rationale for explaining autoimmune disorders . We reported that mortality from viral encephalitis is primarily linked to exaggerated innate inflammatory monocyte and neutrophil responses , rather than viral replication induced tissue damage [41 , 42] . In the current report , we establish a novel role for IFNγ in the development and resolution of protective CNS innate immune responses , after infection with HSV , a neurotropic 〈-herpesvirus . In the absence of IFNγ , production of G-CSF , but not GM-CSF or IL-17 , was greatly increased resulting in the generation of apoptosis-resistant , spontaneously degranulating neutrophils . These neutrophils invaded the CNS in large numbers , causing fatal HSV-induced encephalitis ( HSE ) . This increase in G-CSF and neutrophils suppressed T-cell functionality and importantly , ablation of G-CSF abolished neutrophilia , restored T cell functionality and protected mice from fatal HSE . Thus , our study reveals a novel regulatory role for IFNγ in the control of G-CSF induced neutrophilia and further illuminates its role in protection from viral encephalitis .
We showed previously that IL-10-deficient and T- and B cell-deficient Rag-/- mice succumb to fatal HSE resulting from excessive infiltration by inflammatory Ly6Chigh monocytes ( IMs ) into the brainstem ( BS ) [42 , 43] . To determine the mechanism ( s ) by which inflammation , specifically IM infiltration , is suppressed by T cells in WT mice , we measured cytokine production by T cells isolated from WT mice at days 6 and 14 post-infection ( pi ) . We determined the T-cell cytokine levels by intracellular cytokine staining ( ICS ) using flow cytometry , and serum cytokine levels by enzyme-linked immunosorbent assay ( ELISA ) . IL-10 and IFNγ were the major cytokines produced by T cells in the BS , draining cervical lymph nodes ( CLN ) and spleen ( Fig 1A–1D ) . At day 6 pi in the BS , we observed 20–40% IL-10+ and IFNγ+ T cells , which almost doubled by day 14 pi ( Fig 1B ) . Notably , most ( > 80% ) of the cytokine-secreting CD4 and CD8 T cells in the BS were IFNγ+ at day 14 pi . Although not as impressive as in the BS , IFNγ+ and IL-10+ T cells comprised the predominant cytokine-producing subsets detected in the CLN and spleen ( Fig 1C and 1D ) . The cytokines and chemokines that were analyzed in the sera and BS of infected WT mice at day 6 pi are listed in S1 and S2 Figs respectively and these were either undetectable or altered relative to day 0 levels . Not surprisingly , the IFNγ-driven chemokines , such as IP-10 ( CXCL10 ) and MIG ( CXCL9 ) , dominated the chemokine profile , concordant with our prior observation that T cells are the predominant cell population in the BS infiltrate of WT mice [42] . Since , the major cytokines detected in serum are IL-27 , IL-13 , IFNγ , IL-10 and IL-18 ( S1 Fig ) , we conclude that the infected WT mice express a balanced Th1/Th2 signature . Previously , we found an anti-inflammatory role for IL-10 , which suppressed the generation of IM , thereby preventing HSE in WT but not IL-10 knockout ( IL-10KO ) mice [42] . We now show that IFNγ , a classical pro-inflammatory cytokine , is present at substantially greater levels than the anti-inflammatory cytokine IL-10 in HSV-infected WT mice . To investigate the role of IFNγ in HSE , we infected WT , IFNγ KO ( GKO ) , IL-10KO , and Rag-/- mice and monitored their survival . Unexpectedly , 80% of the GKO mice succumbed to infection within 14 days ( Fig 2A ) , in contrast to the WT mice , which all survived . As previously reported , 60% of the IL-10KO mice and all of the Rag-/- mice succumbed to HSV infection ( Fig 2A ) [42 , 43] . Since Rag-/- mice lack T and B cells and GKO and IL-10KO mice lack IFNγ and IL-10 respectively , these results indicate that IFNγ and IL-10 secreted by infiltrating T cells are required for optimal protection from HSE mortality [42 , 43] . To determine if GKO mice succumbed due to uncontrolled virus replication , we measured the levels of infectious virus in the trigeminal ganglion ( Tg ) and BS tissues from GKO mice . HSV1 was undetectable after day 8 pi in the Tg and BS of GKO mice , similar to WT and IL-10KO mice ( Fig 2B and S3A Fig ) and most of the GKO mice died after day 8 pi , which discounts virus-induced cytopathology as the cause of mortality . As expected , virus replication was not controlled in Rag-/- mice ( Fig 2B ) . These results confirm that T cells control acute HSV replication via an IFNγ independent mechanism , consistent with prior studies in mice and humans [32 , 33 , 43–45] . Excessive CNS infiltration by the pathogenic CD11b+ Ly6Chigh IM subset , which is characteristic of fatal HSE , is limited by the anti-inflammatory cytokine IL-10 [41 , 42] . Because IFNγ plays an important role in myeloid responses , we investigated whether GKO mice succumbed to HSE due to inefficient control of BS inflammation , specifically IM infiltration . As early as day 4 pi , CD45high infiltrates composed 65% of the mononuclear cell population within the BS of GKO mice and remained the dominant population in the BS of GKO mice at day 6 pi , ( Fig 3A and 3C ) . Notably these cells were also present at high levels in the brain and spinal cord of GKO but not WT mice ( S3B Fig ) . Relative CD45high infiltrate levels in the BS were ranked in the order: GKO > IL-10KO > Rag-/- >>> WT mice ( Fig 3C ) , which implies surprisingly that suppression of CNS infiltration requires T cells secreting both IFNγ and IL-10 . We next determined if , similar to IL-10KO mice , IMs were the major CD45high infiltrating subset in the BS of the GKO mice [42] . As anticipated , most of the monocytes / macrophages ( CD45high SSClow CD11b+ CD115+ Ly6G- F480+/lo ) in the BS of GKO , IL-10KO and Rag-/- mice , but not WT mice , expressed high levels of Ly6C , characteristic of the IM subset ( Fig 3A , 3D and 3E ) [42] . However , infiltration of monocytes / macrophages into the GKO BS was significantly reduced compared to WT , IL-10KO and Rag-/- mice ( Fig 3D ) . Interestingly , although macrophages only accounted for a minor fraction ( 25% ) of the total CD45high infiltrate in the GKO BS , conversion to numbers revealed a three fold increase in macrophage numbers in the BS of GKO mice compared to WT mice ( Fig 3D ) . These data suggest that an alternate CD45high cell subset , possibly neutrophils , dominates BS infiltrates in GKO mice . In prior studies , we could not determine the specific role of neutrophils in HSE due to cross reactivity of the neutrophil specific antibodies available at that time [41] . We next determined whether neutrophils ( PMN ) dominated the BS infiltrates in GKO mice . Neutrophils ( CD45high SSChigh CD11b+ Ly6G+ CD115- ) comprised ~60–75% of the CD45high infiltrates in the BS of GKO mice at days 4–12 pi , but were reduced in the BS of WT mice at days 4–6 pi ( ~30% ) and diminished after day 6 pi ( Fig 3A and 3F ) . Amazingly by day 8 pi when PMNs were on the decline in the BS of WT mice , PMN numbers were increased 30 fold in the GKO BS compared to the WT BS ( Fig 3F ) . Furthermore , neutrophils isolated from either the BM or blood of GKO mice were increased compared to WT mice ( Fig 3B , 3G and 3H ) . Thus , in the absence of IFNγ , neutrophils were the predominant BM-derived population in the BS of GKO mice . Importantly , the ratio of PMNs to IMs in the blood of GKO mice was much higher than in WT mice , and increased from day 2 to 6 pi ( Fig 3B and 3I ) . Because IL-10 is important for inhibiting neutrophil responses [15 , 46] , we analyzed the ratio of neutrophils to IMs in the blood of IL-10KO and Rag-/- mice . The ratio was reversed for IL-10KO but not Rag-/- mice at days 4 and 6 pi , implying that control of IM , but not neutrophil expansion , requires IL-10 ( S3C Fig ) . Furthermore , IL-10KO T cells secreted substantial amounts of IFNγ , similar to WT T cells further emphasizing IFNγ’s role in neutrophil contraction ( S3D Fig and Fig 1B ) . These data suggest that during viral infection , IFNγ rather than IL-10 is the major regulator of neutrophil output , especially at later time points after infection . Since FoxP3+ Tregs and ICOS+ CD4 T cells have been shown to moderate inflammation , we examined GKO and WT mice for regulatory CD4 T cells during HSE . IL-10-secreting FoxP3+ Tregs and ICOS+ FoxP3- CD4 Tr1 cells , previously shown to protect against HSV challenge , were expanded in the CLNs and spleens of WT mice ( Fig 4A and 4B ) [42] . We have shown previously that Tregs were induced in peripheral lymphoid organs of WT mice but were not detected in the BS [42] . To determine if these cells were induced in GKO mice , we analyzed CLN , spleen , and BS for FoxP3 and ICOS expression on CD4 T cells ( Fig 4C , S4A and S4E Fig ) . Unexpectedly , Tregs were reduced in the spleens of GKO mice compared to WT mice and absent in the BS of GKO mice ( Fig 4C and S4E Fig ) . Also , adoptive transfer of FoxP3+ CD25+ CD4 Tregs or ICOS+ CD4 T cells from infected WT , but not GKO or naïve WT mice protected naïve WT recipients from HSE , which suggests the GKO Tregs were unable to suppress inflammatory innate immune responses ( Fig 4D ) ; we have shown previously that IL-10 secreting Tregs prevented HSE by suppressing innate inflammatory responses [42] . Intriguingly , the IL-10+ CD4 T cells that expanded in WT mice ( Fig 1 ) [42] were not detected in the BS , CLN or spleen of GKO mice ( Fig 4E and 4F: top row , S4B–S4D Fig ) , consistent with their inability to protect from HSE ( Fig 4D ) . Similarly , IL-4+ and TNF+ CD4 T cells were also absent in spleen ( S4B Fig ) . IFNγ , the protypical Th1 cytokine , is known to suppress Th2 cells and Tregs , but our data suggest paradoxically that during HSV infection IFNγ may be responsible for the fitness and function of regulatory T cells , revealing the complexity of IFNγ’s regulatory mechanisms [15 , 35] . We next asked whether suppression of effector T-cell function during acute infection of GKO mice results from induction of myeloid- or granulocyte-derived suppressor cells ( MDSCs / GDSCs ) [47] . Spleen cells isolated at day 6 pi from GKO mice ( containing high numbers of neutrophils ) or WT mice ( containing few neutrophils ) were labeled with CFSE , then incubated with heat-killed HSV ( HK-HSV ) and analyzed at various time points for dilution of CFSE . Although neither WT nor GKO T cells proliferated after 4 h of culture ( S5A Fig ) , by 24 h WT CD4 and virus-specific CD8 T cells proliferated more rapidly than their GKO counterparts ( Fig 5A–5C ) . However , after 48 h of culture , proliferation rates were similar for both WT and GKO T cells ( Fig 5A–5C ) , suggesting that the suppressive activity of GKO neutrophils declines over time , likely due to their rapid death in culture . Furthermore , in vitro WT effector CD4 and CD8 T cell proliferation was suppressed in the presence of GKO CD11b+ Ly6G+ PMN but not CD11b+ Ly6G- monocytes or CD11c+ DCs ( M / DCs ) that were isolated at day 6 pi from blood of HSV infected GKO mice , confirming that suppression was mediated by GKO GDSCs and not other suppressor cells including Tregs ( Fig 5D ) . To determine if similar suppressive effects were observed for memory T cells , CD11b+ Ly6G+ neutrophils or M / DCs isolated from the blood of infected GKO mice at day 6 pi were incubated with CFSE-labeled spleen cells isolated from HSV immunized WT mice at day 21 pi and memory T-cell proliferation was analyzed in vitro . Indeed , WT memory CD4 , CD8 and virus specific gB498-505 tetramer+ ( Tet+ ) CD8 T cells reactive to virus antigens ( HK-HSV ) proliferated at reduced rates ( ~10% ) when cultured with GKO neutrophils , compared to m / DCs ( Fig 5E ) . Although , WT memory CD4 and CD8 T cells proliferation was reduced in the presence of GKO PMNs compared to WT PMNs presenting HK-HSV antigen ( S5B and S5C Fig ) , virus-specific WT and GKO CD8 T cells cultured with the immunodominant H-2Kb HSV gB498-505 peptide proliferated at similar rates ( S5D Fig ) , suggesting that the suppressive effects of GKO GDSCs depends on the cytosolic proteolysis of viral antigens but can be overcome with an optimal concentration of immunodominant peptide . To determine if suppression by GDSC can also be overcome by stimulation with αCD3 and αCD28 , CFSE-labeled splenocytes containing memory CD4 or CD8 T cells isolated from HSV-infected WT or GKO mice at day 25 pi were incubated with soluble αCD3 and αCD28 in the presence of Ly6G+ neutrophils isolated from blood of infected GKO mice at day 6 pi . Similar rates of proliferation were observed for both WT and GKO memory T cells ( S5E and S5F Fig ) . Additionally , very few exhausted CD4 and CD8 T cells were observed in the two groups , as <5% of cells expressed high levels of PD-1 ( S5B–S5D Fig ) . Overall , these data suggest that the suppressive effects of GDSCs are specific to virus antigens that require cytolytic processing but could be overcome by stimulation with αCD3 and αCD28 or immunodominant viral peptides . Several reports have shown that IFNγ regulates the generation of IL-23-driven pathogenic Th17 cells producing IL-17 and GM-CSF . These Th17 cells have been implicated in several autoimmune disorders associated with dysregulated IM and neutrophil production [25 , 26 , 28] . Although a small population of IL-17+ γδ T cells were detected in the CLN ( 15% ) and spleen ( 7% ) at day 6 pi ( Fig 4E and 4F: bottom row ) , we did not detect IL-17- or GM-CSF-secreting CD4 T cells in the BS , CLN or spleen ( Fig 4E and 4F: top row , S4B–S4E and S6 Figs ) of GKO mice . Moreover , depletion with either 3 or 8 doses of αGM-CSF mAb did not influence the survival of GKO mice , thereby excluding IL-17 and GM-CSF as candidate regulators of the neutrophilia observed in GKO mice ( S7 Fig ) . To determine the mechanism by which IFNγ controls neutrophil output from BM , we screened sera and BS obtained at various time points from infected GKO and WT mice for cytokines and chemokines ( S1 , S2 and S6 Figs ) . IL-17 , IL-6 , G-CSF and GM-CSF are potent inducers of neutrophils in the BM [6 , 7 , 26] . S6 Fig shows that many pro and anti-inflammatory cytokines and chemokines , including IFNα4 , IL-4 , IL-10 , IL-17 and GM-CSF , were undetectable in the sera of infected GKO mice , consistent with the absence of Th17 and Th2 cells in the spleens and CLNs of these mice ( Fig 4E and 4F and S4 Fig ) . IL-27 , a potent inducer of IL-10 and Tr1 cells , was present at similar levels in the sera of both WT and GKO mice ( S1 and S6 Figs ) [48] . Nevertheless , IL-10 was undetectable in CD4 T cells isolated from BS , spleen or CLN of GKO mice , and both Tregs and Tr1 cells obtained from infected GKO mice were unable to protect GKO mice from HSV challenge ( Fig 4D–4F and S4 Fig ) . The major cytokines upregulated in the sera and BS of infected GKO mice were IL-6 and G-CSF , along with the CXC chemokines CXCL1 ( KC ) and CXCL2 ( MIP-2 ) , which elicit neutrophil production and egress from BM ( Fig 6A–6D , S2 and S6 Figs ) . Neutrophil and monocyte chemoattractants such as CCL3 ( MIP-1α ) , CCL4 ( MIP-1β ) and CCL5 ( RANTES ) expression were also induced at high levels in BS but present at reduced levels in sera of GKO mice ( S2 and S6 Figs ) . Astonishingly , G-CSF was increased ~3–4 fold in the sera of GKO mice compared to WT mouse sera ( Fig 6A ) . Notably , although G-CSF levels declined in WT mice after day 6 pi ( day 8 pi: < 1500 pg/ml ) , G-CSF levels remained extremely high in the sera of GKO mice ( day 6 pi: ~15 , 000 pg/ml , and day 8 pi: ~10 , 000 pg/ml ) ( Fig 6A ) . Also , because the serum levels of G-CSF in GKO mice ( 735 pg/ml ) were ~2-fold greater than in WT mice as early as day 2 pi ( 304 pg/ml ) we speculate that control of G-CSF production was impaired at , or soon after , day 2 pi in GKO compared to WT mice . Depletion of Gr-1-expressing Ly6G+ neutrophils and Ly6Chigh IMs in GKO mice , using an αGr1 mAb , did not protect the mice ( S7 Fig ) . Also surprisingly , depletion of macrophages with chlodronate liposomes enhanced mortality in GKO mice , compared to PBS liposome-treated mice ( S7 Fig ) . This result argues against macrophages or IM being causally involved in induction of HSE in GKO mice . Because neutrophils expanded more than other cell subsets in HSV-infected GKO mice , we depleted the neutrophils using a αLy6G-specific mAb . Remarkably , ~50% of the GKO mice survived HSV infection after Ly6G+ neutrophil depletion ( Fig 7A ) , but the majority of mice died following cessation of mAb treatment . Because G-CSF was dramatically increased ( ~3–4 fold ) in the sera of GKO mice , and G-CSFR is expressed predominantly by neutrophils and their progenitors in the BM , we depleted G-CSF using 3 doses of αG-CSF mAb to determine if reducing G-CSF levels could prevent neutrophilia and thus fatal encephalitis . As expected , G-CSF depletion protected the GKO mice from HSE ( Fig 7A ) with only one mouse succumbing on day 15 pi . Importantly , after G-CSF depletion , levels of circulating neutrophils in GKO mice were significantly reduced compared to untreated GKO mice , at days 4 ( S8A and S8B Fig ) and 6 pi ( Fig 7B ) . CNS infiltration by CD45high cells , Ly6Chigh IMs , and Ly6G+ neutrophils was reduced in the G-CSF-depleted GKO mice , rendering them similar to WT mice ( Fig 7C and 7D , S8C and S8D Fig ) . Similarly , G-CSF depletion drastically reduced the levels of degranulating neutrophils in the BS of GKO mice compared to the vastly increased numbers ( ~30X ) in non-depleted GKO mice ( Fig 7E ) . These data show that αG-CSF mAb treatment normalized the neutrophil and monocyte levels to that observed in WT mice , thus confirming that excessive levels of G-CSF drive neutrophillia in GKO mice . These data highlight the importance of G-CSF and IFNγ in balancing the neutrophil and monocyte output from the BM following infection with a viral pathogen . Several cell types , including hematopoietic and non-hematopoietic cells , secrete G-CSF [11] . We found that CD11b+ cells , including macrophages and neutrophils in the blood and spleen ( Fig 7F: left plot ) , secrete G-CSF in response to HSV infection . We found similar results for most cell types in the BS , including CD45neg CD11b- neural / glial cells , such as astrocytes , CD45int CD11b+ microglia , and CD45high CD11b+ monocytes and neutrophils ( Fig 7G: top row ) . G-CSF secretion was greatly diminished by treatment of infected GKO mice with αG-CSF mAb ( Fig 7F and 7G ) . Importantly , neutralizing G-CSF in GKO mice reinstated IL-10 secretion by ICOS+ CD4 T cells in the blood and BS ( Fig 7F: right plot and G: bottom row ) and reduced IM and neutrophil output from BM and infiltration into the BS ( Fig 7D , S8A and S8B Fig ) . This result further validates the suppressive effects exerted by G-CSF induced GDSCs on T cell proliferation and cytokine secretion ( Figs 5 and 7G ) . G-CSF is known to activate STAT3 , thereby promoting neutrophil turnover and activation; however , SOCS3 mediated inhibition of STAT3 signaling curtails neutrophil expansion [18] . We observed that pSTAT3 expression was upregulated in neutrophils isolated from the blood of GKO mice at day 6 pi , compared to WT mice ( S9A , S9B and S9C Fig ) . In contrast , SOCS3 expression was increased in neutrophils isolated from the blood and BM of WT mice , compared to GKO mice ( Fig 8A ) . Futhermore , ex vivo treatment of neutrophils isolated at day 6 pi from blood of WT , GKO or G-CSF-depleted GKO mice with recombinant IFNγ , prior to treatment with recombinant G-CSF ( S9B and S9C Fig ) , reduced pSTAT3 expression . Consistent with our in vivo results , GKO mouse blood-derived neutrophils , which were treated ex vivo with recombinant IFNγ , upregulated SOCS3 expression ( Fig 8A ) . It was previously shown that in vitro treatment of naïve BM cells with recombinant IFN© upregulates SOCS3 expression , which in turn inhibits STAT3 signaling [49] . Our results extend this observation to an in vivo viral encephalitis model and show that IFN© is critical for regulation of G-CSF-mediated emergency hematopoiesis . Since G-CSF is a survival factor for neutrophils , we asked whether GKO mice that have elevated G-CSF levels , exhibit reduced apoptosis of neutrophils . We found that indeed , the elevated G-CSF levels also increased the survival of circulating neutrophils as revealed by reduced Annexin V+ neutrophils in blood and BS of GKO , compared to αG-CSF treated GKO mice or WT mice , resulting in neutrophilia ( Fig 8B and S8D Fig ) . These data are consistent with a previously described role for SOCS3 in suppression of G-CSF signaling and neutrophil apoptosis during emergency hematopoiesis [18] . Reduced neutrophil apoptosis and chronic inflammation have also been linked to increased IL-17 secretion . Although , we did not find CD4 T cells secreting IL-17 ( Fig 4E and 4F ) , ~50% of the γδ T cells in the CLN and 30% in the spleen of GKO mice were IL-17+ at day 14 pi ( Fig 8C and 8D ) compared to 15% and 7% at day 6 pi in CLN and spleen respectively ( Fig 4E and 4F ) . These data reveal a complex interaction network involving several pro- and anti-inflammatory cytokines that regulate CNS inflammation during viral infection . Our data support a model wherein a normal response to HSV infection depends on IFNγ-induced suppression of G-CSF and IL-6 levels ( S10 Fig ) . This suppression of G-CSF allows for neutrophil apoptosis and the secretion of anti-inflammatory IL-10 from CD4 T cells , which controls IM expansion . In the absence of IFNγ , excessive neutrophil production and diminished IL-10 secretion predominate , leading to fatal HSE . This work demonstrates a mechanism by which IFNγ , a prototypical pro-inflammatory cytokine , can exert anti-inflammatory effects on innate inflammatory responses during viral infection of the CNS .
Dysregulated CNS inflammation is a hallmark of viral encephalitis . Our previous studies have shown an anti-inflammatory role for IL-10 , which , by suppressing the generation of IMs , prevents HSE in WT mice [41 , 42] . The present study uncovered a novel and complex interplay among IFNγ , G-CSF and IL-10 , which determines the induction of neutrophila and HSE as shown schematically in S10 Fig . IFNγ was present at substantially greater levels than IL-10 in HSV-infected WT mice and played a unique role in the development and resolution of protective innate inflammatory responses in the CNS . Contrary to prior studies of neuroinflammatory diseases , we found that G-CSF and CXCL1 , but not GM-CSF or IL-17 , increased drammatically in HSV-infected GKO mice , resulting in invasion of the CNS by massive numbers of apoptosis-resistant , inflammatory neutrophils . Remarkably , we found that IFNγ controls G-CSF signaling by increasing SOCS3 expression in neutrophils and thereby induces apoptosis . Our study reveals that during viral encephalitis , it is G-CSF rather than GM-CSF that is the critical regulator of emergency hematopoiesis . The elevated levels of G-CSF induced production of GDSCs , which suppressed T-cell proliferation and function , including IL-10 secretion by regulatory T cells . Importantly , G-CSF depletion eliminated neutrophilia and restored protective IL-10 secreting regulatory T cells , thus preventing fatal HSE . GM-CSF has recently been ordained as a central mediator of tissue inflammation during emergency hematopoiesis and as a crucial conduit between tissue invading lymphocytes and myeloid cells [25 , 30 , 50] . However , our study reveals a novel anti-inflammatory role for IFNγ , where G-CSF but not GM-CSF suppression results in control of expansion and survival of potentially pathogenic neutrophils . Thus , the antagonistic G-CSF-IFNγ interactions emerge as a key regulatory node in control of the innate inflammatory response to virus infection of the CNS . Neutrophils are the most abundant immune cell subset and are produced daily in prodigious amounts . Earlier studies established that neutrophils play indispensable roles in various aspects of host immunity , including defense against pathogens [2 , 4 , 12 , 51] . Recent awareness that excessive numbers of neutrophils in tissues are associated with inflammatory diseases , has led to the re-evaluation of neutrophils as the primary drivers of inflammation associated pathology [2 , 11 , 13 , 14 , 52] . Consequently , neutrophil numbers and activity require precise regulation to avert the inflammatory pathology linked to various inflammatory diseases , including infections , autoimmunity and chronic disorders [11 , 12 , 52–55] . This regulation is imposed mainly by the G-CSFR / STAT3 / SOCS3 axis [18 , 20 , 56] . G-CSF is a key regulator of production , activation , and survival of neutrophils both during steady state and specifically during emergency hematopoiesis [56] and its levels are tightly controlled to avoid emergence of pathogenic neutrophils as a result of chronic G-CSF signaling [11] . We found that in contrast to WT mice , GKO mice were unable to suppress G-CSF levels after HSV infection , causing neutrophilia and death , demonstrating for the first time that IFNγ controls G-CSF levels and importantly neutrophil expansion and survival in vivo . In addition , several studies have documented the importance of GM-CSF and the Th17-related cytokines IL-23 and IL-17 for induction of inflammatory and autoimmune disease when IFNγ is lacking [25–28 , 30] . In contrast , our data reveals that G-CSF , rather than GM-CSF , IL-23 or IL-17 is the primary cytokine that provokes neutrophilia and fatal HSE [52] . These results highlight the importance of IFNγ interaction with G-CSF signaling for regulation of neutrophil responses . We have identified a novel regulatory role for IFNγ as a suppressor of neutrophil proliferation during viral infection . Although IFNγ’s role in contraction of T-cell responses after the resolution of infections is well defined , its role in neutrophil contraction has been less clear . This work highlights the complexity of antiviral inflammatory responses , which depends on the virulence of the infectious agent and the extent of emergency hematopoiesis . The resolution of an inflammatory response is initiated by apoptosis of neutrophils , which are then programed for phagocytosis by macrophages that release anti-inflammatory mediators to resolve inflammation [2] . However , during severe acute infections , apoptotic neutrophils can stimulate IL-17 secretion by either γδ or CD4 T cells , resulting in chronic inflammation [57] . Alternatively , reduced apoptosis of neutrophils , can sustain chronic inflammation [55] . Our results show that GKO mice have increased IL-17-secreting γδ T cells at day 14 pi , as well as reduced apoptosis of neutrophils , which likely accounts for impaired resolution of inflammation in GKO mice . Furthermore , depletion of G-CSF in GKO mice induced apoptosis of neutrophils and resolved inflammation , implicating impaired neutrophil apoptosis as the cause of chronic inflammation in the GKO mice . Increased SOCS3 in neutrophils and BM cells is essential for desensitizing G-CSFR signaling to terminate neutrophil production [56] . Myeloid expression of SOCS3 is also crucial for controlling neuroinflammatory diseases [53 , 58] . Our data show reduced SOCS3 expression in GKO , compared to WT , neutrophils , which likely impeded resolution of neutrophil responses , and is consistent with the established role for SOCS3 in regulating G-CSF signaling to prevent neutrophil-mediated disease . We have previously shown that IL-10 suppressed expansion of IM in WT mice [59] . In our current study , excessive G-CSF in GKO mice was accompanied by deficient IL-10 production by CD4 T cells , which importantly was restored by neutralization of G-CSF . The mechanism linking IFNγ deficiency , G-CSF and lack of IL-10 production by CD4+ Tregs and Tr1 cells is intriguing and remains to be resolved . High levels of IL-27 , which induce Tr1 cells , IL-10 production and suppression of Th17 cells [48 , 60] , were present in both WT and GKO mice , which explains the absence of Th17 cells but not the loss of IL-10 in GKO mice . In addition to the loss of IL-10 production , there are several possible reasons for the failure of GKO Tregs and Tr1 cells to protect from HSE , including effects of chronic inflammation in GKO mice , differences in TLR signaling or metabolic programs compared to WT Tregs and Tr1 cells [61 , 62] . Also , the reduced SOCS3 expression , which resulted in sustained G-CSF signaling and neutrophil production , could contribute to IL-10 deficiency . Although IL-10 has been implicated in the resolution of neutrophil responses in a viral model of CNS infection [46] , in our model IL-10KO mice displayed increased IM , but not neutrophil expansion; this difference likely results from the different virus-mouse strain combinations used in the two studies . Furthermore , IL-10KO T cells secreted increased IFNγ compared to WT T cells , which considered together with the reduced neutrophil levels in IL-10KO mice , clearly implicates IFNγ rather than IL-10 as an important regulator of neutrophils during inflammation . Thus our data reveals a “division of labor” for IL-10 and IFNγ in the resolution of inflammation following viral infection , with IL-10 being important for control of IMs and IFNγ being essential for termination of neutrophil responses . IFNγ’s role in balancing the neutrophil-to-monocyte output from BM and regulation of neutrophil apoptosis during HSV infection , emphasizes its importance in controlling emergency hematopoiesis and neutrophil responses via regulation of G-CSF . Interestingly , a few studies have shown that the absence of type I IFNs results in CXCR2-driven neutrophil accumulation in the lungs or sensory ganglia following viral infections [63 , 64] . Although we did not observe increased IFNα/β expression , increased expression of serum CXCL1 and CXCL2 which are CXCR2 ligands , was observed in GKO mice , compared to WT mice . Therefore , it is plausible that neutrophil expansion in the absence of IFNα/β [63 , 64] might involve interference with the G-CSF / STAT3 / SOCS3 axis , similar to our results . Importantly , G-CSF signaling is crucial for the down-regulation of CXCR4 , which permits neutrophil egress from BM [65] . The continued G-CSF signaling observed in GKO mice could account for the unabated production and egress of pathogenic neutrophils , and fatal HSE . Although the G-CSF receptor is present only on neutrophils , infiltration of other immune cells including IM into the BS of G-CSF depleted GKO mice was markedly reduced . Because neutrophils are the first cells to infiltrate an inflamed tissue , where by stimulating chemokine and integrin expression at the site of infection they enable other cells to enter the target organ , depletion of neutrophils likely resulted in reduced infiltration by macrophages along with all other cell types [66–68] . In various animal models of neutrophil-mediated inflammatory disease [13] , administration of αG-CSF Ab resolves the disease by controlling neutrophil numbers and function . Thus , in the clinical setting , management of G-CSF levels with antibodies or analogous inhibitors could be beneficial in diseases where chronic inflammation is associated with aberrant inflammatory neutrophil responses . Our study corroborates a growing body of evidence emphasizing the importance of neutrophils in neuroinflammatory disorders , including viral-mediated encephalitis . We propose identifying novel factors that target the activation and function of neutrophils , rather than T cells , as a novel therapeutic approach for neuroinflammatory disease .
129S6 WT ( 129S6/SvEvTac ) and Rag2-/- ( 129S6/SvEvTac-Rag2tm1Fwa ) mice were obtained from Taconic ( Hudson , NY ) while 129 IL-10KO ( 129 ( B6 ) -IL10tm1Cgn/J ) mice were obtained from Jackson Laboratories ( Bar harbor , Maine ) . 129 GKO mice were derived in this laboratory and have been described previously [45] . 129S6 WT , 129 GKO , 129 Rag2-/- ( abbreviated to Rag-/- ) and 129 IL-10KO mice were bred in the Animal Research Facility at City of Hope . Both male and female mice , at 6–8 weeks of age , were infected with the HSV1 strain 17+ ( 3200 PFU ) via corneal scarification , as previously described [41 , 42] . To prevent increased mortality after infection , all mice received 4 mg intravenous immunoglobulin intraperitoneally at 24 h pi unless otherwise mentioned and were monitored daily for signs of encephalitis , as previously described [42] . Only WT recipients adoptively transferred with Tregs ( Fig 4D ) did not receive IVIG following HSV infection . G-CSF depletion was performed by administering 3 doses of 250 μg of αG-CSF Abs ( R&D Systems ) on days 0 , 1 and 4 pi . 3 doses or 8 doses of αGM-CSF Ab ( clone MP1-22E9 ) given every two days by ip injections was used to deplete GM-CSF , and its absence in serum confirmed by ELISA . Anti Gr-1 ( clone RB6-8C5 ) was used to deplete Gr-1+ monocytes and neutrophils and anti-Ly6-G ( clone 1A8 ) Abs to specifically deplete Ly6G+ neutrophils; depletion of neutrophils were determined by flow cytometry . Depletion of G-CSF using αG-CSF mAb was confirmed in serum by ELISA and its effects on neutrophil numbers were assessed by flow cytometry; ~ 90% of neutrophils were depleted in blood and ~85% in the BS . Virus titers in Tg and BS were determined by the plaque assay , as previously described [42] . Isolation of mononuclear cells from spleen , CLN , blood and CNS have been described previously [42] . Briefly , brains , spinal cords , and BS were removed separately from mice perfused with PBS , minced , and digested with collagenase and DNase for 30 min prior to centrifugation at 800 x g for 25 min on a two-step Percoll gradient . Trigeminal ganglia were treated like CNS cells but instead of using a Percoll gradient , they were centrifuged at 400 x g for 10 min before passing through a nylon mesh . The purified cell fractions were then used for FACS . <5% CD45high cells were present in the CNS of naïve and mock infected mice and these were mostly macrophages and DCs; very few T cells if any were detected . Day 6–8 pi time point was chosen to show cell infiltration into the BS as this time point represented peak infiltration by immune cells into the CNS of infected mice . CD4+ or CD8+ T cells , CD25+ or ICOS+ CD4 T cells and CD11b+ monocytes and neutrophils , were isolated from spleen or blood of WT or GKO mice using cell-specific Ab tagged with magnet-conjugated nanobeads or by negative isolation EasySep kits according to the manufacturer’s instructions ( Stemcell Technologies ) . Flow-sorted ( FACSAria , BD Biosciences ) CD11b+ Ly6G+ neutrophils isolated from blood were used for RT-PCR analysis . For adoptive transfers , CD25+ FoxP3 GFP+ Tregs ( 5x106 ) or ICOS+ CD4 T cells ( 107 ) were re-suspended in RPMI and injected intra-venously into the recipients prior to challenge with HSV 17+ by ocular scarification . Neutrophil and macrophage degranulation was determined in vitro in the absence of stimulation , or after stimulation of cells for 4–5 h with heat-killed HSV in the presence of αCD107a/b Abs to capture cell surface-associated LAMPs [42] . Resting macrophages did not express surface CD107a/b . To determine cell surface expression , Ab-labeled cells were acquired on a BD Fortessa Analyzer ( BD Biosciences , San Jose , CA ) and flow cytometry analysis was performed using FlowJo software ( Tree Star Inc . ) . Doublets were excluded from live cell populations . CD45 was used to distinguish BM-derived CD45high leukocytes from CD45int CD11b+ microglia and CD45neg neural / glial cells as shown in Fig 3A . Mock infected mice contain < 5% CD45high leukocytes in the BS . Neutrophils were determined by their SSChigh , Ly6G+ , CD115- phenotype ( Fig 3A and 3B ) . CD4+ Tregs were determined by reactivity to CD25 and intracellular FoxP3 expression ( Fig 4A and 4B ) . Monocytes / macrophages were determined by a SSClow CD115+ CD11b+ F480+/low Ly6G- phenotype , whereas IMs expressed high levels of Ly6C molecules ( Fig 3A and 3B ) . Annexin reactivity to determine apoptosis in untreated ex vivo neutrophils was performed based on the manufacturer’s protocol using an Annexin V-specific Ab and 7-AAD ( Biolegend Inc . , San Diego , CA ) . Intracellular staining was performed as previously described [42] . Analysis of cytokine secretion during acute infection was done at day 6 pi because T cell responses were maximal between day 6–8 pi in the BS , CLN and spleen . Day 14 pi was chosen to study T cell responses following resolution of viral infection , because infectious virus could not be detected at this time point . Briefly , 106 cells were stimulated for 4–6 h with or without peptide ( CD8: gB498-505; CD4: heat killed ( HK ) -HSV; or PMA + ionomycin ( Sigma-Aldrich ) + HK-HSV , in the presence of protein transport inhibitors containing Brefeldin A or monensin ( eBiosciences ) . Following FcR blocking , surface expression , using lineage specific antibodies , was determined . Then , the cells underwent fixation and membrane permeabilization using ebioscience IC fixation / permeabilization buffers ( ebioscience ) , and the permeabilized cells were probed with α cytokine Abs to detect cytokines . pSTAT1 and pSTAT3 were determined using manufacturer’s protocol ( BDPhosFlow , BD Biosciences , San Jose , CA ) for mouse spenocytes . Briefly , mononuclear cells isolated from spleen , BM or blood were incubated with one of the following stimulations: recombinant IFNγ ( 20 ng/ml ) for 2 h , recombinant G-CSF ( 5 ng/ml ) for 20 min , PMA + ionomycin for 20 min or just medium control . In some cases , cells were treated first with recombinant IFNγ for 2 h , followed by recombinant G-CSF for 20 min . Following the stimulations , cells were fixed in pre-warmed 1x Lyse/fix buffer for 10–12 min at 37°C , permeabilized with chilled BD Perm Wash buffer III for 30 min , and probed for Ab specific for pSTAT1 and pSTAT3 and lineage specific surface markers ( BD Biosciences , San Jose , CA ) . CD11b+ Ly6G+ Neutrophils ( PMN ) and CD11b+ Ly6G- Ly6C+ monocytes or APCs ( Ly6G- Ly6C- CD11b+ / CD11c+ and B220+ cells ) were isolated from blood of WT and GKO mice at day 6 pi because their levels were maximal at this time point; negative enrichment EasySep kits ( Stemcell Tech . , Vancouver BC ) or flow sorting ( FACSAria , BD Biosciences ) was used for purification of cells . Memory T cells were obtained from spleens of HSV immunized WT mice at day 21 pi , while HSV specific effector T cells present in spleens of HSV infected WT and GKO mice at day 6 pi were used as effector cells . In some experiments ( S5E and S5F Fig ) , memory CD4 and CD8 T cells were obtained from HSV infected WT and GKO mice at day 25 pi . CFSE labeled ( 5 μM ) splenocytes or T cells ( 5x105/well ) and purified GKO or WT PMNs ( suppressors; 5x104 ) were cultured at 10:1 ratio in the presence or absence of HK-HSV ( for CD4s and CD8s ) , H-2Kb HSV-1 gB498-505 peptide ( for CD8s only ) or with soluble α CD3 ( 2 μg/ml ) and α CD28 ( 1 μg/ml ) for 48–72 h to determine CD4 and CD8 T cell proliferation [47] . For ex vivo suppression of T cell proliferation , splenocytes isolated at day 6 or 8 pi were labeled with CFSE and cultured in the presence of HK-HSV for various times to determine suppression by neutrophils or IMs present within the respective groups ( Fig 5 ) . Since GKO spleens have increased numbers of neutrophils compared to WT spleens ( 9% Vs 5% ) , GKO PMN ( Ly6G+ CD11b+ ) or monocyte / DCs ( M/DC: CD11b+ Ly6G-/CD11c+ ) were added to the culture at a 1:10 ( GKO innate cell: T cell ) ratio along with CD19+ B cells in Fig 5D and 5E . Dilution of CFSE on labeled T cells revealed rates of proliferation at the different time points analyzed . Comparison with and without presence of suppressors in the presence or absence of antigen revealed the rate of suppression by neutrophils . Serum obtained from wt or GKO mice ( n = 3 mice per group ) at day 0 or different time pi were analyzed for a panel of 36 cytokines and chemokines using the ProCarta Plex Mouse Cytokine and Chemokine 36plex kit ( Affymetrix eBioscience , San Diego , Ca ) , performed on the Bio-Rad Bio-Plex HTF System at the Clinical Immunobiology Correlative Studies Laboratory Core at City of Hope , Duarte , CA . We chose day 6 pi to show data for the majority of cytokines and chemokines in S1 , S2 and S6 Figs as this time point reflected peak expression . Total RNA was isolated from homogenized and lysed GKO and WT BS samples using the RNeasy Mini Kit ( Qiagen , Valencia , CA ) and following genomic DNA elimination , cDNA synthesized from mRNA using the RT2 first strand kit ( Qiagen ) , according to manufacturer’s instructions . Chemokine and Cytokine gene expression was analyzed using Syber Green based RT2 Profiler PCR Arrays ( PAMM-011z for Inflammatory Cytokines & Receptors ) in a 96 well plate format . RT-PCR was performed using manufacturers protocol and analysis performed using the manufacturer provided Analysis template . Briefly , data analysis was performed based on the ΔΔCT method , with normalization of raw data ( Gene of interest = GOI ) to 3–5 housekeeping genes ( HKG ) that were not been altered following infection ( <1 . 5 difference in CT value between day 0 and day 6 pi ) . The ΔΔCT value was calculated by subtracting the averaged ΔCT value of the GOI at day 6 pi BS ( test group ) from the ΔCT value of the day 0 BS ( control group ) and the fold change ( up- or down- regulation ) calculated as 2^ ( -ΔΔCT ) . Graph Pad Prism Software was used to analyze mortality data by the log rank ( Mantel Cox or Gehan-Breslow-Wilcoxon ) test , considering both the time of death and mortality rates . Statistical differences between groups of mice were calculated using two-way ANOVA with multiple pairwise comparisons ( Turkeys or Sedaks ) to determine the effects of time intervals between the different groups on cell populations and infiltrations or Student’s t tests for other calculations , with p ≤0 . 05 considered significant in the GraphPad Prism 6 software . All animal procedures were performed in compliance with the City of Hope Beckman Research Institute Institutional Animal Care and Use Committee ( IACUC ) and within the framework of the Institute for Laboratory Animal Research ( ILAR ) Guide for the care and use of laboratory animals and all regulations of the United States Deparment of Agriculture ( USDA ) which implement the Animal Welfare Act ( AWA ) and the Public Health Service ( PHS ) Policy on Humane Care and Use of laboratory animals . All work done on mice used in this study was done under the approval of the IACUC of the City of Hope Beckman Research Institute that reviewed and approved the relevant protocol #07043 . | Successful resolution of infections requires a balanced immune response that recognizes and destroys invading pathogens , without provoking excessive damage to the host . Interferon gamma ( IFNγ ) is an important pleiotropic cytokine , which regulates host immunity at key stages of the immune response . However , much less is known about its regulatory activities during inflammation , especially in viral infections . Here , we show a novel role for IFNγ in regulating innate immune responses during a viral infection . HSV1 induced encephalitis is triggered by inflammatory monocytes and neutrophils invading the brainstem . In the absence of IFNγ , G-CSF and IL-6 levels but not IL-17 and GM-CSF , were highly elevated . This resulted in uncontrolled expansion and infiltration of pathogenic neutrophils into the CNS causing fatal encephalitis in IFNγ deficient ( GKO ) but not wildtype mice . Depletion of G-CSF in GKO mice abolished neutrophil expansion , reinstated IL-10 secretion by T cells , resulting in protection from encephalitis . Our study reveals a novel G-CSF regulated pathway , independent of GM-CSF and IL-17 , that controls neutrophil responses to viral infection . Importantly , IFNγ emerges as a key regulator of the inflammatory response to virus infection and this functionality is distinct from its direct anti-microbial activities . | [
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"developm... | 2018 | IFNγ inhibits G-CSF induced neutrophil expansion and invasion of the CNS to prevent viral encephalitis |
RIT1 belongs to the RAS family of small GTPases . Germline and somatic RIT1 mutations have been identified in Noonan syndrome ( NS ) and cancer , respectively . By using heterologous expression systems and purified recombinant proteins , we identified the p21-activated kinase 1 ( PAK1 ) as novel direct effector of RIT1 . We found RIT1 also to directly interact with the RHO GTPases CDC42 and RAC1 , both of which are crucial regulators of actin dynamics upstream of PAK1 . These interactions are independent of the guanine nucleotide bound to RIT1 . Disease-causing RIT1 mutations enhance protein-protein interaction between RIT1 and PAK1 , CDC42 or RAC1 and uncouple complex formation from serum and growth factors . We show that the RIT1-PAK1 complex regulates cytoskeletal rearrangements as expression of wild-type RIT1 and its mutant forms resulted in dissolution of stress fibers and reduction of mature paxillin-containing focal adhesions in COS7 cells . This effect was prevented by co-expression of RIT1 with dominant-negative CDC42 or RAC1 and kinase-dead PAK1 . By using a transwell migration assay , we show that RIT1 wildtype and the disease-associated variants enhance cell motility . Our work demonstrates a new function for RIT1 in controlling actin dynamics via acting in a signaling module containing PAK1 and RAC1/CDC42 , and highlights defects in cell adhesion and migration as possible disease mechanism underlying NS .
RIT1 belongs to the RAS superfamily of low molecular weight GTP-binding proteins that function as guanine nucleotide-regulated molecular switches in the cell by changing between an active GTP-bound and an inactive GDP-bound state [1 , 2] . The RAS GTPases contain five well-conserved amino acid motifs , with G1 and G3 involved in phosphate binding , G2 in effector binding and G4 and G5 in GTP binding and hydrolysis [1] . Rit1 is expressed in a variety of tissues and throughout development [3] . A series of studies in primary neurons and pheochromocytoma ( PC ) cell lines indicated roles of Rit1 in neuronal morphogenesis [4 , 5] , neural differentiation [6–8] , and cell survival [2 , 8–10] . Detailed biochemical analyses showed that Rit1 mediates cell survival via the p38-MK2-HSP27 and mTORC2-Akt signaling pathways [9–12] and regulates MEK-ERK signal transduction upon activation of particular upstream cell surface receptors in certain cell types [6 , 8 , 13] . The recent identification of germline gain-of-function RIT1 mutations in patients with Noonan syndrome ( NS ) demonstrated the importance of this small GTPase for embryonic development [14] . NS is a genetically heterogeneous , autosomal dominant disorder characterized by craniofacial dysmorphism , growth retardation , cardiac abnormalities , and learning difficulties [15] . Individuals with a RIT1 mutation have a higher prevalence of cardiovascular manifestations and lymphatic problems compared with other NS subtypes , and the frequency of RIT1 mutations in NS is at least 5% [14 , 16–18] . NS-associated RIT1 mutations reported to date particularly affect codons 57 , 82 and 95 and result in amino acid changes in the switch I and II regions ( Fig 1A ) [14 , 16–30] . These two protein motifs are involved in nucleotide , effector and regulator binding and thereby ensure molecular functioning of RIT1 [31 , 32] . Almost all causative genes for NS and clinically overlapping diseases encode components or regulators of RAS-mediated signaling , and dysregulated RAS-MAPK signaling has been postulated to be the pathogenic mechanism shared among these disorders , summarized as RASopathies [33–35] . Rit1 shares effector molecules with Ras , such as Raf1 and the p110 catalytic subunit of PI3K [2 , 36] . Accordingly , EGF-induced ERK activation has previously been found to be enhanced and/or sustained in T-REx293T cells expressing NS-associated RIT1 mutants [21] , and ectopic expression of NS- and cancer-related RIT1 mutants in PC6 cells induced phosphorylation of both MEK and ERK [25 , 37] . Similarly , the NS-associated RIT1 mutants p . A77S and p . M90I , also found in lung adenocarcinoma , stimulated phosphorylation of AKT in PC6 cells [37] . Collectively , the data suggest that RIT1 is a positive modulator of RAF-MEK-ERK and PIK3-AKT signaling , and NS-associated mutants cause sustained activation of both pathways . With the identification of RRAS as RASopathy-associated gene [38] , that encodes a small GTPase controlling cell adhesion , spreading and migration [39–41] , the question arose whether signaling routes other than RAF-MEK-ERK and PIK3-AKT may also be relevant in the pathogenesis of RASopathies . In line with this , expression of the constitutively active Rit1 mutant Q79L changes NIH 3T3 cellular morphology , characterized by membrane extensions with some ruffle-like structures at their end [42] . Induction of these actin-dependent structures by active Rit1 suggests an involvement of this small GTPase in cytoskeletal rearrangements . In general , the Rho GTPases Rho , Rac and Cdc42 act as key regulators of actin dynamics and associated processes , such as cell migration [43] . Indeed , Rac/Cdc42 was found in a ternary complex with Rit1 and the cell-polarity protein Par6 [44 , 45] . Here we provide evidence for a novel signaling node containing RIT1 and major cytoskeleton regulators and emphasize altered actin dynamics as another aspect in the molecular pathogenesis of NS .
We studied the signaling consequences of wild-type RIT1 ( WT ) and six NS-associated RIT1 mutations , including p . K23N and p . G31R in the P-loop , p . A57G in the switch I region , and p . F82L , p . M90V and p . G95A in the switch II region ( Fig 1A ) [14 , 16–18 , 27] . Serum stimulation promoted a sound ERK1/2 phosphorylation response in HEK293T cells expressing wild-type RIT1 , which was increased compared to control cells ( Fig 1B ) , indicating that RIT1 affects MEK-ERK signaling . All six RIT1 mutants induced elevated and prolonged phosphorylation of ERK1/2 upon serum stimulation compared with RIT1 WT ( Fig 1B and S1A Fig ) . This increase was statistically significant for RIT1 p . F82L ( 5 min ) , p . G95A ( 30 min ) and p . M90V ( 5 , 15 and 30 min ) ( Fig 1B and S1A Fig ) . Only RIT1 p . G95A was able to ( statistically significantly ) stimulate ERK1/2 phosphorylation under serum-deprived culture condition suggesting a growth factor-independent function of this mutant ( Fig 1B and S1B Fig ) . Under basal , steady-state condition ( 10% serum ) , RIT1 wildtype expression resulted in a moderate activation of ERK1/2 that was elevated in cells expressing any of the RIT1 mutants ( Fig 1C ) . Post hoc testing revealed statistical significance for the RIT1 mutant p . K23N ( Fig 1C ) . A minor effect of the p . G31R mutant on ERK1/2 activation was confirmed under steady-state condition ( Fig 1C and S1C Fig ) . Together , these data confirm a role of RIT1 in ERK activation and demonstrate that NS-associated RIT1 mutants intensify ERK1/2 phosphorylation . Next , we determined phosphorylation levels of AKT at both serine 473 and threonine 308 in HEK293T cells expressing RIT1 wildtype and three of the mutants . Phosphorylation of serine 473 was similar in all analyzed cell lysates , and even stimulation with serum did not induce AKT phosphorylation at this residue ( S2A Fig ) . Phosphorylation of threonine 308 could be slightly stimulated by EGF , however , there was no difference in AKT phosphorylation between control and RIT1 wildtype expressing cells ( S2B Fig ) . These data indicate that AKT signaling is very robust in HEK293T cells , and responsiveness to serum factors is limited . To identify novel effector molecules of RIT1 and study their biological relevance , we performed binding assays under different culture conditions ( 0 . 1% serum , 10% serum basal condition , and 0 . 1% serum followed by 20 min EGF stimulation ) . We first tested three known RAS effectors and could pull down RIT1 with GST-RALGDS[RA] , GST-PLCE1[RA] and GST-PIK3CA[RBD] ( as positive control ) under all tested cell culture conditions ( Fig 2A ) . As a negative control , we used GST alone ( Fig 2A ) . The amount of HA-RIT1 co-precipitated with GST-RALGDS[RA] or GST-PLCE1[RA] was the same or even less in the presence of serum or upon EGF stimulation compared with serum-starved condition ( 0 . 1% serum ) ( Fig 2A and 2B ) . In contrast , we observed a statistically significant increase of HA-RIT1 in PIK3CA::RBD precipitates from cells cultured in the presence of serum factors compared to serum-starved cells ( Fig 2A and 2B ) . Together , the data suggest that RALGDS and PLCE1 are binding to RIT1 , however , the interaction is not stimulated by growth or serum factors . PIK3CA seems to be a physiological relevant RIT1 binding partner , as has been demonstrated by others [37 , 46] . Rit1 has been found in a protein complex with Rac/Cdc42 and Par6 [44 , 45] . p21-activated kinases ( PAKs ) act downstream of Rho family members and are critical for multiple signaling pathways associated with cell growth , cytoskeletal dynamics , cell polarity , survival , and development [47 , 48] . We tested if RIT1 is part of a PAK-containing protein complex and used the GST-tagged Rac/Cdc42 interactive binding ( CRIB ) domain of PAK1 ( PAK[CRIB] ) in pull down assays . Indeed , HA-RIT1 co-precipitated with PAK[CRIB] under all culture conditions tested ( Fig 2A ) . We detected a significantly increased amount of RIT1 in precipitates from cells cultured in the presence of serum factors ( 10% serum ) compared to serum-starved ( 0 . 1% serum ) cells ( Fig 2B ) . This data suggests physiological relevance of a RIT1- and PAK1-containing protein complex in HEK293T cells . Next , we aimed to investigate if NS-associated RIT1 mutations affect formation of the novel RIT1-PAK1 complex . We pulled down HA-tagged RIT1 wildtype or mutants from HEK293T lysates by using PAK[CRIB] . Compared with wild-type RIT1 , RIT1K23N and RIT1G95A significantly increased the amount of co-precipitated RIT1 ( Fig 3A ) . Enhanced co-precipitation of RIT1G31R , RIT1A57G , RIT1F82L and RIT1M90V with PAK[CRIB] was detected in every single experiment , however , statistical significance was not reached ( Fig 3A ) . We independently confirmed our findings by co-immunoprecipitation ( co-IP ) experiments . Both HA-RIT1 wildtype and HA-RIT1G95A co-IPed with endogenous PAK1 from lysates of serum-deprived cells ( 0 . 1% serum ) and from cells cultured in 10% serum ( Fig 3B ) . In serum-deprived cells , co-precipitation of HA-RIT1F82L , HA-RIT1M90V and RIT1G95A with PAK1 tended to be stronger than of RIT1 wildtype; co-IPed HA-RIT1M90V was significantly increased ( Fig 3C ) . The trend of RIT1 mutations to increase the amount of co-precipitated RIT1 with PAK1 also turned out by using a second anti-PAK1 antibody ( S3A Fig ) . Under basal , steady-state condition ( 10% serum ) , binding of RIT1 wildtype and mutants to endogenous PAK1 was observed , but no difference in the amount of co-precipitated RIT1 wildtype or mutants was detected ( S3B Fig ) . Interaction between endogenous PAK1 and RIT1 wildtype and the p . G95A mutant was also shown in COS7 cells ( S4A–S4C Fig ) . As PAKs fall into two categories , group I and group II , and PAK1 belongs to group I [48] , we tested a member of group II , PAK4 , for RIT1 binding . Neither wild-type HA-RIT1 nor the p . G95A mutant was detected in GFP-PAK4 precipitates ( S4D Fig ) , indicating that preferentially group I PAKs form complexes with RIT1 . By using an in-vitro binding assay , we detected a direct interaction of His-tagged RIT1 with GST-PAK[CRIB] that was slightly increased when RIT1 was in the GTPγS-bound state compared with the GDP-bound state ( Fig 4A ) . RIT1 interacts with PAK[CRIB] in the nanomolar range indicating a medium binding affinity ( Fig 4B ) . The switch I and II regions of RIT1 are involved in nucleotide , effector and regulator binding [31 , 32] . Therefore , we analyzed the two mutants His-RIT1A57G and His-RIT1F82L affecting switch I and II , respectively ( Fig 1A ) , in the in-vitro binding assay . Both showed a direct binding to PAK[CRIB] ( Fig 4C ) . Taken together , our data demonstrate that RIT1 directly interacts with PAK1 , and NS-associated RIT1 mutations enhance the protein-protein interaction and uncouple it from serum factors . The strongest effects were seen for mutations located in the switch II region . As RIT1 and PAK1 are in a protein complex and PAKs are prominent effectors of CDC42 and RAC1 [48] , we tested for an association between RIT1 and CDC42 or RAC1 . HA-RIT1 wildtype co-IPed with endogenous CDC42 from lysates of serum-deprived HEK293T cells ( 0 . 1% serum ) and more efficiently from cells cultured in 10% serum ( Fig 5A ) . The p . G95A mutation uncoupled enhanced complex formation between RIT1 and CDC42 from serum factors ( Fig 5A ) . By performing further CDC42 co-IPs , we found RIT1 protein enriched in the precipitates from serum-deprived cells expressing either of the RIT1 mutants compared to RIT1 wildtype ( Fig 5B ) . Post hoc testing showed statistically significant enrichment for the RIT1 p . G95A mutant compared with wildtype ( Fig 5B ) . Under basal , steady-state condition ( 10% serum ) , the amount of RIT1 wildtype and each of the mutants that co-IPed with CDC42 was not significantly different ( S5A Fig ) . In an analogous set of experiments , we immunoprecipitated endogenous RAC1 and detected RIT1 wildtype in the precipitate; the amount of RIT1 p . G95A was increased in the RAC1 precipitates under both culture conditions ( 0 . 1% and 10% serum; Fig 5C ) . Similarly , each of the six RIT1 mutations enhanced co-immunoprecipitation of RIT1 with RAC1 in serum-deprived cells ( Fig 5D ) . Post hoc testing revealed a significant enhanced amount of co-IPed RIT1 p . G95A ( Fig 5D ) . In cells cultured under basal condition , the amount of co-IPed RIT1 was similar for RIT1 wildtype and mutants ( S5B Fig ) . We confirmed association of RAC1 or CDC42 with RIT1 by co-IPs and myc-trap assays in HEK293T cells . Wild-type RIT1 co-IPed with ectopically expressed RAC1 or CDC42 , and the p . G95A substitution strengthened co-immunoprecipitation efficiency ( S6A and S6B Fig ) . To demonstrate specificity of RAC1 and CDC42 binding to RIT1 , we used RHOA , another member of the RHO family of GTPases , in GFP-trap assays; neither RIT1 wildtype nor the p . G95A mutant was found in the precipitates of ectopically expressed EGFP-tagged RHOA ( S6C Fig ) . By using the Flp-In system that allows integration of the gene of interest at a specific genomic location , we generated isogenic Flp-In 293 cell lines stably and moderately expressing the RIT1 mutant p . G31R or p . A57G . We used these cell lines , which fairly reflect disease relevant conditions , to demonstrate interaction of endogenous CDC42 with RIT1 p . G31R and p . A57G . Both HA-tagged RIT1 p . G31R and p . A57G co-immunoprecipitated with CDC42 ( S6D Fig ) . Finally , by in-vitro binding assays , we tested if interaction between RIT1 and RAC1 or CDC42 is direct . RIT1 and CDC42 as well as RIT1 and RAC1 show a direct binding , and both of which seemed to be independent of the bound guanine nucleotide ( GDP vs . GTPγS ) ( S7A and S7B Fig ) . Taken together , our data demonstrate that RIT1 can directly bind to PAK1 and also to RAC1 and CDC42 , suggesting the existence of a multiprotein signaling module . The major function of RAC1 , CDC42 , and PAK1 is the regulation of the actin cytoskeleton to modulate cell shape , motility and adhesion [48–50] . Active Pak1 promoted the loss of actin stress fibers [51 , 52] . Stress fibers are contractile actin-myosin filaments that are directly linked to focal adhesions which provide the main sites of cell adhesion to extracellular matrix [53] . To investigate a possible role of RIT1 in stress fiber and focal adhesion regulation , we utilized COS7 cells because HEK cells were loosely adherent and exhibited a poorly organized actin cytoskeleton ( S8A Fig ) [54 , 55] . While cells transfected with empty vector possessed well-organized , prominent bundles of actin stress fibers throughout the cell bodies , RIT1-expressing cells showed a drastic loss of stress fibers from the cell interior ( Fig 6A ) . The same effect , i . e . dissolution of internal stress fibers was detected in cells expressing any of the six NS-associated RIT1 mutants ( Fig 6A ) . To exclude that the morphological changes were due to protein overexpression in COS7 cells , we transiently expressed RAP1B , another small GTPase of the RAS family , and observed the same distribution and morphology of stress fibers as in cells transfected with empty vector ( Fig 6A ) . To quantify the data , we determined the number of cells with absent or reduced stress fibers ( S8B Fig ) . In COS7 cells expressing RIT1 wildtype or any of the mutants , 53–64% of cells showed no or a reduced number of stress fibers ( Fig 6B ) . In contrast , only 31% and 16% of cells expressing RAP1B and transfected with empty vector , respectively , showed a reduced number or no stress fibers ( Fig 6B ) . It has been reported that loss of stress fibers promoted by Pak1 was accompanied by disappearance of focal adhesions [51 , 52] , and Pak-induced phosphorylation of the focal adhesion-specific protein paxillin increased adhesion turnover [56 , 57] . Thus , we studied the effect of RIT1 wildtype and mutants on the size and shape of paxillin-containing focal adhesions in COS7 cells . By quantification of both nascent ( dot-like ) and mature ( stripe-like ) focal adhesions , we observed a shift from mature to nascent focal adhesions: in COS7 cells expressing RIT1 wildtype or mutants , 71–74% of focal adhesions were nascent , while 58% and 56% of focal adhesions in RAP1B expressing cells and those transfected with vector , respectively , were nascent ( S9A and S9B Fig ) . The data suggest that expression of RIT1 , either wildtype or disease-associated mutants negatively affects the stability of stress fibers and maturation of focal adhesions . Expression of constitutively active forms of RAC1 and CDC42 ( Q61L ) also was associated with loss of stress fibers , and parallel blocking of PAK1’s activity prevented stress fiber dissolution [51 , 58] . We analyzed the consequences of dominant-negative PAKK299A , RAC1S17N and CDC42S17N mutants on actin stress fiber turnover and found no effect on stress fiber distribution and morphology in COS7 cells ( Fig 7 and S10 Fig ) . Co-expression of each of the S17N mutants with wild-type RIT prevented stress fiber dissolution ( S10 Fig ) , and expression of the kinase-dead PAK1 mutant K299A [59] together with RIT1 wildtype also reverted the phenotype of COS7 cells expressing RIT1 alone ( compare Fig 6A and Fig 7 ) . These data suggest that RIT1 , RAC1/CDC42 , and PAK1 may act in a signaling module regulating stress fiber turnover and cytoskeletal re-organization . PAK1 was found to control protrusive activity and cell migration [60] and increase adhesion turnover [56] . We studied the consequences of RIT1 on invasion and migration of HEK293T by using transwell migration assay . Transfection efficiency was about 99% for each RIT1 construct ( S11 Fig ) . Cells transiently expressing RIT1 wildtype showed increased migration and/or invasive and chemotactic behavior compared to cells transfected with empty vector ( Fig 8A ) ; the same applies to cells expressing any of the RIT1 mutants ( ANOVA P <0 . 001; Fig 8A ) . Taken together , our findings suggest that RIT1 may regulate actin-depending cell motility .
Rit1 shares effector molecules with Ras , such as Raf1 and the p110 catalytic subunit of PI3K [36] . We detected binding of RIT1 to PIK3CA and also to RALGDS and PLCE1 . We show that RIT1 promotes phosphorylation of ERK1/2 in HEK293T cells upon serum factor stimulation , which is further intensified by the six NS-associated RIT1 mutants tested here . Similarly , EGF-induced ERK activation was previously found to be enhanced and/or sustained in T-REx293T cells expressing NS-associated RIT1 mutants [21] , and ectopic expression of NS- and cancer-related RIT1 mutants in PC6 cells induced phosphorylation of both MEK and ERK [25 , 37] . Collectively , the data indicate that RIT1 is a positive modulator of RAF-MEK-ERK signaling , and NS-associated mutants cause sustained MEK-ERK activation upon stimulation rather than a constitutive hyper-activation . RIT1 binding to PIK3CA was enhanced upon serum stimulation of HEK293T cells suggesting that the RIT1-PI3K interaction is physiologically relevant . However , we could not detect any effect of the RIT1 mutants on AKT phosphorylation levels downstream of PI3K that is in line with previous experiments , in which expression of Rit1 in PC6 cells and the human neuroblastoma cell line SH-SY5Y did not induce AKT activation [8 , 62] . In another study , however , oncogenic RIT1 mutants , including p . A77S and p . M90I also found in NS , stimulated phosphorylation of AKT in PC6 cells [37] . RIT1-dependent AKT activation in adult hippocampal neuronal precursor cells suggested a role of this signaling cascade in neurogenesis [63] . Taken together , data on AKT activation by RIT1 are controversial , and functional relevance of RIT1-PIK3-AKT signaling may significantly vary between different cell types . We identified direct binding of RIT1 to PAK1 as well as RAC1 and CDC42 . Formation of these protein complexes is stimulated by serum factors , and all disease-associated RIT1 amino acid substitutions enhance complex formation and uncouple binding of RIT1 to PAK1 and RAC1/CDC42 from extracellular stimuli . Our results strengthen previous data demonstrating Rit1 in a complex with Rac1 or Cdc42 [44 , 45] , both of which are master regulators of the actin cytoskeleton [49 , 50] . PAK1 acts directly downstream of the two GTPases and primarily controls polymerization of actin structures [48] . Expression of activated forms of Pak1 resulted in loss of stress fibers [37 , 51 , 58 , 64] , disassembly of focal adhesions [51 , 52] , and adhesion turnover by paxillin phosphorylation [56] . Here we show that expression of wild-type RIT1 and NS-associated RIT1 mutants leads to dissolution of stress fibers and a shift from mature to nascent paxillin-containing focal adhesions in COS7 cells . Moreover , co-expression of RIT1 with a dominant-negative form of CDC42 , RAC1 or PAK1 blocked the phenotypic effects on stress fibers induced by RIT1 . This data suggests that RIT1 , PAK1 , RAC1/CDC42 act together in a signaling module controlling actin-dependent processes ( Fig 8B ) . Indeed , Rit1 p . Q79L has been previously demonstrated to initiate cytoskeletal changes , such as membrane extensions with ruffle-like structures in NIH 3T3 cells and the formation of neurite-like extensions in PC6 and SH-SY5Y cells [42 , 62] . Reorganization of stress fibers and focal adhesion turnover are fundamental processes for cell adhesion and motility [65] . Both Rac and Cdc42 and their effector Pak play a key role in regulating cell migration [43] . For example , Pak1 operates downstream of Rac to regulate F-actin turnover in the lamellipodium and spatially and temporally organizes the interplay between actin , myosin and focal adhesion dynamics [60 , 66] . Thus , the observed trend toward enhanced migration of COS7 cells expressing either wild-type RIT1 or a NS-associated RIT1 mutant and the finding of RIT1 as positive regulator of neurite outgrowth [6 , 62] suggest an important role of this small GTPase in coordinating the cytoskeletal system during cell migration ( Fig 8B ) . However , the impact of NS-associated RIT1 mutants on stress fiber dissolution , maturation of focal adhesions and cell migration/invasion was similar to wild-type RIT1 suggesting that ( I ) most mutations do not exert a gain-of-function effect in this specific cellular context or ( II ) ectopic overexpression of wild-type RIT1 is sufficient to induce the observed cellular effects . NS belongs to the RASopathies , a group of syndromes caused by germline gain-of-function mutations in genes encoding components or regulators of the RAS-MAPK pathway . Thus , dysregulated RAS-MEK-ERK signaling is the well-established biological mechanism shared among these disorders [33 , 67] . Our data point to a novel aspect in the molecular pathogenesis of RASopathies as we uncovered RIT1 as regulator of cytoskeletal changes through the RHO GTPases RAC1 and CDC42 and their effector PAK1 . This is in line with recent findings reported by Martinelli et al . ( 2018 ) . They demonstrated that specific CDC42 mutations are associated with a RASopathy-like phenotype . Cells expressing the CDC42 mutants p . Cys81Phe , p . Ser83Pro and p . Ala159Val showed enhanced polarized migration and cell growth [68] . Similarly , RAC1 germline missense mutations cause varying developmental disorders depending on whether they act as dominant-negative or gain-of-function alleles . RAC1 mutations had variable effects on fibroblast morphology during spreading; for example , expression of RAC1Tyr64Asp resulted in a greater proportion of cells with lamellipodia and ruffles , similar to constitutive active RAC1 [69] . Other proteins encoded by disease genes for NS [70] have been also linked to the RHO GTPase pathway and F-actin dynamics . For example , RRAS promotes actin polymerization and focal contact formation via the regulation of Rac activity [39–41] . RRAS also associates with Pak1 and the actin binding protein filamin A , thereby mediating cytoskeletal reorganization [71] , cell migration and integrin activation [72] . The two RASopathy-related proteins SHOC2 and PPP1CB regulate cell migration [73 , 74] , and SOS1 , a dual guanine nucleotide exchange factor for Ras and Rac , induces Rac-dependent actin remodeling [75] . By introducing the NS-associated PTPN11 mutation p . N308D in Xenopus , the encoded protein phosphatase SHP-2 was found to regulate actin dynamics in the developing heart in vivo [76] . Similarly , valve cell migration was increased by expression of the SHP-2 mutant Q510E [77] . These and our data indicate that in addition to deregulated RAS-MEK-ERK signaling , dysfunction in actin dynamics and associated processes are pathophysiological mechanisms underlying the group of RASopathies . Thus , altered actin-dependent processes such as cell spreading , adhesion and migration may explain certain clinical features in patients with NS [38 , 68] .
The RAS-binding domain ( RBD ) of PI3K ( PIK3CA ) ( amino acids 127–314 ) , the RAS-association ( RA ) domain of RALGDS ( amino acids 777–872 ) , the RA domain of PLC1 ( PLCE1 ) ( amino acids 2130–2240 ) and the CRIB domain of PAK1 ( amino acids 58–141 ) were used to specifically pull down GTP-bound RIT1 from cell extracts . Preparation of GST-RBD/RA/CRIB beads , cell lysis and precipitation of GTP-bound GTPases have been described previously [78] . Transiently transfected HEK293T cells were cultured as indicated in the respective experiment . Cells were lysed in ice-cold co-immunoprecipitation buffer [150 mM NaCl , 50 mM HEPES pH 7 . 5 , 0 . 2% Nonidet P40 , 1 mM Glycerol; supplemented with complete Mini Protease Inhibitors ( Roche ) ] , and cell lysates were clarified by centrifugation . RAC1 immunoprecipitation shown in S5B Fig was performed with an alternative co-immunoprecipitation buffer [50 mM Tris-HCl pH 8 , 120 mM NaCl , 1 mM EDTA , 0 . 5% Nonidet P40; supplemented with complete Mini Protease Inhibitors ( Roche ) ] . After removing small aliquots ( total cell lysates ) , supernatants were pre-cleared for 45 min with 20 μl Protein A agarose beads ( Roche ) or Protein G sepharose beads ( GE Healthcare ) on a rotator at 4°C followed by centrifugation . 1–2 μg of antigen-specific primary antibody or an IgG isotype control antibody were added to the supernatants . In some experiments , the pre-cleared lysates were split into two aliquots and two different antigen-specific antibodies were used for immunoprecipitation . Solutions were incubated on an end-over-end rotator overnight at 4°C . Subsequently , 20 μl Protein A agarose beads ( Roche ) for antibodies raised in rabbit or 20 μl Protein G sepharose beads ( GE Healthcare ) for mouse IgG antibodies were added and mixtures were incubated for 1 h at 4°C on a rotator . For ectopically expressed proteins , the supernatants were transferred to either 20 μl EZview Red Anti-Myc Affinity Gel , 20 μl EZview Red Anti-FLAG Affinity Gel ( Sigma-Aldrich ) or GFP-Trap A ( Chromotek ) and incubated for 2 h at 4°C on a rotator . Precipitates were collected by repeated centrifugation and washing with co-immunoprecipitation buffer ( 50 mM Tris-HCl , pH 7 . 4; 150 mM NaCl ) , resuspended in sample buffer ( 33% glycerol , 80 mM Tris-HCl , pH 6 . 8 , 0 . 3 M Dithiothreitol , 6 . 7% sodium dodecyl sulphate , 0 . 1% bromophenol blue ) and subjected to SDS-PAGE and immunoblotting . PAK1 and CDC42 co-immunoprecipitations shown in S3B , S5A and S6D Figs were performed with magnetic Dynabeads Protein G ( Thermo Fisher Scientific ) . Therefore , 1–2 μg of anti-PAK1 or anti-CDC42 antibodies were bound to Dynabeads on a rotator for 10 min at room temperature followed by a washing step with PBST . Cells were lysed in 500 μl mRIPA buffer [50 mM HEPES pH 7 . 5 , 150 mM NaCl , 1% Nonidet P40 , 0 . 5% sodium deoxycholate , 0 . 05% sodium dodecyl sulphate , 1 mM EDTA; supplemented with complete Mini Protease Inhibitors and PhosphoStop ( Roche ) ] for 15 min at 4°C , and cell debris was cleared by centrifugation for 20 min . After removing an aliquot ( total cell lysate ) , the remaining supernatant was precleared with Dynabeads Protein G for 1 h at 4°C followed by incubation with the antibody-bound Dynabeads overnight at 4°C on a rotator . Next day , the Dynabeads were pelleted and washed five times with mRIPA buffer . The bound target proteins were eluted by resuspending the beads in 25 μl 1x sample buffer and subjected to SDS-PAGE and immunoblotting . GST-PAK[CRIB] , GST-CDC42 ( amino acids 1–178 ) and GST-RAC1 ( amino acids 1–181 ) fusion proteins were isolated from E . coli strain BL21 transformed with pGEX-2TK-PAK[CRIB] , pGEX-4T3-CDC42 and pGEX-4T3-RAC1 , respectively , as described previously [78] . GST fusion proteins were coupled to glutathione-bound agarose beads ( CAS 64-17-5; Macherey-Nagel ) , washed in wash buffer ( 50 mM Tris-HCl , pH 8 . 0; 50 mM NaCl; 5 mM MgCl2 ) and finally diluted to 50% slurry with wash buffer . His-RIT1WT , His-RIT1A57G and His-RIT1F82L ( amino acids 1–201 each ) were purified from E . coli BL21 cells transformed with pET151/D-TOPO-RIT1WT , pET151/D-TOPO-RIT1A57G and pET151/D-TOPO-RIT1F82L , respectively , by using the Champion pET Directional TOPO Expression Kit ( K151-01 , Life Technologies ) according to manufacturer’s instructions . Depending on the experimental design , recombinant His-RIT1WT/A57G/F82L , GST-CDC42 and GST-RAC1 were coupled to GDP ( G7127 , Sigma-Aldrich ) or non-hydrolyzable GTPγS ( G8634 , Sigma-Aldrich ) for 30 min by incubation with 20 mM Tris-HCl , 100 mM NaCl , 0 . 1% Triton X-100 , 1 mM DTT , 2 . 5 mM EDTA and 1 mM of GDP or GTPγS at room temperature . Reaction was stopped by adding 20 mM MgCl2 . GST-PAK[CRIB] , GDP/GTPγS-loaded GST-CDC42 and GDP/GTPγS-loaded GST-RAC1 fusion proteins coupled to glutathione-bound agarose beads were mixed with different concentrations of GDP/GTPγS-loaded His-RIT1WT/A57G/F82L protein—as indicated in the respective experiment—in binding buffer ( 20 mM Tris-HCl , 100 mM NaCl , 0 . 1% Triton X-100 , 1 mM DTT , 100 mM MgCl2 ) . After incubation for 30 min at room temperature , precipitates were collected by centrifugation and washed with binding buffer ( 4 times ) and proteins were analyzed by western blotting . Purified GST-SAPAP2 ( NM_004745 . 4 ) ( expressed from plasmid pGEX-6P-1 ) was a gift from Stefan Kindler ( Institute of Human Genetics , University Medical Center Hamburg-Eppendorf , Hamburg , Germany ) . Coverslips were coated with 10 μg/ml collagen type I ( #08–115 , EMD Millipore ) in PBS for 1 hour at room temperature . Excess collagen was removed , COS7 cells were seeded on coverslips , transfected with expression constructs and serum-deprived overnight . Subsequently , cells were rinsed with PBS , fixed with 4% paraformaldehyde ( Sigma-Aldrich ) in PBS and washed three times with PBS . After treatment with permeabilization/blocking solution ( 2% BSA , 3% goat serum , 0 . 5% Nonidet P40 in PBS ) , cells were incubated in antibody solution ( 3% goat serum and 0 . 1% Nonidet P40 in PBS ) containing appropriate primary antibodies . Cells were washed with PBS and incubated with Fluorophore-conjugated secondary antibodies or Texas Red-X phalloidin ( Life Technologies ) in antibody solution . After extensive washing with PBS cells were embedded in ProLong Diamond Antifade Mountant with DAPI ( P36962 , Life Technologies ) on microscopic slides . Cells were examined in epifluorescence mode of an Olympus cell tool TIRFM system equipped with a 60x oil immersion objective lens and pictures were taken of representative cells to visualize the observed morphological changes . Phalloidin-stained cells were categorized in two different groups: cells with normal and cells with reduced or absent actin stress fibers ( exemplary photographs are shown in S8B Fig ) . A minimum of 50 cells per dataset were analyzed . The number of paxillin-positive ( dot-like and stripe-like ) structures per cell was determined in a minimum of 30 cells per dataset by using ImageJ software ( NIH; http://rsb . info . nih . gov/ij/index . html ) . Cell invasion was investigated by using Costar Transwell cell culture inserts for 24 well plates with 8 μm pores ( Corning ) , which were coated with 100 μg/cm2 growth factor reduced Matrigel Basement Membrane Matrix ( Corning ) according to manufacturer´s instructions . Briefly , 105 HEK293T cells transiently expressing various RIT1 protein variants were seeded in transwell upper compartment in a volume of 100 μl of serum-free DMEM . For chemo attraction , 600 μl of DMEM supplemented with 10% FBS were added into the lower compartment . 48 h later cells that moved to the lower compartment were carefully detached by trypsinization and transferred to FACS tubes . Cell numbers were determined by flow cytometry using AccuCheck Counting Beads ( Thermo Fisher Scientific ) . Transfection efficiency was determined by staining of cells with anti-HA-fluorescein antibody and quantification of HA-fluorescein positive cells . To determine cell numbers , detached cells were washed twice with PBS . To each FACS tube , 20 ml of AccuCheck Counting Beads ( Thermo Fisher Scientific ) was added . Flow cytometry was carried out using a FACS Calibur ( BD Biosciences ) . In each sample , 3000 beads were acquired and the number of cells , which were collected in the meanwhile , was determined using CellQuestPro Software ( BD Biosciences ) . Signals on autoradiographs were quantified by densitometric analysis using the ImageJ software ( NIH; http://rsb . info . nih . gov/ij/index . html ) . Quantitative data are presented as the mean ± standard deviation ( SD ) performed by GraphPad prism7 software ( Instat , GraphPad Software ) . Statistical significance was assessed by Student's t-test for pairwise comparisons or one-way ANOVA for multiple comparisons . In the latter case , statistically significant differences were identified by post hoc analysis using Student's t-test followed by Bonferroni correction . Data shown in graphs of Fig 1B and S1A Fig were derived from various independent immunoblottings/autoradiographs; thus , ANOVA was not applicable for all RIT1 protein variants without normalization to RIT1 wildtype . Therefore , data were split into data sets consisting of RIT1 wildtype and one RIT mutant in each case ( both of which were derived from the same experiment/autoradiograph ) , and separate graphs for these data sets are presented ( Fig 1B and S1A Fig ) . For the same reason , data shown in Fig 1C were split into two data sets consisting of RIT1 wildtype and three RIT1 mutants in each case ( all were derived from the same immunoblotting/autoradiograph ) . Data shown in Fig 3C and S3A Fig were derived from two independent experiments; therefore , ANOVA was not applicable as it needs at least three data points . Assessments were considered significant at P value <0 . 05 . | Noonan syndrome ( NS ) belongs to the RASopathies , a group of developmental diseases caused by mutations in genes encoding RAS-MAPK pathway components . Germline mutations in RIT1 have been identified in NS . RIT1 belongs to the RAS superfamily , however , the cellular function of RIT1 remains elusive . We show that RIT1 binds p21-activated kinase 1 ( PAK1 ) , an effector of the RHO GTPases RAC1 and CDC42 , which are important regulators of cytoskeletal dynamics . NS-associated RIT1 mutants enhance complex formation between RIT1 , RAC1/CDC42 and PAK1 . Expression of wild-type or mutant forms of RIT1 caused loss of stress fibers and mature focal adhesions and enhanced cell motility . Our data suggest that dysfunction in actin dynamics is a novel aspect in the pathophysiology of RASopathies . | [
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"structures",... | 2018 | RIT1 controls actin dynamics via complex formation with RAC1/CDC42 and PAK1 |
Notwithstanding the well-characterised roles of a number of oncogenes in neoplastic transformation , microRNAs ( miRNAs ) are increasingly implicated in several human cancers . Discovery of miRNAs in several oncogenic herpesviruses such as KSHV has further highlighted the potential of virus-encoded miRNAs to contribute to their oncogenic capabilities . Nevertheless , despite the identification of several possible cancer-related genes as their targets , the direct in vivo role of virus-encoded miRNAs in neoplastic diseases such as those induced by KSHV is difficult to demonstrate in the absence of suitable models . However , excellent natural disease models of rapid-onset Marek's disease ( MD ) lymphomas in chickens allow examination of the oncogenic potential of virus-encoded miRNAs . Using viruses modified by reverse genetics of the infectious BAC clone of the oncogenic RB-1B strain of MDV , we show that the deletion of the six-miRNA cluster 1 from the viral genome abolished the oncogenicity of the virus . This loss of oncogenicity appeared to be primarily due to the single miRNA within the cluster , miR-M4 , the ortholog of cellular miR-155 , since its deletion or a 2-nucleotide mutation within its seed region was sufficient to inhibit the induction of lymphomas . The definitive role of this miR-155 ortholog in oncogenicity was further confirmed by the rescue of oncogenic phenotype by revertant viruses that expressed either the miR-M4 or the cellular homolog gga-miR-155 . This is the first demonstration of the direct in vivo role of a virus-encoded miRNA in inducing tumors in a natural infection model . Furthermore , the use of viruses deleted in miRNAs as effective vaccines against virulent MDV challenge , enables the prospects of generating genetically defined attenuated vaccines .
Oncogenic viruses account for nearly a fifth of all human cancers [1] . In addition to their devastating effects on human health , virus-induced neoplastic diseases are valuable models in understanding the molecular pathways and dynamics of cancer . Although most of the past studies on the molecular mechanisms of cancer induced by oncogenic viruses have primarily centred on classic oncogenes [2] , increasing demonstration of the widespread role of micro ( mi ) RNAs in cancer has added new dimensions to the molecular mechanisms of neoplastic transformation [3] , [4] . Moreover , the identification of miRNAs encoded by human oncogenic viruses such as Kaposi's sarcoma-associated herpesvirus ( KSHV ) and Epstein-Barr virus ( EBV ) suggested that virus-encoded miRNAs may also contribute towards the oncogenicity of the virus . The potential of some of these miRNAs to target a number of host genes associated with the oncogenic pathways has further strengthened the case for their role in inducing malignancies [5] , [6] . However , despite the identification of several potential targets [7] , direct in vivo role of virus-encoded miRNAs in the induction of malignancies is yet to be demonstrated , due at least in part , to the absence of suitable animal disease models . Marek's disease ( MD ) , a naturally occurring neoplastic disease of poultry [8] , is an excellent model for herpesvirus-induced lymphomas [9] , [10] , [11] , [12] . All birds exposed to the causative Marek's disease virus ( MDV ) , unless protected by vaccination or have genetic resistance to the disease , develop rapid-onset T-cell lymphomas usually from 3–4 weeks after infection . Among the virus-encoded genes , Meq protein encoded from the MDV EcoRI Q fragment is considered to be primarily associated with the oncogenicity of the virus , since its deletion [13] or inhibition of its interaction with host proteins such as c-Jun , c-Fos and C-terminal binding protein [11] , [14] , [15] can abolish the oncogenic potential of the virus . Although these studies clearly demonstrate the contribution of Meq in oncogenesis , other viral genes could also be important in the induction of MD lymphomas . We and others have shown that MDV also encodes 14 miRNAs located within three clusters from each of the long ( IRL/TRL ) and short ( IRS/TRS ) repeat regions of the viral genome [16] , [17] . Most of these miRNAs are expressed at high levels in MD lymphomas and transformed T-cell lines [18] suggesting their direct role in transformation . The six miRNAs in cluster 1 , thought to be processed from a single primary transcript upstream of the Meq locus [16] included miR-M4 , the functional ortholog of miR-155 [19] and KSHV-miR-K12-11 [20] , [21] . MDV miR-M4 has recently been shown to inhibit the translation of viral proteins involved in the cleavage and packaging of herpesvirus DNA [22] . However , based on the direct association of miR-155 in several cancers [23] , [24] , including the recent findings on its roles in TGF-β pathway and lymphomagenesis [25] and modulation of mismatch repair and genomic instability [26] , we hypothesised that miR-M4 has a major role in MDV oncogenicity . This was also supported by recent observations of high levels of its expression in tumors correlated with the pathogenicity of viral strains [17] . In the present study , we explored the functional role of the miRNAs encoded within the cluster 1 of the MDV genome , including that of the miR-155 functional ortholog miR-M4 , in inducing T-cell lymphomas using the well-established models of MD in the natural chicken hosts of the virus . Using a series of mutant viruses generated by reverse genetics techniques [27] , [28] on the full-length infectious bacterial artificial chromosome ( BAC ) clone of the highly oncogenic RB-1B virus [29] , [30] , we were able to directly examine the in vivo functions of MDV-encoded miRNAs in the induction of MD lymphomas . Our studies demonstrate the critical role of the cluster 1 miRNAs , especially of the miR-M4 , in the induction of lymphomas , thus providing the direct evidence on the in vivo function of miRNAs in virus-induced cancer .
Members of the Family Herpesviridae account for most of the currently identified virus-encoded miRNAs , and these are thought to play important roles in the distinct biological and pathogenic features of these viruses . MDV encoded-miRNAs are expressed as 3 clusters from the repeat regions , making each miRNA present as two identical copies in the viral genome ( Fig . 1 ) . We have previously demonstrated high levels of expression of these miRNAs both in vitro and in vivo [16] , [31] . However , these studies did not show whether any of these miRNAs are essential for viral replication . In order to address this , we examined the role of cluster 1 miRNAs using a series of mutant viruses generated by reverse genetics techniques on the pRB-1B5 BAC clone [29] . In the mutant virus construct miR-00 , the region encoding the miRNA cluster and the overlapping R-LORF8 in the TRL or IRL regions were deleted by recombination using galK-kan and thyA-spec antibiotic selection cassettes . While galK-kan cassette replaced the entire region encoding all the six miRNAs from one of the repeat regions , thyA-spec cassette that targeted a shorter region removed all the miRNAs except miR-M9 and miR-M4 from the other repeat region . For generation of viruses with mutations in all of the miRNAs within the cluster , we synthesised a gene designed to introduce mutations into the stem loop structures of each of the six miRNAs in the cluster 1 without affecting the R-LORF8 frame ( Fig . 2 ) . In the miR-S0 and the miR-SS constructs respectively , one or both copies of the miRNA locus were replaced with this synthetic gene sequence , preventing them expressing any of cluster 1 miRNAs . The revertant miR-W0 and miR** viruses both expressed all the miRNAs , but in the latter , the introduction of a stop codon is expected to abolish the translation of R-LORF8 . These two constructs were included to delineate between the functions of miRNAs and the overlapping R-LORF8 gene . The details of mutations in each of the individual recombinant viruses are summarised in Table 1 . Reconstitution of the viruses in primary chicken embryo fibroblast ( CEF ) transfected with the BAC DNA demonstrated the infectivity of each of the above constructs . Demonstration of viral proteins Meq , pp38 and pp14 in CEF infected with the parent pRB-1B5 and different mutant viruses by western blotting showed that the mutations did not affect the expression of these proteins ( Fig . 3A ) . This was further confirmed by specific staining of Meq and pp38 in the nucleus and cytoplasm respectively of CEF infected with a selection of the mutant viruses ( Fig . 3B ) . Moreover , analysis of the in vitro growth kinetics showed very similar replication kinetics for both the parent and mutant viruses ( Fig . 4A ) indicating that these miRNAs are not essential for replication of MDV in vitro . For further analysis of the effect of mutations in the miRNA cluster on MDV gene expression , we compared the relative levels of ICP4 and Meq transcripts by quantitative RT-PCR in CEF infected with the wild type RB-1B , parent pRB-1B5 and the mutant viruses . MDV-transformed lymphoblastoid cell line 265L and CEF infected with the Meq-deletion mutant ( RB-1BΔMeq ) virus were used as controls . These studies showed that the transcription of ICP4 and Meq were not affected by mutations in the miRNA locus ( Fig . 5A–B ) , although the levels of ICP4 transcripts were low with variations among different viruses in comparison to the Meq transcripts . In order to examine whether these miRNAs are essential for MDV replication in vivo , we asked whether viruses with mutations in the miRNA locus could be re-isolated from birds in later stages of infection . Successful isolation of MDV by co-culturing of the peripheral blood leukocytes ( PBL ) of birds infected with different viruses 7–8 weeks after infection confirmed that the mutations in the miRNA locus did not affect the viral replication and their ability to establish latent infection in vivo . Moreover , quantitative RT-PCR data of Meq , LAT and ICP4 transcripts in CEF co-cultured with the PBL from infected birds ( Fig . 5C ) indicated that the modifications in the miRNA locus did not affect the expression of these transcripts in viruses isolated from these birds . All these results confirmed that the miRNAs encoded from this locus did not affect MDV replication either in vitro or in vivo . Having shown that the mutations in the miRNA locus did not affect the replication and viral gene expression in vitro or the establishment of latency in vivo , we investigated whether the miRNAs encoded within this cluster are associated with the oncogenicity of the virus using the established infection models in the natural chicken hosts [29] . Compared to the 100 percent incidence of MD in birds infected with the parent pRB-1B5 virus ( Fig . 6A ) , none of the birds infected with the miR-00 , miR-S0 and miR-SS viruses developed any gross or microscopic lesions of MD , showing that the loss of expression of the cluster 1 miRNAs directly affected the oncogenic potential of MDV . Rescue of the oncogenic phenotype by the revertant miR-W0 virus to the levels close to that of the parent virus , even by restoring only one copy of the miRNA cluster , provided the strong evidence on the distinct role of these miRNAs in the induction of lymphomas . Moreover , the induction of MD in 90% of the birds by the miR** virus ( Fig . 6A ) demonstrated that miRNAs , but not R-LORF8 , are involved in the induction of lymphomas . Having demonstrated that the miRNAs encoded in the cluster 1 are essential for the oncogenic potential of the virus , we wanted to refine our investigation to identify whether any single miRNA within this cluster plays a more important role in the oncogenic potential of the virus . Since MDV miR-M4 in this cluster is a functional ortholog of the oncogenic miR-155 [19] implicated in a number of neoplastic disorders [23] , we decided to focus on this particular miRNA . For this , we used the modified pRB1B5 clone [30] to generate two additional mutant viruses where only the miR-M4 was made non-functional . These included the constructs miR-4-00 , where both copies of the pre-miR-M4 sequences were deleted , and the miR-4-0-mu2 , where one of the deleted copies was replaced with a mutant miR-M4 carrying a 2-nucleotide mutation in the seed sequence ( Fig . 7 ) . We have previously shown that this mutation in the seed region abolished miR-M4 function [19] . We also determined the complete sequence of the ∼178-kb genome of the miR-4-0-mu2 construct by deep sequencing and ruled out unintended mutations or rearrangements generated during the BAC manipulation . Revertant construct miR-4-0-R was also generated by restoring one copy of the wild type miR-M4 sequence . A summary of the miRNA expression profiles of these constructs are in Table 1 . The mutant viruses showed similar growth kinetics in CEF further demonstrating that miR-M4 deletion did not affect replication in vitro ( Fig . 4B ) . We also examined the expression levels of MDV-encoded miRNAs miR-M4 , miR-M5 and miR-M6 by qRT-PCR on RNA extracted from CEF infected with wild type RB-1B , the parent pRB-1B5 and the different mutant viruses ( Fig . 8A–C ) . MDV-transformed cell line 265L and RB-1BΔMeq virus were used as controls . The absence of miR-M4 and miR-M5 transcripts in the cells infected by miR-S0 and miR-SS viruses ( the low level of miR-M4 but not miR-M5 in miR-00 virus is due to the presence of one copy of miR-M4 ) , confirmed the deletion of these miRNAs in these constructs ( Fig . 8A , B ) . As expected , the expression of miR-M6 located within the LAT region was not affected by the mutations within the cluster 1 miRNA locus ( Fig . 8C ) . When the oncogenicity of these viruses was evaluated in MD lymphoma model in the natural chicken hosts , the parent pRB-1B5 virus induced 100 percent disease ( Fig . 6B ) . Compared to this , none of the birds infected with miR-4-0mu2 virus showed any clinical evidence , gross tumors or microscopic lesions in any of the organs . There was 90% reduction of MD incidence in birds infected with miR-4-00 virus ( Fig . 6B ) , clearly demonstrating the important role of miR-M4 in inducing lymphomas . The rescue of the oncogenic phenotype in 70% of birds by the revertant miR-4-0R virus even by restoring one copy of the wild type of miR-M4 , further confirmed the significant role of miR-M4 in the induction of lymphomas . Since miR-M4 is a functional ortholog of the host-encoded gga-miR-155 [19] , we wanted to examine whether gga-miR-155 can replace the oncogenic function of miR-M4 in MDV . For this , we generated an additional MDV construct miR-4-0-155 ( Table 1 ) , in which gga-miR-155 pre-miRNA including the loop sequence of the chicken BIC transcript [32] was introduced in the position of the viral miR-M4 pre-miRNA . After demonstrating that the in vitro growth kinetics on CEF was similar ( Fig . 4B ) , we examined the oncogenic potential of miR-4-0-155 virus in the MD lymphoma models in chickens . Demonstration of the incidence of MD at similar levels ( 70% ) in birds infected with miR-4-0-155 virus and the miR-4-0R revertant virus ( Fig . 6B ) indicated that gga-miR-155 can function as an oncogenic miRNA in the context of the MDV cluster 1 miRNAs . Although the levels of MD incidence were similar , the onset of tumors induced by miR-4-0-155 virus was slow , with most of the tumors detected much later than those induced by the parent pRB-1B5 or the miR-4-0R revertant viruses ( Fig . 6B ) . Tumours induced by the mutant viruses , including the chimeric miR-4-0-155 virus , were typical MD lymphomas with neoplastic lymphoid lesions in multiple organs ( Fig . 9A ) . In order to demonstrate the expression of gga-miR-155 or miR-M4 in the tumors induced by miR-4-0-155 and miR-4-0-R viruses respectively , we carried out quantitative RT-PCR on RNA samples extracted from the lymphomas collected from infected birds at post mortem . Demonstration of expression of miR-M4 , but not gga-miR-155 , in lymphoid tumors harvested from birds infected with miR-4-0-R virus ( bird numbers #2166S , 2171S , 2172S and 2178K ) indicated the direct correlation between the expression of miR-M4 and the induction of lymphomas ( Fig . 9B–C ) . Despite the low levels of expression , detection of gga-miR-155 but not miR-M4 in tumors of birds infected with miR-4-0-155 virus ( bird numbers #2153L , 2153S , 2163L and 2183S ) by quantitative RT-PCR and Northern blotting ( Fig . 9D ) suggested a direct role for gga-miR-155 in induction of these tumors . Demonstration of the total loss of oncogenicity persuaded us to investigate whether MDV made non-oncogenic by inactivation of miRNAs can function as effective vaccines against virulent MDV infection . For this , groups of birds were vaccinated with a recombinant ( pCVI988-10 ) clone [33] of the widely used CVI988 vaccine strain or miR-SS virus that has mutations in all the miRNAs encoded from both copies of the cluster 1 . Birds from all the groups were challenged with the very virulent plus MDV ( vv+MDV ) strain 675A along with unvaccinated controls . Comparison of the incidence of MD in the 3 groups during the 68-day experimental period showed that miR-SS virus provided the same level of protection as the widely used Rispens vaccine against infection by the vv+MDV strain 675A that induced 100 per cent disease in unvaccinated chickens ( Fig . 10 ) .
Despite the increasing evidence of the potential role of herpesvirus-encoded miRNAs functioning as determinants of oncogenicity [34] , direct role of any of these miRNAs in the induction of tumors in vivo has not yet been demonstrated . Using lymphoma models in the natural chicken hosts infected with modified recombinant viruses generated from infectious BAC clones of the oncogenic RB-1B strain of MDV , our study provides the first direct evidence of the role of miRNAs in the induction of tumors . The total abolition of tumors in birds infected by viruses that do not express miRNAs in cluster 1 , and subsequent rescue of the oncogenic phenotype by the revertant miR-W0 virus demonstrated the direct role of these miRNAs in the induction of MD lymphomas . Moreover , the inability of miR-S0 and miR-SS viruses to induce lymphomas and restoration of the oncogenic phenotype by miR** virus revealed that the miRNAs , and not the overlapping R-LORF8 gene , are the major determinants of oncogenicity . Important role of these miRNAs in oncogenesis is supported by the high levels of their expression in MD tumors and transformed cell lines [16] , [18] . Although other miRNAs encoded within this cluster might be contributing to the induction of MD lymphomas [35] , we focussed mainly on examining the role of miR-M4 for various reasons . Firstly , MDV miR-M4 is expressed at very high levels in lymphomas , in some cases accounting for even up to 72% of all MDV-encoded miRNAs [16] , [17] , [18] , supporting its potential as a major determinant of MDV oncogenicity . Secondly , based on the seed sequence homology and the potential for regulating common targets such as Pu . 1 , BACH1 , CEBPβ , HIVEP2 , BCL2L13 and PDCD6 , we have previously shown that miR-M4 is a functional ortholog of gga-miR-155 [19] , a miRNA known to be directly associated with several cancers [23] , [36] and molecular mechanisms of cancer pathogenesis [25] , [26] . Finally , overexpression of miR-155 has been shown to be associated with lymphocyte transformation by viruses such as EBV [37] and reticuloendotheliosis virus strain T [38] . Significant reduction in the incidence of MD in birds infected with miR-4-00 virus , and the total loss of oncogenicity of miR-4-0-mu2 virus clearly indicated that miR-M4 plays a critical role in the induction of MD lymphomas . Although we have not carried out similar investigations on other miRNAs in this cluster , the dramatic suppression of oncogenicity subsequent to the loss of miR-M4 function to the levels similar to those shown by viruses with mutations in the whole cluster 1 miRNAs , suggested that miR-M4 is very important for the oncogenicity of MDV . However the expression of miR-M4 alone appeared to be not sufficient since miR-00 virus expressing low levels of miR-M4 ( Fig . 8A ) remained non-oncogenic , suggesting the contribution of other miRNAs in the cluster to the oncogenicity . This is also supported by our recent observation that viruses deleted in miR-M5 , another miRNA within this cluster , retained oncogenicity although not to the same extent as the wild type viruses ( unpublished data ) . The exact mechanisms of the loss of oncogenic phenotype by the miR-M4-deleted/mutated viruses remain to be investigated . However , it is not due to the lack of expression of viral genes such as Meq , ICP4 and LAT as we were able to demonstrate the expression of the proteins or transcripts in infected CEF or PBL ( Fig . 3 and 5 ) . It is also not due to the inability to express miRNAs such as miR-6 outside the cluster 1 ( Fig . 8C ) . MDV miR-M4 is known to influence directly the expression of a number of viral and cellular targets [19] , [22] . The regulation of gene expression by these miRNAs can also be due to indirect effects , and may include genes such as Meq . Although the expression levels of Meq in CEF infected with miR-M4-deleted/mutated viruses were not affected ( Fig . 5B ) , the relative levels of Meq in the PBL of infected birds were lower than those from birds infected with viruses expressing the miRNAs ( Fig . 5C ) . Meq is well-known for its key role in MDV oncogenesis [11]–[15] . Although Meq by itself is weak in its oncogenic potential , modulation of the levels of Meq expression by these miRNAs could be important in MDV oncogenicity . Our recent studies have shown that Meq may have a regulatory role in the expression of cluster 1 miRNAs by binding to its promoter region ( unpublished ) suggesting that Meq-miRNA regulatory loop could be important in the induction of tumors . Thus viral oncogenesis should be seen as a very complex process involving the interaction of multiple factors and regulatory processes including those by the virus-encoded miRNAs . Disruption of any one of these key elements could have a significant effect on the oncogenic pathways . Although the precise understanding of all the molecular processes would need further studies , the present findings demonstrate that miR-M4 , most likely acting through the miR-155 pathway , functions as a key factor contributing to MD oncogenesis . It is remarkable to show that a two-nucleotide mutation in the miR-M4 seed sequence in the context of the whole viral genome was sufficient for the total inhibition of oncogenicity of the miR-4-0-mu2 virus ( Fig . 6B ) . One of the birds ( #2168L ) infected with miR-4-00 virus did develop MD lymphoma even in the absence of miR-M4 ( Fig . 9B , C ) . This is unlikely to be due to any spurious mutations or recombination events in the virus , as we were able to confirm the absence of miR-M4 or miR-155 expression in the tumor tissues of this bird ( Fig . 9D ) . We are yet to identify the exact mechanisms underlying the development of lymphomas in this bird in the absence of miR-M4 . However , occurrence of such tumors underlines the complex and multifactorial nature of oncogenic processes; moreover the individual differences between birds in immunocompetence and genetic susceptibility could also contribute to the onset of such tumors . Nevertheless , the near total elimination of oncogenicity in the absence of miR-M4 , and the rescue of oncogenicity in up to 70 per cent of the birds when the miR-M4 expression was restored in the revertant miR-4-0-R virus , provided the first clear evidence on the in vivo role of a virus-encoded miRNA in the induction of tumors . The conservation of the seed sequence and the ability to regulate common sets of targets by miR-M4 and gga-miR-155 [19] prompted us to examine whether the miR-M4 functions of MDV can be replaced by gga-miR-155 . The demonstration of the ability of the chimeric miR-4-0-155 virus to restore the oncogenic potential to the same levels as the miR-4-0-R ( Fig . 6B ) , further demonstrated the significance of the miR-M4/miR-155 pathway in MD lymphomagenesis . Although the types of tumors induced by recombinant viruses expressing miR-M4 or gga-miR-155 were not distinguishable ( Fig . 9A ) , the onset of tumors in birds infected miR-4-0-155 virus was slow ( Fig . 6B ) , possibly due to the differences in the functional context of the two miRNAs or in their processing and expression . This was also evident from the expression levels of miR-M4 and miR-155 in the primary tumor samples collected from birds infected with the two viruses . Compared to the high levels of miR-M4 in tumor samples induced by miR-4-0-R virus ( Fig . 9B ) , the levels of miR-155 in the tumors induced by miR-4-0-155 virus were much lower ( Fig . 9B ) . The lack of expression of miR-M4 and weak expression of miR-155 in the tumors induced by miR-4-0-155 virus was also confirmed by Northern blot ( Fig . 9D ) . Interestingly , there is consistent downregulation of gga-miR-155 in MD tumors and MDV-transformed cell lines [17] , [18] . The downregulation of miR-155 in MDV-transformed tumor cell lines is not a permanent defect , as these cells can be induced to express miR-155 by co-infection with retroviruses-expressing v-Rel ( unpublished data ) . Moreover , recent studies have demonstrated differences between miR-M4 and gga-miR-155 in targeting genes such as UL28 despite having identical seed sequences highlighting the significance of the non-seed regions of these miRNAs [22] . Tumors induced by the chimeric miR-4-0-155 virus are the first examples demonstrating the expression of gga-miR-155 in MD tumors . Although our study does not identify the various genes linked to the miR-M4-mediated oncogenesis , demonstration of the significance of miR-M4 in the induction of lymphomas is a major step in understanding the molecular oncogenic mechanisms in MD . From the demonstration of the conserved functions of miR-M4 and miR-155 in the induction of tumors , it is clear that MDV is able to restore the functions of miR-155 through the expression of high levels of the functional homolog miR-M4 . Although it is unclear how MDV is able to downregulate miR-155 , some feedback regulatory mechanisms are the most likely mechanisms . Interestingly in KSHV-induced tumors also , there is upregulation of the miR-155 functional homolog miR-K12-11 , at the expense of significant downregulation of miR-155 [39] . On the other hand , EBV does not encode any functional ortholog but induces miR-155 in transformed B-cells [37] , and a recent study has demonstrated its role in the induction of B-cell transformation by EBV in vitro [40] . It is unclear what advantages MDV and KSHV do have in choosing this rather complicated pathway of encoding and expressing high levels of the functional orthologs of a host miRNA , the expression of which is repressed in the transformed cells . One possible advantage of encoding a viral ortholog is the potential to achieve high levels of expression as seen in MDV- and KSHV-induced tumors , overriding the tighter cellular regulatory controls associated with the c-bic/miR-155 expression . Moreover , although miR-155 and the two viral orthologs may regulate common set of target proteins through the conserved seed sequences , other potential differences in their functions , especially due to the differences in the non-seed sequences , may also exist . It is not known whether the changes in the non-seed sequences do affect the functions of these miRNAs . Nevertheless , the differences in the speed in the onset of tumors between MDV expressing miR-M4 and gga-miR-155 would suggest that such differences could be important . The role of miR-M4 and the rescue of the oncogenic phenotype by miR-155 provide further evidence of the conserved biological functions of miRNA orthologs , a finding of major importance in elucidating the functions of other viral orthologs such as KSHV miR-K12-11 . The study also highlights the use of tumor virus disease models as powerful tools to reveal fundamental molecular determinants that trigger the development of cancers . Finally , the demonstration of protection induced by the miR-SS virus against infection by the vv+MDV strain 675A to the same levels as the widely used CVI988 vaccine strain ( Fig . 10 ) demonstrated the prospects of generating molecularly-defined attenuated vaccines by specific deletion of oncogenic sequences such as the miRNAs .
All animal experiments were performed in accordance with the United Kingdom Home Office guidelines under the provisions of the Project License approved by the Institute for Animal Health Ethical Committee . Primary chicken embryo fibroblast cultures ( CEF ) were prepared from 10-day old chicken embryos from SPF eggs as previously described [29] . Reconstitution of recombinant viruses was achieved by transfection of 1–2µg BAC DNA into the CEF using Lipofectamine ( Invitrogen ) . Infectious BAC clone pRB-1B5 [29] , [30] was used for the generation of the mutant constructs [41] . List of primers used for the construction are shown separately ( Table S1 ) . Selection markers galK-kanamycin ( galK-Kn ) [42] and thymidylate synthase-spectinomycin ( ThyA-spec ) [43] cassettes were used in consecutive steps for the deletion of the two copies of the miRNA cluster . For this , the fragment containing the miRNA cluster ( GenBank EF523390 - Nucleotides 134362 to 136848 ) was amplified with 5′ GCCAACTGTACACGCAGGGACGT 3′ and 5′ GTGCAGTGCCTTTGATGTCTG 3′ primers and cloned into pCR8-TOPO vector ( Invitrogen ) . From this vector , the 1665-bp PshAI-StuI fragment ( 134527–136183 ) encompassing all the six miRNAs in the cluster 1 from miR-M9 to miR-M4 ( Fig . 1 ) was replaced with galK-Kn cassette to generate the -recombination –construct for replacing the first copy of the microRNA cluster 1 . Similarly , an XhoI fragment ( 135221–135959 ) from this vector was replaced with a ThyA-spec cassette to make another construct for the specific replacement of the second copy of the miRNA cluster . This shorter ( 783-bp ) deletion removed all the miRNAs except miR-M9 and miR-M4 . The two copies of the miRNA cluster were sequentially deleted using recombineering techniques [41] , [44] to generate the miR-00 construct . For generation of viruses with mutations in all of the miRNAs within the cluster , a 1 . 45 kb NgoMIV-EcoRV fragment corresponding to the positions 134785–136216 of the pRB-1B5 sequence was synthesised . The synthetic gene was designed through alternative codon usage so as to destroy all the pre-miRNA's hairpin structures , but retaining the R-LORF8 open reading frame ( Fig . 2 ) . The first copy of the miRNA cluster 1 was replaced using a RecA-based strategy [28] . Recombinant clones detected by PCR ( primers 5′ GTAGTGTATCGGTCTTCGTG 3′ and 5′ CCCGAATACAAGGAATCCTG 3′ ) were digested with BglII to identify the clones with the replaced synthetic sequence that contained a unique BglII site . The virus reconstituted from this construct with one copy of the miRNA cluster replaced with the synthetic sequence and the other copy deleted by the insertion of the galK-kn was designated miR-S0 . The galK-kn selection marker in the miR-S0 construct was then replaced with the pKOV-miR-syn construct to generate the construct with both copies of the miRNA cluster 1 replaced by synthetic sequences , and named miR-SS . We also generated a revertant construct miR-W0 , in which we replaced one copy of wild type miRNA cluster sequence from pRB-1B5 , while the other copy remains deleted . We mutated one copy of R-LORF8 start codon by inserting FRT-Kn cassette amplified by PCR using R-LORF8-Kn-For and R-LORF8-Kn-Rev primers ( Table S1 ) . The FRT-Kn cassette was ‘Flipped-off’ in E coli strain SW105 , leaving only a ‘scar’ sequence . In order to prevent the translation of R-LORF8 , we introduced stop codon replacing the ATG initiation codon in the construct miR** . For this , we first destroyed the gene by introducing the galK-kn cassette into the locus . This was then replaced with a modified R-LORF8 sequence generated by PCR using R-LORF8-stop-For and R-LORF8-stop-Rev primers ( Table S1 ) . Detailed protocols for the manipulation of the BAC constructs are provided separately [41] . The accuracy of the mutations in different constructs was checked by sequencing . In order to examine the role of miR-M4 in MDV oncogenicity , we also constructed a series of viruses with mutations only in the miR-M4 , by standard mutagenesis techniques [45] on the pRB-1B5 clone [29] . First we deleted one copy of the pre-miR-M4 with FRT-Kn cassette using PCR product derived with miR-M4-kn-For and miR-M4-kn-Rev primers . After excision of the Kn cassette , the second copy was deleted with galK cassette using PCR products derived with miR-M4-galK-For and miR-M4-galK-Rev primers . This construct with deletion of both copies of the miR-M4 was designated miR-M4-00 and the galK locus in this construct was used for generating additional constructs . For the construction of a seed mutant of miR-M4 , we generated a PCR product with miR-M4-mu2-Top and miR-M4-mu2-Bottom primers , designed to introduce two-nucleotide mutations in the miR-M4 seed region that has been previously shown to be sufficient to abolish the function of miR-M4 . The galK selection marker in the miR-M4-00 construct was replaced with the mutated miR-M4 to generate the miR-4-0-mu2 construct . In order to generate another construct in which the miR-M4 was replaced with the miR-155 sequence , we generated the gga-miR-155 pre-miRNA along with the loop sequence using miR-M4-155-Top and miR-M4-155-Bottom primers . The galK selection marker in the miR-4-00 construct was replaced with the annealed above oligonucleotides to generate the miR-4-0-155 . Finally , the revertant construct miR-4-0-R was generated in which the wild type miR-M4 sequence was restored . The accuracy of all the modifications was also confirmed by sequencing the modified regions of the constructs . The whole genome of the miR-4-0-mu2 was determined by deep sequencing to confirm the mutations and to rule out any unexpected mutations or recombinations . All animal experiments were carried out under licence from the UK Home Office in dedicated negative pressure rooms . Groups ( n = 10–12 ) of one day-old line P ( MHC type B19/B19 ) chicks were infected with 1 , 000 plaque forming units ( pfu ) of miR-00 , miR-S0 , miR-SS , miR-W0 , miR** , miR-4-00 , miR-4-0-mu2 , miR-4-0-155 and miR-4-0-R and pRB1B5 viruses as described . Blood samples collected at regular intervals or post-infection ( d . p . i . ) were used for quantitation of virus load or for the extraction of RNA from the PBL . Methods for quantitative RT-PCR to measure miRNA/transcript levels have been described [31] . Virus isolations were also performed by inoculating 1×106 PBL to a well of a 6-well plate of CEF and incubated at 38 . 5 C in 5% CO2 until the appearance of virus plaques . The birds were inspected regularly and were sacrificed at clinical end-points and samples taken post-mortem for histology . Birds showing gross or histological lesions of lymphomas in any of the tissues collected at post-mortem were diagnosed as MD-positive , and the data from the incidence of MD from each of the groups were used to calculate the cumulative survival rates . Groups ( n = 10 ) of one-day old birds were vaccinated via the intra-muscular route with either sham vaccine , or 925 pfu of miR-SS or pCVI988 viruses . One week after infection , all the birds were infected with 1450 pfu of the vv+MDV strain 675A via the intra-peritoneal route . Birds were observed for the onset of clinical disease and the incidence of MD was recorded up to 68 days post infection . All the birds were necropsied at the end of the experiment and the incidence of MD from the gross and microscopic lesions was used to calculate the survival rates from virus infection . Western blotting to detect the viral proteins Meq , pp38 and pp14 , and the chicken CtBP1 was carried out using methods previously described [11] . Immunofluorescence staining and confocal microscopy were carried out on CEF cultures on 13 mm glass coverslips infected with the mutant viruses using methods described [12] . The infected cell were fixed in 4% paraformaldehyde , permeabilised with 0 . 1% Triton x-100 and stained with anti-Meq antibody ( FD7 ) or anti-pp38 ( BD1 ) antibodies and detected with Alexa Fluor 488/568-conjugated goat anti-mouse reagents ( Invitrogen ) . Images were taken using Leica TCS SP5 confocal laser scanning microscope . | MicroRNAs ( miRNAs ) , encoded in the genomes of a number of organisms including several viruses , belong to a class of small RNA molecules that can function as key regulators of gene expression influencing various biological processes and diseases including cancer . Among all the miRNAs , miR-155 has been well documented for its direct role of oncogenesis in a number of species including chickens . Remarkably , miR-K12-11 and miR-M4 , the miRNAs encoded by the oncogenic Kaposi's sarcoma-associated herpesvirus ( KSHV ) and Marek's disease virus ( MDV ) respectively , have been shown to be functional orthologs of miR-155 . There are no animal models of KSHV-induced tumors to examine the oncogenic potential of miR-K12-11 . However , using recombinant mutant viruses in excellent models of MDV-induced lymphomas in their natural chicken hosts , we demonstrate that miR-M4 is critical for the induction of tumors . This is the first study that clearly demonstrates a direct role for a single miRNA in inducing cancer in an in vivo animal model . The ability of gga-miR-155 to rescue the oncogenic potential of miR-M4-deleted virus demonstrated the conservation of oncogenic functions the two miRNAs . Moreover , we show that virus attenuated by deleting the miRNAs can function as vaccines against virulent virus infection , enabling the prospects of generating novel molecularly-defined vaccines . | [
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"oncology/hemat... | 2011 | Critical Role of the Virus-Encoded MicroRNA-155 Ortholog in the Induction of Marek's Disease Lymphomas |
During transcription , the nascent RNA can invade the DNA template , forming extended RNA-DNA duplexes ( R-loops ) . Here we employ ChIP-seq in strains expressing or lacking RNase H to map targets of RNase H activity throughout the budding yeast genome . In wild-type strains , R-loops were readily detected over the 35S rDNA region , transcribed by Pol I , and over the 5S rDNA , transcribed by Pol III . In strains lacking RNase H activity , R-loops were elevated over other Pol III genes , notably tRNAs , SCR1 and U6 snRNA , and were also associated with the cDNAs of endogenous TY1 retrotransposons , which showed increased rates of mobility to the 5′-flanking regions of tRNA genes . Unexpectedly , R-loops were also associated with mitochondrial genes in the absence of RNase H1 , but not of RNase H2 . Finally , R-loops were detected on actively transcribed protein-coding genes in the wild-type , particularly over the second exon of spliced ribosomal protein genes .
During transcription , the RNA polymerase opens the DNA duplex , and in the process rotates the DNA double helix by approximately one turn per 10 bp . This generates positive torsional stress ahead , and negative torsional stress in the wake , of the transcribing polymerase [1] . Positive stress impedes further unwinding of the DNA duplex , potentially stalling the polymerase . In contrast , negative torsion can lead to DNA strand separation and opening of the duplex . The resulting template single-stranded DNA region can base-pair with the nascent RNA transcript , generating an RNA-DNA duplex and an unpaired non-template DNA strand , giving rise to the term “R-loop” for such structures ( for reviews see [2] , [3] , [4] , [5] , [6] , [7] , [8] ) . Other features besides negative topological stress strongly influence R-loop formation [3] , e . g . the G . C content of the inherent sequence . In particular , R-loop formation can be favoured by a high guanine ( G ) density in the non-template DNA strand ( property known as positive GC skew , see [9] , [10] ) , and this is specifically due to the higher thermodynamic stability of RNA-DNA hybrid sequences endowed with “G-rich purine RNA”/“C-rich pyrimidine DNA” duplexes [9] , [10] , [11] , [12] , [13] , [14] . Importantly , R-loops rich in G-clusters have been linked to immunoglobulin class switch recombination and CpG methylation in mammals [9] , [10] , [14] , [15] . R-loops are generally regarded as highly deleterious , since the single stranded DNA is susceptible to damage . Moreover , it is believed that the structure can block both transcription and DNA replication , creating replicative stress and potentially causing further DNA damage ( for reviews see [2] , [3] , [5] , [6] , [7] . Highly transcribed genes in yeast exhibit greater mutation and recombination rates than genes transcribed at lower rates ( reviewed in [7] ) , which might be related to R-loop formation . R-loops can be resolved by RNase H1 and/or RNase H2 ( Rnh201 is the catalytic subunit of a three subunit enzyme ) , either of which can cleave the RNA component in the RNA-DNA hybrid , albeit with different efficiencies ( reviewed in [16] ) . However , loss of both RNase H1 and H2 activity is not lethal in yeast [17] , strongly indicating that other cellular activities can resolve R-loops , such as the helicase Sen1/Senataxin , THO/TREX RNA packaging complexes and the RNA exosome [2] , [5] , [8] . Moreover , RNase H2 plays dual roles in preserving genome integrity , processing both R-loops and ribonucleotides mis-incorporated in to DNA during replication , whereas RNase H1 is reported to resolve only R-loops ( reviewed in [16] , [18] ) . In mammals both RNase H1 and H2 are required for cell viability and for embryonic development , and mutations in any of the three subunits of RNase H2 have been reported to cause the neuro-inflammatory disease Aicardi-Goutières syndrome ( AGS ) [19] , [20] , [21] , [22] . In previous analyses of transcription by RNA polymerase I ( Pol I ) on the yeast ribosomal DNA ( rDNA ) , we observed that R-loops are common at specific sites , in particular within the 5′-region of the 18S rDNA [23] . These were readily detected in wild-type strains , although their abundance was increased in strains lacking the activity of DNA Topoisomerase I ( Top1 ) , which can resolve negative torsion behind the RNA polymerase ( for a review on DNA topoisomerases see [24] ) , and further increased in the absence of RNase H activity . R-loop formation by Pol I on the highly transcribed rDNA array is favored by negative torsional stress [23] , [25] , suggesting the possibility that R-loop formation in wild-type cells might also be associated with other RNA polymerases , particularly on actively transcribed genes . Here we determined the genome-wide distribution of RNA-DNA hybrids in budding yeast using chromatin immunoprecipitation ( ChIP ) with antibody S9 . 6 [26] , [27] , followed by deep sequencing of immunopurified DNA fragments ( ChIP-seq ) . The conclusions are related to , but not identical with , the results of recent microarray analyses [11] . R-loops were strongly associated with actively transcribed loci by all RNA polymerases including the mitochondrial RNA polymerase ( mtRNAP ) . Notably , R-loops accumulated unevenly across intron-containing genes with the highest peak over exon 2 . We show that R-loop accumulation at tRNA genes leads to reduced pre-tRNA synthesis specifically in mutants lacking both RNase H and Top1 , or also Top2 activities . We also show that integration of TY1 retrotransposons in 5′-flanking regions of tDNAs is favored in cells depleted of both Top1 and cellular RNase H activities . We present evidence that both RNase H1 and RNase H2 are involved in cleaving RNA-DNA hybrids associated with cDNAs of TY1 retrotransposons . Unexpectedly , we also show that only RNase H1 is involved in processing of co-transcriptional R-loops at mtDNA transcription units .
We previously reported that R-loops can be identified robustly by ChIP-QPCR analyses using the S9 . 6 antibody [23] , which is specific for the structure of RNA-DNA duplexes independently of their sequence [26] , [27] . To assess the genome-wide distribution of R-loops , formaldehyde-crosslinked and sonicated chromatin was incubated with antibody S9 . 6 . The immunoprecipitated DNA and input chromatin were processed for high-throughput sequencing . Recovered DNA sequences were then mapped to the yeast genomic sequence ( GEO Series accession number GSE53420 ) . ChIP-seq was applied to wild-type and to mutants double rnh1Δ rnh201Δ and triple PGAL::TOP1 rnh1Δ rnh201Δ ( Fig . S1 ) . For Top1 depletion , cells were shifted from medium containing galactose plus sucrose and harvested after 6 h in glucose-containing medium . Sequenced reads over each target were normalized to the genome-wide mean of all intergenic regions ( arbitrarily set as sequencing background , see Materials and Methods ) , so changes in hit densities are relative differences compared to all other targets . Reads mapped to the rDNA in strains WT , rnh1Δ rnh201Δ and PGAL::TOP1 rnh1Δ rnh201Δ ( depleted of Top1 ) , were greatly enriched in the S9 . 6 ChIP-seq data over the input chromatin ( Fig . S1B ) . This is a good indication that R-loops are strongly associated with this locus , as also observed in S9 . 6 ChIP-QPCR ( Figs . 1A–B; and [23] ) . For the Pol I transcribed , 35S pre-rRNA region of the rDNA , the strongest peak detected in the wild-type strain was located over the 5′ segment of the 18S rDNA ( region ∼210 nt to ∼580 nt at the beginning of 18S rRNA; triple asterisk in Fig . S1A ) . Additional peaks were located over the 25S rDNA ( e . g . quadruple asterisk in Fig . S1A ) . In strains lacking both cellular RNase H and Top1 a new peak appeared over the Pol I promoter and 5′ETS regions at the 5′ end of the 35S pre-rRNA ( double asterisk in Fig . S1A; see also ChIP-QPCR in Fig . 1A ) . A further prominent peak was seen over the 5S rDNA , which is transcribed by RNA Pol III in the opposite direction to the 35S pre-rRNA ( single asterisk in Fig . S1A ) . Notably , R-loops over the 5S rDNA were strongly increased in strains lacking RNase H and even more when Top1 was also absent ( single asterisk in Fig . S1A; see also ChIP-QPCR in Fig . 1A ) . Comparison to DNA base-composition indicated that the uneven distribution of R-loops over the transcribed regions of the rDNA partially reflects a preference for C+G rich sequences ( Fig . S1A ) . To confirm that the signals detected by the antibody represent bona fide sites of RNA-DNA hybrids , wild-type ChIP samples were treated on-beads with recombinant E . coli RNase HI in vitro followed by recovery and analysis of the bound DNA ( Fig . 1B ) . A strong reduction in the R-loop signal was seen over the rDNA region in the RNase H treated samples . ChIP-QPCR for the 5S rRNA , the cytoplasmic RNA scR1 , the small nuclear RNA U6 ( SNR6 ) and three tRNA genes ( Fig . 1A ) revealed that R-loops are also associated with these loci . In the wild-type strain R-loops are detected at the 5S rDNA ( compare dark blue +Ab and light blue −Ab bars ) , as also observed for the 35S rDNA . However , for other Pol III transcripts R-loops were strongly increased by the absence of RNase H activity ( rnh1Δ rnh201Δ , red +Ab bars ) . In each case , R-loop accumulation was further increased by depletion of Top1 , although this increase was relatively small in the case of tRNA genes ( PGAL-TOP1 rnh1Δ rnh201Δ , light green +Ab bars ) . In vitro treatment of S9 . 6 ChIP samples with recombinant RNase H strongly reduced R-loops associated with Pol III genes ( Fig . 1B ) . This confirms that Pol III fragments , which are immunoprecipitated by antibody S9 . 6 indeed represent sites of RNA-DNA hybrid formation . The conclusion that R-loop accumulation is highly increased over tRNA genes in strains lacking RNase H activities was supported by ChIP-Seq data ( Figs . 1C–D ) . The expression levels of tRNA genes are correlated with the usage of the corresponding codon in mRNAs , but this is offset by the greater numbers of genes encoding isoacceptors for the most common codons [28] , [29] , [30] . Separation of the tRNAs into 41 gene families , ranked by codon usage , indicated that R-loop occupancy is heterogeneous between isogenic tRNAs of each family ( Figs . 1C–D; number 1 indicates the most common anticodon ) . Importantly also , some tRNA genes were enriched over their entire transcribed region , some showed higher levels of enrichment over their 5′ region , and some other showed enrichment over their 3′ region . This heterogeneity in R-loop occupancy between and within tRNA genes is likely to reflect heterogeneity in tRNA transcription rates among isoacceptors [28] , [29] , [30] , as well as differences in the thermodynamic stability of the RNA-DNA hybrids [12] , [13] . It should be noted that the fold enrichment of R-loops at tRNA genes in strains lacking RNase H ( or also Top1 ) relative to input chromatin ( or to the WT ) in ChIP-Seq data were generally lower than those in ChIP-QPCR data ( compare Figs . 1C–D with 1A ) . This may reflect differences in ChIP-seq efficiencies within and between samples . For tRNA heatmaps in Figs . 1C–D , only sequence reads mapping to unique locations on the genome were used , thus excluding the possibility that hits at one tRNA isogene could be erroneously attributed to other family members . Notably , the distribution of hits extends beyond the ends of the mature tRNA species into the non-conserved flanking regions ( indicated by dotted lines in Figs . 1C–D ) , confirming that the recovered sequences are unique and arise from the genomic loci . The R-loops overlapping 5′ and 3′ flanking regions of mature tRNA species potentially play roles in initiation and termination of transcription by Pol III ( [31] , [32] ) . The substantial increase in R-loops on tRNA genes seen in strains lacking RNase H activity presumably reflects their high transcription rates , whereas low levels of R-loops in wild-type strains apparently shows that these are normally cleared rapidly by RNase H . However , the ChIP-QPCR data ( Fig . 1A ) revealed little further increase in strains also depleted of Top1 . We speculate that due to their generally short lengths , Pol III genes are less dependent on topoisomerase activity than are the long Pol I transcripts [23] , [25] , [33] . The presence of R-loops is expected to impede transcription elongation , increasing the time required for pre-tRNA synthesis . In contrast , the accumulation of negative supercoiling behind the polymerase in strains with reduced topoisomerase activity can increase transcription initiation rates via promoter opening . To assess the outcome of these potentially competing effects , pre-tRNA levels were assessed in strains genetically depleted of Top1 ( single PGAL-TOP1 ) , or of both Top1 and Top2 ( double PGAL-TOP1 PGAL-TOP2 , designated PGAL-TOP1/TOP2 in Fig . 2 ) , or also lacking RNase H activity ( triple PGAL-TOP1 rnh1Δ rnh201Δ and quadruple PGAL-TOP1 PGAL-TOP2 rnh1Δ rnh201Δ ) . Pre-tRNA synthesis was slightly affected under conditions of partial induction of PGAL-TOP1 , specifically at 0 h depletion in medium containing galactose plus sucrose ( which provides the cells with limited amounts of glucose ) , in both the triple and quadruple mutant strains , relative to the strains with functional RNase H ( Figs . 2A–D panels I , compare lanes 7 and 13 with 1 , 4 and 10; quantified in Figs . 2F–I ) . Following a shift to repressive , glucose-containing media , Top1 depletion in the single mutant resulted in elevated pre-tRNA levels , and this was further increased when Top2 was also depleted in the double mutant ( Figs . 2A–D , panels I , lanes 5–6 and 11–12; quantified in Figs . 2F–I ) . Elevated levels were seen for the unprocessed primary transcripts and the unspliced but end-matured pre-tRNAs ( Figs . 2B–D , lanes 5–6 and 11–12 , panels I ) . Similarly , elevated levels were also seen for the intronless pre-tRNA species ( Fig . 2A , lanes 5–6 and 11–12 , panel I ) . However , loss of RNase H activity reversed this accumulation in each case . Indeed , the triple and quadruple mutant strains had a reduced ratio of precursor to mature tRNA for several species tested at 0 h and 6–9 h post-shift to glucose-containing medium ( Figs . 2A–D , lanes 7–9 and 13–15 , compare panels I and II ) . Changes in transcription elongation rates are reported to impact on pre-mRNA and pre-rRNA maturation pathways ( reviewed in [34] , [35] ) , and our data indicate that this may also be the case for pre-tRNAs . Ty1 LTR-retrotransposons are composed of 2 direct long terminal repeats ( LTRs ) flanking the TYA and TYB open reading frames ( see Fig . 3B; and [36] , [37] ) . TYA encodes the Gag structural proteins of the virus-like particle ( VLP ) , whereas TYB encodes the protease , the integrase and the reverse-transcriptase/RNase H ( RT/RNase H ) . ChIP-QPCR analyses revealed only low levels of RNA-DNA hybrids over Ty1 retrotransposons in the wild-type strain , but notable accumulation was seen in the double mutant rnh1Δ rnh201Δ , and even more in the triple mutant PGAL-TOP1 rnh1Δ rnh201Δ following depletion of Top1 for 6 h ( Fig . 3A ) . S9 . 6 ChIP-seq profiles showed that RNA-DNA hybrids are unevenly enriched across the Ty1 elements in the RNase H mutants ( Figs . 3B and S2 ) . In vitro treatment of S9 . 6 ChIP samples of double mutant rnh1Δ rnh201Δ with recombinant RNase H strongly reduced the signals over Ty1 retrotransposons confirming thus that these elements are associated with RNA-DNA prone sites ( Fig . 1B ) . The life-cycle of Ty1 involves transcription of a chromosomal element by Pol II , reverse transcription of Ty1 genomic mRNA into cDNA by the RT/RNase H inside the VLPs and incorporation of the cDNA into the nuclear genome ( reviewed in [36] , [37] ) . This raised the question of whether RNA-DNA hybrids mapped to Ty1 elements in the absence of cellular RNase H enzymes are produced by Pol II co-transcriptionally on the chromosomal elements ( R-loops ) or by reverse transcription in the VLPs ( Ty1cDNA::RNA hybrid molecules ) . Transposition of endogenous Ty1 elements is most frequent during growth at 22°C and below , much less active at 30°C and undetectable at 37°C [38] . Ty1 cDNA levels were quantified by Southern analysis of PvuII digested , total DNA . This showed an ∼3 fold increase in cDNA molecules in the double rnh1Δ rnh201Δ strain relative to the wild-type , in cultures grown at 22°C ( Fig . S3B ) . To directly assess the role of RT in generating RNA-DNA duplexes on TY1 , cultures of double rnh1Δ rnh201Δ strains were treated with the RT inhibitor phosfonoformic acid ( PFA ) ( e . g . see [39] ) . This greatly reduced the accumulation of RNA-DNA hybrids at TY1 ( see S9 . 6 ChIP-QPCR in Fig . S4 ) , but not at other sites . This indicates that RNA-DNA hybrids over TY1 retrotransposons in rnh1Δ rnh201Δ strains are mostly associated with Ty1 cDNA molecules . During growth at 30°C , Ty1 cDNA accumulated in the triple PGAL-TOP1 rnh1Δ rnh201Δ mutant following 6 h depletion of Top1 ( Fig . S3C; compare lane 12 with 4 ) . Loss of the intron-lariat debranching enzyme Dbr1 was reported to silence Ty1 retrotransposition , possibly by suppressing Ty1 replication in the VLPs [40] . The basis of this effect is unclear , but may be a consequence of the accumulation of high levels of intron lariats . The triple PGAL-TOP1 rnh1Δ rnh201Δ and quadruple PGAL-TOP1 rnh1Δ rnh201Δ dbr1Δ strains were compared following 6 h depletion of Top1 at 30°C . Loss of Dbr1 reduced the accumulation of RNA-DNA hybrids at Ty1 elements in ChIP-QPCR analyses ( Fig . 3A ) , and Ty1 cDNA in Southern analysis ( Fig . S3C , compare lanes 12 and 16 ) . Western blotting showed that the abundance of the Gag protein p45 was slightly increased in the PGAL-TOP1 rnh1Δ rnh201Δ strain depleted of Top1 , relative to the wild-type , at 30°C ( Fig . S5 ) . Together these data indicate that most RNA-DNA hybrids mapped to Ty1 elements in the absence of RNase H are associated with TY1 mRNAs , undoubtedly arising during reverse transcription of these into cDNAs in the VLPs ( see model in Fig . 3E ) . However , we cannot exclude the possibility that some of these RNA-DNA hybrids are co-transcriptional R-loops generated by Pol II transcription of chromosomal Ty1 loci , as recently proposed [11] . To assess the effects of loss of RNase H and Top1 on endogenous Ty1 retromobility , we used a BY4741 strain carrying a his3 ( AI ) construct inserted into a chromosomal Ty1 element ( TY1his3AI-[Δ1]-3114 , see ) [41] . The his3 ( AI ) gene does not produce functional HIS3 mRNA , however , an intact HIS3 gene can be regenerated by splicing , cDNA synthesis and retrotransposition . Ty1 mobility can therefore be quantified by measuring the rate of His+ prototroph formation . In strains carrying double rnh1Δ rnh201Δ or single top1Δ mutations , the rates of TY1his3AI transposition were ∼12 . 5 and ∼5 fold higher , respectively , than the isogenic wild-type ( Fig . 3C ) . Yeast strains are unable to grow when all three enzymes Top1 , RNase H1 , and RNase H201 are absent [23] . We therefore complemented the triple mutant PGAL-TOP1 rnh1Δ rnh201Δ by expression of the AGS-related mutant protein Rnh201G42S that shows reduced cleavage activity of RNA-DNA hybrids [42] . Ty1 mobility in this strain was ∼30 fold higher than in the wild-type and ∼3 fold greater than the rnh1Δ rnh201Δ strain ( Fig . 3C ) . These data show that RNase H and Top1 act together to suppress endogenous Ty1 retromobility ( see model in Fig . 3E ) . Ty1 preferentially integrates in a ∼1 kb window upstream of Pol III-transcribed genes , at the nucleosomal H2A/H2B interface , with an approximate 80-bp periodicity between integration hotspots [43] , [44] . To determine whether Top1 and/or RNase H play roles in the targeting of endogenous Ty1 to the 5′-flanking sequences of tRNA genes , we made use of a qualitative PCR assay ( Fig . 3D ) . This yields a PCR product whenever a TY1 element integrates upstream from any of the 16 different tRNAGLY genes . Analysis of DNA samples from the cultures used in Fig . 3C showed a large increase in Ty1 integration upstream of tRNAGLY in the strain PGAL-TOP1 rnh1Δ rnh201Δ , depleted of Top1 and expressing the AGS-related protein Rnh201G42S ( Fig . 3D , lanes 21–25 ) , relative to the wild-type ( Fig . 3D , lanes 1–5 ) . However , there was only a small increase in Ty1 integration in the double rnh1Δ rnh201Δ mutant ( Fig . 3D , lanes 6–10; see also Fig . S6 , odd numbered lanes , rnh1Δ rnh201Δ mutants ) . Integration of Ty1 at tRNAGLY was strongly suppressed in strains lacking only Dbr1 or both Dbr1 and RNase H , relative to the loss of only RNase H ( Fig . S6 , even numbered lanes ) . We conclude that Top1 and RNase H act together to restrict Ty1 integration at sites 5′ to tRNA genes , presumably by suppressing R-loop formation , with integration also dependent on intact debranching activity ( see model in Fig . S7 ) . Analysis of the S9 . 6 ChIP-Seq data showed that , unexpectedly , mitochondrial DNA ( mtDNA ) sequences were enriched with RNA-DNA hybrids ( Fig . 4A ) . At all sites in the mtDNA , R-loop formation was stronger in strains lacking RNase H activity than in the wild-type strain , but this was not further increased by the additional loss of Top1 activity ( Figs . 4A and S8 ) . Notably , the degree of R-loop accumulation varied within and between mt transcription units ( Figs . 4 and S8 , e . g . compare the different regions in the relatively long ∼12 . 88 Kb COX1/Q0045 gene with other mt genes in Fig . 4A ) , possibly reflecting variations in the transcription initiation and elongation rates of mtRNAP [45] , as well as differences in the thermodynamic stability of the RNA-DNA hybrids [12] , [13] . Reverse transcriptase activity has been reported in mitochondria of S . cerevisiae [46] . However , our data show that RNA-DNA hybrids accumulated on mtDNA transcription units are generated through transcription by mtRNAP rather than reverse transcription ( Fig . S4 ) . In order to assess the contributions of RNase H1 and RNase H2 in resolving R-loops in mitochondria , we performed S9 . 6 ChIP-QPCRs in single rnh1Δ , single rnh201Δ and double rnh1Δ rnh201Δ mutants ( Fig . 4B ) . Loss of RNase H1 ( +Ab red bars ) , but not of RNase H201 ( +Ab green fluorescent bars ) , highly increased R-loop levels over the COX1 and 21S rDNA mt genes . Loss of both RNase H1 and RNase H201 in double mutants resulted in similar or lower levels of R-loop formation than the RNase H1 single mutant ( +Ab blue fluorescent bars ) . Notably , the no antibody controls ( −Ab bars ) showed no enrichment above background in any strain tested . The recovery of R-loops that mapped to the mtDNA was substantially reduced by treatment of ChIP samples from the WT and double rnh1Δ rnh201Δ mutant strains with recombinant RNase HI in vitro ( Fig . 1B ) , strongly indicating that the mtDNA regions recovered with antibody S9 . 6 represent bona fide sites of R-loop formation . We conclude that nuclear-encoded RNase H1 , but not RNase H2 , can degrade R-loops in yeast mitochondria . In the wild-type strain , clear enrichment for R-loops in the S9 . 6 ChIP-seq data relative to the input chromatin was seen at highly expressed mRNA genes ( Fig . 5A ) . Most mRNA genes showing clear enrichment for R-loops also have relatively high G . C contents ( Fig . 5B ) . The ChIP-seq findings were confirmed by ChIP-QPCR for the highly expressed genes ADH1 , ACT1 and RPL28 , which showed a small but significant enrichment in R-loops ( +Ab red bars ) relative to no-antibody control ( −Ab black bars ) ( Fig . 5C; see also gene PMA1 in Fig . S9 ) . The levels of R-loops detected by ChIP-QPCR at Pol II genes ACT1 , ADH1 , PMA1 and RPL28 were similar in the wild-type and the double mutant rnh1Δ rnh201Δ ( Fig . S9 ) . Since the numbers of mapped sequences in the S9 . 6 ChIP-seq samples are expressed relative to total reads , the high increase in signal over Pol III genes , retrotransposons and mitochondria in mutants double rnh1Δ rnh201Δ and triple PGAL-TOP1 rnh1Δ rnh201Δ is expected to overshadow the effects seen elsewhere , precluding genome-wide analysis of mRNA genes in these strains . Treatment of S9 . 6 ChIPs with recombinant RNase HI in vitro lead to slight decreases in R-loop signals over all mRNA genes tested ( Fig . S9 ) . Note , however , that this is a ChIP experiment in which the RNA , DNA and chromatin are all formaldehyde crosslinked , and these conditions , together with the bound antibody , potentially hinder cleavage of RNA-DNA hybrids by recombinant E . coli RNase HI , which has different hybrid hydrolysis properties from eukaryotic RNase H enzymes ( for a review on the mechanisms of action of RNase H enzymes see [16] ) . In the cases of Pol I , Pol III and mtDNA genes ( Fig . 1B ) , RNA-DNA hybrids may be much more accessible to recombinant RNase HI due to the relative lack of nucleosomes at these loci [29] , [47] , [48] , [49] and/or to the length/complexity of the RNA-DNA hybrids [50] . At intron-containing genes ( designated here “i-genes” ) the distribution of S9 . 6 ChIP-seq reads was distinctly different over exon and intron sequences ( Figs . 5D–E ) . The density of R-loops was notably higher over the second exon ( exon 2 ) than the first exon ( exon 1 ) , or intron , particularly for spliced ribosomal protein genes ( RPG i-genes ) ( Figs . 5D–E ) . This was confirmed by ChIP-QPCR for the RPG i-gene RPL28 ( Fig . 5C ) . The majority of yeast i-genes harbor a relatively short exon1 ( <150 bp; see Fig . S10B ) . The asymmetric distribution pattern of R-loops over i-genes , with low relative hit densities over exon 1 , was most marked for this class and particularly for the highly expressed RPGs ( Fig . S11 ) . This pattern was also visible when individual , well-expressed i-genes were examined ( i-genes colored in red in Fig . S12 ) . The distribution of hits along i-genes was very different from that seen on intronless genes ( designated here “e-genes” ) , clearly showing it to be splicing-specific ( compare Figs . 5D–E and 5F ) . Moreover the signal on exon 2 of RPG i-genes was clearly higher than on the RPG e-genes ( Fig . S13 ) . Remapping sequence reads across splice junctions using STAR , revealed no sequenced reads that map across annotated splice junctions in neither the S9 . 6 CHIP-seq nor the input chromatin . This confirms that R-loops were accumulated over genomic loci and not associated with spliced mRNAs . This is in contrast to RNA-DNA hybrids accumulated over TY1 elements in the double mutant rnh1Δ rnh201Δ which could be associated with TY1 cDNAs i . e . products of reverse transcription ( see Figs . S3 , S4 ) . Analysis of G . C content across i-genes revealed higher enrichment over exon 2 than exon1 and intron regions , in particular the RPGs ( Fig . S14 ) . Prediction of the thermodynamic stability patterns of ( pre-mRNA ) /DNA and DNA/DNA duplexes across i-genes , indicated that ( pre-mRNA ) /DNA stability is likely to be particularly weak in the intron region adjacent to the 3′ splice site , relative to exon 2 , in particular for the RPGs ( Fig . S14 , for a description of ΔG9 calculations see Protocol S1 and [12] ) ) . This potentially contributes to the sharp rise in R-loops seen at the intron-exon2 boundary ( Figs . 5D–E and S11 ) . Altogether these data clearly show that there is a correspondence between G . C content , thermodynamic stability of ( pre-mRNA ) /DNA duplexes , R-loop accumulation and transcription activity over yeast i-genes . On spliced genes , R-loops are less abundant over exon1 and intron sequences than on the second exon , particularly on highly expressed intron-genes . We speculate that R-loops are suppressed at intron regions to ensure proper recognition of 5′ and 3′ splice sites by the splicing machinery , whereas they are favored over exon 2 to decelerate elongation of Pol II and by doing so to promote co-transcriptional splicing [51] , [52] , [53] .
Here we report the application of ChIP-seq to systematically identify sites of RNA-DNA duplex formation throughout the nuclear and mitochondrial genomes in budding yeast . Numerous , prominent sites of R-loop enrichment were identified in actively transcribed genes by all RNA polymerases , Pol I , II , III and mtRNAP . Over the Pol I transcribed rDNA , the distribution of R-loops agreed well with the pattern previously observed in conventional ChIP analyses [23] . In addition , loss of cellular RNase H activity resulted in strong accumulation of RNA-DNA hybrids at Pol III transcribed genes , Ty1 retrotransposons , the second exon of spliced genes and over transcription units of the mtDNA . While this work was in preparation , an analysis of R-loop prone-sites in budding yeast arrived at similar , but not identical , conclusions [11] . R-loops were strongly detected over the Pol I transcribed 35S rDNA and the 5S rRNA genes in the wild-type . Various other Pol III transcripts including tRNAs , scR1 , U6 snRNA and the snoRNA snRNA52 were also enriched in R-loops particularly in the absence of RNase H activities . Over the Pol I promoter and the rDNA 5′-ETS , R-loop formation was increased in mutants lacking both Top1 and RNase H ( data herein and [23] ) , presumably also reflecting the effects of DNA strand separation over these regions ( see Fig . 4 in [25] ) . The unusual high rates of transcription initiation by Pol I and Pol III ( reviewed in [31] , [32] , [35] , [54] ) may be facilitated by negative DNA supercoiling and strand separation over the promoter regions [33] , [55] , [56] . However , this can also favor formation of R-loops that interfere with transcription elongation [23] , [25] . The accumulation of negative supercoils behind transcription bubbles is expected to be enhanced by loss of Top1 or also Top2 [23] , [25] , [33] , [57] . Consistent with this , increased pre-tRNA accumulation was observed in strains lacking Top1 or also Top2 . However , the increase in pre-tRNAs was reversed when RNase H was also absent . We therefore propose that increased transcription initiation of tRNA genes due to promoter opening , particularly when Top1 ( or also Top2 ) is absent , can be offset by impaired elongation due to stable R-loop accumulation in strains also lacking RNase H . This is reminiscent with the reduced rates of pre-rRNA synthesis in strains lacking both Top1 and RNase H due to stable R-loop accumulation at the rDNA repeats ( data herein and [23] , [25] ) . Our data agree with the recent report [11] that R-loops at tRNA genes are processed by RNase H . It is possible that nascent tRNAs engaged in R-loops in wild-type yeast are rapidly cleaved by RNase H and/or resolved by helicase Sen1/Senataxin [11] and targeted to degradation by the TRAMP/exosome 3′-5′ surveillance machinery [15] , [23] , [58] , [59] , [60] . Analyses of the Ty1 class of endogenous LTR-retrotransposons in strains lacking cellular RNase H and Top1 activities revealed marked increases in the abundance of RNA-DNA hybrids and in the frequency of retrotransposition . Most of the RNA-DNA hybrids mapped to TY1 elements in strains lacking cellular RNase H or also Top1 activities are associated with TY1 cDNAs rather than chromosomal genes . These hybrids are products of reverse transcription that may have escaped cleavage by the RT/RNase H protein ( for a review on Ty1 replication see [61] ) . TY1 RNA:cDNA hybrid molecules could be produced during synthesis of either the first ( minus ) DNA or the second ( plus ) DNA strand in the VLPs . Minus strand synthesis requires reverse transcription of the highly structured Ty1 genomic RNA , which could be hampered by potential RT pausing/stalling events [62] . Plus strand synthesis requires priming at specific polypurine tracts , which are resistant to cleavage by the RNase H domain of the RT [61] . Ty1 cDNAs and/or retromobility are increased in a variety of different genome-maintenance mutants ( reviewed in [36] ) . It is possible that DNA damage inflicted on the genome by co-transcriptional R-loops , e . g . in mutants lacking cellular RNase H or also Top1 , leads to the alleviation of Ty1 dormancy ( see model in Fig . 3E ) . Additionally , RNase H1 and H2 may directly cleave RNA-DNA hybrids generated by reverse transcription of TY1 genomic RNAs . Cleavage of TY1 RNA:cDNA hybrid molecules by cellular RNase H could occur inside and/or outside the VLPs ( see model in Fig . 3E and [36] , [63] ) . Notably , RNA-DNA hybrids associated with cDNAs of endogenous retroelements may play roles in the pathogenesis of autoimmune diseases in humans , e . g . in RNase H2-AGS [64] , [65] . The LTR-retrotransposons TY3 and TY5 in S . cerevisiae and Tf1 in S . pombe are selectively targeted to nuclear genomic regions through interactions between the retrotransposon and specific transcription factors and/or chromatin ( reviewed in [36] , [37] , [66] ) . Ty1 incorporation most commonly occurs by integrase-mediated integration at the nucleosomal H2A/H2B interface upstream of Pol III-transcribed genes [43] , [44] , [67] , with a periodicity of ∼80 bp that is mediated by interactions between the ATP-dependent chromatin remodeling factor Isw2 and the TFIIIB transcription complex [68] , [69] . While pre-tRNA synthesis was reduced in strains lacking both Top1 and cellular RNase H activities , the integration of Ty1 at target sites upstream of tRNAGLY was on the contrary strongly increased in these mutants ( see model in Fig . 3E ) . tRNA genes act as nucleosome phasing signals in both directions , possibly due to specific properties of the TFIIIB-TFIIIC transcription complex [49] , and R-loops might affect the stability/flexibility of this complex leading to altered nucleosome dynamics/phasing , thus creating an environment that is conducive to Ty1 integration ( see model in Fig . S7 ) . It is possible that collisions between the DNA replication machinery and Pol III associated R-loops may also play a role in TY1 integration [70] . We observed the accumulation of R-loops over the mitochondrial DNA ( mtDNA ) transcription units , specifically in strains lacking RNase H1 . The ∼80 Kb S . cerevisiae mt chromosome comprises relatively long transcription units such as the genes COX1/Q0045 and COB/Q0105 , which expression and polycystronic structure ( with multiple exons and introns ) are extremely complex [71] . Mitochondrial transcription-translation coupling is expected to suppress R-loop formation , as is believed to be the case in bacteria ( reviewed in [72] , [73] ) . However , gene structure and expression , poor packaging of nascent transcripts , G . C composition of the sequence ( see Fig . 4A ) , transcription-mediated topological stress and other factors may favor R-loop formation on mtDNA [4] . The yeast mt DNA encodes proteins and RNAs with key roles in mitochondrial function [74] , [75] , [76] , but most proteins that are needed in mitochondria are encoded by the nuclear genome and imported from the cell cytoplasm . A mitochondrial function for RNase H1 has previously been reported in mammals [19] , [77] , but this was not known to be the case for yeast . However , dramatic accumulation of R-loops was seen over transcription units in the mtDNA in the absence of RNase H1 , but not RNase H2 , strongly indicating that RNase H1 does function in this organelle . Notably , the absence of Top1 failed to exacerbate the accumulation of R-loops at mtDNA in yeast strains lacking also RNase H activity . In yeast the mtDNA is not essential for viability on glucose-containing medium and many mutations cause instability of the mtDNA , leading to a complete loss ( rho ( 0 ) petites ) or truncations ( rho ( - ) petites ) of this genome [47] , [74] . Yeast strain W303 grows slowly on glycerol-containing medium , on which mtDNA function is required , and shows a relatively high rate ( ∼15% ) of formation of rho ( - ) petites containing non-functional mtDNA on glucose medium . Loss of RNase H1 in this yeast background increased rho ( - ) petite formation 2–3 fold , to ∼45% ( Fig . S15 ) . Processing of R-loops by RNase H1 may therefore be important for the maintenance and expression of yeast mtDNA . RNA-DNA hybrids are extensively formed in the circular 16 . 5 Kb mt chromosome of mammalian cells where they are believed to play important roles during DNA replication [50] , [78] , and RNase H1 may generate/remove RNA primers during this process [19] , [79] . However , the mechanism of mtDNA replication in S . cerevisiae is expected to be more similar to the fungus C . albicans , which is mediated mainly by recombination-driven replication [80] . The role of RNase H1 in yeast mitochondria may predominately involve the resolution of cotranscriptional R-loops , as in the nuclear genome , rather than direct involvement in the mtDNA replication process . As for RNA Pol I , Pol III and mtRNAP genes , the actively transcribed mRNA genes were also associated with R-loops , albeit at much lower rates . Thus there appear to be a general link between transcriptional activity and R-loop formation in budding yeast , as recently reported in [11] . Competition between RNA packaging and R-loop formation is a normal feature of mRNA synthesis . R-loop formation at mRNA genes could be dictated by factors including increased residence in proximity to the DNA template of poorly packaged transcripts and the G . C content of the sequence ( reviewed in [2] , [3] , [5] ) . Indeed the thermodynamic helical stability of ( pre-mRNA ) /DNA duplexes at highly expressed mRNA genes in yeast is higher than DNA/DNA duplexes mostly due to the relatively higher content in G . C of these genes in comparison to the less-well expressed mRNA genes ( Fig . S16 ) . More generally , there was a correspondence between transcription activity , R-loop formation and G . C . content at most yeast mRNA genes ( see Figs . 5A–B and [11] ) . Yeast mutants of RNA biogenesis factors , including the helicase Sen1/SENATAXIN , the THO/TREX RNA packaging complexes , the RNA exosome , the RNA-binding protein Npl3 and components of mRNA 3′ cleavage and polyadenylation ( mCP ) machinery [60] , [81] , [82] , [83] , [84] , have all been associated with R-loop formation , with deleterious effects on genome stability . In mammalian cells , R-loops can result in silencing of protein coding genes , with potentially pathogenic outcomes [8] , [85] , [86] . Most intron-containing genes in yeast , particularly ribosomal protein genes , have a short exon 1 , and this correlates with higher levels of expression relative to genes with longer exon 1 ( see Fig . S10 ) . The close proximity of the 5′ splice site to the promoter region may stimulate transcription via coupling of the splicing and Pol II initiation machineries [87] , [88] . In the S9 . 6 ChIP-seq data , R-loops were reduced over short exon 1 regions and the accompanying intron , relative to the second exon , on most spliced genes ( see Figs . 5D–E and S11 ) . It was recently proposed that R-loop formation downstream from CpG-rich regions of strong promoters of highly expressed spliced genes in mammalian cells may be more favored over longer than shorter first exon regions [9] . R-loops can slow down elongation of the RNA polymerase ( reviewed in [2] , [3] ) , and co-transcriptional splicing is kinetically coupled to transcription elongation by Pol II ( reviewed in [89] ) . We speculate that co-transcriptional R-loops have been counter selected over short exon 1 and the associated introns . This may promote high expression of these genes , together with proper recognition of their 5′ and 3′ splice sites ( SS ) . Notably , depletion of the splicing factor ASF/SF2 ( alternative splicing factor/splicing factor 2 ) in mammalian cells can lead to increased R-loop formation and genome instability [53] , [90] . Whereas R-loops were reduced over exon 1 and intron regions of spliced genes , they were increased over exon 2 ( see Figs . 5D–E and S11 ) . We speculate that R-loops over exon 2 could decelerate elongation of Pol II [2] , [3] , and/or create a chromatin environment favorable for Pol II pausing [89] , thus promoting co-transcriptional splicing [51] , [52] . Supporting these models , a computational , thermodynamic study covering many genomes including H . sapiens , predicted that R-loops will generally be less stable around the 5′ and 3′ SS , due to differences in the helical stability of ( pre-mRNA ) /DNA and DNA/DNA duplexes [12] . The application of this approach to yeast indicated that ( pre-mRNA ) /DNA duplexes are indeed intrinsically less favored on introns compared to exons , in particular around the 5′ and 3′ SS ( see Fig . S14 ) . Most strikingly , predicted ( pre-mRNA ) /DNA duplexes are particularly disfavored around the 3′ SS regions of ribosomal protein genes ( see Fig . S14 ) , and this is potentially related to the high splicing efficiency of these pre-mRNAs [91] .
Yeast strains and plasmids used in this study are listed in Table S1 . Growth and handling of S . cerevisiae were by standard techniques . For Top1 and Top2 depletion , cells ( PGAL-TOP1 and PGAL-TOP1/2 strains ) were grown at 30°C to OD600 ∼0 . 3–0 . 4 in complete Kaiser synthetic SGS minimal medium ( 2% galactose , 2% saccharose ) then transferred to the pre-warmed complete SD minimal medium ( 2% glucose ) . Growth was continued for several hours and maintained in exponential phase by dilution with pre-warmed SD medium . Immunoglobulins IgG2a of monoclonal antibody S9 . 6 [26] , [27] were purified from mouse hybridoma cell line supernatants by Eurogentec . ChIP of RNA-DNA hybrids using the antibody S9 . 6 was performed mainly as described in [23] . Crosslinking of exponentially growing cells ( OD600 ∼0 . 6 , 50 OD600/ChIP sample ) with formaldehyde ( 1% ) was for 25 min at room temperature . Pellets were resuspended with 400 µL of FA-1 lysis buffer [50 mM HEPES-KOH at pH 7 . 5 , 140 mM NaCl , 1 mM EDTA at pH 8 , 1% Triton X-100 , 0 . 1% w/v sodium deoxycholate , plus CPI-EDTA 1× ( Protease inhibitor cocktail , Roche 11697498001 ) ] , mixed with 500 µL of glass beads ( Sigma , G8772 ) , and vortexed ( Vortex Genie 2T , Scientific Industries ) for 45 min at full speed at 4°C . Glass beads were removed and cross-linked chromatin was recovered by centrifugation at full speed for 10 min at 4°C ( supernatant discarded ) . Eight-hundred microliters of FA-1 buffer were added on the top of the pellet . Sonication of chromatin was performed for 2 min ( 10 sec ON , 15 sec OFF , 20% amplitude; Branson Digital Sonifier ) to yield an average DNA fragment size of ∼500 bp . Sonicated chromatin were spun for 15 min at full speed at 4°C and glycerol 5% was added to supernatants . Sonicated chromatin were mixed with sepharose Cl-4B beads ( Sigma CL4B200 ) and cleared for 1 h at 4°C . Twenty microliters were kept for control Input chromatin . Immunoprecipitations were performed by mixing ‘cleared-sonicated chromatin’ with 35–40 µg of IgG2a of antibody S9 . 6 together with 100 µl bed of Protein A sepharose CL-4B beads ( GE Healthcare 17-0780-01 ) on a rotating wheel overnight at 4°C . To assess contribution of background , a ‘beads-only’ internal control was prepared in parallel to immunoprecipitated samples but without addition of any antibody . Beads were recovered ( see also paragraph ‘Treatment of S9 . 6 ChIP with recombinant RNase H’ ) and washed successively with FA-1 buffer ( plus CPI-EDTA 1× ) , FA-2 buffer ( as FA-1 buffer but with 500 mM NaCl , plus CPI-EDTA 1× ) , FA-3 buffer ( 10 mM Tris-HCl at pH 8 , 0 . 25 M LiCl , 0 . 5% NP-40 , 0 . 5% w/v sodium deoxycholate , 1 mM EDTA at pH 8 , plus CPI-EDTA 1× ) , and TE 1× ( 100 mM Tris-Cl at pH 8 , 10 mM EDTA at pH 8 ) at 4°C . Cross-link reversal of sonicated- chromatin from samples ‘input chromatin’ , ‘beads-only’ and ‘S9 . 6 immunoprecipitate’ were performed by incubating the washed beads overnight at 65°C in 250 µL of TE buffer containing 1% SDS and 1 mg/mL proteinase K . DNA was purified using Qiagen PCR purification kit and eluted with 55 µL of buffer EB containing RNase A ( 0 . 5 µg/mL ) . 10–20 ng/µl DNA were recovered from ‘S9 . 6 immunoprecipitates’ from wild-type cells as measured by Qubit dsDNA HS Assay Kit ( Invitrogen , Q32851 ) . Quantitative PCRs ( qPCRs ) were performed in triplicate in a MX3005P real-time PCR machine ( Agilent Technologies ) in 10 µl reaction containing: 5 µl of 2× TaKara SYBR premix Ex Taq II Tli Rnase H Plus ( Clontech RR820L ) , 1 µl DNA ( but 10% of input chromatin ) , 0 . 4 µl of 10 µM primers ( see Table S3 ) , 0 . 04 µl of Rox II and 3 . 56 µl of water . Values for ChIPs were calculated using the formulas ΔΔCt “no antibody” = 2− ( Ct ‘beads only’ - Ct ‘input chromatin’ ) and ΔΔCt “S9 . 6 immunoprecipitate” = 2− ( Ct ‘S9 . 6 immunoprecipitate’ - Ct ‘input chromatin’ ) . The ‘S9 . 6 immunoprecipitate’ and ‘input chromatin’ were further processed for ChIP-seq as described below . 200 ng of DNA ( ‘S9 . 6 immunoprecipitate’ and ‘input chromatin’ ) were linker-ligated as described mainly in the Illumina's hand book with some modifications . Step1 “Repair DNA ends”: 100 µl reactions contained , DNA , 1× T4 DNA ligase buffer ( NEB , B0202S ) , 0 . 4 mM dNTP mix , 15 units T4 DNA polymerase ( NEB , M0203L ) , 50 units T4 Polynucleotide Kinase ( NEB , M0201L ) and 5 units DNA Polymerase I- Large ( Klenow ) Fragment ( NEB , M0210 ) . Reactions were incubated at 20°C for 30 min . DNA was purified using QIAquick PCR Purification Kit ( Qiagen , 28106 ) . DNA columns were centrifuged several times up to 10 min to ensure that no residual traces of ethanol are left in the column . DNA was eluted with 32 µl EB buffer ( 10 mM Tris-HCl , pH 8 . 5 ) which was pre-heated to 55°C . Step2 “Add A”: 50 µl reactions contained DNA from step 1 , 1× NEB buffer 2 ( NEB , B7002S ) , 0 . 2 mM dATP and 15 units Klenow Fragment 3′→5′ exo minus ( NEB , M0212L ) . Reactions were incubated for 30 min at 37°C . DNA was purified using MinElute spin column ( Qiagen , 28006 ) and eluted with 10 µl EB buffer as described in step 1 . Step 3 “Ligation with standard pair-end ( PE ) adapters”: 30 µl reactions contained DNA from step 2 , 1× Quick DNA ligase buffer ( NEB , M2200S ) , 3 . 33 nM PE adapter mix and 1600 units Quick T4 DNA ligase ( NEB , M2200S ) ( for primer sequences see Table S3 ) . Reactions were incubated at room temperature ( 18–22°C ) for 30 min . DNA was purified and eluted with 36 . 5 µl EB buffer as described in step 1 . Step 4 ‘PCR amplification’: 50 µl reactions contained: DNA from step three , 1 unit Phusion high fidelity ( HF ) DNA polymerase ( NEB , B05185 ) , 1× HF buffer ( NEB , B05185 ) , 0 . 2 µM ‘primer 1 . 2’ , 0 . 2 µM ‘primer 2 . 2’ and 0 . 2 mM dNTP mix ( for primer sequences see Table S3 ) . Cycling conditions were 98°C for 30 sec; followed by 18 cycles ( 98°C for 10 sec , 65°C for 30 sec and 72°C for 1 min ) ; followed by 72°C for 5 min; followed by cooling to 4°C . PCR DNA was purified using MinElute spin column ( Qiagen , 28006 ) and eluted with 10 µl EB buffer as described in step 1 . Step 5 “size selection”: DNA from step 4 was well resolved on 2% agarose gel [mixture 3::1 of standard agarose:: Metaphor agarose ( Lonza , 50180 ) ] in 1× TBE , alongside with a DNA ladder , stained with SYBR safe ( Invitrogen ) and visualised with Fuji FLA-5100 PhosphorImager . 300±50 bp DNA were excised from the gel and purified using QIAquick Gel Extraction Kit ( Qiagen , 28706 ) . Note that in order to improve the representation of A+T rich-DNA sequences agarose gel slices were melted at room temperature ( 18–22°C ) . DNA was eluted using MinElute spin column ( Qiagen , 28006 ) with 10 µl EB buffer as described in step 1 and sent for high-throughput sequencing . One-hundred microliters of bed of Protein A beads incubated with sonicated-crosslinked-chromatin and antibody S9 . 6 ( see paragraph ‘Chromatin immunoprecipitation analyses of RNA-DNA hybrids’ ) were washed successively with FA-1 buffer ( plus CPI-EDTA 1× ) , TE 1× buffer ( plus CPI-EDTA 1× ) and 10 mM Tris-HCl pH 8 ( plus CPI-EDTA-free 1× , Roche 11873580001 ) . Washed beads were re-suspended in 300 µl of reaction buffer containing RNAse H buffer 1× ( NEB , M0297L ) , 4% glycerol and 20 µg/ml BSA . Beads were incubated for 2 . 5 h at 37°C in absence or presence of 15 µl of recombinant E . coli RNase HI ( 75 units , NEB , M0297L ) , with shaking at 1000 rpm ( Eppendorf Thermomixer ) . RNase H reactions were stopped by adding 10 mM EDTA . Beads were washed successively with buffers FA-2 , FA-3 , and TE 1× , sonicated-chromatin was reverse-crosslinked , and DNA was recovered and analysed by q-PCR as described for the standard ChIP protocol . Equal amounts of total RNA ( 10 µg ) were resolved on standard 8% polyacrylamide-8 . 3M urea gels for low molecular weight RNAs . Northern hybridizations for tRNAs were performed overnight at 37°C in ULTRAHyb-oligo buffer ( Ambion , Invitrogen , AM8663 ) and washes done at 37°C in SSC 6× . Northern signals were generated by a Fuji FLA-5100 PhosphorImager and quantified with AIDA software ( Raytest ) . For primer sequences see Table S2 . In strain JC3212 ( BY4741 , TY1his3AI-[Δ1]-3114 ) HIS3 gene was inserted in the TYB sequence of a Ty1 element in the antisense orientation ( see Table S1 and [41] ) . HIS3 RNA synthesis from TY1his3AI element was interrupted by an artificial AI intron which is only spliced during transcription of TY1 RNA . Ty1his3AI retromobility occurs only when the Ty1his3AI RNA is spliced , reverse transcribed and the resulting Ty1HIS3 cDNA is incorporated into the genome by integration or recombination . For the activation of Ty1 retrotransposition in conditions non-permissive for Top1 expression ( Figs . 3C–D ) , cells were: 1 ) grown overnight at 30°C in Kaiser synthetic SGS Drop-Out minimal medium , 2 ) diluted in SD Drop-Out medium to OD600 ∼0 . 05 and grown for 3 doublings at 30°C , 3 ) re-diluted to OD600 ∼0 . 01 in the same medium and aliquoted in 5 cultures of 10 ml each , and , 4 ) grown at 18°C until saturation ( 4–6 d ) . Cells were harvested , washed and re-suspended in 5 ml sterile water . For total number of colonies , aliquots of each culture ( dilution 1∶106 ) were plated on SD Drop-Out -Leu minimal medium and incubated at 30°C . For HIS+ colonies , aliquots of each culture ( dilution 1∶2 ) were plated on SD Drop-Out -Leu-His plates and incubated at 30°C . The rate of Ty1his3AI transposition is the number of HIS+ colonies divided by the total number of colonies ( as described in [92] ) . Spontaneous Ty1 insertions upstream of 16 tRNAGLY genes were detected as described previously [93] , [94] with some modifications . To confirm that the genomic DNA samples were in the linear range for PCR , DNA concentrations were measured with a Qubit dsDNA BR Assay Kit ( Invitrogen , Q32850 ) and equal amounts of 6 or 30 ng DNA were assayed by PCR . Reactions of 50 µl contained 1× Phusion HF buffer ( NEB , B05185 ) , 0 . 2 µM primer ‘TYB OUT’ , 0 . 2 µM primer ‘SUF16’ ( for primer sequences see Table S3 ) , 0 . 2 mM dNTP mix , 1 unit Phusion HF ( NEB , B05185 ) , and genomic DNA . Cycling conditions were 98°C for 30 sec; followed by 30 cycles ( 98°C for 10 sec , 57°C for 30 sec and 72°C for 1 min ) ; followed by 72°C for 10 min; followed by cooling to 4°C . PCR DNA fragments were resolved on a standard 1 . 5% agarose gel ( stained with SYBR safe , Invitrogen ) in 1× TBE and visualized with a Fuji FLA-5100 PhosphorImager . For primer sequences see Table S3 . Total genomic DNA were extracted by standard glass-bead/phenol lysis ( e . g . see [95] ) . DNA concentrations were measured with a Qubit dsDNA BR Assay Kit ( Invitrogen , Q32850 ) . 2 µg DNA were incubated overnight at 37°C in presence of 200 units of restriction endonuclease PvuII-HF ( NEB , R3151 ) . DNA samples were resolved on a standard 1% agarose gel ( stained with ethidium bromide ) in 1× TBE . Washes and blotting of the gel were performed mainly as described in [95] , but the depurination step was omitted . DNA random priming probes were prepared using DECAprime II Random Primed DNA Labelling Kit ( Ambion , Invitrogen , AM1455; for primer sequences see Table S2 ) , hybridized overnight at 42°C in Hybridization Buffer ( 50% formamide , 5× SSC , 5× Denhardt's solution , 0 . 5% SDS and 100 µg/ml sonicated salmon sperm DNA ) , and washed at 55°C with 2× SSC , 0 . 1% SDS and 0 . 1× SSC , 0 . 1% SDS . Southern signals were generated by a Fuji FLA-5100 PhosphorImager and quantified with AIDA software ( Raytest ) . Total protein extracts and Western blot analysis were performed using standard procedures . Mouse anti-Ty1 Gag antibodies , raised against the Glu-Val-His-Thr-Asn-Gln-Asp-Pro-Leu-Asp peptide ( Diagenode , anti-Ty1-tag , MAB-054-050; and see [96] ) , and rabbit anti-beta-actin antibodies ( Abcam , ab 34731 ) were used as primary antibodies . Horseradish peroxidase-conjugated antibodies ( GE Healthcare ) were used as secondary antibodies . The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [98] and are accessible through GEO Series accession number GSE53420 . | R-loops ( RNA-DNA hybrids ) are potentially deleterious for gene expression and genome stability , but can be beneficial , for example , during immunoglobulin gene class-switch recombination . Here we made use of antibody S9 . 6 , with specificity for RNA-DNA duplexes independently of their sequence . The genome-wide distribution of R-loops in wild-type yeast showed association with the highly transcribed ribosomal DNA , and protein-coding genes , particularly the second exon of spliced genes . On RNA polymerase III loci such as the highly transcribed transfer RNA genes ( tRNAs ) , R-loop accumulation was strongly detected in the absence of both ribonucleases H1 and H2 ( RNase H1 and H2 ) , indicating that R-loops are inherently formed but rapidly cleared by RNase H . Importantly , stable R-loops lead to reduced synthesis of tRNA precursors in mutants lacking RNase H and DNA topoisomerase activities . RNA-DNA hybrids associated with TY1 cDNA retrotransposition intermediates were elevated in the absence of RNase H , and this was accompanied by increased retrotransposition , in particular to 5′-flanking regions of tRNAs . Our findings show that RNase H participates in silencing of TY1 life cycle . Surprisingly , R-loops associated with mitochondrial transcription units were suppressed specifically by RNase H1 . These findings have potentially important implications for understanding human diseases caused by mutations in RNase H . | [
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... | 2014 | Genome-Wide Distribution of RNA-DNA Hybrids Identifies RNase H Targets in tRNA Genes, Retrotransposons and Mitochondria |
Malaria epidemics in regions with seasonal windows of transmission can vary greatly in size from year to year . A central question has been whether these interannual cycles are driven by climate , are instead generated by the intrinsic dynamics of the disease , or result from the resonance of these two mechanisms . This corresponds to the more general inverse problem of identifying the respective roles of external forcings vs . internal feedbacks from time series for nonlinear and noisy systems . We propose here a quantitative approach to formally compare rival hypotheses on climate vs . disease dynamics , or external forcings vs . internal feedbacks , that combines dynamical models with recently developed , computational inference methods . The interannual patterns of epidemic malaria are investigated here for desert regions of northwest India , with extensive epidemiological records for Plasmodium falciparum malaria for the past two decades . We formulate a dynamical model of malaria transmission that explicitly incorporates rainfall , and we rely on recent advances on parameter estimation for nonlinear and stochastic dynamical systems based on sequential Monte Carlo methods . Results show a significant effect of rainfall in the inter-annual variability of epidemic malaria that involves a threshold in the disease response . The model exhibits high prediction skill for yearly cases in the malaria transmission season following the monsoonal rains . Consideration of a more complex model with clinical immunity demonstrates the robustness of the findings and suggests a role of infected individuals that lack clinical symptoms as a reservoir for transmission . Our results indicate that the nonlinear dynamics of the disease itself play a role at the seasonal , but not the interannual , time scales . They illustrate the feasibility of forecasting malaria epidemics in desert and semi-arid regions of India based on climate variability . This approach should be applicable to malaria in other locations , to other infectious diseases , and to other nonlinear systems under forcing .
Epidemic or ‘unstable’ malaria occurs in areas of marginal environmental conditions for the development of the parasite and the population dynamics of the mosquito vector , at the edge of the distribution of the disease . Millions of people live in the highlands and desert fringes around the tropics in Africa , Asia and South America . It is in these regions , where temperature or rainfall limit transmission , that climate variability and climate change have the potential to most strongly impact the population dynamics of the disease . Determining the role of climate variability is fundamental to evaluate both the feasibility of early-warning systems for infectious diseases based on climate , as well as the consequences of longer-term trends in climate . The intermittent nature of epidemics in unstable malaria regions results in populations that cannot sustain high levels of acquired immunity and are therefore susceptible to high morbidity and mortality; it also poses a different challenge for control efforts than the more stable , high-transmission intensity , endemic regions [1] . The ability to forecast and identify epidemic events becomes one important component of control efforts that can contribute to the timely implementation of effective prevention and treatment , as recognized by on-going efforts to develop malaria early-warning systems ( MEWS ) [1]–[3] . Studies of the role of climate variability , not just in malaria but also in other infectious diseases , have been limited by the scarcity of long temporal records of disease incidence , and by the difficulties of addressing the role of climate forcing in the context of the nonlinear dynamics of infectious diseases . These systems are well-known to behave as seasonally-forced nonlinear oscillators , capable of generating substantive variation from year to year on their own , in the complete absence of any year-to-year variation in an external driver such as climate [4]–[7] . The waxing and waning of immunity in the population has long been recognized as the key mechanism behind this intrinsic ability of disease systems to cycle on characteristic time scales longer than one year , leading to inter-annual variation in the size of outbreaks . The severity of epidemics is influenced by the size of the non-immune population , which in turn decreases as levels of infection rise , and rebuilds with the loss of functional immunity and demographic processes such as birth and immigration [8] , [9] . This dynamic feedback within the disease system underlies different conclusions on the role of climate variability in the inter-annual variation of vector-borne diseases , especially for malaria in E . African highlands [7] , [10]–[13] . It can also modulate the sensitivity of the response to climate drivers by generating periods of time that are refractory to forcing because of the temporary depletion of the non-immune population that fuels transmission [14] . Other feedbacks such as those generated by control efforts that are reactive to previous levels of infection , or behavioral responses , can also generate interannual cycles and refractory periods . Extensive epidemiological records for the past two decades in desert and semi-arid districts of India provide an opportunity to examine the role of rainfall on malaria epidemics of Plasmodium falciparum , while also taking into account disease dynamics . The periodically recurring epidemics in the ( semi ) arid parts of India , particularly the Punjab , were amongst the most devastating described in the history of malaria [15] . Early efforts to understand and forecast malaria epidemics included not just rainfall but also spleen rate , the proportion of children with enlarged spleens , which reflected recent exposure and provided an indirect measure of population levels of immunity [16]–[18] . After the malaria eradication efforts in India were abandoned in the 1970s , the epidemic belt shifted to the more arid and increasingly populous regions of Gujarat and Rajasthan . In the last decades the severity of these epidemics have made developing a MEWS based on rainfall [19] and rainfall forecasts [20] a public health priority . Quantifying the role of climate variability , and doing so in the context of epidemiological dynamics , remains an important open problem for these regions and for epidemic malaria in general . Recent developments on parameter estimation for nonlinear dynamical systems now make possible the consideration of epidemiological models that can be confronted to noisy and incomplete data [21] , [22] . We show here that these recent developments provide a basis for a formal statistical comparison of rival hypotheses on extrinsic drivers vs . intrinsic feedbacks , or more specifically , on climate vs . nonlinear disease dynamics , represented in mechanistic dynamical models . This differs from previous efforts to answer this same question , that did not provide a formal statistical comparison of hypotheses , based on models whose structures were constrained to simple forms by the inference methods , and that did not include the climate covariate explicitly ( e . g . [10] , [14] ) . We formulate here a dynamical model of malaria transmission that incorporates rainfall explicitly . The sequential Monte Carlo methods allow for more flexible representations , as well as the consideration of process and measurement noise , because their computational implementation only requires the numerical simulation of the dynamical models . Desert malaria provides an ideal initial application of this approach , given previous correlative evidence for an association between desert malaria and rainfall from shorter records in Africa [9] , [23] , and the potentially-weak dynamical role of immunity in these epidemic regions at the edge of the geographical distribution of the disease . Our analysis shows a strong and significant effect of rainfall in the inter-annual variability of epidemic malaria that involves a threshold in the disease response . Simulations of the transmission model for twenty years capture remarkably well the observed epidemic patterns when rainfall is prescribed . We demonstrate the high prediction skill of the model for yearly cases in the transmission season following the monsoonal rains . We compare this skill to that of statistical models , in particular a mixture model that incorporates a threshold , to examine the feasibility of forecasting epidemics in these regions with simpler , more phenomenological , models of malaria's response to rainfall . We end by showing the robustness of the results to a more complex transmission model that includes clinical immunity , and the value of the dynamical models for predicting the time course of epidemics .
A maximum correlation between monthly cases and rainfall was found when rainfall was accumulated for the five to six previous months ( Figure 1B and Figure S3C ) . A nonlinear response of cases to accumulated rainfall is evident in Figure 1C , with no apparent response of the disease below a threshold of approximately 200 mm , and an increase in both cases and variability above this threshold . Based on these observations , we incorporated accumulated rainfall and a threshold response in the force of infection of a model of malaria transmission ( Methods and Figure 2 ) , where both the value of the threshold and the length of the relevant time window previous to reported cases are parameters to be estimated . Our models for P . falciparum malaria were developed to capture some key aspects of the human , parasite and vector dynamics while remaining sufficiently parsimonious for parameters to be estimated directly from the available time series data . The structures of the two models are shown in Figure 2 and their formulation described in the Methods and in Text S1 . We begin with the simpler model , with the simpler representation of waning immunity , in which immune individuals are temporarily protected from both ( clinical ) disease and infection . We follow with a more realistic representation of malarial immunity in which we differentiate susceptibility to disease from that to infection [24]–[26] . The VSEIRS model that includes rainfall in the force of infection performs better than the model without rainfall , based on log-likelihoods ( respective log-likelihoods for Kutch are −1265 . 0 and −1275 . 0; , chi-square likelihood ratio test ) . We can also compare these values to those obtained for a linear seasonal autoregressive moving-average models ( SARIMA ) with and without rainfall . These comparisons are meant only as a general point of reference: if our mechanistic models fit better , or at least not substantially worse , than flexible statistical models then we conclude that they are capable of explaining most features of the available time series data . Both disease models , with and without rainfall , are better supported by the data than their SARIMA counterparts ( SARIMA , respective log-likelihoods are −1322 . 6 and −1329 . 0 ) ( Table S1 ) . This is also the case if we base this comparison on the Akaike Information Criterion ( AIC ) to take into account model complexity and penalize the likelihood based on the number of parameters ( Table S1 ) . Figure 3A shows numerical simulations of the VSEIRS model that includes rainfall together with the malaria data . This model captures well the observed patterns of the epidemics , in particular the pattern of large outbreaks followed by smaller ones , with exceptions during 1999–2001 ( including the large earthquake of 2001 ) . The similarity is striking given that these simulations are not next-step predictions , but predicted trajectories for the whole twenty years starting only from estimated initial conditions in 1987 . When rainfall is not included , simulations of the VSEIRS model ( Figure 3B ) have a poor resemblance to the data , and generate interannual cycles of approximately 5 years . Similar results were obtained for the district of Barmer and are shown in Text S1 and Figure S5 . In particular , Barmer experienced an extremely large epidemic , of nine thousand reported cases , in 1994–1995 , coincident with an extreme in precipitation ( Figure S3A and Figure S3B ) . This epidemic is five times larger than any of the outbreaks in the data , and makes the fitting of the models more challenging as reflected in likelihoods comparable to those of SARIMA ( with and without rainfall respectively ) ( Table S1 ) . One difference between the parameters of the two VSEIRS models is the duration of ‘effective’ immunity at the population level ( in Table 1 ) . Immunity is shorter when rainfall is included , lasting approximately 2 months , instead of 8 years without rainfall , in the respective models with the best likelihoods . Despite fairly broad confidence intervals , some interesting patterns are apparent in the profiles of this parameter ( Figure S6 ) . In particular , the profile for the model with rainfall is bimodal which gives rise to a discontinuous confidence region , including short durations of immunity ( below one year , corresponding to the maximum likelihood estimate , MLE ) , and longer durations around five years . This second peak maps approximately to the MLE of the model without the climate covariate . In other words , the model with rainfall fits the data in two different ways: for short immunity , interannual variability would be mostly driven by rainfall , given that the depletion of susceptibles is short-lived and within the epidemic season; whereas for longer-lasting immunity , both rainfall and the nonlinear intrinsic dynamics of the system would play a role . The corresponding log-likelihoods of these two solutions ( −1265 . 0 and −1266 . 8 , respectively ) suggest that the short and effectively negligible duration of immunity provides a better explanation for the data , though both modes , since they fall in the confidence region , are statistically consistent with the data . To compare these further , we examine the resulting dynamics of the cases from the perspective of the dominant periodicities using wavelet analysis ( see [27] , [28] for a description of this method in the context of population dynamics ) . Figure S12 shows that the wavelet spectrum of the cases corresponds closely to that of rainfall , with the variability at a period of one year reflecting seasonality and showing the timing of large annual epidemics . Variability is also apparent transiently at a number of longer periods , including values of approximately 3 years , especially in the first decade and after 2002 , between 4 and 5 years from 1990 to 2000 , and two years from 2000 . This similarity supports the role of rainfall variability as a main driver of the dynamics of cases , and a response of the system that does not involve the nonlinear dynamics of the disease itself . Representative wavelet spectra for simulations with the three models with the best likelihoods support this conclusion . These are shown in Figure S13 , for the model without rainfall , and the model with rainfall but with the two different lengths of immunity . When rainfall is not included , the model tends to generate cycles of periods from four to five years , and large epidemics whose timing does not match that of observations ( see regions of high power at period one in the wavelet spectrum Figure S13 A and compare with Figure S12 B ) . Another feature that compares poorly with the data are the long intervals with no variability for periods between one and two years that follow large epidemics . These refractory intervals during which there is little signal , even at seasonal time scales , result from the relatively long duration of immunity which reduces the pool of susceptibles after large events . When rainfall is added in the model with similarly long immunity , the timing of epidemics improves but the existence of these refractory intervals persists ( Figure S13 C ) . Finally , simulations from the model with rainfall and a negligible duration of immunity are better able to capture both the timing of the large ( annual ) events , and also many of the interannual features of the variability , in terms of timing and periods ( compare the spectra in Figure S12 B and Figure S13 B ) . To further explore the possibility of an interaction between the longer immunity and rainfall forcing , we considered an intermediate model , with the epidemiological parameters fixed at the values of the best VSEIRS model without rainfall ( see Text S1 ) . Only the rainfall and noise parameters were then fitted to the data . This intermediate model was unable to reproduce the inter-annual oscillations observed in the reported temporal series ( Figure S9 ) . Another parameter of interest is the delay between the latent and current force of infection . This was estimated to be approximately 10 days in the model without rainfall ( in Table 1 ) and around 11 days when rainfall is included ( Table 1; Figure S8 ) . These values are consistent with empirical values of the parasite's development given the observed temperatures in these regions . In this case , the delay represents the combined effect of multiple processes that establish a lag in transmission , relative not only to previous levels of infection but also to precipitation , such as the development of larvae into adult mosquitoes . The value of the delay will further depend on the specific variable chosen to represent rainfall's influence; here , accumulated rainfall in the previous five months . Another parameter estimate of interest is the reporting rate which was found to be low , with a small fraction of the infected population detected by the surveillance system in the two districts ( see in Table S2 , Table S3 , Table S4 and Figure S7 ) . The performance of our best VSEIRS model suggests the feasibility of forecasting malaria cases as a function of previous rainfall . Moreover , the short duration of immunity , which here involves full protection to both disease and infection , further suggests that the depletion and replenishment of susceptibles , that is at the heart of intrinsic interannual cycles in infectious disease dynamics , does not play an important role here , despite the low reporting rate . This is confirmed by simulations with a fixed and large value of that display essentially the same patterns as those of the best-fit model . Hence , the nonlinear dynamics of the disease in this model are not crucial for the interannual cycles and simpler statistical models may also perform well for forecasting cases aggregated for the whole season . We considered both a standard linear regression model and a mixture model that effectively implements a threshold response to rainfall and an increasing variance as a function of rainfall ( Figure 1C ) ( see Methods and Text S1 for details on the statistical models and measures to evaluate predictions ) . For Barmer , the VSEIRS model with rainfall exhibits the best performance overall , regardless of how we measure prediction performance , with the mixture model second in terms of prediction likelihoods and with fairly high values of prediction skill for both models ( Table 2 ) . For Kutch , high values of prediction skill are also found especially for the VSEIRS model with rainfall ( Table 2 ) . Prediction likelihoods place the VSEIRS model with rainfall on top ( Table 2 ) . Overall , the VSEIRS model without rainfall exhibits a very low predictive ability for Kutch , as expected . This is not so for Barmer , possibly because the impact of rainfall is strongly concentrated in one single extreme event , for the extreme rains of 1994 . We have considered above forecasts of the total accumulated cases for the whole epidemic season . It is also of interest to predict the time course of cases during the epidemic season . The dynamical model of transmission with rainfall appears valuable for this purpose . This is illustrated by Figure 4 with hindcast predictions from one to four months ahead , for the rise of the peaks , immediately after the monsoons at the end of August , and for their decay , after the typical timing of the main peak , at the end of December . We examined the robustness of our results by considering the data for Kutch and the more complex model , in which individuals can acquire clinical immunity and contract infections that lack severe symptoms ( and do not contribute to reported cases ) but retain the ability to transmit to mosquitoes . This formulation explicitly differentiates susceptibility to disease from that to infection , and does not allow for full immunity to both disease and infection . Interestingly , the likelihood of this model when rainfall is included ( −1251 . 0 ) justifies the added complexity ( Table S1 ) . Also , this model performs better than its counterpart without rainfall ( −1261 . 1 ) ( see Table S1 ) , as reflected by the substantial decrease in the levels of process noise ( Table S2 and Table S3 ) . This performance is also evident in a high prediction skill ( 90% ) and prediction log-likelihood ( −161 . 3 ) ( Table 2 and Figure S11 ) . The prediction performance of the model can be further evaluated by defining an epidemic ( or extreme event ) based on a threshold for the accumulated total number of cases during the epidemic season . We can then quantify the forecasted probability of an epidemic and compare this to the observed occurrence of large outbreaks ( Table 3 ) . Simulations of the model ( and the resulting medians for the total cases in the epidemic season ) correctly predict four out of five epidemic years , with 2 false positives and one false negative ( Table 3 ) . Despite the higher likelihood of this model and its predictive ability , several parameters are poorly identified , as shown by their large confidence intervals , in particular the parameters determining the duration of the classes associated with clinical immunity ( and ) ( Table S3 ) . By contrast the estimate of the delay in the force of infection improves , with the confidence interval reduced to ( 6 . 2–28 . 4 ) days with the best likelihood corresponding to a value around 12 days ( Figure S10 ) . The wavelet spectrum of simulations generated with this model closely resemble that of the cases , as illustrated with a representative simulation in Figure S12 A . Finally , to further compare the temporal patterns generated by this model to the data in a way that accounts for the uncertainty in the parameters , we considered the correlation value between total rainfall , accumulated during the monsoon season , and the cases , accumulated during the epidemic season . This value is influenced by both the timing and the interannual variability of the peaks in cases . We simulated repeatedly from the fitted model by resampling the sets of plausible parameters identified while investigating the parameter space , with probabilities according to their likelihood . For each simulation , we computed the predicted correlation and generated in this way a distribution for this statistic for the two models ( the model with and without rainfall ) . Figure 5 shows the comparison of the observed correlation to the two distributions of correlations from simulations of these models . Whereas the distribution of the model without rainfall results in a very low probability of the observed association ( ) , its counterpart for the model with rainfall peaks at the observed correlation ( ) . This further supports rainfall as the main driver behind the temporal variability of cases , a conclusion that is robust to the uncertainty in parameter values . A more detailed comparison of the models will be reported elsewhere [29] . We discuss below the implications of these findings .
We have proposed a computational approach to formally compare hypotheses on the respective roles of extrinsic forcings vs . intrinsic feedbacks in dynamical systems for which time series data are available but provide only a partial measurement of the relevant variables . This extends previous efforts on this inverse problem by incorporating climate covariates explicitly in dynamical models that also include both measurement uncertainty and dynamic noise . Our approach complements time series methods based on phenomenological , autoregressive models , developed to address the role of covariates and density-dependence in ecology and epidemiology ( e . g . [30]–[32] ) . Concerns on the limitations of ( linear ) correlative analyses to infer the role of climate drivers in the interannual variability of malaria and other infectious diseases [33] can be addressed directly by formulating different hypotheses as epidemiological models . Beyond a better understanding of temporal disease patterns , such models can also contribute valuable tools for forecasting purposes . Ultimately , any early-warning system should benefit from an ensemble of models , including epidemiological models driven by climate variability . We have applied the proposed approach to demonstrate the impact of rainfall on the interannual cycles of epidemic malaria in desert regions of India , by incorporating a climate variable into a stochastic dynamical model of disease transmission . Our approach directly confronted different hypotheses on the population dynamics of the disease based on time series data . The findings on the predominant role of rainfall are robust to consideration of more complex epidemiological models of malaria transmission with a different representation of immunity [25] . The same approach should be applicable to other nonlinear systems and to other vector-transmitted diseases in particular , including those for which the nonlinear population dynamics of the disease is especially relevant . One example is dengue for which evidence of a temporal association with the El Niño Southern Oscillation ( ENSO ) differs across geographic location and study [34] , [35] , consistent with the complex multi-strain dynamics of the disease [36] . For malaria , the role of climate variability in East African highlands could be revisited , given previous conflicting evidence in these areas where the interplay of immunity and climate is likely [7] , [9] , [10] , . Even in more ‘stable’ malaria regions with seasonal behavior , the question of interannual climate variability might be of relevance and worth examining in retrospective records . Our results with the simpler model suggest that immunity , in the sense of complete protection to both infection and disease , is of short duration and negligible at the population level in these semi-arid regions . These regions are expected to lie at one extreme of continuum in the gradient from stable to unstable , or endemic vs . epidemic . In this case , climate variability and not epidemiological dynamics are expected to be the main driver , as proposed for desert fringes in Africa [9] , [38] . We have used , however , the term ‘effective’ population immunity to emphasize that our results apply to the dynamical role of ( full ) immunity at the spatial scale of districts , and do not necessarily imply that individual immunity is also short . Disease risk may be , for example , spatially heterogeneous within districts . We have tested this possibility by considering a reduced total population of susceptibles , systematically lower than that of the whole district , but obtained similar results regarding immunity and rainfall . In a more complex situation , high risk areas may shift their location from year to year , tending to mask the effect of immunity at the more aggregated spatial level of districts . Detection of immunity patterns in this case would require analyses and data at higher spatial resolutions; although they might still be effectively inconsequential at the aggregated level of districts . The bimodal character of the likelihood surface with two peaks , corresponding respectively to two different durations of immunity , raises the possibility that longer time series with a higher number of interannual cycles may support an interaction of rainfall with the nonlinear dynamics of the disease ( via the longer-lasting immunity ) . However , the analyses of the patterns of variability generated by the model for these two peaks in parameter space do not support this conjecture . Additional data could be considered in future work by identifying multiple districts , or different areas within districts , for which it is sensible to fit more than one time series simultaneously . Our second model follows naturally from the result that immunity as included in a typical SEIRS framework , meaning protection against disease and infection , appears negligible . The better performance of the model with clinical immunity indicates that a more complex representation of the epidemiological dynamics is warranted , and further suggests the importance of the contribution of asymptomatic infections to transmission , especially as a reservoir during the low season . In particular , the incorporation of two pathways , associated with more than one time scale of the recovery to disease susceptibility , appears warranted . This better performance is consistent with more detailed models of malaria transmission in the literature that consider the different effects or underlying mechanisms of immunity in malaria ( e . g . [24] , [26] , [39] ) . These are typically confronted to age prevalence curves especially for endemic regions; it is of interest here , that time series patterns per-se also support a more complex structure of immunity than that typically used for childhood diseases , and do so for an epidemic region . Long time series patterns for malaria have not been analyzed before from this perspective; they are of particular relevance in epidemic regions where the interannual variation is high . It is also of interest to note that most mathematical arguments on the role of intrinsic disease dynamics in the interannual variability of epidemic malaria ( in highland regions ) rely essentially on analytical approaches related to dominant or resonant frequencies of SEIRS-type models ( e . g . [7] , [13] ) . The understanding of intrinsic interannual cycles in infectious diseases in general is influenced by the rich literature on this subject in childhood diseases that confer full immunity ( see [40] ) . The biology of malaria and our results here suggest that such understanding is not likely to simply transfer in a relevant way . The consideration of epidemiological models in the analysis of population-level time series provides a natural link to , and opens the door for , analytical approaches [13] , [41] to malaria's cycles based on empirical patterns . However , we are here at the limit of model complexity that retrospective data on a single epidemiological variable ( the number of cases ) can support , as shown by the poor identifiability of specific parameters , especially those associated with the duration of clinical immunity . This is not surprising given the obvious trade-offs between epidemiological parameters that become possible in this more complex model: for example , individuals may remain for shorter times in the infectious but clinically immune class if we increase their contribution to the force of infection . Despite this limitation , it is interesting that even for an epidemic region , where transmission is intermittent and at its limit for disease persistence , we can clearly improve the fit of the data by including asymptomatic infections . The low estimate of the reporting rate also suggests the possibility of a considerable number of asymptomatics and a larger disease burden than reflected by the detected cases . Microscopy has been shown to underestimate the diagnosis of malaria compared to PCR ( polymerase chain reaction ) in similarly low prevalence settings [42] . This may be particularly relevant in the Indian context , as part of the slides examined for malaria are not obtained from patients seeking medical care but from active surveillance ( see Methods ) . In our model , the reservoir provided by infectious individuals that lack severe symptoms plays a dynamical role primarily at seasonal scales in the decay and trough of epidemics [29] rather than at interannual ones , with the interannual signal in the data largely captured by the variability of the rainfall forcing . Future work with this more realistic model should consider independent measurements of specific epidemiological parameters , as well as the age distribution of clinical and non-clinical infections , to constrain the dimensionality of the search . This would allow a better understanding of the possible mechanisms and temporal scales of a reservoir in transmission , given that class plays two different roles in this model that are difficult to separate: it provides a reservoir for infection at low levels and for longer times than the symptomatic class , while also keeping individuals protected from clinical disease . Our results suggest that this protection might be long lasting , once acquired , requiring only low levels of re-infection ( with a low value of the coefficient ) . This may reflect a low ( antigenic ) diversity of P . falciparum in these regions of low transmission intensity . In its current formulation , this kind of long-lasting protection is obtained by making long lasting with only a very small fraction of individuals in this class contributing to the force of infection . A more realistic formulation , currently under investigation , would require and extension of the model that incorporates more continuous levels of susceptibility and infection without increasing model complexity . Alternatives to our likelihood-based approach for inference on nonlinear dynamic systems include spectral matching [43] , gradient matching [44] and Bayesian methodology [45] , [46] . Our choice of likelihood-based methods was influenced by their statistical efficiency ( even in the face of poor estimability of some parameters [47] ) , the availability of computationally efficient numerical algorithms [21] , and the lack of scientifically supported prior distributions for a Bayesian analysis . However , alternative methods could lead to complementary perspectives on data analysis . All statistical methods can be expected to give valid conclusions only when the model under consideration is adequate for the investigation at hand . By comparing a range of models , including empirical statistical models , we can be confident that our mechanistic models have a reasonable level of statistical fit , even once they are penalized for additional model complexity . We cannot , however , rule out the possible existence of superior models leading potentially to differing conclusions . Indeed , we hope and expect that future work will refine the models that we have presented . We anticipate the development of a body of research investigating and explaining fluctuations in epidemic malaria , based on confronting dynamic models to population-level time series data . Finally , our results indicate the feasibility of forecasting malaria epidemics in these desert and semi-arid regions of India based on climate variability . Our epidemiological models including rainfall exhibited high prediction skill for seasonal cases as a function of monsoonal rainfall . This skill is comparable to , and even higher than , that of a purely statistical model that incorporates a threshold and increasing uncertainty with rainfall . Thus , these aspects of the nonlinear response to climate variability , and not the nonlinear dynamics of the disease itself , appear key to variation and prediction of total epidemic size in these regions . However , disease dynamics appear useful to predict the time course of the epidemic curve , that is , the rise and fall of the individual outbreaks following the monsoons . Future investigations should consider other districts to encompass a larger geographic area , as well as the effects of control efforts , socio-economic conditions , and related land-use patterns including irrigation , to tackle the remaining unpredictability in the size of large epidemics . Other aspects of rainfall variability , in particular those pertaining to the monsoon season , should be examined to consider not just short-term prediction but possible implications of changes in the intensity and frequency of extremes with climate change in India [48] . Longer lead times for prediction should also be explored based on climate dynamics and global scale drivers of rainfall .
The malaria data consists of monthly clinical cases from positive slides of Plasmodium falciparum from 1987 to 2007 in Kutch and from 1985 to 2005 in Barmer , two large semi-arid and desert districts of North-West India in the states of Gujarat and Rajasthan respectively ( Figure 1A and Figure S3A; see also map in Figure S1 ) . The epidemiological data reported by the health system are based on two mechanisms: ( a ) active surveillance on a fortnight basis: collection of blood slides from fever patients by house to house visits by a health worker and examination of these slides for malaria parasites at the Primary/Community Health Center of that area; ( b ) passive surveillance: examination of blood slides from fever patients reporting directly to the Primary/Community Health Center . Both types of data are pooled and analyzed for each village . For this study , epidemiological data were collected from the office of the District Malaria Officer . Monthly accumulated rainfall for 20 years of data was obtained from local weather stations ( Figure S2 ) . Monthly rainfall data were supplied by the Indian Meteorological Department , Pune ( India ) . For Kutch rainfall was recorded at Bhuj located at 23 15′ N , 69 49′ E and for Barmer at a station located at 25 45′ N , 71 25′ E . Time series for total population size were obtained via interpolation from census data available every 10 years , i . e . , in 1990 and 2000 . For our malaria transmission models , we do not rely on the well-known Ross-Macdonald formulation and its extensions [49] , [50] because they assume that the human and mosquito populations are constant , and track the respective fractions infected . In epidemic regions , mosquito abundances are highly dynamic . Malaria further differs from the well-known models of childhood diseases such as measles that have been studied extensively on the parameter inference front [45] , [51] , in two ways: transmission by a vector and more complex , waning patterns of immunity that have been represented with different structures of the human population ( e . g . [25] , [26] ) . An innovative feature of our model is the inclusion of a parsimonious representation of the vector dynamics motivated by our inference goals . The stage κ represents the latent force of infection or latent per-capita rate of infection from an infected to a susceptible human . This is not the realized force of infection because a mosquito that acquires infection by biting an infective or infectious human cannot immediately transmit the disease through a second bite . The Plasmodium parasite needs to first complete its incubation period , and to do so before the vector dies . To account for the development of the Plasmodium parasite among surviving mosquitoes , we introduce a second variable , λ , representing the current force of infection and consisting of the latent infection lagged by a distributed delay . We specifically consider Gamma-distributed transitions [52] , [53] for the latent period of the force of infection , these are more flexible than the more standard exponential and better suited to developmental times that give rise to a ( temperature-dependent ) lower bound . This parsimonious representation of the vector can be mapped explicitly to , and derived from , the well-known parameters and treatment of mosquitoes in standard malaria models , by rewriting these models as non-autonomous with mosquito abundance as the forcing ( see Text S1 ) . By representing mosquito dynamics implicitly through a model for the force of infection of humans , we avoid explicit consideration of mosquito abundance , survival and behavior . In the absence of mosquito data , we limit our inclusion of vector dynamics to the aspect that is most directly relevant to the human disease . We consider that the main stochasticity in this system arises from variations in vector abundance and behavior , and model this by forcing the rate of change of κ with three different sources of exogenous variability , namely seasonality , climate covariates ( here rainfall ) , and random environmental noise ( multiplicative Gamma noise ) ( see Text S1 for details ) . The form of the climate covariate in this forcing is given by with , a function of the accumulated rainfall over the past months . Accumulated rainfall is given by where is a spline interpolation of the discretely measured monthly rainfall . Here , is a threshold for accumulated rainfall , and is a constant coefficient . From preliminary investigations based on likelihood profiles , we selected months and for Kutch and Barmer . Seasonality is modeled nonparametrically . For the human component , we adopt first the well-established practice of subdividing the population into the following distinct classes: , susceptible to infection ( and disease ) ; , exposed ( i . e . , carrying Plasmodium parasites which have not yet matured into gametocytes ) ; , infected and gametocytemic ( infectious ) ; and , recovered and protected from all but asymptomatic and negligibly gametocytemic reinfections ( Figure 2A ) . The total population size is supposed known by interpolation from census , and the birth rate into the susceptible class is set to ensure that . We refer to this model hereafter as VSEIRS ( for the vector and population classes respectively ) . To examine the robustness of our results , we consider then a more complex representation of immunity that differentiates between two classes of infected individuals , and in so doing , adds the possibility of clinical immunity ( Figure 2B , ) . This formulation follows that of [25] and differentiates between susceptibility to disease and infection [24] . It is one of several possible representations that follow from the pioneer work of Dietz et al . ( 1974 ) on malaria models that recognize different levels and roles of immunity . We incorporate here two classes of infected individuals , corresponding to two levels of infection , for clinical and asymptomatic cases , respectively . The latter retain the ability to transmit to mosquitoes but at a reduced rate . Two classes of susceptibles allow us to differentiate individuals lacking protection to clinical disease from those protected from disease but retaining susceptibility to ( asymptomatic ) infection . Thus this model incorporates clinical immunity ( classes and ) but does not allow for the development of full immunity to infection; instead , individuals can be re-infected and maintain in this way their protection to disease . This is meant to represent in a simple way that immunity to disease is acquired by repeated exposure [54] , [55] , and its maintenance depends on repeated re-infection [26] , [50] , [56]–[58] . The model effectively introduces two pathways , and therefore , two different temporal scales , for the acquisition and loss of immunity: a first pathway between fully susceptible individuals and severe infection and back , which allows for repeated symptomatic infections and the resulting acquisition of protection from disease; a second pathway through less severe infections , which potentially allows for a longer lasting removal from the pool of individuals susceptible to disease . Sustaining clinical immunity ( by keeping individuals in this pathway ) would require very different levels of re-infection ( from to ) in different regions depending on the parasite's ( antigenic ) diversity and therefore , on transmission intensity . In epidemic regions , where such diversity is presumably low , the rate of repeated re-infections would also be low but individuals may be nevertheless effectively protected from clinical disease for a long time once they have transitioned to asymptomatic infection because they have been exposed to much of the existing diversity . The corresponding system of stochastic differential equations and details on the seasonal and stochastic forcing are given in Text S1 . To complete the model , we need to specify the relationship between the continuous-time dynamic system and the data on monthly reported malaria cases at the discrete set of observation times . We assume that , on average , a fraction of the people moving from class to class are detected by the surveillance system . Specifically , we model conditional on the history of the dynamic system as ( 1 ) where is the negative binomial distribution with mean and variance . The negative binomial distribution provides a model for count data that includes the possibility of overdispersion relative to Poisson or binomial models , and permits both under-reporting and over-reporting . To estimate parameters but also to compare among different models representing different hypotheses on the origin of the interannual variation of malaria epidemics , we used a recently developed likelihood-based inference technique for stochastic differential equations based on iterating filtering , a sequential Monte Carlo optimization method [21] , [22] , [51] . The method allows the comparison of different mechanistic models that include stochasticity , non-linearity , and non-observed states ( see Text S1 ) . The algorithm is briefly explained and summarized in Text S1 . We implemented iterated filtering via the mif function of the R package pomp [59] , which carries out the algorithm detailed in the supplement to [22] . To investigate the information in the data about specific parameters , in the absence of constraints on other parameters , we used profile likelihood methods . The confidence intervals resulting from these profiles are based on likelihood ratio tests . Thus , our confidence intervals enjoy the properties of likelihood ratio tests and are in particular robust to weak identifiability of other parameters [47] . Of course , inasmuch as the parameter itself is weakly identified , its profile will be flat and its confidence interval wide . To investigate forecasting , we focus on the task of predicting the total malaria incidence in the peak transmission months of September through December based on information available at the end of August . We challenge the different models to predict the reported incidence rather than the actual number of cases: the former measures the burden on the public health services and the latter is an unobserved quantity which is linked to the reported cases through an unknown reporting rate . We compare mechanistic models with and without rainfall , a linear prediction based on accumulated rainfall during the May through August monsoon season , and a nonlinear mixture model where the chance of the epidemic component of the mixture depends on accumulated rainfall [60] . The main idea of the mixture model is to capture the threshold response of cases as a function of an environmental covariate ( here , rainfall ) , and allow for different means and variances as a function of this covariate . We constructed a mixture model motivated by [60] in which the Poisson mixture components of [60] were replaced with a negative binomial distribution ( see details in Text S1 ) . This overdispersed distribution is better suited to the large variance of the data ( Figure 1C and Figure S3C ) , given the limitation that the variance equals the mean imposed by a Poisson distribution . The predictive ability of the models was evaluated retrospectively in two different ways: first , prediction skill was measured by comparing for each season the error of the model's prediction to that of a trivial and uninformative model that simply predicts the mean total cases , with both errors normalized by a variance ( see Text S1 ) . This tells us how much better is our ability to predict than that of trivially using the mean . A value close to one indicates high prediction skill , whereas a value close to zero or even negative indicates poor skill . The second approach is based on likelihoods and evaluates how likely the observed total number of cases is for a given season , given forecasts from the different models . This approach is illustrated in detail in Text S1 and Figure S4 . To examine the dominant temporal scales present in the observed and simulated time series , we used wavelet analysis ( e . g . [27] , [28] ) . The wavelet spectrum differs from its predecessor , the Fourier power spectrum , in that it describes the distribution of the variance in the data not just as a function of the different frequencies but also as a function of their localization in time . This is achieved by decomposing the signal with a family of functions ( wavelets ) whose support is local , and differs in this way from the sines and cosines of Fourier analysis . The local nature of the wavelet power spectrum makes it better suited to characterize patterns of variability whose dominant periods change over time . See Cazelles et al . [27] for a detailed explanation of wavelet analysis in the context of population dynamics , and [28] for an application to epidemiology . | Malaria epidemics can exhibit pronounced variation from year to year that can be driven by external forcings , such as climate , or can be generated instead by dynamic feedbacks within the disease system itself . For example , levels of immunity in the population ( or control efforts ) can rise and fall as the result of past levels of infection . This type of feedback is found in the dynamics of all ( nonlinear ) biological systems . Feedbacks can interact in complex ways with external drivers , for example by creating refractory periods . It remains a challenge to identify internal feedbacks vs . external forcings from available temporal records of aggregated reported cases and forcing variables . We propose a quantitative approach that can statistically compare the hypotheses of feedbacks vs . forcings ( epidemiological vs . climate ) based on dynamical and mechanistic models . Our approach is computational , based on a large number of computer simulations of the different models . We illustrate and apply the approach to the analysis of extensive monthly records for malaria incidence in desert regions of India that span two decades . Our analyses confirm the strong role of rainfall , and quantify this effect with transmission model ( s ) for malaria that include rainfall and are shown to exhibit a remarkable prediction skill . | [
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] | 2010 | Forcing Versus Feedback: Epidemic Malaria and Monsoon Rains in Northwest India |
The spread of dengue ( DEN ) worldwide combined with an increased severity of the DEN-associated clinical outcomes have made this mosquito-borne virus of great global public health importance . Progress in understanding DEN pathogenesis and in developing effective treatments has been hampered by the lack of a suitable small animal model . Most of the DEN clinical isolates and cell culture-passaged DEN virus strains reported so far require either host adaptation , inoculation with a high dose and/or intravenous administration to elicit a virulent phenotype in mice which results , at best , in a productive infection with no , few , or irrelevant disease manifestations , and with mice dying within few days at the peak of viremia . Here we describe a non-mouse-adapted DEN2 virus strain ( D2Y98P ) that is highly infectious in AG129 mice ( lacking interferon-α/β and -γ receptors ) upon intraperitoneal administration . Infection with a high dose of D2Y98P induced cytokine storm , massive organ damage , and severe vascular leakage , leading to haemorrhage and rapid death of the animals at the peak of viremia . In contrast , very interestingly and uniquely , infection with a low dose of D2Y98P led to asymptomatic viral dissemination and replication in relevant organs , followed by non-paralytic death of the animals few days after virus clearance , similar to the disease kinetic in humans . Spleen damage , liver dysfunction and increased vascular permeability , but no haemorrhage , were observed in moribund animals , suggesting intact vascular integrity , a cardinal feature in DEN shock syndrome . Infection with D2Y98P thus offers the opportunity to further decipher some of the aspects of dengue pathogenesis and provides a new platform for drug and vaccine testing .
Dengue ( DEN ) virus belongs to the Flaviviridae family , Flavivirus genus , and is the causative agent of DEN disease , a mosquito-borne illness that is endemic in subtropical and tropical countries [1] . With approximately half of the world's population residing in DEN endemic regions [2] and more than 50 million new infections projected to occur annually [3] , DEN certainly poses as a global economic and health threat . Infection with one of the four DEN serotypes can be asymptomatic or trigger a wide spectrum of clinical manifestations , ranging from mild acute febrile illness to classical dengue fever ( DF ) , and to severe dengue hemorrhagic fever/dengue shock syndrome ( DHF/DSS ) , characterized by fever , hemorrhagic tendency , thrombocytopenia , and capillary leakage according to the WHO guidelines [4] . Despite the increasing attention and research efforts devoted to DEN in recent years , the cellular and molecular mechanisms responsible for DEN pathogenesis remain largely unknown . Current hypotheses for the development of severe DEN that involve dysfunction of the host immune system include enhancing mechanisms induced by sub-neutralizing cross-reactive antibodies and memory T cells [3] , [5] . Other non-enhancing mechanisms implicating the immune system include auto-immune responses against cross-reactive viral components , such as DEN non-structural 1 ( NS1 ) protein [6] , [7] . Platelet lysis , nitric oxide-mediated apoptosis of endothelial cells and complement activation have also been proposed to mediate thrombocytopenia and vascular leakage [8] . In addition , host genetic predisposition [9]–[11] and virus virulence [12] , [13] were reported as risk factors for the development of severe DEN . No effective drugs or vaccines against DEN are currently available on the market [14] . Undeniably , progress in deciphering the mechanisms responsible for DEN pathogenesis and in developing effective prophylactic and/or therapeutic treatments has been impeded by the lack of suitable animal models [15] . Humans and mosquitoes represent so far the only natural hosts for DEN virus . Non-human primates have been reported to be permissive to DEN infection but no apparent clinical symptoms of the disease were observed [16] , [17] , although a recent study reported signs of hemorrhage in rhesus macaques intravenously infected with a high dose of a DEN2 virus strain [18] . In addition , since the infected animals develop transient viremia and antibody responses , they have been useful for evaluating the efficacy of vaccine and antiviral candidates prior to clinical trials in humans [19] , [20] . However , for ethical and economical reasons , non-human primates do not represent a sustainable option for DEN research . Alternatively , the mouse model has been explored [15] . However , most of the DEN virus laboratory strains and clinical isolates do not replicate efficiently in mice . Mouse-adapted DEN virus strains displayed a higher infectivity but led to irrelevant clinical manifestations such as paralysis [21] , [22] . Alternatively , a variety of mouse genetic backgrounds have been explored that displayed greater susceptibility to DEN infection [23]–[30] . Among them , AG129 mice , deficient in interferon ( IFN ) - α/β and -γ receptors , were shown to allow effective replication of DEN virus [30]–[33] . However , great heterogeneity in the susceptibility of these mice to DEN virus strains , even within the same serotype , was reported [32] with none or few of DEN disease manifestations [30] . Moreover , administration of high viral doses was necessary to trigger a virulent phenotype which resulted in the animals' death within few days at the peak of viremia [30] . This is in contrast to humans for whom signs of severe DEN generally occur during or after defervescence when DEN virus is no longer detectable in the patient's blood [3] , [34] , [35] . Here we describe a unique non mouse-adapted strain of DEN virus serotype 2 ( D2Y98P ) which is highly infectious in AG129 mice upon intraperitoneal administration . Infection with a high viral dose of D2Y98P resulted in an acute model of infection with mice dying at the peak of viremia , whereas infection with a low viral dose led to asymptomatic dissemination and replication of the virus followed by death of the animals after the virus has been cleared from its host .
All the animal experiments were carried out under the guidelines of the National University of Singapore animal study board . The virus strain used in this study ( D2Y98P ) derives from a 1998 DEN2 Singapore human isolate that has been exclusively passaged for about 20 rounds in Aedes albopictus C6/36 cells . C6/36 cells ( ATCC# CRL-1660 ) were maintained in Leibovitz's L-15 medium ( GIBCO ) supplemented with 5% fetal calf serum ( FCS ) , and virus propagation was carried out as described previously [32] . Virus stocks were stored −80°C . When necessary , heat-inactivation of the virus was performed at 55°C for 15 min . Plaque assay was carried out to quantify the number of infectious viral particles using BHK-21 ( Baby Hamster Kidney , ATCC# CCL-10 ) cells as described previously [36] with slight modifications . Briefly , BHK cells were cultured to approx . 80% confluency in 24-well plates ( NUNC , NY , USA ) . The virus stock was 10-fold serially diluted from 10−1 to 10−8 in RPMI 1640 ( GIBCO ) . BHK-21 monolayers were infected with 100 ul of each virus dilution . After incubation at 37°C and 5% C02 atmosphere for 1 hr with rocking at 15 min intervals , the medium was decanted and 1 ml of 1% ( w/v ) carboxymethyl cellulose in RPMI supplemented with 2% FCS was added to each well . After 4 days incubation at 37°C in 5% CO2 , the cells were fixed with 4% paraformaldehyde and stained for 30 min with 200 µl of 1% crystal violet dissolved in 37% formaldehyde . After thorough rinsing with water , the plates were dried and the plaques were scored visually . AG129 [129/Sv mice deficient in both alpha/beta ( IFN-α/β ) and gamma ( IFN-γ ) interferon receptors] were obtained from B&K Universal ( UK ) . They were housed under specific pathogen-free conditions in individual ventilated cages . Eight to 9 week-old mice were administered with 107 to 102 plaque forming units ( PFU ) of D2Y98P via the intraperitoneal ( ip . ) route ( 0 . 4 ml in sterile PBS ) . Where indicated , mice were inoculated with the same dose and volume of heat-inactivated D2Y98P . Systemic antibody titres against D2Y98P were determined by enzyme-linked immunoadsorbent assay ( ELISA ) as described previously [32] . Briefly , 96-well plates ( Corning costar , NY , USA ) were coated overnight at 4°C with 105 PFU of heat-inactivated D2Y98P virus in 0 . 1M NaHCO3 buffer at pH 9 . 6 . Two-fold serially diluted serum samples ( 1∶25 to 1∶25 , 600 ) were added to the wells and incubated for 1 hr at 37°C . HRP-conjugated anti-mouse IgM ( Chemicon ) or IgG ( H+L ) ( Bio-rad ) secondary antibody were used at a 1∶3 , 000 dilution . Detection was performed using SigmaFast™ O-phenylenediamine dihydrochloride substrate ( Sigma Aldrich ) according to the manufacturer's instructions . The reaction was stopped with 75 µl of 1M H2SO4 and absorbance was read at 490 nm using an ELISA plate reader ( Bio-rad model 680 ) . ELISA titres were defined as the reciprocal of the highest serum dilution that equals to 3 times the absorbance reading from uninfected mouse serum sample . PRNT was carried out as described previously [36] with modifications . Briefly , mouse serum samples were heated at 56°C for 30 min to inactivate complement . Two-fold serial dilutions of the sera ( 1∶10 to 1∶10 , 240 in RPMI 1640 ) were mixed in 96-well plates with an equal volume containing 30 PFU of D2Y98P , and incubated at 37°C for 1 hr with rocking every 15 min . Each mix ( 100 µl ) was transferred onto BHK monolayers grown in 24-well plates , and incubated at 37°C for 1 hr . The mix was decanted , and plaque assay was carried out as described above . The percentage of plaque reduction was derived relative to the control consisting of virus mixed with uninfected serum: [1- ( number of plaques in test wells/number of plaques in control wells ) *100] . Fifty percent neutralization titres ( PRNT50 ) were determined for each sample by fitting a variable sigmoidal curve in GraphPad Prism 5 . 00 ( GraphPad Software ) . Data are expressed as the reciprocal of the highest serum dilution for which PRNT50 is obtained . Blood samples were collected in 0 . 4% sodium citrate and centrifuged for 5 min at 6 , 000 g to obtain plasma . The presence of infectious viral particles was determined by plaque assay as described above . To assess the levels of infectious virus in the tissues from infected mice , the animals were euthanized and perfused systemically with 50 ml sterile PBS . Whole tissue from the brain , intestines , liver and spleen were harvested from individual mice , kept on ice and their wet weights were recorded prior to any further processing . Samples were then trimmed and homogenized using a mechanical homogenizer ( Omni ) for 5 minutes in 1 ml RPMI 1640 at medium speed on ice . Thoroughly homogenized tissues were clarified by centrifugation at 14 , 000 rpm for 10 min at 4°C to pellet debris . The supernatant was filter-sterilized using a 0 . 22 µm diameter pore size filter and the volume was recorded . The level of infectious virus within the filtrate is thus considered representative of the total level of infectious virus present in the harvested organ . Ten-fold serial dilutions of each filtrate ( from neat to 1∶105 ) were assayed in a standard virus plaque assay on BHK-21 cells as described above . Triplicate wells were run for each dilution of each sample . Data are finally expressed as log10 [mean ± SD] in PFU per gram of wet tissue with a limit of sensitivity set at 1 . 0 log10 PFU/g of tissue . Five mice per time point per group were assessed . Results are representative of two experiments . Mice were euthanized , and tissues were harvested and immediately fixed in 10% formalin in PBS . Fixed tissues were paraffin embedded , sectioned and stained with Hematoxylin and Eosin ( H&E ) . Vascular leakage was assessed using Evans Blue dye as a marker for albumin extravasation as described previously [30] , [37] with modifications . Briefly , 0 . 2 ml of Evans blue dye ( 0 . 5% w/v in PBS ) ( Sigma Aldrich ) were injected intravenously into the mice . After 2 hrs , the animals were euthanized and extensively perfused with sterile PBS . Vascular permeability in the tissues was determined visually and quantitatively; the tissues were harvested and weighed prior to dye extraction using N , N-dimethylformamide ( Sigma; 4 ml/g of tissue wet weight ) at 37°C for 24 hrs after which absorbance was read at 620 nm . Data are expressed as fold increase in OD620nm per g of tissue wet weight compared to the uninfected control . Cytokine ( IFN-γ , TNF-α and IL-6 ) expression levels were measured in individual serum samples using individual detection kits ( R&D ) , according to the manufacturer' instructions . After incubation with detection antibodies and streptavidin-PE complexes , absorbance was read at 450 nm . Five mice per group and per time point were used . Mouse blood samples were collected in K2EDTA and serum tubes ( Biomed Diagnostics ) . Whole blood was immediately analysed for cell counts using automated hematology analyzer Cell Dyn – 3700 ( Abbott ) . Serum alanine ( ALT ) and aspartate ( AST ) aminotransferases , and albumin levels were quantified using chemistry analyzer COBAS C111 ( ROCHE ) . The results were analyzed using the unpaired Student t test . Differences were considered significant ( * ) at p value <0 . 05 .
To test the infectious potential of the D2Y98P strain , AG129 mice were intraperitoneally ( ip . ) infected with 10-fold serially diluted viral doses ranging from 107 to 102 PFU . Survival rates indicated that infection with 104 PFU and above induced 100% mortality whereas 20% and 90% survival rates were observed in animals infected with 103 and 102 PFU , respectively ( Fig . 1 ) . Moreover , in mice infected with lethal doses , a clear correlation between viral dose and time-of-death was observed , with increased heterogeneity as the infectious dose is lower . Upon infection with 107 and 106 PFU , initial clinical signs included ruffled fur and hunched posture , which further progressed to bloatedness , lethargy , diarrhoea-like symptoms , moribund state and finally death of the animals . None of the mice exhibited paralysis or significant body weight loss during the course of infection ( Fig . 2A ) . In contrast , upon infection with 105 PFU and below , no signs of diarrhoea were observed and near moribund state , rapid body weight loss was measured ( Fig . 2B ) . Mice ip . inoculated with heat-inactivated D2Y98P ( 107 PFU equivalent ) displayed none of the disease manifestations or death . In addition , neither disease manifestation nor transient viremia was observed in immunocompetent Balb/c and C57Bl/6 mice ip . infected with 107 PFU of D2Y98P ( data not shown ) . Although both viral doses eventually induced 100% mortality in AG129 mice , ip . infection with 107 and 104 PFU of D2Y98P gave very different disease kinetics , suggesting that different mechanisms and players are involved in the disease progression . We thus decided to further characterize both the “acute” and “delayed” models of DEN infection . Systemic virus titres were monitored over the course of infection for both viral doses . In mice infected with 107 PFU , the peak of viremia ( 105 PFU/ml ) coincided with the animals' death at 5 days p . i . ( Fig . 3A ) . In contrast , in mice infected with 104 PFU , viremia peaked at around 104 PFU/ml at 6 days p . i . , followed by viral clearance from the blood circulation prior to animal death ( Fig . 3B ) , similar to the disease kinetic described in severe DEN patients [3] , [34] , [35] , [38] . Furthermore , specific IgM and IgG antibody titres were monitored over the course of infection . Significant IgM but weak IgG responses were measured in mice infected with 107 PFU which both peaked at the time of death , 5 days p . i . ( Fig . 3C ) . Instead , in mice infected with 104 PFU , significant IgG antibody titers were detected which progressively increased over time , while the IgM antibody response peaked at day 10 p . i . and waned by day 18 p . i . ( Fig . 3D ) . Neutralizing antibody titres correlated with the IgG antibody responses ( Fig . 3E&F ) . Gross pathological examination of the organs within the intraperitoneal cavity from moribund animals infected with 107 PFU of D2Y98P revealed overt abnormalities that included a severely distended stomach , a significantly enlarged spleen and focal areas of haemorrhage in the liver , observable after systemic perfusion of the mice with saline ( Fig . 4A ) . These features were not observed in moribund animals infected with 104 PFU ( data not shown ) . Tissue tropism and kinetic of viral replication were determined in the intestines , liver , spleen , and brain from animals infected with either 107 or 104 PFU of D2Y98P . No infectious viral particles were detected in the intestines . In the spleen , liver and brain , the kinetic of the virus titers corresponded to the viremia profile; in animals infected with 107 PFU , virus titres in the infected organs increased logarithmically in conjunction with disease advancement , reaching their highest at the time of death ( Fig . 4B-D ) . Instead , in animals infected with 104 PFU , the virus titres peaked at 5 or 6 days p . i . in the liver , spleen and brain , and progressively dropped until complete clearance by day 8 p . i . ( Fig . 4B-D ) . Interestingly , the peak of virus titres achieved in the liver and spleen was comparable in both animal groups whereas peak titres in the brain ( Fig . 4D ) and plasma ( Fig . 3A&B ) were about 1 log higher in mice infected with 107 PFU . Brain , spleen , liver and intestines were harvested from mice infected with 107 or 104 PFU of D2Y98P over the course of infection . Histological examination of H&E stained-sections from animals infected with 107 PFU revealed progressive damage at both tissue and cellular levels which culminated at the time of death ( Fig . 5A ) . The well defined limits of the splenic red and white pulp began to blur by day 3 p . i . ( data not shown ) and the spleen architecture was completely lost by day 5 p . i . ( Fig . 5A ) . A larger magnification revealed the presence of apoptotic debris . The liver displayed focal areas of haemorrhage and edema of cell masses . Lymphoid aggregates and inflammatory infiltrates were also detected at the portal tract and within the sinusoidal spaces of the liver ( data not shown ) . At the cellular level , extensive cytopathic effects that included hepatocyte swelling , cytoplasmic vacuolation and degeneration were observed . Liver damage was reflected by the significantly increased levels of aspartate ( ALT ) and alanine ( AST ) transaminases measured in the serum of the infected animals ( Fig . 5B ) . Interestingly , despite the absence of detectable virus particles in the intestines , these tissues displayed marked infiltration of inflammatory cells and extensive architectural distortion at moribund state ( Fig . 5A ) . Severe detachment and disintegration of the intestinal villi resulting in a debris-filled intestinal lumen was noted . In animals infected with 104 PFU of D2Y98P , no visible organ damage was noticeable at the peak of viremia , 6 days p . i . ( Fig . 5A ) . However , at moribund state , the splenic architecture was severely impaired to an extent comparable to that observed in animals infected with 107 PFU . In contrast , the liver and intestines were moderately affected with only localized areas of visible damage . Moderate but significant increase in the systemic levels of ALT and AST was measured at moribund state ( Fig . 5B ) , indicative of some liver dysfunction . Apart from slight vascular congestion , brain sections from both animal groups did not display any significant pathological changes at any time post-infection ( Fig . 5A ) . Vascular leakage , a hallmark of severe DEN infection in humans , was investigated in D2Y98P-infected AG129 mice using Evans blue dye extrusion assay [30] , [37] . At moribund state , severe vascular leakage was observed ( Fig . 6A ) and measured ( Fig . 6B ) in the spleen , liver and intestines from animals infected with 107 PFU compared to uninfected controls . Consistently , significant decreased levels in serum albumin were measured in these infected animals , indicative of plasmatic proteins leakage ( Fig . 6C ) . In animals infected with 104 PFU , marginal dye extrusion was observed in the liver , intestines and spleen at the peak of viremia ( 6 days p . i . ) whereas at moribund state , dye extrusion was markedly increased in all the organs examined ( Fig . 6A&B ) . The extent of leakage in the liver and intestines was lesser than that observed in mice infected with 107 PFU , whereas dye extrusion in the spleen was as high as in the animals infected with 107 PFU ( Fig . 6B ) . Interestingly , and in contrast to animals infected with 107 PFU , serum albumin concentration measured in animals infected with 104 PFU was significantly higher than that measured in uninfected control animals ( Fig . 6C ) , suggestive of hemoconcentration . Enhanced cytokine production may lead to increased vascular permeability and has been proposed to contribute to DHF/DSS pathogenesis [39] , [40] . The expression profile of three key pro-inflammatory cytokines , namely IFN-γ , IL-6 and TNF-α , was monitored over the course of infection in the serum of animals infected with 107 or 104 PFU of D2Y98P . In animals infected with 107 PFU , the cytokine expression levels increased consistently over time and peaked at the time of death of the animals ( Fig . 7 ) . In contrast , in animals infected with 104 PFU , the production of these pro-inflammatory cytokines corresponded to the viremia profile , peaking at day 6 p . i . , followed by a progressive decline to reach basal production levels at moribund stage ( Fig . 7 ) . Of note , peak values of the systemic levels of these three cytokines were significantly higher in animals infected with 107 PFU compared to animals infected with 104 PFU . Hematological disorders have been associated with DEN disease and tentatively used as diagnostic and prognostic markers [41] , [42] . Total counts of red blood cells ( RBC ) , white blood cells ( WBC ) , lymphocytes , platelets and neutrophils were monitored in D2Y98P-infected mice over the course of infection ( Table 1 ) . In animals infected with 107 PFU , significant increase in RBC concentration and hematocrit was measured at day 3 p . i . compared to uninfected controls , indicative of hemoconcentration . At moribund state however ( day 5 p . i . ) , the levels of RBC and hematocrit dropped , suggestive of hemorrhage . However , the levels of WBC , neutrophils and platelets increased substantially over time . Transient depletion in lymphocyte counts was observed at day 3 p . i . followed by significant increase at day 5 p . i . In animals infected with 104 PFU , progressive increase in RBC counts and hematocrit was observed over the course of infection , indicative of hemoconcentration . WBC , neutrophils , and platelets levels similarly increased progressively and reached peak values at 10 days p . i . At moribund state however , the levels measured were comparable to those measured in uninfected controls . Transient lymphopenia was observed at the peak of viremia ( day 6 p . i . ) followed by a very significant increase at day 10 p . i . Basal lymphocytes level was measured at moribund state . Altogether , the hematological parameters indicate that infection with 107 PFU of D2Y98P led to haemorrhage tendency , whereas infection with 104 PFU resulted in hemoconcentration . Remarkably , no evidence of thrombocytopenia was observed in the infected animals as reflected by the platelets counts which were not found statistically different from the uninfected controls .
A growing number of immunocompetent , immunosuppressed and humanized mouse models of DEN infection have been explored , using an increasing number of mouse-adapted or cell-culture passaged DEN virus strains . However , none of these have so far managed to recapitulate all the clinical symptoms and manifestations of DEN disease as observed in humans . As humans and mosquitoes represent the only two natural hosts for DEN virus , it is unrealistic to hope address all the features of DEN pathogenesis in a single mouse model . However , previous studies have shown that it is possible to reproduce , and thus study , one or few aspects of DEN pathogenesis in a specific mouse model of DEN infection defined by a particular mouse background infected with a specific DEN virus strain through a particular route of administration and at a particular infectious dose . For example , a mouse model of DEN hemorrhage has recently been reported through intradermal infection of immunocompetent mice with a high dose of the non-mouse adapted DEN2 virus strain 16681 originally isolated from a DHF patient [43] , [44] . Likewise , a humanized mouse strain infected subcutaneously with various DEN virus strains reportedly displayed clinical signs of DEN fever , including fever , viremia , erythema , and thrombocytopenia [45] . Similarly , the AG129 mouse model has allowed the investigation of some aspects of DEN pathogenesis including virus tropism , vascular leakage , and pathogenesis in context of a functional adaptive immune system [33] . Furthermore , the AG129 mouse background has proven useful for vaccine and drug testing [31] , [32] . However , the lack of IFN α/β− and γ−signalling draws some limitations and calls for cautious interpretation of the findings and observations made in this mouse model . Furthermore , the susceptibility of AG129 mice to DEN infection appears to greatly depend on the DEN virus strain [32] and a limited number only have so far been reported to result in a productive infection with no , few or irrelevant clinical manifestations [30] , [32] . Moreover , administration of high viral doses was necessary to trigger a virulent phenotype which resulted in animal death within few days at the peak of viremia [30] . Here we describe a non mouse-adapted DEN virus strain , D2Y98P , which is highly infectious in AG129 mice . D2Y98P is a serotype 2 DEN virus strain originally isolated in 1998 from a Singapore DEN-infected patient whose disease status at the time of sample collection , and disease outcome are unfortunately not known . The virus has been exclusively amplified in mosquito cells for less than 20 rounds . Interestingly , an earlier passage ( P13 ) displayed a more attenuated virulent phenotype upon infection of AG129 mice ( G . Tan , personal communication ) . This observation therefore suggests that mutation ( s ) have occurred in the viral genome upon amplification in mosquito cells that rendered the virus more virulent . Identification of the nucleotide changes between the two virus passages is currently in progress in our laboratory . Infection of AG129 mice with a high dose ( 107 PFU ) of D2Y98P induced an acute lethal DEN infection where the peak of viremia and virus titres in the infected organs coincided with death of the animals , accompanied by cytokine storm , massive organ damage , and severe vascular damage leading to haemorrhage . It is thus likely that in this acute model of DEN infection , the pathological events are a consequence of both virus-induced cell death and massive inflammation reaction [39] , [40] . Such virulent phenotype is similar to that described previously by Shresta and colleagues using the D2S10 DEN virus strain [30] . In contrast , infection of AG129 mice with a lower dose ( 104 PFU ) of D2Y98P led to a transient asymptomatic systemic viral infection followed by death of the animals few days after viral clearance , similar to the disease kinetic described in humans [3] , [38] . A strong neutralizing IgG antibody response was measured in the infected animals and is likely to be involved in the viral clearance . Although increased vascular permeability ( as indicated by increased serum albumin concentration and Evan's blue dye extrusion ) was observed in the moribund animals , the actual cause of the animals' death remains elusive . Apparent destruction of the splenic architecture and liver dysfunction at moribund stage are likely to contribute to the sickness . Furthermore , as the disease progressed , infected animals appeared lethargic and displayed reduced motility . This may result in reduced water intake and dehydration of the animal , hence contributing to the sharp body weight loss observed towards moribund stage and consequently leading to animal death . Widespread immune activation in response to acute DEN infection has been well documented in DEN patients , and circulating levels of various pro-inflammatory cytokines were found to be elevated in patients with severe DEN [40] . Likewise , the levels of three key pro-inflammatory cytokines implicated in DF/DHF , namely IL-6 , TNF-α and IFN-γ , were significantly elevated in the D2Y98P-infected AG129 mice and were directly dependent on the initial infectious dose . Consistently , extensive damage of various organs including the spleen , liver and intestines was observed in animals infected with a high viral dose ( 107 PFU ) . In contrast , lower levels of cytokine production in animals infected with a low viral dose ( 104 PFU ) correlated with milder organ damage except for the spleen that appeared at moribund stage , to be as extensively damaged as in animals infected with a high viral dose; the absence of infectious viral particles in the moribund animals excludes a direct virus cytopathic effect but rather suggests some immunological disorder that may arise from the overstimulation of immune cells possibly by persistent viral antigens . In contrast to the liver and spleen , no histological damage or abnormalities were detected in the brain of animals infected with 107 PFU or 104 PFU , although infectious viral particles were readily detected in this tissue after systemic perfusion . This observation suggests that the virus is capable of extravasating from the systemic circulation and cross the blood-brain barrier but may not effectively replicate in the brain . Therefore , in this mouse model , and as reported in dengue patients [46] , [47] , meningitis and/or encephalitis may not contribute significantly to disease severity . The action of a variety of cytokines , chemokines , and other soluble mediators on endothelial cells has been proposed to affect vascular permeability during DEN infection [39] . Vascular leakage is a hallmark of DHF/DSS leading to hemoconcentration and hemorrhagic manifestations [41] , [48] , as observed in mice infected with 107 PFU of D2Y98P for whom focal areas of haemorrhage were observed in the liver , and low hematocrit and serum albumin levels were measured . In this animal group , high levels of pro-inflammatory cytokines are likely responsible for the observed severe vascular leakage , particularly in the intestines where no infectious viral particles were detected . However , in mice infected with 104 PFU , neither significant vascular leakage nor hemorrhage was detected at the peak of viremia despite elevated levels of IFN-γ , IL-6 and TNF-α . Instead , increased vascular permeability was clearly observed at moribund stage where the production of these three cytokines has returned to basal level . This observation suggests that other pro-inflammatory cytokines may be involved in the increased vascular permeability observed in this low viral dose infection model . Indeed , in addition to IFN-γ , IL-6 and TNF-α , a number of cytokines , chemokines and other soluble mediators have been demonstrated or proposed to play a role in vascular leakage in DEN disease [39] . Alternatively or additionally , other mediators previously proposed to increase vascular permeability such as immune complexes [49] , nitrite oxide production [39] , or cross-reactive anti-NS1 antibodies [6] , [7] , may be at play . Furthermore , hemoconcentration and increased serum albumin level suggests that fluid only but not proteins or cells , leaks from the blood vessels . Increased vascular permeability without morphological damage of the capillary endothelium is believed to be the cardinal feature of DSS [39] , [49] and thus appears to be reproduced in this mouse model of DEN infection . Further investigation is however needed to decipher the actual mechanisms underlying this phenomenon . Remarkably , thrombocytopenia , a hallmark of severe disease in DEN patients , was not detected in the animals infected with D2Y98P virus , regardless of the initial infectious dose . Transient drop in platelet counts has been previously observed in a number of mouse models of DEN infection [15] including AG129 [33] , ruling out the possibility that the lack of IFNγ signalling in these mice would impair the mechanism ( s ) involved in thrombocytopenia . The absence of thrombocytopenia in our model may thus be inherent to the D2Y98P virus strain . A number of immunological mechanisms and effectors have been proposed to play a role in thrombocytopenia during DEN infection [50]–[53] , but the differential ability of DEN virus strains to induce thrombocytopenia in a single model of DEN infection has never been investigated . In conclusion , the attractiveness of the D2Y98P strain lies in its ability to induce , without the need for mouse-adaptation and upon peripheral administration of a low viral dose , a virulent phenotype in AG129 mice with a productive viral replication and dissemination accompanied by some relevant clinical manifestations , including disease kinetic , organ damage/dysfunction and increased vascular permeability . This model thus offers the opportunity to further dissect some of the mechanisms involved in DEN pathogenesis with the caveat that AG129 mice are defective in IFN signalling . Furthermore , the induction of a disease kinetic where the time-of-death window is distinct from the viremic phase makes this low viral dose model unique and an attractive platform for assessing the efficacy of DEN vaccine and drug candidates . | The spread of dengue ( DEN ) worldwide combined with an increased severity of the DEN-associated clinical outcomes have made this mosquito-borne virus of great global public health importance . Infection with DEN virus can be asymptomatic or trigger a wide spectrum of clinical manifestations , ranging from mild acute febrile illness to classical dengue fever and to severe DEN hemorrhagic fever/DEN shock syndrome ( DHF/DSS ) . Progress in understanding DEN disease and in developing effective treatments has been hampered by the lack of a suitable animal model that can reproduce all or part of the disease's clinical manifestations and outcome . Only a few of the DEN virus strains reported so far elicit a virulent phenotype in mice , which results at best in an acute infection where mice die within few days with no , few or irrelevant disease manifestations . Here we describe a DEN virus strain which is highly virulent in mice and reproduces some of the aspects of severe DEN in humans , including the disease kinetics , organ damage/dysfunction and increased vascular permeability . This DEN virus strain thus offers the opportunity to further decipher some of the mechanisms involved in DEN pathogenesis , and provides a new platform for drug and vaccine testing in the mouse model . | [
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] | 2010 | A Non Mouse-Adapted Dengue Virus Strain as a New Model of Severe Dengue Infection in AG129 Mice |
Mycoplasma pneumoniae is a causative agent of atypical pneumonia . The formation of hydrogen peroxide , a product of glycerol metabolism , is essential for host cell cytotoxicity . Phosphatidylcholine is the major carbon source available on lung epithelia , and its utilization requires the cleavage of deacylated phospholipids to glycerol-3-phosphate and choline . M . pneumoniae possesses two potential glycerophosphodiesterases , MPN420 ( GlpQ ) and MPN566 . In this work , the function of these proteins was analyzed by biochemical , genetic , and physiological studies . The results indicate that only GlpQ is an active glycerophosphodiesterase . MPN566 has no enzymatic activity as glycerophosphodiesterase and the inactivation of the gene did not result in any detectable phenotype . Inactivation of the glpQ gene resulted in reduced growth in medium with glucose as the carbon source , in loss of hydrogen peroxide production when phosphatidylcholine was present , and in a complete loss of cytotoxicity towards HeLa cells . All these phenotypes were reverted upon complementation of the mutant . Moreover , the glpQ mutant strain exhibited a reduced gliding velocity . A comparison of the proteomes of the wild type strain and the glpQ mutant revealed that this enzyme is also implicated in the control of gene expression . Several proteins were present in higher or lower amounts in the mutant . This apparent regulation by GlpQ is exerted at the level of transcription as determined by mRNA slot blot analyses . All genes subject to GlpQ-dependent control have a conserved potential cis-acting element upstream of the coding region . This element overlaps the promoter in the case of the genes that are repressed in a GlpQ-dependent manner and it is located upstream of the promoter for GlpQ-activated genes . We may suggest that GlpQ acts as a trigger enzyme that measures the availability of its product glycerol-3-phosphate and uses this information to differentially control gene expression .
Pathogenic bacteria have developed a large battery of enzymes and mechanisms for extracting nutrients from their hosts , and the requirement for nutrient acquisition can be regarded as one of the driving forces for virulence [1]–[3] . In consequence , the metabolic capabilities of a pathogen reflect its adaptation to a particular niche in a particular host . Mycoplasma pneumoniae is a causative agent of atypical pneumonia , however implication of this bacterium in several additional infections including encephalitis , aseptic meningitis , acute transverses myelitis , stroke , and polyradiculopathy has been reported [4]–[7] . These bacteria are members of the taxonomic class Mollicutes that are characterized by an extreme reductive evolution that results in the smallest genomes that allow independent life . Moreover , the mollicutes have lost the cell wall and most metabolic pathways , since they obtain the building blocks for their cellular macromolecules from the host tissue . However , even in these minimal pathogens , there is a close relation between metabolism and virulence ( for a review , see [8] ) . M . pneumoniae thrives at the apical surface of lung epithelia . Thus , these bacteria must have evolved to utilize the carbon sources present in this niche . The pulmonary surfactant is composed of about 90% phospholipids and 10% proteins [9] . This suggests that phospholipids play a major role in the nutrition of M . pneumoniae . Glycerophospholipids , the major building blocks of the cell membrane in bacteria and eukaryotes , are degraded in several steps . First , the fatty acids are cleaved from the phospholipids resulting in the formation of glycerophosphodiesters . In these molecules , the phosphate group of glycerol-3-phosphate is linked to another compound , called the head group . In eukaryotes , choline is by far the most abundant head group , and lecithin , the choline-containing phospholipid accounts for about 80% of all phospholipids in human lung cells [9] . In the second step , the choline head group is cleaved due to the activity of a glycerophosphodiesterase resulting in the formation of glycerol-3-phosphate that can feed into glycolysis after oxidation to dihydroxyacetone phosphate ( see Figure 1 ) . In M . pneumoniae , the latter reaction is catalyzed by the glycerol-3-phosphate oxidase GlpD [10] . GlpD transfers the electrons to water resulting in the formation of hydrogen peroxide , the major virulence factor of M . pneumoniae [11] . In consequence , the virulence of M . pneumoniae glpD mutant cells is severely attenuated [10] . While the metabolism of glycerol has been well studied in M . pneumoniae and other mollicutes such as M . mycoides [10] , [12] , [13] , only little is known about the glycerophosphodiesterases required for lipid utilization . Many bacteria encode multiple glycerophosphodiesterases . In E . coli , both enzymes are enzymatically active in lipid degradation; however , they are differentially regulated , with GlpQ and UgpQ being induced in the presence of glycerol-3-phosphate and under conditions of phosphate starvation , respectively [14] , [15] . In B . subtilis , out of three putative glycerophosphodiesterases , only GlpQ has been studied . The corresponding gene is under dual control and its expression is induced when phosphate becomes limiting and glycerol is available [16] , [17] . Moreover , glpQ expression is repressed if more favourable carbon sources such as glucose are present [18] . In Haemophilus influenzae , another bacterium thriving in the respiratory tract , the glycerophosphodiesterase is involved in pathogenicity . The enzyme generates choline , which in turn is used for the biosynthesis of the bacterial lipopolysaccharide layer - a major virulence determinant of Gram-negative bacteria [19] , [20] . Similarly , glycerophosphodiesterase activity is implicated in virulence of different Borrelia species . The enzyme is only present in the relapsing fever group and may help the bacteria to reach a higher cell density in the blood of the host as compared to Lyme disease spirochaetes [21] . In many bacteria , central enzymes of metabolism do not only fulfil their catalytic function , but in addition , they are also involved in signal transduction . In this way , the information on the availability of important metabolites can be directly determined by the enzyme in charge of their conversion , and this information is then often transferred to the transcription machinery . Collectively , such enzymes have been termed trigger enzymes [22] . They can control gene expression by directly acting as DNA- or RNA-binding transcription factors as the E . coli proline dehydrogenase and the aconitase or by controlling the activity of transcription factors by covalent modification or a regulatory protein-protein interaction as observed for several sugar permeases of the bacterial phosphotransferase system and the B . subtilis glutamate dehydrogenase , respectively [23]–[26] . In this work , we have analyzed the role of the two potential glycerophosphodiesterases encoded in the genome of M . pneumoniae . Biochemical and physiological studies demonstrate that one of the two proteins , GlpQ , is a functional glycerophosphodiesterase . GlpQ is essential for hydrogen peroxide formation in the presence of deacylated phospholipids as the carbon source and , in consequence , for cytotoxicity . Moreover , GlpQ may act as a trigger enzyme by controlling the expression of a set of genes encoding lipoproteins , the glycerol facilitator , and a metal ion ABC transporter .
Since phospholipids are the most abundant potential carbon sources for M . pneumoniae living at lung epithelial surfaces , we considered the possibility that these bacteria synthesize enzymes that cleave the polar head groups from the glycerophosphodiesters to produce glycerol-3-phosphate that can be utilized by the enzymes of glycerol metabolism [10] ( Figure 1 ) . Two genes that potentially encode such enzymes are present in the genome of M . pneumoniae , i . e . mpn420 ( renamed to glpQ ) and mpn566 . An alignment of the corresponding proteins to glycerophosphodiesterases from other bacteria is shown in the supporting information ( Figure S1 ) . In order to assess the biochemical properties and physiological relevance of the putative glycerophosphodiesterases , their corresponding genes , glpQ and mpn566 , were cloned into the expression vector pGP172 , thus allowing a fusion of the proteins to an N-terminal Strep-tag facilitating purification . The recombinant proteins were purified and the activities were first determined using glycerophosphocholine ( GPC ) as the substrate and a set of divalent cations . As shown in Figure 2 , purified GlpQ was active against GPC , and the activity was highest in the presence of magnesium ions ( 10 mM ) . Manganese and zinc ions did also support activity , although to a lesser extent ( Figure 2 ) . In contrast , the enzyme was inactive in the presence of calcium and cobalt ions ( data not shown ) . The activity assay with purified MPN566 revealed no activity with GPC , irrespective of the cation present in the assay ( data not shown ) . We did also test the activity of both proteins with glycerophosphoethanolamine and glycerophosphoglycerol . However , neither protein was active with any of these substrates . Thus , our data demonstrate that GlpQ is active as a glycerophosphodiesterase , whereas MPN566 does not exhibit such an activity . The two proteins GlpQ and MPN566 share ∼58% identical residues . Thus , it seems surprising that MPN566 was inactive in the enzymatic assay . However , several residues that are known to be important for the activity of glycerophosphodiesterases are conserved in GlpQ but not in MPN566 ( Figure S1 ) . These residues include Trp-36 and Glu-38 as well as the conserved HD motif ( Asn-51 and Leu-52 in MPN566 ) and Phe-110 . Interestingly , a similar arrangement with two GlpQ-like proteins is also observed in M . genitalium , and as in M . pneumoniae , one protein has all the conserved residues characteristic for glycerophosphodiesterases , whereas the second protein has similar deviations from the consensus as MPN566 ( Figure S1 ) . In order to test whether a restoration of the conserved residues would also convert MPN566 to a biologically active glycerophosphodiesterase , we replaced the five amino acids that differ from the consensus by those residues present in GlpQ . The resulting mutant allele was cloned into pGP172 , and purification attempted . Unfortunately , this protein was highly unstable and purification was impossible . The analysis of mutants is one of the most powerful tools for studying gene functions and bacterial physiology . The isolation of desired M . pneumoniae mutants became possible only recently by the introduction of the “Haystack mutagenesis” [27] . To get more insights into the physiological role of the glycerophosphodiesterases GlpQ and its paralog MPN566 , we attempted to isolate mutants affected in the corresponding genes . The strategy of “Haystack mutagenesis” is based on an ordered collection of pooled random transposon insertion mutants that can be screened for junctions between the transposon and the gene of interest due to transposon insertion . The 64 pools were used in a PCR to detect junctions between the glpQ or mpn566 genes and the mini-transposon using the oligonucleotides SS35 and SS40 ( for the respective genes ) and SH30 ( for the mini-transposon ) [28] ( Figure 3 ) . Positive signals were obtained for both genes . From pools that gave a positive signal , colony PCR with the 50 individual mutants resulted in the identification of the desired glpQ and mpn566 mutants . The presence of the transposon insertion in both genes was verified by Southern blot analysis ( Figure 3 ) . To test whether these strains contained only unique transposon insertions , we did another Southern blot using a probe specific for the aac-aphD resistance gene present on the mini-transposon . As shown in Figure 3 , only one band hybridizing with this probe was detected in each strain , moreover , this fragment had the same size as the AgeI or PstI/SacI fragment hybridizing to the glpQ and mpn566 probe , respectively ( Figure 3 ) . The isolated glpQ and mpn566 mutant strains were designated GPM81 and GPM82 . The position of the transposon insertion in the two genes was determined by DNA sequencing . The glpQ gene was disrupted between nucleotides 517 and 518 , resulting in a truncated protein of 172 amino acids with one additional amino acid and the following stop codon encoded by the inserted mini-transposon . The disruption of the mpn566 gene was located between nucleotides 157 and 158 , resulting in a truncated protein of 52 amino acids with one additional amino acid and the following stop codon . First , we compared the ability of the wild type strain and the two mutant strains to utilize glucose and glycerol as the single carbon sources ( Figure 4 ) . As an additional control , we used the glpD mutant strain GPM52 . This strain is defective in glycerol-3-phosphate oxidase and therefore unable to utilize glycerol as the only carbon source [10] . As shown in Figure 4A , the wild type and the glpD and mpn566 mutant strains grew well with glucose . In contrast , the glpQ mutant GPM81 grew more slowly and did not reach the final biomass as compared to the other strains . As reported previously , the wild type strain exhibited very slow growth with glycerol as the only carbon source [29] . In this respect , the glpQ and mpn566 mutants were indistinguishable from the wild type . As reported previously , the glpD mutant strain did not grow at all in glycerol-containing medium [10] . In conclusion , the active glycerophosphodiesterase GlpQ is required for maximal growth in the presence of glucose , whereas its absence does not interfere with the slow growth in the presence of glycerol . Since the disruption of glpQ affected the growth properties of the bacteria , we wondered whether this might reflect changes in cell morphology and in the movement of the bacteria . The morphology of the wild type and mutant bacteria was analyzed by scanning electron microscopy , and no differences were detected ( Figure S2 ) . An analysis of the gliding velocities of the three strains revealed that the wild type strain glided with a velocity of 0 . 32±0 . 09 µm/s , whereas the glpQ and mpn566 mutants exhibited velocities of 0 . 2±0 . 08 µm/s and 0 . 3±0 . 1 µm/s , respectively ( Videos S1 , S2 , and S3 ) . Thus , the active glycerophosphodiesterase GlpQ is required for full gliding velocity of the bacteria . The utilization of glycerol or glycerophosphodiesters results in the generation of hydrogen peroxide , the major cytotoxic product of M . pneumoniae . We asked therefore whether the glpQ and mpn566 disruptions would affect hydrogen peroxide formation and if so , whether it also affects cytotoxicity . Hydrogen peroxide formation was assayed in M . pneumoniae cultures that contained glucose , glycerol , GPC , glycerol-3-phosphate or no carbon source . In the absence of an added carbon source , neither the wild type strain nor the mutants formed substantial amounts of hydrogen peroxide ( Figure 5 ) . It is interesting to note that the wild type and the mpn566 mutant formed some hydrogen peroxide even in the absence of any added carbon source . This might result from the presence of low concentrations of phospholipids in the medium . Similarly , essentially no hydrogen peroxide was produced in the presence of glucose . If glycerol was available , maximal hydrogen peroxide formation ( 9 . 5 mg/l ) was observed in the wild type strain . In the glpD mutant that served as a control , no hydrogen peroxide was formed . This is in good agreement with previous reports on the increase of hydrogen peroxide generation in the presence of glycerol and its dependence on a functional glycerol-3-phosphate oxidase [10] . The hydrogen peroxide production in the glpQ and mpn566 mutants was similar to that observed in the wild type strain . This result reflects that the metabolite glycerol is downstream from the glycerophosphodiesterase activity . In the presence of GPC , the wild type strain produced similar amounts of hydrogen peroxide ( 9 mg/l ) as in the presence of glycerol . In contrast , no hydrogen peroxide formation was detected for the glpQ mutant GPM81 , whereas the disruption of mpn566 did not have any effect on the production of hydrogen peroxide ( Figure 5 ) . This result is in good agreement with the enzymatic activities of the two proteins: GlpQ is the only active glycerophosphodiesterase in M . pneumoniae , and no glycerol-3-phosphate , the substrate of GlpD , can be formed in its absence , whereas MPN566 is dispensable for the utilization of GPC . We also tested the ability of the M . pneumoniae strains to form hydrogen peroxide in the presence of glycerophosphoethanolamine and glycerophosphoglycerol . These compounds did not stimulate hydrogen peroxide in any of the strains tested ( data not shown ) . This is in excellent agreement with the result of the enzyme assay that suggested that neither GlpQ nor MPN566 is able to degrade these substances . Finally , we tested whether hydrogen peroxide was formed in the presence of glycerol-3-phosphate . As shown in Figure 5 , no significant formation of hydrogen peroxide was observed in any of the strains tested . This suggests that the uptake of glycerol-3-phosphate is rather inefficient . To assess the cytotoxicity of the different M . pneumoniae strains , we infected confluently grown HeLa cell cultures with M . pneumoniae cells ( multiplicity of infection: 2 ) . The cytotoxicity of the mutants was compared to that of the wild type strain and M . pneumoniae GPM52 that is affected in glpD . As shown in Figure 6 , the HeLa cells had undergone nearly complete lysis after four days upon infection with wild type M . pneumoniae ( cytotoxicity of 89% ) . As observed previously , the glpD mutant GPM52 has a reduced cytotoxicity ( 51% ) resulting in a large portion of viable cells after infection [10] . For the glpQ mutant GPM81 , nearly all HeLa cells had survived the infection suggesting that GlpQ is essential for cytotoxicity . In contrast , cytotoxicity induced by the mpn566 mutant strain GPM82 was equivalent to that of the wild type strain ( Figure 6 ) . These data clearly demonstrate that the active glycerophosphodiesterase GlpQ is required for host cell damage , whereas MPN566 is not . Moreover , they support the assumption that hydrogen peroxide formation is the major factor that contributes to host cell damage . In order to exclude the possibility that the phenotypes observed with the glpQ mutant are due to a polar effect on the downstream alaS gene , we compared the alaS transcript levels in the wild type strain and the glpQ mutant . We observed fourfold increased amounts of alaS mRNA levels in the glpQ mutant , most likely due to the presence of a strong promoter in the transposon ( data not shown ) . The expression of the alaS gene in the glpQ mutant strongly suggests that the observed phenotypes are the result of the glpQ disruption rather than of a polar effect . However , to provide unequivocal evidence for the implication of GlpQ in the growth phenotype as well as in hydrogen peroxide production and cytotoxicity , we performed a complementation assay . For this purpose , the M . pneumoniae glpQ gene with its own promoter was cloned into the integrative vector pMTnTetM438 and introduced into the chromosome of the glpQ mutant GPM81 ( for details , see “Materials and Methods” ) . The resulting complementation strain GPM92 and the isogenic glpQ mutant GPM93 carrying the empty vector integrated into the chromosome were analysed for growth in the presence of glucose and glycerol , for hydrogen peroxide formation and for cytotoxicity . As shown in Figure 4A , the complemented mutant grew in the presence of glucose as the wild type strain . In contrast , the control strain carrying the empty vector grew slowly in the presence of glucose and was in this respect indistinguishable from the original glpQ mutant . These data clearly establish that the growth defect of the glpQ mutant in Hayflick medium containing glucose is a specific result of the glpQ inactivation . Similar results were observed for hydrogen peroxide production . Ectopic expression of the glpQ gene in the mutant strain restored the wild type phenotype , i . e . strong hydrogen peroxide formation in the presence of GPC . Again , the empty vector did not alter the phenotype of the glpQ mutant ( see Figure 5 ) . Finally , we assessed the cytotoxicity of the complemented strain towards HeLa cells . As one would expect from the restoration of hydrogen peroxide production upon complementation , the complemented mutant GPM92 was toxic for the HeLa cells , whereas the mutant carrying the control vector was not ( Figure 6 ) . In conclusion , ectopic expression of glpQ complemented all mutant phenotypes thus demonstrating , that the active glycerophosphodiesterase GlpQ is indeed essential for hydrogen peroxide production in the presence of the major substrate glycerophosphocholine and for cytotoxicity of M . pneumoniae . As reported above , the glpQ mutant exhibits multiple phenotypes related to motility , metabolism , and pathogenicity . We asked therefore whether some of the effects are due to changes in the proteome of the glpQ mutant GPM81 . To answer this question , we compared the total protein profiles of the wild type strain and the glpQ and mpn566 mutants , GPM81 and GPM82 , respectively , after growth in glucose and glycerol . While the protein patterns in the mpn566 mutant were indistinguishable from the wild type strain under both conditions , several differences were noted for the glpQ mutant . To identify those proteins that exhibit altered accumulation in the glpQ mutant , the total proteins of the wild type and the glpQ mutant strains were identified by mass spectrometry . For the protein extracts from glucose-grown cells , 532 different proteins were identified . This corresponds to about 77% of the theoretical proteome of M . pneumoniae . In the presence of glycerol , 473 proteins corresponding to 69% of the theoretical proteome were identified . The differences in protein expression between glucose- and glycerol-grown cells as well as proteins that could not be detected at all are summarized in Tables S1 and S2 . A detailed list of the differences of the protein profiles between the wild type strain and the glpQ mutant is presented in Tables S3 and S4 . As expected , the GlpQ protein was detected in the protein extracts of the wild type strain but not in those of the glpQ mutant strain . In glucose-grown cells , 33 and 21 proteins were in elevated and reduced amounts , respectively , in the glpQ mutant . The strongest increase was observed for the glycerol facilitator GlpF and the uncharacterized lipoprotein MPN162 . A strongly reduced accumulation was observed for the lipoprotein MPN506 . In the presence of glycerol , five induced and five repressed proteins were detected ( see Table S4 ) . Five proteins were subject to a identical regulation under both conditions ( Table 1 ) . It has been shown before that changes at the proteome level may result from altered gene expression or from changes in protein stability [30] , [31] . Therefore , we studied the expression of the genes corresponding to the most prominently regulated proteins and of genes encoding potential regulators , transport systems and potential pathogenicity factors . For this purpose , we isolated RNA from cultures grown in modified Hayflick medium supplemented with glucose and performed slot blot analyses ( Figures 7 and S3 ) . These studies demonstrated that the regulation of the glycerol facilitator GlpF and the lipoproteins MPN162 and MPN506 occurs at the level of transcription ( Table 1 ) . Moreover , our results confirmed the higher expression of glpF and mpn162 and the repression of mpn506 in the glpQ mutant . For the other proteins that were induced in the presence of glucose , with exception of plsC and mpn566 ( nearly two-fold higher transcript levels ) , similar accumulation of the mRNAs compared to the protein amount was observed ( Table S3 and Figure S3 ) . In contrast , for the proteins that were present in reduced amounts in glucose-grown cells , no changes of the corresponding mRNAs were observed for all transport proteins . Interestingly , the lipoprotein MPN083 showed a similar pattern at the level of transcription as the induced proteins and the ribonucleoside-diphosphate reductase ( encoded by nrdFIE ) was the only protein with reduced mRNA amounts , however changes in transcript level were not significant ( Table S3 and Figure S3 ) . The proteome and transcription analyses identified three genes that are significantly regulated - either induced or repressed - in a GlpQ-dependent manner . An inspection of the upstream region of these genes revealed the presence of a common palindromic DNA motif ( Figure 8 ) . To exclude the possibility that this motif is randomly distributed in the genome of M . pneumoniae because of the extremely AT-rich consensus sequence , we tested its presence in the genome using the GLAM2SCAN algorithm [32] . In nine cases ( matching score cut-off ≥30 ) , this potential motif was located upstream of open reading frames , among them the three genes mentioned above . Therefore , the expression of the remaining six genes was tested by slot blot analysis , and for two of these genes , cbiO and mpn284 , a significant accumulation and reduction of the mRNA , respectively , was observed ( data not shown; Figure 7 ) . Interestingly , the corresponding proteins , a subunit of a putative metal ion ABC transporter CbiO and the uncharacterized lipoprotein MPN284 were found to be present in higher or lower amounts in the glpQ mutant in glycerol-grown cell . Thus , there is a very good agreement between the regulatory effect of GlpQ at the proteome level , the regulation at the level of transcription , and the presence of the cis-acting element .
Two cytotoxicity factors are known in M . pneumoniae: The formation of hydrogen peroxide and the CARDS toxin that possesses ADP-ribosyltransferase activity [11] , [33] . This work establishes that the glycerophosphodiesterase GlpQ of M . pneumoniae is essential for cytotoxicity of these bacteria . This is in excellent agreement with previous reports that carbon metabolism is intimately linked to virulence in pathogenic bacteria , including M . pneumoniae and other mollicutes [1] , [2] , [8] . The utilization of glycerol and phospholipids plays a particularly important role in the virulence of Mycoplasma species: Hydrogen peroxide , the major cytotoxic substance produced by these bacteria , is generated as a product of glycerol metabolism , and both glpD and glpQ mutants are severely affected in pathogenicity [10 , this work] . In M . mycoides , pathogenicity is associated with the presence of a highly efficient ABC transporter for glycerol . Non-pathogenic strains of M . mycoides rely on the less efficient glycerol facilitator for glycerol uptake [34] . In M . pneumoniae , GlpQ is not only important for virulence but also for growth in the commonly used medium in the laboratory , i . e . Hayflick medium with glucose as the added carbon source ( see Figure 4A ) . This observation is in good agreement with a recent analysis of the M . pneumoniae metabolism that suggested that glycerol is essential for growth of M . pneumoniae [35] . Accordingly , no difference between the wild type strain and the glpQ mutant was observed during growth in the presence of glycerol ( see Figure 4B ) . Therefore , it is tempting to speculate that some glycerophosphodiesters in the Hayflick medium support growth . In addition to GlpQ , M . pneumoniae encodes a second paralogous protein . However , as shown in this work , this protein does not exhibit enzymatic activity nor does the inactivation of the corresponding gene ( mpn566 ) cause any detectable phenotype . This lack of detectable activity of MPN566 is easily explained by the lack of conservation of amino acid residues that are essential for the activity as a glycerophosphodiesterase . Interestingly , a very similar arrangement with two glpQ-like genes is also present in M . genitalium and Mycoplasma alligatoris . Based on the conservation of the catalytically important residues ( see Figure S1 ) , there is an active and an inactive enzyme in M . genitalium , as observed here for M . pneumoniae . In M . alligatoris , both potential glycerophosphodiesterases contain all the important amino acids suggesting that both proteins are enzymatically active . It is tempting to speculate that the possession of two active glycerophosphodiesterases is related to the fact that M . alligatoris is the only mollicute that obligatorily causes fatal infections [36] . In the syphilis spirochaete , Treponema pallidum , one glpQ-like gene is present; however , the encoded protein is not active as a glycerophosphodiesterase . Again , the inactivity is most likely caused by the lack of conservation of functionally important amino acids [37] , [38] . The presence of inactive GlpQ-like proteins in several pathogens , including a spirochaete and M . genitalium , the bacterium with the smallest genome , suggests that these proteins have other functions that have yet to be identified . Unfortunately , the experiments reported in this study did not give any hints as to a putative function of MPN566 . Many proteins have activities in addition to their primary functions . On one hand , this allows gene duplication and specialization to non-related functions of similar proteins . On the other hand , a protein may acquire a second useful activity and act as a so-called moonlighting protein [39] . The former is very common and might apply to the putative functional specialization of GlpQ and MPN566 . In contrast , the latter phenomenon is true for all trigger enzymes that measure the availability of their respective metabolites and transduce this information to the regulatory machinery of the cell . In mammals , a glycerophosphodiesterase controls the development of skeletal muscles independent from its enzymatic activity [40] . Our results suggest that GlpQ might also have such a second activity . Indeed , the expression of the glycerol facilitator GlpF , a lipoprotein , and the ATP-binding subunit of a metal ion ABC transporter are strongly overexpressed in the glpQ mutant , whereas two uncharacterized lipoproteins are less expressed in the mutant . Interestingly , the genes that appear to be repressed by GlpQ are more strongly transcribed in the presence of glycerol as the carbon source ( as compared to glucose ) . In contrast , the two lipoprotein genes mpn284 and mpn506 that require GlpQ for expression are only weakly expressed in the presence of glycerol , but they are strongly induced if glucose is used as the carbon source . These observations might be explained as follows: In the presence of glucose , only little glycerol or glycerol-3-phosphate ( the product of the reaction catalyzed by GlpQ ) is present in the cell . Free GlpQ might then directly bind DNA or trigger the DNA-binding activity of another , yet unknown transcription factor , resulting in repression or activation of the two sets of genes . In the presence of glycerol , glycerol-3-phosphate would be formed due to the activity of glycerol kinase , and this metabolite might then prevent GlpQ from its regulatory activity . As a result , those genes that are subject to GlpQ-dependent repression ( glpF , mpn162 , and cbiO ) are stronger expressed than in the presence of glucose , whereas the GlpQ-activated genes ( mpn284 , mpn506 ) would be less expressed . Finally , in the glpQ mutant , the former set of GlpQ-repressed genes is highly constitutively expressed , and only a very low level of transcription can be detected for the two GlpQ-dependent lipoprotein genes . Since glycerol-3-phosphate is the product of the glycerophosphodiesterase reaction , this metabolite is an excellent candidate for detection by GlpQ . Moreover , the glpQ gene is constitutively expressed and the GlpQ protein was detected in M . pneumoniae cells irrespective of the carbon source used in similar amounts in this study [41 , this study] . Thus , GlpQ is available in the cell under all conditions to cause regulation . In a recent study on the phosphoproteome of M . pneumoniae , phosphorylation of GlpQ was observed [42]; however , no precise phosphorylation site could be detected and predicted , respectively . Therefore , the functional relevance of this modification remains unknown so far . As observed for several other transcription regulators and trigger enzymes , GlpQ exerts both an activating and repressing effect on gene expression . The location of the putative cis-acting element correlates perfectly with the regulatory effect: Those genes that seem to be repressed by GlpQ-dependent manner have this element overlapping or in the very close vicinity of the -10 region of the promoters . This element is the only conserved promoter element in M . pneumoniae and it is sufficient for transcription initiation [30] , [41] , [43] . Binding of GlpQ or of a transcription factor that is controlled by GlpQ would prevent a productive interaction with RNA polymerase and therefore cause transcription repression . On the other hand , the cis-acting elements that may be involved in the regulation of the GlpQ-activated genes are located upstream of the promoters . This is usually the case for binding sites of transcription activators and fits perfect with the observed regulation . Our future work will focus on the elucidation of the mechanism ( s ) by which GlpQ controls gene expression . Moreover , we will address the functions of the lipoproteins that are subject to glycerol- and GlpQ-dependent regulation .
The M . pneumoniae strains used in this study were M . pneumoniae M129 ( ATCC 29342 ) in the 32nd broth passage , and its isogenic mutant derivatives GPM52 ( glpD::mini-Tn , GmR ) [10] , GPM81 ( glpQ::mini-Tn , GmR ) , and GPM82 ( mpn566::mini-Tn , GmR ) . M . pneumoniae was grown at 37°C in 150 cm2 tissue culture flasks containing 100 ml of modified Hayflick medium as described previously [29] . Carbon sources were added to a final concentration of 1% ( w/v ) . Growth curves were obtained by determining the wet weight of M . pneumoniae cultures as described previously [29] . Strains harboring transposon insertions were cultivated in the presence of 80 µg/ml gentamicin and/or 2 µg/ml tetracycline as required . Escherichia coli DH5α and BL21 ( DE3 ) /pLysS [44] were used as host for cloning and recombinant protein expression , respectively . The sequences of the oligonucleotides used in this study are listed in Table S5 . To achieve complementation of the glpQ mutant , we constructed strain GPM92 as follows: The M . pneumoniae glpQ gene including its own promoter was amplified using the primer pair SS245/SS267 . The PCR product was digested with EcoRI and XhoI and cloned between the EcoRI/SalI sites of the integrative plasmid pMTnTetM438 [45] . The resulting plasmid , pGP695 , was introduced by electroporation into the genome of the M . pneumoniae glpQ mutant GPM81 . As a control , we transformed GPM81 with the empty vector pMTnTetM438 . The resulting strain was M . pneumoniae GPM93 . To exclude multiple insertions of the integrative plasmids in the two constructed strains , we performed Southern blot analyses with both mutants using a probe specific for the tetracycline resistance gene . In both cases , unique insertion events were detected . M . pneumoniae chromosomal DNA was prepared as described previously [28] . Finally , digests of chromosomal DNA were separated using 1% agarose gels and transferred onto a positively charged nylon membrane ( Roche Diagnostics ) [44] and probed with Digoxigenin labeled riboprobes obtained by in vitro transcription with T7 RNA polymerase ( Roche Diagnostics ) using PCR-generated fragments as templates . Primer pairs for the amplification of glpQ , mpn566 , aac-ahpD , and tet gene fragments were SS42/SS43 , SS44/SS45 , SH62/SH63 , and SS272/SS273 , respectively ( Table S5 ) . The reverse primers contained a T7 RNA polymerase recognition sequence . In vitro RNA labeling , hybridisation and signal detection were carried out according to the manufacturer’s instructions ( DIG RNA labeling Kit and detection chemicals; Roche Diagnostics ) . The M . pneumoniae genes encoding proteins similar to glycerophosphodiesterases ( glpQ and mpn566 ) were amplified with chromosomal DNA as the template and the primer pairs SS34/SS35 and SS39/SS40 , respectively . The PCR products were digested with SacI and BamHI and cloned into the expression vector pGP172 that allows the fusion of the target proteins to a Strep-tag at their N-terminus [46] . The resulting plasmids were pGP1018 and pGP1020 . Since the glpQ gene contains three TGA codons that are recognized as stop codons in E . coli , these codons were replaced by TGG specifying tryptophan as in M . pneumoniae . For this purpose we applied the multiple mutation reaction [47] using the phosphorylated mutagenesis primers SS36 , SS37 , and SS38 and the external primers SS34 and SS35 . The PCR product was digested and cloned into pGP172 as described above . The resulting expression vector was pGP1019 . The plasmids pGP1019 and pGP1020 allowed the purification of the putative M . pneumoniae glycerophosphodiesterases ( GlpQ and MPN566 ) carrying an N-terminal Strep-tag . A mutant variant of MPN566 was obtained by the multiple mutation reaction using pGP1020 as the template and the phosphorylated mutagenesis primers SS192 , SS193 , and SS194 and the external primers SS39 and SS40 . The PCR product was cloned into pGP172 as described above and the resulting plasmid was pGP661 . The putative glycerophosphodiesterases were overexpressed in E . coli BL21 ( DE3 ) /pLysS . Expression was induced by the addition of IPTG ( final concentration 1 mM ) to exponentially growing cultures ( OD600 of 0 . 8 ) . Cells were lysed using a french press ( 20 . 000 p . s . i . , 138 , 000 kPa , two passes , Spectronic Instruments , UK ) . After lysis the crude extracts were centrifuged at 15 , 000 g for 60 min . The crude extract was passed over a Streptactin column ( IBA , Göttingen , Germany ) . The recombinant proteins were eluted with desthiobiotin ( IBA , final concentration 2 . 5 mM ) . After elution the fractions were tested for the desired protein using 12% SDS-PAGE . Only fractions that contained the desired protein in apparent homogeneity ( content of the specific protein >95% ) were used for further experiments . The relevant fractions were combined and dialyzed overnight . Protein concentration was determined according to the method of Bradford using the Bio-Rad dye-binding assay where Bovine serum albumin served as the standard . Glycerophosphodiesterase activity was measured in a coupled spectrophotometric assay as described previously [48] . The enzyme assay is based on the formation of glycerol-3-phosphate and the subsequent oxidation by the glycerol-3-phosphate dehydrogenase and the formation of NADH . Briefly , 5 µg of glycerophosphodiesterase were incubated with 20 U of rabbit muscle glycerol-3-phosphate dehydrogenase ( Sigma ) in a 0 . 9 M glycine-hydrazine buffer containing 0 . 5 mM glycerophosphodiester and 0 . 5 mM NAD+ in a volume of 1 ml . Divalent cations were added as indicated . NADH formation was determined photospectrometrically at 340 nm . The hydrogen peroxide production in M . pneumoniae was determined using the Merckoquant peroxide test ( Merck , Darmstadt , Germany ) as previously described [10] . Briefly , growing cells were resuspended in assay buffer and after incubation for 1 h at 37°C , glucose , glycerol , glycerol-3-phosphate or glycerophosphodiesters ( final concentration 100 µM ) were added to one aliquot . An aliquot without any added carbon source served as the control . The test strips were dipped into the suspensions for 1 s and subsequently read . Whole cell extracts of the different M . pneumoniae strains were prepared as described previously [31] . In order to analyze the complete proteome , 15 µg of the cell extracts were separated by one-dimensional 12% SDS-PAGE and the gels subsequently stained with Coomassie Brillant Blue R250 dye ( Serva ) . For protein identification , each running lane was cut out into 15 pieces followed by a separate analysis by mass spectrometry . The proteome analyses were performed in triplicate . Gel pieces were washed twice with 200 µl 20 mM NH4HCO3/30% ( v/v ) acetonitrile for 30 min , at 37°C and dried in a vacuum centrifuge ( Concentrator 5301 , Eppendorf ) . Trypsin solution ( 10 ng/µl trypsin in 20 mM ammonium bicarbonate ) was added until gel pieces stopped swelling and digestion was allowed to proceed for 16 to 18 hours at 37°C . Peptides were extracted from gel pieces by incubation in an ultrasonic bath for 15 min in 20 µl HPLC grade water and transferred into micro vials for mass spectrometric analysis . The tryptic digested proteins obtained from the one-dimensional SDS PAGE gel pieces were subjected to a reversed phase column chromatography ( Waters BEH 1 . 7 µm , 100-µm i . d . ×100 mm , Waters Corporation , Milford , Mass . , USA ) operated on a nanoACQUITY UPLC ( Waters Corporation , Milford , Mass . , USA ) . Peptides were first concentrated and desalted on a trapping column ( Waters nanoACQUITY UPLC column , Symmetry C18 , 5 µm , 180 µm × 20 mm , Waters Corporation , Milford , Mass . , USA ) for 3 min at a flow rate of 1 ml/min with 0 . 1% acetic acid . Subsequently the peptides were eluted and separated with a non-linear 80-min gradient from 5–60% acetonitrile in 0 . 1% acetic acid at a constant flow rate of 400 nl/min . MS and MS/MS data were acquired with the LTQ Orbitrap mass spectrometer ( Thermo Fisher , Bremen , Germany ) equipped with a nanoelectrospray ion source . After a survey scan in the Orbitrap ( r = 30 , 000 ) , MS/MS data were recorded for the five most intensive precursor ions in the linear ion trap . Singly charged ions were not taken into account for MS/MS analysis . Tandem mass spectra were extracted using Sorcerer v3 . 5 ( Sage-N Research ) . All MS/MS samples were analyzed using SEQUEST ( Thermo Fisher Scientific , San Jose , CA , USA; version 2 . 7 , revision 11 ) . Database searching was performed against a target decoy database of M . pneumoniae with added common laboratory contaminant proteins . Cleavage specificity for full tryptic cleavage and a maximum of 2 missed cleavages was assumed . SEQUEST was run with a fragment ion mass tolerance of 1 . 00 Da and a parent ion tolerance of 10 ppm . Oxidation of methionine ( +15 . 99492 Da ) and phosphorylation of serine/threonine/tyrosine ( +79 . 966331 Da ) were specified in SEQUEST as variable modifications . Proteins were identified by at least two peptides applying a stringent SEQUEST filter ( Xcorr vs . charge state: 1 . 8 for singly , 2 . 2 for doubly , 3 . 3 for triply , and 3 . 5 for higher charged ions ) . To address protein amount differences between the M . pneumoniae wild type and mutant strains , fold-changes were calculated by comparing number of assigned spectra for each protein ( mutant vs . wild type strain ) . Preparation of total M . pneumoniae RNA was done as previously described [29] . For slot blot analysis , serial twofold dilutions of the RNA extract in 10x SSC ( 2 µg–0 . 25 µg ) were blotted onto a positively charged nylon membrane using a PR 648 Slot Blot Manifold ( Amersham Biosciences ) . Equal amounts of yeast tRNA ( Roche ) and M . pneumoniae chromosomal DNA served as controls . DIG-labelled riboprobes were obtained by in vitro transcription from PCR products that cover ORF internal sequences using T7 RNA polymerase ( Roche ) . The reverse primers used to generate the PCR products contained a T7 promoter sequence ( Table S5 ) . The quantification was performed using the Image J software v1 . 44c [49] . Infection of HeLa cell cultures with M . pneumoniae cells was done as described previously [10] , [31] . After four days upon infection , HeLa cells cultures were stained with crystal violet and photographed . Additionally , lactate dehydrogenase ( LDH ) release of HeLa cell cultures after 2 h of infection was used as an index of cytotoxicity . LDH release was measured with the CytoTox 96 Non-Radioactive Cytotoxicity Assay ( Promega ) according to the manufacturer’s instructions . Results are expressed as cytotoxicity calculated as the percentage of total LDH release after cell lysis with the lysis buffer provided in the kit . The cytotoxicity assays were performed in triplicate . After growing of M . pneumoniae cultures in 5 ml volumes to mid-log , the cells were scraped off and passed ten times through a syringe . Then , 20 µl of this cell suspension were inoculated to 2 ml of Hayflick medium in a Lab-Tek chamber slide ( Nunc ) . After growing cells to mid-log phase , the medium was removed and the cells were washed three times with PBS and fixed with 1% glutaraldehyde for 1 h . The samples were washed three times with PBS and then dehydrated sequentially with 30 , 50 , 70 , 90 , and 100% ethanol for 10 min each . Immediately , the critical point dried of samples was performed ( K850 critical point drier; Emitech Ashfort , United Kingdom ) and sputter coated with 20 nm of gold . Samples were observed using a Hitachi S-570 ( Tokyo , Japan ) scanning electron microscope . After passing through a syringe cells grown in a 5 ml culture , 20 µl of disaggregated cells were inoculated to 2 ml of modified Hayflick medium including 3% gelatine in 14 mm glass bottom culture dishes plates ( MatTek ) . Cell movement was examined at 37°C using a Nikon Eclipse TE 2000-E microscope , and images were captured at intervals of 2 s for a total of 2 min with a digital sight DS-SMC Nikon camera controlled by NIS-Elements BR software . Tracks from 50 individual motile cells corresponding to 2 min of observation and 2 separated experiments were analyzed to determine the gliding velocity and gliding motile patterns . | Mycoplasma pneumoniae serves as a model organism for bacteria with very small genomes that are nonetheless independently viable . These bacteria infect the human lung and cause an atypical pneumonia . The major virulence determinant of M . pneumoniae is hydrogen peroxide that is generated during the utilization of glycerol-3-phosphate , which might be derived from free glycerol or from the degradation of phospholipids . Indeed , lecithin is the by far most abundant carbon source on lung epithelia . In this study , we made use of the recent availability of methods to isolate mutants of M . pneumoniae and characterized the enzyme that generates glycerol-3-phosphate from deacylated lecithin ( glycerophosphocholine ) . This enzyme , called GlpQ , is essential for the formation of hydrogen peroxide when the bacteria are incubated with glycerophosphocholine . Moreover , M . pneumoniae is unable to cause any detectable damage to the host cells in the absence of GlpQ . This underlines the important role of phospholipid metabolism for the virulence of M . pneumoniae . We observed that GlpQ in addition to its enzymatic activity is also involved in the control of expression of several genes , among them the glycerol transporter . Thus , GlpQ is central to the normal physiology and to pathogenicity of the minimal pathogen M . pneumoniae . | [
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"medical",
"microbiolog... | 2011 | A Trigger Enzyme in Mycoplasma pneumoniae: Impact of the Glycerophosphodiesterase GlpQ on Virulence and Gene Expression |
Mycobacterium leprae is not cultivable in axenic media , and direct microscopic enumeration of the bacilli is complex , labor intensive , and suffers from limited sensitivity and specificity . We have developed a real-time PCR assay for quantifying M . leprae DNA in biological samples . Primers were identified to amplify a shared region of the multicopy repeat sequence ( RLEP ) specific to M . leprae and tested for sensitivity and specificity in the TaqMan format . The assay was specific for M . leprae and able to detect 10 fg of purified M . leprae DNA , or approximately 300 bacteria in infected tissues . We used the RLEP TaqMan PCR to assess the short and long-term growth results of M . leprae in foot pad tissues obtained from conventional mice , a gene knock-out mouse strain , athymic nude mice , as well as from reticuloendothelial tissues of M . leprae–infected nine-banded armadillos . We found excellent correlative results between estimates from RLEP TaqMan PCR and direct microscopic counting ( combined r = 0 . 98 ) . The RLEP TaqMan PCR permitted rapid analysis of batch samples with high reproducibility and is especially valuable for detection of low numbers of bacilli . Molecular enumeration is a rapid , objective and highly reproducible means to estimate the numbers of M . leprae in tissues , and application of the technique can facilitate work with this agent in many laboratories .
Because M . leprae can not be grown on synthetic media , the bacilli must be enumerated by direct microscopic counting . Originally developed by Shepard [1] in the 1960's , this technique has survived as the “gold standard” for enumerating M . leprae for almost 50 years . Unfortunately , it is a highly specialized procedure , cumbersome to perform and limited in terms of sensitivity and specificity . Only a few laboratories today have retained the ability to enumerate M . leprae using direct microscopy [2] , [3] . Various methods have been described to minimize error in direct microscopic counting of M . leprae , including the use of special slide coatings , staining procedures , and methods to calibrate microscopes [2] , [4] . However , these steps add to the complexity of the technique and the inherent insensitivity of the method requires that multiple samples be processed in large group sizes in order to reduce error . In addition , direct microscopy has limited clinical utility . For example , M . leprae cannot be differentiated from other acid-fast bacteria by microscopic examination alone , and clinical assessment of suspect biopsies requires that additional tests also be applied when a mixed infection is suspected [5]–[10] . With the development of nucleic acid-based amplification assays , the identification of difficult to grow microorganisms in tissues , including M . leprae , has become routine [11]–[16] . These assays have enhanced our awareness of clinical disease processes , and in some cases have produced new ways to diagnose and monitor mycobacterial infections . Implementing real-time PCR assays adds another potential advantage of direct or indirect quantitation of target DNA . Therefore , we investigated this approach seeking a more precise and reproducible assay for enumerating M . leprae in tissues based on the M . leprae DNA content of tissue specimens using real-time PCR . The M . leprae chromosome contains a family of dispersed repeats ( RLEP ) of variable structure and unknown function [17] . Twenty-nine copies of RLEP exist in the chromosome , each containing an invariant 545-bp core flanked in some cases by additional segments ranging from 44 to 100 bps . We identified DNA sequences for TaqMan PCR primers and fluorescent probe from the M . leprae-specific , invariant region of RLEP . We tested the specificity of the assay against a number of microorganisms , including cultivable mycobacteria and evaluated the sensitivity of the assay for detecting M . leprae by comparing it with direct microscopic counting for accuracy in estimating the number of M . leprae under a variety of experimental conditions employing both the mouse foot pad ( MFP ) model and infected armadillos .
M . leprae , strains Thai-53 or NHDP98 were isolated as previously described [18] and maintained in continuous serial passage in nude mice ( Hsd∶Athymic Nude-Foxn1nu , Harlan Sprague Dawley Inc . , Indianapolis , IN ) . Briefly , M . leprae were harvested from nude mouse foot pad tissues after infection for approximately 6 months . Following CO2 asphyxiation the hind feet are removed and cleaned with 70% ethanol and Betadine to kill surface contaminants . The skin is removed aseptically and the highly bacilliferous tissue excised , minced and homogenized in 10 ml of Middlebrook 7H12 medium without catalase . Tissue debris is removed by slow speed centrifugation ( 50×g ) for 10 minutes and the bacilli-rich supernatant is pelleted ( 10 k×g×10 min ) , resuspended and washed extensively in TE buffer to remove extraneous tissue debris associated with the intact bacilli . The suspension is then enumerated using the method of Shepard et al [1] as described in the MFP Technique below , and viability assessed in axenic culture by the oxidation of 14C-palmitate . Viable M . leprae obtained through serial passage in nude mice were used to infect other mice and armadillos used in this study [19] . Cultivable mycobacteria were grown to late log phase in Middlebrook 7H9 media plus glycerol , Tween 80 and OADC at appropriate temperatures for optimal growth ( Table 1 ) . M . lepraemurium was purified from infected mouse spleens and was a gift from I . Brown , Middlesex , England . Genomic DNA was purified from all mycobacteria by enzymatic lysis as described by Belisle and Sonnenberg [20] . Purified genomic DNA from Streptococcus pyogenes , Staphylococcus epidermidis , Clostridium perfringens , Escherichia coli and Corynebacterium glutamicum were purchased from American Type Culture Collection ( Manassas , VA ) . Growth of M . leprae in the mouse foot pad is determined by direct enumeration of bacilli using the method of Shepard et al [1] , [2] , [21] . Generally , the bacilli are first inoculated through the planter surface of the foot in 30 ul volumes . A localized infection is established and the bacilli are harvested after a suitable time , often 6 months or more . For enumeration , mice are sacrificed and the plantar surfaces of both hind feet are excised with scalpel and forceps . The tissue is minced with scissors before being transferred to a motorized Potter-Elvehjem tissue grinder where it is homogenized to a fine paste for 1 minute . Trypsin-EDTA ( GibcoBRL , Life Technologies , Grand Island , NY ) ( 1 ml ) is added and homogenized with the tissue for an additional 30 seconds before the entire preparation is incubated for 15 minutes at 37°C . After incubation the tissue is ground an additional 30 seconds and the entire contents transferred to a glass Mickle homogenizer with 25 glass beads ( 3 mm ) , capped , and vibrated for 2 minutes . For bacterial enumeration , 10 ul of the homogenized liquid is added to 10 ul of calf serum containing 2% phenol . The suspension is mixed thoroughly and spread evenly over three , 1 cm2 area circles on a premarked counting slide ( Bellco Glass , Inc . , Vineland , NJ ) . After drying in air , slides are fixed in formalin vapor for 3 minutes , using a covered staining dish containing 700 ul of formalin . The fixed slides are then heated on a glass plate over a boiling water bath for 2 minutes . Warmed slides are twice flooded and drained of distilled water containing 0 . 5% gelatin and 0 . 5% phenol , and then heated again for 2 minutes between each treatment , and again before being stained . The bacilli are stained using a modified Fite carbol-fuschin for 20 minutes , and decolorized for 30–40 seconds with 5% sulfuric acid in 25% ethanol . Slides are counterstained with crystal violet before a final wash and air drying [1] , [4] , [21] . Acid-fast bacilli ( AFB ) are then enumerated by direct examination of 20 oil emersion fields in each of the three , 1 cm2 circles , scanning along the horizontal axis of the stained smear using a calibrated microscope . The average number of bacilli in each of three smears is determined and multiplied by the appropriate calibration factor to yield a mean and standard deviation for the AFB count . Care is taken to enumerate only fully stained and intact bacilli avoiding partially stained organisms or those with atypical morphological shapes . Three strains of mice were utilized to assess growth and counting efficiency of M . leprae using real-time PCR . The strains were 1 ) fully immunocompetent C57BL/6 mice which permit M . leprae growth over an approximate 2-log range of growth from 104 to 106; 2 ) immune-compromised tumor necrosis factor receptor 1 ( TNFR1 ) knock out ( KO ) mice ( B6 . 129-Tnfrsf1atm1Mak; The Jackson Laboratory , Bar Harbor , ME ) . This KO strains exhibits a reduced capacity to control multiplication of M . leprae , although not to the extent seen in nude mice , permitting M . leprae growth over a 3-log range from 104 to 107; and 3 ) Athymic nude mice which lack T-cells making them unable to control M . leprae infections permitting growth over a 6-log range from 104 to 1010 . All studies with animals were previously approved and conducted within the ethical guidelines outlined under the U . S . Public Health Service policy for the care and use of laboratory animals ( NHDP IACUC assurance number A3032-01 ) . Primers and probe for the RLEP TaqMan PCR were selected from a common region of the RLEP family of dispersed repeats . M . leprae RLEP DNA sequences were acquired from the Sanger Center ( www . sanger . ac . uk ) and aligned for regions of identity using Omiga 2 . 0 software ( Oxford Molecular Ltd . , Madison , WI ) . RLEP primers and fluorescent probe were chosen using Primer Express software ( PE Applied Biosystems , Foster City , CA ) based on criteria established for TaqMan PCR reactions . All reagents used in the TaqMan assay were recommended by the manufacturer ( PE Applied Biosystems ) , including AmpErase UNG enzyme and AmpliTaq Gold DNA polymerase . PCR cycling conditions were 40 cycles with 60°C annealing/extension temperature for 60 seconds and 95°C denaturating temperature for 15 seconds . PCR and data analyses were performed on a 7300 RealTime PCR System ( Applied Biosystems , Foster City , CA ) .
RLEP TaqMan primers and probe were selected by aligning DNA sequences from RLEP 1 , 2 , 3 and 4 . A region of RLEP was selected in which the four families of dispersed repeats were identical and analyzed for optimal TaqMan primers and probe . The sequence selected was 5′-GCAGTATCGTGTTAGTGAACAGTGCAtcgatgatccggccgtcggcgGCACATACGGCAACCTTCTAGCG-3′ . Capital letters in bold represent the sequence on which the forward and reverse primers were built . The sequence in lower case italics was selected for building the fluorescent TaqMan probe . When forward and reverse primer sequences were blasted against the M . leprae genome , 19 regions were identified with identical sequences . Another 8 regions were identified with high homology to the primers but amplification would not be likely because of 3-prime mismatches with these primers . Accordingly , amplification of a single M . leprae chromosome with these primers should result in 19 copies of RLEP . Sensitivity of the RLEP TaqMan PCR assay was tested with both purified M . leprae DNA and nude mouse-derived M . leprae . A titration of M . leprae DNA in the TaqMan PCR using RLEP primers/probe gave a lower limit of detection of 10 fg equaling approximately 3 organisms based on the M . leprae chromosome of approximately 3 . 27 Mb ( data not shown ) . While these conditions measure the sensitivity of the assay under ideal circumstances ( no inhibitors ) , a more realistic assessment of the detection limit was determined using M . leprae harvested from infected mouse tissues . Using nude mouse-derived M . leprae as a source of DNA the RLEP TaqMan PCR was able to detect approximately 300 M . leprae ( Fig . 1 ) . Specificity of the RLEP TaqMan PCR for M . leprae DNA was determined by testing purified genomic DNA from 16 mycobacterial species , 10 of which are associated with human diseases , three gram positive microorganisms often associated with skin infections and E . coli ( Table 1 ) . In order to monitor genomic DNA for efficient amplification by PCR , samples were tested for reactivity in a separate PCR designed to detect 16S rDNA [23] . All samples tested for 16S rDNA gave a strong signal based on agarose gel electrophoresis when amplifying 10 pg of genomic DNA for 35 cycles . In contrast , RLEP TaqMan PCR was positive only for M . leprae DNA when samples were tested at the same concentration using 40 cycles . After enumeration by direct microscopic counting , we extracted DNA for enumeration by RLEP TaqMan PCR using the highest enumerated sample of each tissue type to establish a standard curve for those tissues . As shown in Figure 2 , direct microscopic counts ranged between 4 . 8×103 and 2 . 3×1010 bacilli . Estimates based on RLEP TaqMan PCR ranged from 623 organisms in conventional mouse foot pad tissues , to 5 . 8×1010 bacilli in each gram of armadillo tissue . For Molecular Enumeration , Coefficient of Variation ( CV ) between individual replicates averaged 14 . 03% ( Mode 0 . 53% , Median 5 . 58% ) . Similar CV data was not available for the direct microscopic counts and no values were excluded based on CV . Enumeration estimates based on RLEP showed good correlation with direct microscopic counting with coefficients ( Pearson's r ) ranging from 0 . 78 to 0 . 89 for individual tissue types examined . Best results were seen with tissue sets that had a broad range of estimated bacillary counts . No significant difference in counting efficiency was seen between the various types ( liver , spleen or lymph node ) of armadillo tissues examined ( data not shown ) . In combination across all tissues examined , RLEP showed a correlation of 0 . 98 ( Pearson's ) with direct microscopic counting .
These results demonstrate that a simple , reproducible test based on genomic DNA can be used to quantify M . leprae in infected tissues . The real time PCR assay yields results similar to those obtained from conventional direct microscopic counting methods , is highly specific , sensitive , and is easily adapted to large scale batch processing of samples . Molecular quantification of M . leprae based on amplification of RLEP TaqMan PCR is a suitable replacement for direct microscopic counting of bacilli . The quantitative sensitivity of RLEP PCR is within the range of other PCR detection assays for M . leprae DNA developed based on a single-copy gene [9] . A major difference between the two assay systems , however , is the time required for analytical testing , and the ability to quantify multiple batch samples at all time points during thermocycling . For example , the 18-kDa traditional PCR with specific probe hybridization , which we developed earlier , requires approximately 48 hours to complete , whereas the RLEP TaqMan PCR can be accomplished with full analysis in as little as 6 hours . Conventional direct microscopic enumeration requires several hours per sample and has no time savings associated with batch processing . The greater sensitivity of the RLEP TaqMan PCR can be especially useful for comparative growth studies in the MFP model and some in vitro techniques . The threshold limit of detection for direct microscopic counting is approximately 1×104 bacilli . Growth results below those levels are not reliable and data baselines in MFP studies are usually plotted as 1×104 or erroneously coded as zero . Since the upper level of growth in the conventional mouse foot pad plateaus at around 1×106 bacilli for BALB/c mice , and perhaps even lower for some other mouse strains , statistical significance in MFP growth results must be drawn from within only a narrow 2 log window . RLEP TaqMan PCR yields reliable quantitative growth results with less variation at a lower detection threshold than direct microscopic counting ( about 300 organisms ) and the counting efficiency is not influenced by cellular immune processes . The greater sensitivity of RLEP TaqMan PCR can benefit discernment of statistically significant results within more narrow ranges . In addition , since M . leprae is also a notoriously slow growing organism , more sensitive enumeration methods also could lead to shortening MFP trials which now often require 7–12 months to reach completion . Most of our knowledge about the microbiological characteristics of M . leprae is derived from mouse foot pad studies . In the classic Shepard model , mice are typically inoculated in the foot pad with between 5000–10000 bacilli , and the growth of these organisms is assessed after 120–360 days . Even though a large bolus is deposited into the foot , Levy and others observed that the number of bacilli retained in the foot pad 1 week after inoculation was too low to visualize with direct microscopy [25] , [26] . The fate of these organisms remains unknown , but our observations that some 90% of the bacilli are lost from the foot within only a few hours after inoculation is in keeping with those original results and confirms a more immediate time for their loss . Foot pad inoculation was originally developed as a means to provide M . leprae a low temperature growth environment . However , the architecture of the foot pad is not ideal for retention of an inoculum or for supporting the growth of obligate intracellular organisms , such as M . leprae . The soft tissue of the foot pad contains few phagocytic cells and consists mainly of dermal and epidermal cells , along with striated muscle . While M . leprae can invade striated muscle cells and other non-professional phagocytes [27] , their preferred host cell is the macrophage , and sustained local growth of M . leprae in the foot pad requires a continuous influx of new macrophages to the site . It is notable that popliteal lymph nodes of the mice studied here showed enlargement within one week of foot pad inoculation , even in absence of detectable bacilli in those nodes . The specific mechanisms potentially involved in recruiting macrophages to the foot pad are well beyond the scope of this paper; however , these observations support the notion that there is some systemic stimulation following inoculation of the foot pad and these processes may play an important role in establishing and maintaining that localized infection . Although MFP is the oldest and most widely used method to propagate M . leprae , there is much that remains unknown about the technique . Methods that might enhance the growth environment for M . leprae in the foot pad by priming the host beforehand , or pre-populating the foot pads with receptive macrophages could benefit our ability to better exploit this model . Regardless , evolution of more sensitive methods to detect M . leprae in tissues , such as RLEP TaqMan PCR , can aid that development and help advance this reliable model . Other gene targets also can likely be used for relative quantification of M . leprae . Our results with the RLEP TaqMan PCR are in keeping with those reported earlier for quantification of M . leprae based on genetic sequences in the proline-rich antigen region that used purified DNA as a comparative standard [28] . However , the accuracy of estimates based on comparison to purified DNA standard depends entirely on the efficiency of DNA isolation from different tissues , and the inter-run reproducibility of the extraction method . The use of pre-enumerated standards as employed here ( and also available from the NIAID Leprosy Research Support Contract ) , can help eliminate the inaccuracy inherent in variable recovery of DNA in different runs or conditions , and permits ready comparison of results between individual laboratories . Molecular enumeration of M . leprae using the RLEP TaqMan PCR is a rapid and more accurate method to quantify M . leprae in tissues that can have wide applicability in research . The DNA based technique is more sensitive and reproducible than direct microscopic counting , requires less technical expertise , and can permit ready comparisons of results between laboratories . Utilization of this or other molecular based techniques to enumerate M . leprae will likely aide more careful investigation of growth results in a variety of model systems , and will enhance our ability to propagate this and other difficult to grow microorganisms . | Mycobacterium leprae is not cultivable in axenic media , and direct microscopic enumeration of the bacilli is complex , labor intensive , and suffers from limited sensitivity and specificity . We describe the use of real-time PCR to provide a rapid , objective and consistent enumeration procedure for M . leprae . The procedure is specific for M . leprae , has a dynamic range of approximately 6 logs and yields results in only a few hours , including processing time . The procedure was applied to M . leprae growing in mouse and armadillo tissues showing excellent correlation with microscopic counting . The benefits of this technique for experimental characterization of leprosy infections and vaccine trials are substantial , and potential applications to clinical specimens could impact patient management by simplifying the assessment of bacterial burden prior to and during drug treatment . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"microbiology"
] | 2008 | Enumeration of Mycobacterium leprae Using Real-Time PCR |
Humans are remarkably adept at generalizing knowledge between experiences in a way that can be difficult for computers . Often , this entails generalizing constituent pieces of experiences that do not fully overlap , but nonetheless share useful similarities with , previously acquired knowledge . However , it is often unclear how knowledge gained in one context should generalize to another . Previous computational models and data suggest that rather than learning about each individual context , humans build latent abstract structures and learn to link these structures to arbitrary contexts , facilitating generalization . In these models , task structures that are more popular across contexts are more likely to be revisited in new contexts . However , these models can only re-use policies as a whole and are unable to transfer knowledge about the transition structure of the environment even if only the goal has changed ( or vice-versa ) . This contrasts with ecological settings , where some aspects of task structure , such as the transition function , will be shared between context separately from other aspects , such as the reward function . Here , we develop a novel non-parametric Bayesian agent that forms independent latent clusters for transition and reward functions , affording separable transfer of their constituent parts across contexts . We show that the relative performance of this agent compared to an agent that jointly clusters reward and transition functions depends environmental task statistics: the mutual information between transition and reward functions and the stochasticity of the observations . We formalize our analysis through an information theoretic account of the priors , and propose a meta learning agent that dynamically arbitrates between strategies across task domains to optimize a statistical tradeoff .
Compared to artificial agents , humans exhibit remarkable flexibility in our ability to rapidly , spontaneously and appropriately learn to behave in unfamiliar situations , by generalizing past experience and performing symbolic-like operations on constituent components of knowledge [1] . Formal models of human learning have cast generalization as an inference problem in which people learn a shared ( latent ) task structure across multiple contexts and then infer which causal structure best suits the current scenario [2 , 3] . In these models , a context , typically an observable ( or partially observable ) feature of the environment , is linked to a learnable set of task statistics or rules . Based on statistics and the opportunity for generalization , the learner has to infer which environmental features ( stimulus dimensions , episodes , etc . ) should constitute the context that signals the overall task structure , and , simultaneously , which features are indicative of the specific appropriate behaviors for the inferred task structure . This learning strategy is well captured by Bayesian nonparametric models , and neural network approximations thereof , that impose a hierarchical clustering process onto learning task structures [3 , 4] . A learner infers the probability that two contexts are members of the same task cluster via Bayesian inference , and in novel situations , has a prior to reapply the task structures that have been more popular across disparate contexts , while also allowing for the potential to create a new structure as needed . Empirical studies have provided evidence that humans spontaneously impute such hierarchical structure , which facilitates future transfer , whether or not it is immediately beneficial—and , indeed , even if it is costly—to initial learning [3–5] . These clustering models can account for aspects of human generalization that are not well explained by standard models of learning . This approach to generalization , treating multiple contexts as sharing a common task structure , is similar to artificial agents that reuse previously learned policies in novel tasks when the statistics are sufficiently similar [6–9] . However , a key limitation to these clustering models of generalization is that policies of the agent are generalized as a unit . That is , in a new context , a previously learned policy can either be reused or a new policy must be learned from scratch . This can be problematic as policies are often not robust to untrained variation in task structure [10–12] . Thus , a previously learned policy can lead to a poor outcome in a new context even if there is a substantial degree of shared structure . Because task structures are either reused or not as a whole , the ability to reuse and share component parts of knowledge is limited; that is , they are not compositional . Compositionality , or the ability to bind ( compose ) information together in a rule governed way , has long been thought to be a core aspect of human cognition [1 , 13] . Importantly , ecological contexts often share a partial structure , limiting the applicability of previously learned policies but nonetheless providing a generalization advantage to a compositional agent . To provide a naturalistic example , an adept musician can transfer a learned song between a piano and a guitar , even as the two instruments require completely different physical movements , implying that goals can be generalized and reused independently of the actions needed to achieve them . A clustering model that generalizes entire task structures cannot account for this behavior , and would require instead that an agent would need to relearn a song from scratch to play it on a new instrument . Worse , this clustering scheme would predict an unlikely interference effect where the similar outcome of playing the same song on two instruments results in the model incorrectly pooling motor policies across instruments . Here , we propose a framework to address one aspect of compositionality by decomposing task structures—and their separable potential for clustering—into reward functions and transition functions . These two independent functions of a Markov decision process are suitable units of generalization: if we assume that an agent has knowledge of a state-space and the set of available actions , then the reward and transition functions are sufficient to determine the optimal policy . In real-world scenarios , a reward function may correspond to the objective of an agent ( what it would like to achieve and the environmental states that produce these goals ) . A transition function determines how the agent’s actions affect its environment ( i . e . , the subsequent states ) . For example , when playing music a reward function might correspond to the desired sequence of notes ( a scale , or a song ) while the transition function might correspond to the actions needed to produce notes on an instrument . When picking up a new form of guitar , it may be sufficient for a musician to play one or two strings which may then afford inference of the entire transition functions ( the tuning: strings and frets needed to obtain each note ) . Here , we are concerned with how the inference of one ( reward or transition ) function affects generalization of the other . We consider two approaches to clustering and compare their relative generalization advantages as a function of environmental statistics . The independent clustering agent supports generalization by clustering contexts into independent sets defined by the reward and transition statistics , respectively . In contrast , the joint clustering agent clusters contexts into a single set of clusters that binds together the transition and reward functions ( hence amounting to previous models of task-set structure that cluster and re-use policies [3–5] ) . Necessarily , independent clustering is compositional and requires the binding of two independent functions . We show that these two models lead to different predictions depending on the task environment , and we provide an information theoretic analysis to formalize and quantify the bounds of these advantages/disadvantages . In environments where there is a clear , discoverable relationship between transitions and rewards , joint clustering facilitates generalization by allowing an agent to infer one function based on observations that are informative about the other . Nonetheless , we show that independent clustering can lead to superior generalization even in such cases when the transition-reward relationship is weak , difficult to discover , or costly to do so . Finally , we develop a meta-structure learning agent that can infer whether the overall environment is better described by independent or joint statistics .
A common strategy to support task generalization is to cluster contexts together , assuming they share the same task statistics , if doing so leads to an acceptable degree of error [7] . This logic underlies models of animal Pavlovian learning and transfer [2] , human instrumental learning and transfer [3 , 4] , and category learning [17 , 18] . Clustering models of human generalization typically rely on a non-parametric Dirichlet process , commonly known as the Chinese restaurant process ( CRP ) , which acts as a clustering prior in a Bayesian inference process . Used in this way , the CRP enforces popularity-based clustering to partition observations , so that the agent will be most likely to reuse those tasks that have been most popular across disparate contexts ( as opposed to across experiences; [4] ) , and has the attractive property of being a non-parametric model that grows with the data [19] . Consequently , it is not necessary to know the number of partitions a priori and the CRP will tend to parsimoniously favor a smaller number of partitions . As in prior work , we model generalization as the process of inferring the assignment of contexts k = {c1:n} into clusters that share common task statistics . But here , we decompose these task statistics to consider the possibility that that all contexts c ∈ k share either the same reward function and/or mapping function , such that Rk ( x , A ) = Rc ( x , A ) ∀ c ∈ k and/or ϕk ( a , A ) = ϕc ( a , A ) ∀ c ∈ k . ( We return to the “and/or” distinction , which affects whether clustering is independent or joint across reward and mapping functions , in the following section ) . Formally , we define generalization as the inference Pr ( c ∈ k | D ) ∝ Pr ( D | k ) Pr ( c ∈ k ) ( 4 ) where Pr ( D | k ) is the likelihood of the observed data D given cluster k , and Pr ( c ∈ k ) is a prior over the clustering assignment . As in previous models of generalization , we use the CRP as the cluster prior . If contexts {c1:n} are clustered into N ≤ n clusters , then the prior probability for any new context cn+1 ∉ {c1:n} is: Pr ( c t + 1 ∈ k | c 1 : n ) = { N k N + α if k ≤ K n α N + α if k = K n + 1 ( 5 ) where Nk is the number of contexts associated with cluster k and Kn is the number of unique clusters associated with the n observed contexts . If k ≤ Kn , then k is a previously encountered cluster , whereas if k = Kn + 1 , then k is a new cluster . The parameter α governs the propensity to assign a new context to a new cluster , that is to create a new task . Higher values of α lead to a greater prior probability that a new cluster is created and favors a more expanded task space overall , leading to reduced likelihood of reusing old tasks . Thus , the prior probability that a new context is assigned to an old cluster is proportional to the number of contexts in that cluster ( popularity ) , and the probability that it is assigned to a new cluster is proportional to α . As a non-parametric generative process , the prior allows the number of clusters to grow as new contexts are observed . This process is exchangeable , and as such , the order of observation does not alter the inference of the agent [19] , though approximate inference algorithms can induce order effects . As we noted above , there are two key functions the agent learns when navigating in a context: ϕc ( a , A ) and Rc ( x , A ) . These functions imply that the agent could cluster ϕc ( a , A ) and Rc ( x , A ) jointly , or it could cluster them independently , such that it learns the popularity of each marginalizing over the other . Formally , the independent clustering agent ( Fig 1 , left ) assigns each context c into two clusters via Bayesian inference as in ( Eq 4 ) and using the CRP prior for each cluster ( Eq 5 ) . The likelihood function for the two assignments are the mapping and reward functions L ( D | k ϕ ) = ϕ k ( a , A ) ( 6 ) and L ( D | k R ) = R k ( x , A ) , ( 7 ) respectively . Conversely , the joint clustering agent ( Fig 1 , right ) assigns each context c into a single cluster k via Bayesian inference ( Eq 4 ) and , like the independent clustering agent , uses the CRP as the prior over assignments ( Eq 5 ) . The likelihood function for the context-cluster assignment is the product of the mapping and reward functions L ( D | k ) = ϕ k ( a , A ) R k ( x , A ) ( 8 ) The joint clustering agent is highly similar to previous non-compositional models of task structure learning and generalization , which have previously been shown to account for human behavior ( but without specifically assessing the compositionality issue ) [3 , 4] . For the purpose of comparison with the independent clustering agent , here the joint clustering agent separately learns the functions R and ϕ . Previous models , in contrast , have learned model-free policies directly . In the general case , joint clustering does not require the separate representation of policy components ( R and ϕ ) nor does it require a binding operation in the form of planning . However , independent clustering does require the separate representation of policy components that must be bound via planning . Hence , one potential difference between the two approaches is algorithmic complexity , where joint clustering may permit a less complex and computationally costly learning algorithm than independent clustering . In the simulations below , we have equated the agents for algorithmic complexity and examine how inferring the reward and transition functions separately or together affect performance across task domains .
In the first set of simulations , we simulated a task domain in which four contexts involving different combinations of reward and transition functions . In every trial , a “goal” location was hidden in a 6x6 grid world . Agents were randomly placed in the grid world and explored action selection until the goal was reached , at which point the trial ended and the next trial began , with the agent again randomized to a new location . The agent’s task is to find the goal location ( encoded as +1 reward for finding the goal and a 0 reward otherwise ) as quickly as possible . The agents had a set of eight actions available to them A = { a 1 , . . . a 8 } , which could be mapped onto one of four cardinal movements A c a r d = { North , South , East , West } . The agents were exposed to four trials , in which goal locations and mappings were stationary , for each context . Each of the four contexts had a unique combination of one of two goal locations and one of two mappings ( Fig 2A ) , and hence knowledge about the reward function or mapping function for any context was not informative about the other function . However , because the reward and mapping functions were each common across two contexts , the independent clustering agent can leverage the structure to improve generalization without being bound to the joint distribution of mappings and rewards . In addition to the independent and joint clustering agents , for comparison , we also simulated a “flat” ( non-hierarchical decomposition of “context” and “state” ) agent that does not cluster contexts at all and hence has to learn anew in each context . ( The flat agent is a special case of both the independent and joint clustering agents such that ki = {ci} ∀ i ) . We used hypothesis-based inference , where each hypothesis comprised a proposal assignment of contexts in to clusters , h: c ∈ k , defined generatively , such that when a new context is encountered the hypothesis space is augmented . For each hypothesis , maximum likelihood estimation was used to generate the estimates ϕ ^ k ( a , A ) = Pr ^ ( A | a , k ) and R ^ k ( x ) = Pr ^ ( r | x ) . To encourage optimistic exploration , R ^ k ( x ) was initialized to the maximum observable reward ( Pr ( r|x ) = 1 ) with a low confidence prior using a conjugate beta distribution of Beta ( 0 . 01 , 0 ) . The belief distribution over the hypothesis space is defined by the posterior probability of the clustering assignments ( Eq 4 ) . Calculating the posterior distribution over the full hypothesis space is computationally intractable , as the size of the hypothesis space grows combinatorially with the number of contexts . As an approximation , we pruned hypotheses with small probability ( less than 1/10x posterior probability of the maximum a posteriori ( MAP ) hypothesis ) from the hypothesis space . We further approximated the inference problem by using the MAP hypothesis , rather than sampling from the entire distribution , during action selection [3 , 22] . Value iteration was used to solve the system of equations defined by ( Eq 3 ) using the values of ϕ ^ k ( a , A ) and R ^ k ( x ) associated with the MAP hypothesis ( es ) . A state-action value function , defined here in terms of cardinal movements Q ^ k ( x , A ) = R ^ k ( x , A ) + γ ∑ x ′ ∈ X T ( x , A , x ′ ) V ^ k ( x ′ ) ( 9 ) was used with a softmax action selection policy to select cardinal movements: Pr ( A | x , k ) = e β Q ^ k ( a , A ) ∑ A ∈ A c a r d e β Q ^ k ( a , A ) ( 10 ) where β is an inverse temperature parameter that determines the tendency of the agents to exploit the highest estimated valued actions or to explore . Lower level primitive actions ( needed to obtain the desired cardinal movement ) were sampled using the mapping function: Pr ( a | x , k ) = ∑ A ∈ A c a r d ϕ ^ k ( a , A ) Pr ( A | x , k ) ( 11 ) We first simulated the independent and joint clustering agents as well as the flat agent on 150 random task domains using the parameter values γ = 0 . 75 , β = 5 . 0 and α = 1 . 0 ( below we consider a more general parameter-independent analysis ) . Each of the four contexts was repeated 4 times for a total of 16 trials . The independent clustering agent completed the task more quickly than either other agent , completing all trials in an average of 205 . 2 ( s = 20 . 2 ) steps in comparison to 267 . 4 ( s = 22 . 4 ) and 263 . 5 ( s = 17 . 4 ) steps for the joint clustering and flat agents , respectively ( Fig 2B ) . ( We confirmed here and elsewhere that these differences were highly significant ( e . g . , here , the relevant comparisons are a minimum of p < 1e−77 ) ) . Repeating these simulations with agents required to estimate the full transition function ( instead of just the mapping function ) led to the same pattern of results , with the independent clustering agent completing the tasks in fewer steps than either the joint clustering or flat agents ( S1 Text , S1 Fig ) . In this case , the performance advantage of independent clustering is largely driven by faster learning of the reward function , as indexed by the KL-divergence between the agents’ estimates of the reward function compared to a flat learner ( Fig 2C , left ) . In contrast , both joint and independent clustering show generalization benefit when learning the mapping function ( Fig 2C , right ) . This difference reflects an information asymmetry: in a new context , more information is available earlier to an agent about the mappings than the rewards , given that the latter are largely experienced when reaching a goal . ( For example , in these environments , the first action in a novel context yields 2 bits of information about the mappings and an average of 0 . 07 bits of information about the rewards ) . As a consequence of this asymmetry , observing an element of a mapping can facilitate generalization of the rest of the mapping via the likelihood function , whereas observing unrewarded squares in the grid world tells the agent little about the location of rewarded squares . In sum , as expected , independent clustering exhibited advantages over joint clustering in a task environment for which the transition and reward functions were orthogonally linked across contexts . We next simulated all three agents on separate task domain in which there was a discoverable relationship between the reward and mapping functions across contexts , such that knowledge about one function is informative about the other . There were with four orthogonal reward functions and four orthogonal mappings across eight contexts , with each pairing of a reward and mapping function repeated across two contexts , permitting generalization ( Fig 3A ) . As before , 150 random task domains were simulated for each model using the parameter values γ = 0 . 75 , β = 5 . 0 and α = 1 . 0 . Each of the eight contexts was repeated 4 times for a total of 32 trials . In these simulations , both clustering agents show a generalization benefit , completing the task more quickly than the flat agent ( Fig 3B ) . The joint clustering agent showed the largest generalization benefit , completing all trials in average of 384 . 2 ( s = 23 . 6 ) steps in comparison to 441 . 5 ( s = 33 . 4 ) for the independent clustering agent and 526 . 0 ( s = 26 . 4 ) steps for the flat agents . Again , these differences were highly significant and the agents that estimated the full transition function displayed the same pattern of results ( S1 Text , S1 Fig ) . As is the previous simulations , the differences in performance between the clustering agents was largely driven by learning of the reward function . Both the independent and joint clustering agents had similar estimates of mapping functions across time ( Fig 3C , right ) whereas the independent clustering agent uniquely shows an initial deficit in generalization of the reward function , as measured by KL divergence in the first trial in a context ( Fig 3C , left ) . The difference in performance between the two clustering models largely occurs for the first trial in a new in a new context , during which time the joint clustering agent had a better estimate of the reward function . As before , this reflects an information asymmetry between mappings and rewards . Generalization is almost synonymous with a reduction of exploration costs: if an agent generalizes effectively , it can determine a suitable policy without fully exploring a new context . In the above simulations , exploration costs were uniform across all contexts . But in real world situations , the cost of exploring can compound as a person progresses through a task . Exploration can become more costly as resources get scarce: for example , on a long drive it is far more costly to drive around looking for a gas station with an empty tank than with a full one because running out of gas is more likely . Likewise , in a video game where taking a wrong action can mean starting over , it is more costly for an RL agent to randomly explore near the end of the game than at the beginning . Thus , the benefits and costs of generalization can compound in task with a sequential structure over multiple subgoals in ways that are not often apparent in a more restricted task domain . Here , we consider a set of task domains in which each context has a different exploration cost , which increases across time . We define a modified ‘rooms’ problem as a task domain in which an agent has to navigate a series of rooms ( individual grid worlds ) to reach a goal location in the last room ( Fig 4A ) . In each room , the agent must choose one of three doors , one of which will advance the agent to the next room , whereas the other two doors will return the agent to the starting position of the very first room ( hence the ‘diabolical’ descriptor ) . Additionally , the mappings that link actions to cardinal movements can vary from room to room , such that the agent has to discover this mapping function separately from the location of the reward ( goal door ) . All of the rooms are visited in order such that if an agent chooses a door that returns it to the starting location , it will need to visit each room before it can explore a new door . Consequently , the cost of exploring a door in a new room increases with each newly encountered room . Botvinick et al . [21] have previously used the original ‘rooms’ problem introduced by Sutton et al . [20] to motivate the benefit of the “options” hierarchical RL framework as a method of reducing computational steps . However , in the traditional options framework , there is no method for reusing options across different parts of the state-space ( for example , from room to room ) . Each option needs to be independently defined for the portion of the state-space it covers . In contrast , hierarchical clustering agents that decompose the state space can facilitate generalization in the rooms problem by reusing task structures when appropriate [3] . However , because it was a joint clustering agent , this previous work would not allow for separate re-use of mapping and reward functions . In this new , diabolical , variant of the rooms problem , we have afforded the opportunity for reuse of subgoals across rooms , but have modified the task not only to allow for different mapping functions , but where there is a large cost when the appropriately learned subgoal ( choosing the correct door ) is not reused , and where this cost is varied parametrically by changing the size or number of the rooms . The rooms problem here is qualitatively similar to the “RAM combination-lock environments” used by Leffler and colleagues [9] to show that organizing states into classes with reusable properties ( analogous to clusters presented in the present work ) can drastically reduce exploration costs . In the RAM combination-lock environments , agents navigated through a linear series of states , in which one action would take agents to the next state , another to a goal state , and all others back to the start . The rooms task environment presented here is highly similar but allows us to vary the cost of exploring each room parametrically by varying its size . We simulated an independent clustering agent , a joint clustering agent , and a flat agent on a series of rooms problems with the parameters α = 1 . 0 , γ = 0 . 80 and β = 5 . 0 . Each room was represented as a new context ( for example , to simulate differences in surface features ) . There were three doors in the corners of the room , and the same door advanced the agent to the next room for every room ( for simplicity , but without loss of generality—i . e . , the same conclusions apply if the rewarded door would change across contexts ) . Agents received a binary reward for selecting the door that advanced to the next room . In the first set of simulations , we simulated the agents in 6 rooms , each comprising a 6x6 grid world , with three mappings ϕc ∈ {ϕ1 , ϕ2 , ϕ3} , each repeated once . Because the cost of exploration compounds as an agent progresses through a task , the ordering of the rooms affects the exploration costs . For simplicity , we simulated a fixed order of the mappings encountered by rooms , defined by the sequence Xϕ = ϕ1ϕ1ϕ2ϕ2ϕ3ϕ3 . In this task domain , independent clustering performed the best with both clustering agents show a generalization benefit as compared to the flat agent ( Fig 4B ) , with the flat agent completing the task in approximately 1 . 9x and 4 . 5x more total steps than joint and independent clustering , respectively . We further explored how these exploration costs change parametrically with the geometry of the environment . First , we varied the dimensions of each room from 3x3 to 12x12 . While both clustering models show increased exploration costs as the area of the grid world increases , the exploration costs for the joint clustering model grow at a faster exponential rate than the independent clustering model ( Fig 4C ) . Similarly , we can increase the exploration costs by increasing the number of rooms in the task domain . We varied the number of rooms in the rooms in the task domain from 3 to 27 in increments of three . As before , the same door in all rooms advanced the agent and three mappings were repeated across the rooms . The order of the mappings encountered is defined by the sequence X ϕ = ϕ 1 ( k ) ϕ 2 ( k ) ϕ 3 ( k ) for k ∈ { 1 , 2 , . . . , 9 } , where k is the number of times a mapping is repeated . Again , both clustering agents experience an increased cost of exploration as the number of rooms increases , but the cost of exploration increases at a faster linear rate for the joint clustering agent than the independent clustering agent ( Fig 4D ) . Thus , in environments where the benefits of generalization compound across time , difference between strategies can be dramatic . Here , we have simulated an environment in which independent clustering leads to better generalization than joint clustering but we could equivalently create an example in which joint clustering leads to better performance ( for example , joint clustering would do better if each mapping uniquely predicted the correct door ) . Consequently , any fixed strategy has the potential to face an exploration costs that grows exponentially with the complexity of the task domain . Thus far , we have examined the performance of hierarchical clustering variants in specific situations in order to demonstrate a tradeoff between strategies . However , while these examples are illustrative , they impose strong assumptions about the task domain , the agents’ knowledge of its structure , exploration policies and planning . In contrast , we are more concerned with the suitability of generalization across ecological environments , rather than the specific task domains we have simulated and the assumptions of planning and exploration . To make a more general normative claim , it is desirable to abstract away the implementation and strictly address the normative basis of context-popularity based clustering as a generalization algorithm by itself . While addressing optimality requires knowledge of the generative process of ecological environments , which is beyond the scope of the current work , we can more formally and generally assess when , and under what conditions , each of the clustering models might be more suitable than the other . To do so , we can frame generalization as a classification problem and quantify how well an agent correctly identifies the cluster in which a context belongs without regard to learning the associated task statistics . This simplifying assumption allows us to examine the CRP as a mechanism for generalization and abstracts away the effect of the likelihood function on generalization . Let k ∈ K be a cluster associated with a Markov decision problem and let context c ∈ C be a context experienced by the agent . Given a history of experienced contexts and associated clusters {c1:n , k1:m} , we cast the problem of generalization as learning the classification function k = f ( c ) that minimizes the risk of misclassification . ( Misclassification risk here is loosely defined: some misclassification errors are worse than others and misidentifying a cluster , and consequently , its MDP , may or may not result in a poor policy if the underlying MDPs are sufficiently similar . As such , the loss function in ecological settings can be a highly non-linear , domain specific function , as we demonstrate with the diabolical rooms problem . ) More formally , we define risk as the expectation E [ L ( p , f ) ] , where L ( p , f ) is the loss function for misclassification and p is the generative distribution over new contexts p = Pr ( k|ct+1 ) . For our purposes here , we will abstract away domain specificity in the loss function L ( p , f ) . Because the CRP is a probability distribution , a reasonable domain-independent loss function is the information gain between the CRP’s estimate of the probability of k and the realized outcome , or L ( p , f ) = - log 2 q K ( 12 ) where qk is the CRP’s estimate of the probability of the observed cluster k in context c . The misclassification risk is thus the cross entropy between the CRP and the generative distribution: E [ L ( p , f ) ] = E p [ - log 2 q ] = H ( p , q ) ( 13 ) Thus , by casting generalization as classification and assuming information gain as a domain-general loss function , we are in effect evaluating the degree to which the CRP estimates the generative distribution . Risk is minimized when q = p , that is , when the CRP perfectly estimates the generative process . There is no upper bound to poor performance , but in many task domains it is possible to make a naïve guess over the space of clusters ( for example , a uniform distribution over a known set of clusters ) . Because any useful generalization model will be better than a naïve guess , we can evaluate whether the CRP will lead to lower information gain than a naïve estimate in different task domains as a function of their statistics . We consider this more quantitatively in the appendix ( S2 Text ) , but the result is intuitive: overall , the CRP will facilitate generalization when the generalization process is more predictable ( less entropic ) and the CRP can lead to worse than chance performance in sufficiently unpredictable domains ( S2 Fig ) . Given that the optimality of each fixed strategy varies as a function of the statistics of the task domain , a natural question is whether a single agent could optimize its choice of strategy effectively by somehow tracking those statistics . In other words , can an agent infer whether the overall statistics are more indicative of a joint or independent structure and capitalize accordingly ? Here , we address this question by implementing a meta-agent that infers the correct policy across the two strategies ( below we also consider a simple model-free RL heuristic for arbitrating between agents , which produces qualitatively similar results ) . For any given fixed strategy , the optimal policy maximizes the expected discounted future rewards and is defined by Eq 1 . Let π m * be the optimal policy for model m . We are interested in whether π m * is the global optimal policy π* , which we can define probabilistically as Pr ( πm*=π* ) =Pr ( πm*=π*|m ) Pr ( m|D ) ( 16 ) = Pr ( m | D ) ( 17 ) where Pr ( m | D ) is the Bayesian model evidence and where Pr ( π m * = π * | m ) ≡ 1 . The Bayesian model evidence is , as usual , Pr ( m | D ) = 1 Z Pr ( D | m ) Pr ( m ) ( 18 ) where Pr ( D | m ) is the likelihood of observations under the model , Pr ( m ) is the prior over models and Z is the normalizing constant . The likelihood function for the independent and joint clustering models is the product of the mapping and reward functions ( as defined conditionally for a cluster in Eqs 6 , 7 and 8 ) . However , estimates of the mapping function is highly similar for both models ( Figs 2C and 3C ) . Thus , as a simplifying assumption , we can approximate the model evidence with how well each model predicts reward: Pr ( m | D ) ≈ 1 Z ( ∏ t = 1 ∞ Pr ( r t | m ) ) Pr ( m ) ( 19 ) where rt is the reward collected at time t . Independent and joint clustering can be interpreted as special cases of this meta-learning agent with the strong prior Pr ( m ) = 1 . Under a uniform prior over the models , this strategy reduces to choosing the agent based on how well it predicts reward , an approach repeatedly used in meta-learning agents [3 , 23 , 24] . We simulated the meta-learning agent with a uniform prior over the two models and used Thompson sampling [25] to sample a policy from joint and independent clustering at the beginning of each trial ( Fig 6A ) . In the first task domain , where independent clustering results in better performance than joint clustering , the performance of the meta-agent more closely matched the performance of the independent clustering agent ( Fig 6B ) . The meta-agent completed the task in an average of 235 . 2 ( s = 35 . 3 ) steps compared to 205 . 2 ( s = 20 . 2 ) and 267 . 5 ( s = 22 . 4 ) steps for the independent and joint clustering agents . In addition , the meta-agent became more likely to choose the policy of the independent clustering agent over time ( Fig 6D ) . In the second task domain , where joint clustering outperformed independent clustering , the meta-agent completed the task in an average of 417 . 5 ( s = 42 . 0 ) steps compared to an average of 384 . 2 ( s = 21 . 2 ) and 441 . 6 ( s = 35 . 9 ) steps for the joint and independent clustering agents , respectively . Likewise , overtime the meta-agent was more likely to choose the policy of the joint clustering agent ( Fig 6E ) . A computationally simple approximation to estimating the model responsibilities is to select agents as a function of their estimated value . In this approximation , a reward prediction error learning rule estimates the value for each model , Qm , according to the updating rule: Q m ← Q m + η ( r t - r ^ t , m ) ( 20 ) where η is a learning rate and r ^ t , m is the reward predicted by the model at time t . These values can be used to sample the models via a softmax decision rule m ∼ 1 Z exp { β Q m } ( 21 ) Simulation with this arbitration strategy with the parameter values β = 5 . 0 , η = 0 . 2 led to a qualitatively similar pattern of results . Performance in both simulations 1 and 2 were not distinguishable from the Bayesian meta agent , with the agent completing simulation 1 in 237 . 2 ( s = 41 . 2 ) steps as compared to the 235 . 2 ( s = 35 . 3 ) found for the Bayesian implementation ( p < 0 . 65 ) and completing simulation 2 in 418 . 7 ( s = 45 . 9 ) steps as compared to 417 . 5 ( s = 42 . 0 ) steps for the Bayesian agent ( p < . 81 ) . Thus , while for both inference and RL versions , the meta-agent did not equal the performance of the best agent in either environment , it outperformed the worse of the two agents in both environments . Normatively , this is a useful property if an agent cares about minimizing the worst possible outcome across unknown task domains ( as opposed to maximizing their performance within a single domain ) , similar to a minimax decision rule in decision theory [26] . This can be advantageous if agent has little information about the distribution of task domains and if the costs of choosing the wrong strategy are large as in the ‘diabolical rooms’ problem . Furthermore , while we have used a uniform prior over the two strategies , varying the prior may result in a better strategy for a given set of task domains . In our information theoretic analysis above , we showed that the task statistics determines the normative strategy depending on which agent is more efficacious in reducing Bayesian surprise about reward . The meta-learning agent capitalizes on this same intuition by using predicted rewards to arbitrate among strategies . More specifically , we argued that the normative value of each strategy varies with mutual information between rewards and mappings . Thus , we assessed whether the the meta-learning agent is also sensitive to mutual information , without calculating it directly , and hence be more likely to choose joint clustering when the mutual information is higher . In simulations 1 and 2 above , we calculated the mutual information each time a new context was added and used this to predict the probability of selecting the joint agent at the end of that trial . Specifically , we define I ( R ; Φ | D ) = H ( Pr ( R | D ) ) - H ( Pr ( R | ϕ = ϕ k , D ) ) ( 22 ) where Pr ( R | D ) ) is the probability each location is rewarded across all contexts seen so far and Pr ( R | ϕ = ϕ k , D ) ) is conditioned on the current mapping . Using logistic regression , we find that I ( R ; Φ | D ) positively correlates with the probability of selecting the joint agent across both simulations , consistent with expectations [Simulation 1: β = 3 . 3 , p < 0 . 003; Simulation 2: β = 9 . 4 , p < 5 × 10−41] .
In this paper , we provide two alternative models of context-based clustering for the purpose of generalization , a joint clustering agent generalizes reward and transition functions together and an independent clustering agent that separately generalizes reward and transition functions . These models are motivated by human learning and performance , which is thought to be structured and compositional [27–29] . Generalization can be seen as a solution to a dimensionality problem . In real-world problems , perceptual space is typically high-dimensional . In order to learn a policy , agents need to learn a mapping between the high-dimensional perceptual space and the effector space . Learning this mapping can require a large set of training data , perhaps much larger than a human would have access to [12 , 21] . Clustering can reduce the dimensionality by projecting the perceptual space onto a lower dimensional latent space in which multiple percepts share the same latent representation . Thus , an agent does not need to learn a policy over the full state space but over the lower dimensional latent space . This is an explicit assumption of the models presented here as well as in other clustering models of human generalization , allowing agents to collapse across irrelevant features and preventing interference between stimulus-response mappings across latent distinct rules [3 , 23] . Related principles have been explored in lifelong learning [6–8] , object-based , and symbol-based approaches [11 , 30–33] . Incorporating compositionality takes this argument further , as multiple policies often share component features . For example , playing the saxophone involves the use of the same movements to produce the same notes for different effect in different songs . Learning a policy as a direct mapping from the low level effector space to reward values fails to take advantage of the structure , even if that policy can be reused as a whole with another instrument . Thus , learning at the component level as opposed to the policy level reduces a high-dimensional problem into multiple lower-dimensional problems . While this adds the additional complexity of the choice of a good set of component features , here we argue the Markov decision process provides a natural decomposition into reward and transition functions . Importantly , this decomposition of the task structure is not equivalent to a decomposition of the policy , which is itself dependent on the joint reward and transition functions . Of course , other decompositions are also possible and useful ( and not mutually exclusive ) . For example , the state-outcome and action-dependent state transition functions of the active inference framework can both be decomposed into “what” and “where” aspects [34 , 35] . While these functions , analogous to reward and transition functions , are linked by a shared latent state representation , this decomposition facilitate generalization across states that share features . Regardless of the choice of component features , a compositional generalization model needs to make assumptions about the relationship between components . We argue here that the proper choice depends on the generative structure , which as an empirical matter , is largely unknown for the ecological environments faced by humans and artificial agents . As we demonstrated in the grid-world simulations above , when there is a strong relationship between components , an agent that assumes as much outperforms an agent that assumes no relationship , and vice-versa ( Figs 2 and 3 ) . With sufficient ( and stationary ) experience , we might expect a model that assumes a learnable relationship between components ( joint clustering ) to perform better in new contexts , since assuming a potential relationship between goals and mappings can be no worse asymptotically than assuming independence ( i . e . , the agent can simply learn that the correlation is zero ) . Nonetheless , how much experience is sufficient for joint clustering to provide a better model is difficult to define in general , and will depend on the statistics of the relationships and the combinatorial explosion of the state space that arises . Furthermore , noise or partial observability further complicates the picture: even when there is exploitable mutual information , independent clustering can yield a better estimate when experience is limited ( Fig 5 ) . Why is this the case ? It may appear puzzling given the asymptotic assurances that joint clustering will be no worse in stationary environments . Here , the comparison between classification and generalization is instructive . We can think of joint clustering in terms of estimating a joint distribution of the generative process and independent clustering in terms of estimating the marginal distribution for each component independently ( similar to a naïve Bayes classifier ) . In this interpretation , independent clustering trades off an asymptotically worse estimate of the generative process for lower variance , with a bias equal to the mutual information between mappings and goals . In problems with limited experience , such as the type presented here , a biased classifier will often perform better than an asymptotically more accurate estimator because misclassification risk is more sensitive to variance than bias [36] . Thus , by ignoring the correlation structure and increasing the bias to generalize goals that are most popular overall , independent clustering may minimize its overall loss . Intuitively , we can think of joint clustering as being potentially overly sensitive to noise . While over infinite time it is always better to estimate the correlations between components , in practice it may not be worth the cost of doing so . This happens when the correlations between transitions and rewards is weak or difficult to determine . For a human learner , an example might be the relationship between how hungry a person is and how heavy it is to carry a plate of food from a buffet . Learning this relationship is guaranteed to be asymptotically better than ignoring it but given the triviality of the benefit and the frequency of the context , it probably isn’t worth the exploration cost . Previous models of compositional generalization have attempted to decompose the space of policies , rather than task structure , in to reusable pieces that can be re-executed [34 , 37] . Because learned policies depend on both the reward and transition functions of a specific task , this decomposition implicitly generalizes these two sources of information together , and thus does not address the set of issues considered here ( i . e . , when the transition function is independent of the reward function across contexts ) . The same issue applies to the options framework and other hierarchical task representations [10 , 32 , 38–40] . As a consequence , reusing policy components will cause the agent to explore regions of the state space that have had high reward value in other contexts , which as we have shown may or may not be an adaptive strategy . For example , successful generalization in the “diabolical rooms” problem presented here , and the “finger sailing” task presented by Fermin and colleagues [14 , 15] , requires a separation of reward from movement statistics . Indeed , the generalization of policy-dependent successor state representations works well only under small deviations of the reward or transition function [10 , 38 , 39] . Thus , the choice of components should be influenced by the robustness to changes in the reward and transition function , which will not necessarily linked to an individual policy . From the perspective of human cognition , compositional representation provides the flexibility to create novel policies in a novel domains in a rule-governed manner . This flexibility , also known as systematicity or generativity , has long been thought to be a key feature of cognition [29 , 41] . As Lake and colleagues note , a person can re-use the learned knowledge of the structure of a task to accomplish an arbitrary number of goals , such as winning a specific number of points in a video game [12] . Strongly linking component properties may impede the potential for systematicity by limiting the flexibility to recombine knowledge . As we have argued above , recombining reward and transition information may be particularly valuable , such that agents that can only generalize policies and reward-sensitive policy components may lack systematicity . An altogether different possibility is that a mix of strategies is appropriate . While we argue that independent clustering is a simpler statistical problem than joint clustering , there are clearly cases where joint clustering is advantageous . As noted before , generalization of successor-state representation partially links transitions and rewards and is nonetheless sufficiently flexible to handle small deviations in policy [10 , 38 , 39] . Furthermore , joint clustering support simpler algorithms , such as the form of temporal difference learning algorithms thought to underlie human fronto-striatal learning [3 , 42] as well as the proposed successor state representations recently proposed to underlie hippocampal-based planning [38 , 39 , 43] . As we have suggested with the meta-learning agent , trading off between joint and independent clustering can reduce the risk of the decision problem . Furthermore , it is not known what a human learner would typically consider to constitute a higher order context variable separate from lower order state variables [3 , 42] . Ecologically , the number of contexts a human could potentially encounter is quite high , in which case they would be able to form a more accurate estimate of the correlation structure between components over time . If this speculation is true , then one potential adaptive strategy would be to assume a weak relationship between components early in learning and increasingly relying on the correlation structure as the evidence supports it . Thus , a hybrid system is supported by both computational and algorithmic considerations . From the perspective of biological implementation , the inference required for context-clustering based generalization can be approximated by a hierarchical cortico-basal ganglia learning system [3] . This framework could be extended to account for independent clustering by allowing for multiple cortical clusters separately representing reward and mapping functions , each of which is learnable by a neural network model [44] . Because joint clustering results in the same policy generalized to each context in a cluster , joint clustering does not require separately estimating the reward and transition functions and instead learned policies ( such as stimulus-action values ) can be generalized directly . This can obviate planning , a challenge for any biological model of any model-based control . Nonetheless , multiple lines of research suggest humans engage in model-based control [14 , 15 , 39 , 45 , 46] and human subjects can re-use arbitrary action-movement mappings ( highly similar to the ones proposed here ) for model-based control , suggesting a compositional representation potentially mediated by the dorsolateral prefrontal cortex , dorsomedial striatum and cerebellum [14 , 15] . Finally , while we have presented independent clustering as motivated by human capabilities for generalization , the question of whether human learning is better accounted for by independent or joint clustering , or a mixture of the two , remains an open question . While models are a generalization of previous models used to account for human behavior [2–4] , they make separate testable predictions for human behavior . Joint clustering predicts that in a generalization task , human subjects will use transition formation to infer the location of an unknown goal . Independent clustering , in contrast , predicts human subjects will ignore transition information when searching for goals , and ignore goals when inferring the transition function . By providing humans subjects an initial set of contexts where the popularity of reward function varies across contexts as a function of the mapping , a novel set of test contexts can be chosen to differentiate the model predictions . Future work will address these predictions and the underlying brain mechanisms . | A musician may learn to generalize behaviors across instruments for different purposes , for example , reusing hand motions used when playing classical on the flute to play jazz on the saxophone . Conversely , she may learn to play a single song across many instruments that require completely distinct physical motions , but nonetheless transfer knowledge between them . This degree of compositionality is often absent from computational frameworks of learning , forcing agents either to generalize entire learned policies or to learn new policies from scratch . Here , we propose a solution to this problem that allows an agent to generalize components of a policy independently and compare it to an agent that generalizes components as a whole . We show that the degree to which one form of generalization is favored over the other is dependent on the features of task domain , with independent generalization of task components favored in environments with weak relationships between components or high degrees of noise and joint generalization of task components favored when there is a clear , discoverable relationship between task components . Furthermore , we show that the overall meta structure of the environment can be learned and leveraged by an agent that dynamically arbitrates between these forms of structure learning . | [
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... | 2018 | Compositional clustering in task structure learning |
Some strains of enterovirus 71 ( EV71 ) , but not others , infect leukocytes by binding to a specific receptor molecule: the P-selectin glycoprotein ligand-1 ( PSGL-1 ) . We find that a single amino acid residue within the capsid protein VP1 determines whether EV71 binds to PSGL-1 . Examination of capsid sequences of representative EV71 strains revealed that the PSGL-1-binding viruses had either a G or a Q at residue 145 within the capsid protein VP1 ( VP1-145G or Q ) , whereas PSGL-1-nonbinding viruses had VP1-145E . Using site-directed mutagenesis we found that PSGL-1-binding strains lost their capacity to bind when VP1-145G/Q was replaced by E; conversely , nonbinding strains gained the capacity to bind PSGL-1 when VP1-145E was replaced with either G or Q . Viruses with G/Q at VP1-145 productively infected a leukocyte cell line , Jurkat T-cells , whereas viruses with E at this position did not . We previously reported that EV71 binds to the N-terminal region of PSGL-1 , and that binding depends on sulfated tyrosine residues within this region . We speculated that binding depends on interaction between negatively charged sulfate groups and positively charged basic residues in the virus capsid . VP1-145 on the virus surface is in close proximity to conserved lysine residues at VP1-242 and VP1-244 . Comparison of recently published crystal structures of EV71 isolates with either Q or E at VP1-145 revealed that VP1-145 controls the orientation of the lysine side-chain of VP1-244: with VP1-145Q the lysine side chain faces outward , but with VP1-145E , the lysine side chain is turned toward the virus surface . Mutation of VP1-244 abolished virus binding to PSGL-1 , and mutation of VP1-242 greatly reduced binding . We propose that conserved lysine residues on the virus surface are responsible for interaction with sulfated tyrosine residues at the PSGL-1 N-terminus , and that VP1-145 acts as a switch , controlling PSGL-1 binding by modulating the exposure of VP1-244K .
Enterovirus 71 ( EV71 ) is a small , non-enveloped positive-stranded RNA virus that belongs to the human enterovirus species A of the genus Enterovirus in the family Picornaviridae [1] . The viral RNA genome is enclosed in a capsid composed of four structural proteins , VP1 , VP2 , VP3 , and VP4 [2] , [3] , [4] . EV71 is a major causative agent of hand , foot , and mouth disease ( reviewed in [5] , [6] , [7] , [8] ) , a febrile illness that commonly affects young children . Although hand , foot , and mouth disease is usually mild and self-limited , EV71 infection may also cause severe diseases including poliomyelitis-like paralysis , brainstem encephalitis , and fatal cardiorespiratory failure . Recent EV71 outbreaks in the Asia-Pacific region have involved millions of children , and have caused thousands of deaths [9] , [10] . Recently , several cell-surface molecules have been identified to be involved in EV71 infection [11] , [12] , [13] , [14] , [15] , [16] . EV71 isolates use at least two transmembrane proteins as receptors ( reviewed in [17] , [18] ) . Scavenger receptor class B , member 2 ( SCARB2 ) , originally identified as an EV71 receptor on rhabdomyosarcoma ( RD ) cells [14] , is expressed on a broad variety of cell types . In contrast , P-selectin glycoprotein ligand-1 ( PSGL-1 ) , which we first identified as an EV71 receptor on Jurkat T cells [12] , is primarily expressed on leukocytes , where it mediates interaction with selectins and thus serves an important function in inflammatory processes [19] , [20] . Whereas SCARB2 serves as a receptor for all EV71 strains tested , as well as for several other viruses ( coxsackieviruses A7 , A14 , and A16 ) that are not associated with severe disease [21] , PSGL-1 interacts with a distinct subset of EV71 strains . According to their PSGL-1-binding capacity , EV71 strains can be classified into two distinct phenotypes— irrespective of their genogroups— which we designate as PSGL-1-binding ( PB ) and PSGL-1-nonbinding ( non-PB ) strains [12] . PB viruses bind to PSGL-1 to replicate in Jurkat cells , and their replication in Jurkat cells is blocked by an anti-PSGL-1 monoclonal antibody ( mAb ) . Non-PB viruses either fail to replicate in Jurkat cells or replicate in a PSGL-1-independent manner . In RD cells , which express SCARB2 , both PB and non-PB isolates replicate independently of PSGL-1 . We previously showed that the N-terminal region of human PSGL-1 ( amino acids 42–61 ) is directly responsible for PSGL-1 binding to EV71 [12] . This N-terminal region is also critical for PSGL-1 binding to P- , E- and L-selectins . Post-translational modifications of the N-terminal region— including tyrosine sulfation and O-glycosylation— are important for recognition by selectins [22] , [23] , [24] , [25] . We have demonstrated that sulfation of three tyrosines ( Y46 , Y48 , and Y51 ) is essential for binding of EV71 to PSGL-1 [26] . We therefore suspected that these negatively charged residues might promote virus binding by interacting with positively charged residues on the virus surface . We have now used a combination of mutational and structural analysis to clarify the molecular basis for EV71 interaction with PSGL-1 . We find that a single amino acid residue , VP1-145 , regulates binding to PSGL-1 by changing the orientation of a critical lysine residue on the virus surface .
To identify potential genetic determinants of PSGL-1 binding we compared the capsid sequences of EV71 strains ( listed in Table 1 ) that we had previously characterized as PB or non-PB [12] , [27] . EV71 isolates are classified into three genogroups ( A , B , and C ) according to their VP1 nucleotide sequences [28] , and genogroups B and C are each further divided into five subgenogroups [29] . We began our analysis by comparing the capsid amino acid sequences of two viruses in subgenogroup C1 , EV71-KED005 ( PB ) and -02363 ( non-PB ) . There were only four differences ( VP3-55 , VP1-98 , VP1-145 , and VP1-262 ) within the 862 amino acid capsid region ( Table 1 ) , as we previously reported [12] . The amino acid at VP1-145 of EV71-KED005 could not be determined because there was a mixture of sequences at this position [27] . We then examined the sequences of all eight strains , noting the amino acids at VP3-55 , VP1-98 , VP1-145 , and VP1-262 ( Table 1 ) . We found a strong relationship between the PSGL-1 binding phenotype and the specific amino acids at VP1-98 and VP1-145: the five PB strains showed a combination of VP1-98E and VP1-145G ( which we designated EG ) , or VP1-98E and VP1-145Q ( EQ ) , whereas non-PB strains showed VP1-98K and VP1-145E ( KE ) , or VP1-98E and VP1-145E ( EE ) . The amino acids at VP3-55 and VP1-262 did not correlate with the PSGL-1 binding phenotypes . Thus we focused on VP1-98 and VP1-145 as possible determinants of PSGL-1 binding . The eight virus isolates we examined above showed four combinations of amino acids at VP1-98 and VP1-145: EG , EQ , EE and KE . To determine how frequently these combinations are found in other isolates , we examined EV71 nucleotide sequences available in GenBank database [as of January 2011 , 1702 sequences included codons for both VP1-98 and VP1-145 ( Table S1 ) ] . Interestingly , we found one of the same four combinations of amino acids in virtually all the available sequences ( Table 2 ) . The EE was seen in 71% of isolates , and the other combinations were each seen in approximately 9% . We focused on these four amino acid combinations for further investigation , as they accounted for 98 . 8% of the total strains . To determine the amino acids that account for the PSGL-1 binding phenotype , we generated infectious cDNA clones with amino acid mutations at VP1-98 and/or VP1-145 . We cloned full-length genomic cDNA of five strains ( C7/Osaka , Nagoya , 1095 , 02363 , and 75-Yamagata ) into pBR322Y plasmids . The cDNA clones of C7/Osaka , 1095 , 02363 , and 75-Yamagata were corrected to match the consensus sequences that had been determined by direct sequencing of reverse transcription polymerase chain reaction ( RT-PCR ) products . The cDNA clone of Nagoya ( GenBank Accession No . AB747373 ) was not corrected , and differed from the full-length consensus nucleotide sequence ( GenBank Accession No . AB747373 ) at eight positions ( Y930C , Y933C , Y1662C , S2005G , S2416C , Y3547T , W3602A , and Y4038T ) . Into each of the cloned viral genomes we introduced mutations to produce each of the four major combinations of residues at VP1-98 and VP1-145 ( residues at these positions in the original isolates are shown in Figure 1A ) . Viral RNAs were transcribed in vitro using T7 RNA polymerase , and transfected into RD cells to produce viruses , which were collected at 24 h post-transfection and amplified once in fresh RD cells . The capsid-encoding regions of each of the amplified virus mutants were determined by direct sequencing of RT-PCR products . The sequences of 18 of the 20 amplified viruses ( apart from codons for VP1-98 and 145 ) were identical to those of the cDNA clones . Amplified Nagoya-EG virus had a single nucleotide substitution , C3152A , which resulted in an amino acid change at VP1-T237N; this mutation is unlikely to affect virus interaction with PSGL-1 , as VP1-237 is located in the βH barrel , not in the most exposed loops [2] , [3] , [4] . Amplified Yamagata-EE had a silent nucleotide substitution , C3054T , which would not affect virus interaction with PSGL-1 . We examined the direct biochemical interaction between EV71 mutants and PSGL-1 by co-precipitating viruses with a soluble form of recombinant PSGL-1 fused to the Fc region of human IgG1 ( PSGL-1-Fc ) , as described previously [12] . EV71 that co-precipitated with PSGL-1-Fc was detected by western blotting with an anti-VP1 mAb . We first tested mutants derived from genogroup B strains ( Figure 1B ) , to determine whether mutation at VP1-98 or VP1-145 abolished PSGL-1 binding by the PB strain ( C7/Osaka ) , or conferred PSGL-1 binding capacity on the non-PB strain ( Nagoya ) . We used the original isolates of C7/Osaka and Nagoya ( not produced from cDNA clones ) as positive and negative controls , respectively . In either the C7/Osaka or the Nagoya background , viruses with EG or EQ , but not EE or KE , immunoprecipitated with PSGL-1-Fc . Thus , the presence of G or Q at VP1-145 , but not E , was associated with binding to PSGL-1 in these two genogroup B strains; the presence of K or E at VP1-98 did not influence binding . Similar results were obtained with the genogroup C strains 1095 ( PB ) and 02363 ( non-PB ) and with 75-Yamagata , a PB strain which normally has a Q at VP1-145 ( Figure 1C ) : in each case , viruses with G or Q— but not those with E— at VP1-145 bound PSGL-1 , irrespective of the residue at VP1-98 . We also substituted VP1-145 with several amino acids found less commonly in GenBank ( Table 2 ) . EV71-1095 with VP1-145A bound to PSGL-1 as had been previously observed for another viral strain [30] . VP1-145R similarly bound to PSGL-1; however , virus with K or D at this position showed little or no binding to PSGL-1 ( Figure S1 ) . In the presence of VP1-145G or Q , viruses with either VP1-98E or VP1-98K bound to PSGL-1-Fc ( KG and KQ in Figure S1 ) . Taken together , these results suggested that the identity of VP1-145 regulates the capacity of EV71 to bind PSGL-1 . We previously found that PB viruses productively infected Jurkat T lymphocytes , and that their replication was inhibited by anti-PSGL-1 mAb [12] . To determine whether mutations at VP1-145 influenced virus tropism for Jurkat cells , we examined replication in Jurkat cells of the wild-type and mutant derivatives of EV71-02363 ( non-PB ) and EV71-1095 ( PB ) . Although all of the cDNA-derived 02363 mutants replicated well in RD cells ( Figure 2A , left ) , no replication was seen with either of the non-PB mutants , either EV71-02363-EE and EV71-02363-KE in Jurkat cells ( Figure 2A , right ) . In contrast , EV71-02363-EG and EV71-02363-EQ replicated well in Jurkat cells , and their replication was inhibited by anti-PSGL-1 mAb ( Figure 2B ) . Thus , a non-PB isolate of EV71 , which did not replicate in Jurkat cells , gained the capacity to replicate in a PSGL-1-dependent manner when VP1-145E was replaced by either G or Q . Conversely , when mutants of 1095 ( EG , a PB strain ) were tested , viruses with G or Q at VP1-145 replicated to high titer , but the virus with E at this position did not ( Figure 2C ) ; although some apparent replication was noted for the 1095-EE mutant , replication was inhibited by anti-PSGL-1 mAb , and sequencing of the recovered virus revealed a reversion of E to G at VP1-145 ( not shown ) , suggesting that replication in Jurkat cells selected for a minor population of PB virus . Taken together with results shown in Figure 1 , the data shown in Figure 2 indicate that the presence of a G or Q —rather than an E— at VP1-145 determines both virus binding to PSGL-1 and PSGL-1-dependent virus tropism for Jurkat cells . We previously reported that EV71-1095 replicates to a limited degree in U937 monocytes as well as in Jurkat cells [12] . To test the role of VP1-145 in this interaction , we exposed U937 cells to PB ( EG and EQ ) and non-PB ( EE and KE ) derivatives of EV71-1095 and measured virus titers over 4 days . The PB , but not the PB derivatives showed a small but significant increase in titer ( data not shown ) , confirming that VP145G/Q is a determinant of productive infection in this cell line . In contrast , PB derivatives of another virus isolate , EV71-02363 , did not appear to replicate in U937 cells . Thus , viral factors other than the capacity to bind PSGL-1 are also likely to be important for productive infection in some leukocytes . Recent crystal structures of the mature EV71 virion [2] , [3] , [4] reveal that VP1-145 is located on the virus surface within the VP1-DE loop , which connects the βD and βE barrels , on a mesa that surrounds the viral five-fold axis of symmetry ( Figures 3 and 4 ) . Based on the observation that virus binding requires negatively-charged sulfated tyrosines within the N-terminal region of PSGL-1 [26]— the site of virus interaction [12]— we suspected that the binding might depend on positively-charged amino acid side chains exposed on the virus surface . Analysis of the electrostatic surface properties of the EV71 crystal structures with UCSF Chimera software showed the region of positive electrostatic potential ( colored blue ) around the five-fold axis contributed by VP1-242K and VP1-244K ( Figure 3 , A and B ) [4] . Interestingly , we found that these lysine residues , VP1-242K and VP1-244K , are in close proximity to VP1-145 ( Figure 3 , B and C ) . In particular , VP1-145 in the DE loop makes close contact with VP1-244K in the HI loop from an adjacent protomer ( Figure 3D ) . Two crystal structures of the EV71 capsid are available . Plevka et al . [2] , [3] reported the structure of an EV71 isolate with Q at VP1-145 , presumably a PB virus ( Protein Data Bank Accession No . 4AED ) ( Figure 3 , A–D , top; Figure 4 ) . In contrast , Wang et al . [4] reported the structure of an isolate with E at VP1-145 , presumably a non-PB isolate ( Protein Data Bank Accession No . 3VBS ) ( Figure 3 , A–D , bottom; Figure 4 ) . Interestingly , the orientation of the basic side chain of VP1-244 is markedly different in the two structures ( Figure 3 , C and D ) ; in the likely PB virus , the VP1-244K side chain projects outward , with the positively charged ε-amino group highly exposed on the virus surface; in the presumed non-PB virus , the lysine side chain is oriented toward VP1-145E which provides negatively charged patches ( Figure 3B , arrowheads ) , and the positively charged group is less exposed . A homology model generated with SWISS-MODEL ( http://swissmodel . expasy . org/ ) [31] , [32] , [33] revealed that VP1-145E to G or Q substitution of 3VBS ( presumed non-PB ) abolished negatively charged patches , whereas VP1-145Q to E substitution of 4AED ( presumed PB ) generated negatively charged patches ( not shown ) . These results suggested that VP1-145 might regulate the PB phenotype by modulating the orientation of the conserved lysine residues , particularly the orientation of VP1-244 . To test the role of these lysines , we introduced VP1-K242A and/or VP1-K244A substitutions into two PB viruses , EV71-1095-EG and EV71-1095-EQ . We found that when these mutant viruses were generated in RD cells , the recovered viruses had a number of undesired mutations at VP1-145 . We reasoned that , because picornavirus replication is highly error prone , fewer mutations might arise in RNA-transfected CHO-K1 cells , which do not support multiple rounds of viral replication [14] . Because only limited amounts of virus could be obtained in this way , we established a highly sensitive EV71–PSGL-1-binding assay based on real-time quantitative RT-PCR . To detect viral RNA within particles bound to soluble PSGL-1 , it is important to note that attachment to PSGL-1 does not cause to release of RNA from virion [34] . Virus-containing cell culture supernatants of RNA-transfected CHO-K1 cells were used directly for co-precipitation assay with PSGL-1-Fc at 4°C , RNA genomes were released from precipitated virus by heating at 95°C [35] , and genome numbers were measured by real-time RT-PCR [36] . Genome numbers in the input viral supernatants were measured by immunoprecipitation of supernatants with anti-VP1 mAb ( Figure 5A ) . When the assay was performed with previously characterized PB viruses ( EV71-1095-EG or EV71-1095-EQ ) and non-PB viruses ( EV71-1095-EE and EV71-1095-KE ) , the results were consistent with those obtained in Figure 1: for the PB viruses , numerous viral genomes were precipitated with PSGL-1-Fc but not with control Fc protein ( Figure 5B , left ) ; in contrast , for the non-PB viruses , few genomes were precipitated with either protein . Having thus determined that the assay could measure virus binding to PSGL-1 , we tested binding of the VP1-K242A and VP1-K244A mutants . The VP1-K242A mutation permitted a low level of residual binding , but the total binding was reduced more than 100 fold . Both the VP1-K244A mutation and the double mutation of VP1-K242A and VP1-K244A abolished PSGL-1 binding . Thus , although lysine residues at VP1-242 and VP1-244 both contribute to binding , only the lysine at VP1-244 — which makes closer contact with VP1-145— is absolutely essential .
The results we report here demonstrate that a single amino acid , VP1-145 , is the critical determinant of EV71 tropism for PSGL-1 . We found that the presence of a G or Q residue at this position permitted viruses belonging to a variety of genogroups to bind PSGL-1 , whereas viruses with E at this position did not bind PSGL-1 . Similarly , viruses with G or Q , but not E at VP1-145 , were found to replicate in Jurkat T lymphocytes , supporting the idea that VP1-145 , by controlling interaction with PSGL-1 , plays an important role in virus tropism for human leukocytes . Sulfated tyrosines in the PSGL-1 N-terminus are critical for interaction with P-selectin , and crystal structure analysis demonstrates that two sulfated tyrosines in the PSGL-1 N-terminus interact directly with positively charged residues in the P-selectin lectin domain [37] . Interaction of the HIV envelope glycoprotein gp120 with the coreceptor CCR5 depends a sulfation of a tyrosine residue within CCR5 , and structural analysis suggests that this tyrosine interacts with a positively charged residue of gp120 [38] . Based on the observation that EV71 binding depends on tyrosine sulfation of PSGL-1 we had expected that binding must involve positively charged residues on the virus surface , and were somewhat surprised to identify VP1-145 G and Q as critical . However , VP1-145 , which is located on a mesa surrounding the five-fold axis , is in close proximity to two highly conserved lysine residues; we find that these lysine residues— in particular , VP1-244 , which is in close contact with VP1-145— are also required for binding to PSGL-1 . Based on the available crystal structures of viruses with Q versus E at VP1-145 , we propose that VP1-145 modulates the orientation of lysine VP1-244 , and thus regulates exposure of the positively charged lysine side chain . Why does EV71 use VP1-145 , rather than the lysines at VP1-242 or VP1-244 , to control the PSGL-1-binding phenotype ? One possible explanation is that these lysines , which are conserved in 1618 ( VP1-242 ) and 1619 ( VP1-244 ) out of 1623 of the EV71 isolates sequenced in GenBank , serve another function critical for virus interaction with host cells . Recently , Tan et al . [13] showed that EV71 binds to heparan sulfate on the cell surface , and suggested that heparan sulfate may bind to positively charged amino acids ( including the lysines at VP1-242 and 244 , as well as an arginine at VP1-161 ) , that form a cluster around the five-fold symmetry axis . Interaction of coxsackievirus A9 with heparan-sulfate proteoglycan has been shown to depend on a similar cluster of charged residues ( in this case , 5 copies of a single arginine ) at the five-fold axis [39] . If interaction with heparan sulfate is critical for EV71 infection , or for persistence of EV71 in the human population , the virus may have evolved an indirect mechanism , using VP1-145 , to control PSGL-1 interaction . We found that more than 80% of EV71 sequences in GenBank had an E residue at VP-145 ( non-PB ) , and approximately 20% had G or Q ( PB ) . When we examined the specific sequences encoding VP1-145 we found that single nucleotide changes in the codons for E ( GAA , GAG ) lead to codons for G ( GGA , GGG ) and Q ( CAA , CAG ) ( Figure 6 ) . Thus , single nucleotide changes are sufficient for replacement of E with Q or G ( or vice versa ) , and for viruses to gain or lose the capacity to bind PSGL-1 . As there are two nucleotide differences between the codons for G and Q , direct conversion between G and Q is less likely to occur; this suggests that PB viruses in the database are more likely to have derived from non-PB viruses with E at VP1-145 than to have evolved from other PB viruses . It is interesting to note that although four codons are available to specify G , two G codons that cannot be directly obtained from E codons are not seen at VP1-145 . Based on an analysis of the ratio of non-synonymous to synonymous substitutions in the capsid proteins of EV71 , VP1-145 has been identified as a major site of positive selection [29] , [40] , [41] . Thus , the rapid amino acid change and polymorphism at VP1-145 may contribute to viral fitness in vivo by changing the cell tropism of EV71 variants . In two recent studies , EV71 sequences from patients were examined in an effort to identify genetic markers of virulence; in both studies VP1-145 was associated with disease severity , and VP1-145E was associated with milder disease [42] , [43] . Although these studies are not definitive , it is tempting to speculate that isolates with G or Q at this position may be virulent because of their capacity to bind PSGL-1 . However , mutations at VP1-145 from G or Q to E have been shown to confer mouse adaptation and virulent phenotypes in different mouse models [44] , [45] , [46]; because mouse PSGL-1 does not bind EV71 [12] , VP1-145 may also influence virulence by mechanisms that do not involve interactions with PSGL-1 . A major event in the entry process is uncoating , release of viral RNA from the capsid into the cytoplasm . EV71 interaction with SCARB2 , particularly under acidic conditions , has been shown to convert the native virion to a 135S A particle [30] , [34] , which is considered to be an intermediate in the uncoating process . In contrast , EV71 interaction with PSGL-1 does not result in formation of A particles [30] , [34] . Capsid residues important for interaction with SCARB2 map to the cleft or canyon that surrounds the five-fold mesa [30] , suggesting that SCARB2— like the poliovirus receptor , which also induces formation of A particles [47]— binds within the canyon . The capsid residues we find to be important for binding to PSGL-1 are remote from the canyon , and cluster at the five-fold axis , surrounding a small cavity remote from the canyon . It is possible that the thin N-terminal region of PSGL-1 inserts into this cavity , and that the interaction is stabilized by interactions between the sulfated tyrosines and lysine residues at VP1-242 and -244 . The acidic side chain of VP1-145E may repel the sulfated tyrosines of PSGL-1 . Analysis using cyro-electron microscopy will be important to clarify the contact sites between EV71 and PSGL-1 , and crystal structures of EV71-EG and -EE ( in addition to EV71-EQ and -KE ) will provide a more detailed picture of the role of the lysine residues . Because PSGL-1 is involved in leukocyte migration , cytokine production , and regulation of immune responses [48] , [49] , [50] , [51] , [52] , virus interaction with PSGL-1 on leukocytes may be important for dissemination within the host , for modulation of antiviral host responses , or for the excessive inflammatory cytokine production seen in patients with severe disease . The work we describe here indicates that EV71 can rapidly change its avidity for PSGL-1 , and its tropism for PSGL-1-expressing leukocytes , as the consequence of a single amino acid change at VP1-145 . Our results are significant for understanding of virus-host interactions , viral evolution , and pathogenesis .
Jurkat cells were obtained from Riken Cell Bank and cultured in RPMI-1640 medium ( Sigma ) supplemented with 10% fetal bovine serum ( FBS ) . RD cells obtained from the US Centers for Disease Control were maintained in DMEM ( Sigma ) supplemented with 10% FBS . CHO-K1 cells were maintained in F-12 nutrient mixture ( Ham ) ( Life technologies ) supplemented with 10% FBS . We used eight representative EV71 isolates characterized in our previous study [12] ( Table 1 ) . BrCr was originally isolated from a meningitis patient [53] . SK-EV006 and C7/Osaka were isolated from a patient with fatal encephalitis [54] . Other strains were isolated from HFMD patients without severe symptom [55] , [56] , [57] , [58] . Stocks of clinical isolates were prepared in RD or Vero cells . For most experiments , virus produced by RNA-transfected cells was amplified by growth in RD cells; supernatants were titered , and clarified by centrifugation in a microfuge at 15 , 000 rpm for 3 min just before use . For the experiments shown in Figure 2 , viruses were concentrated by ultracentrifugation , as follows: culture supernatant was centrifuged at 10 , 000 rpm in a Beckman SW32Ti rotor for 1 h to precipitate cell debris; the supernatant was then spun in the same rotor at 32 , 000 rpm for 2 h , and the resulting pellet was resuspended in phosphate-buffered saline ( + ) at 4°C overnight . For experiments shown in Figure 5 , virus was produced in RNA-transfected CHO cells; debris was pelleted in a microfuge at 15 , 000 rpm for 3 min , and the supernatant was used for binding experiments . In all cases , viral titers were determined by a microtitration assay using 96-well plates and RD cells as previously described [56] . Briefly , 10 wells were used for each viral dilution and the viral titers were expressed as 50% cell culture infectious dose ( CCID50 ) . We used the EV71-specific mAb MA105 ( mouse IgG2b ) ( Y . Tano et al . , unpublished ) . The mAb to human PSGL-1 ( KPL1; mouse IgG1 ) was purchased from BD Biosciences . For negative controls , mouse IgG1 ( MOPC-21 ) and IgG2b ( MOPC-141 ) were purchased from BioLegend and Sigma , respectively . Soluble recombinant forms of human proteins fused to the Fc region of human IgG1 ( PSGL-1-Fc and CTLA-4-Fc ) were purchased from R&D Systems . The genomic sequence of EV71 was determined as described previously [27] . Briefly , we extracted viral genomic RNA from the culture supernatant of infected RD cells . We performed RT-PCR preparation of DNA fragments for direct DNA sequencing . The 5′ and 3′ ends of the viral genome were sequenced using the conventional RACE methods . Viral cDNA was reverse transcribed using CDS III/3′ PCR Primer ( 5′-ATTCTAGAGGCCGAGGCGGCCGACATG-dT ( 30 ) -VN-3′ ) ( Takara ) and Super Script II polymerase ( Life technologies ) . Full genomic cDNA was amplified by PCR using the primers in Table S2 . For cloning of the full genomic cDNA , we introduced the multi cloning sites into the pBR322 plasmid ( Takara ) . The EcoRI-BsmI fragment of pBR322 was replaced with 5′- GAATTCCTTAAGCTCGAGTCTAGACCCGGGGGATCCGTGCACAGGCCTCG -3′ ( EcoRI+AflII+XhoI+XbaI+SmaI+BamHI+ApaLI+StuI+cg ) to produce pBR322Y . The full genomic cDNA was cloned into pBR322Y and Escherichia coli strain XL10-Gold ( Agilent technologies ) was used for the preparation of the plasmids . The nucleotides different from those obtained from direct sequencing of RT-PCR products were corrected by site directed mutagenesis using PCR or by replacing them with the restriction enzyme-digested DNA fragment from a plasmid with the correct nucleotide sequence . We used the genomic RNA of C7/Osaka , Nagoya , 1095 , 02363 , and 75-Yamagata strains of EV71 as the template for RT-PCR and named the resultant plasmids as pBREV71-C7/Osaka-EG , pBREV71-Nagoya-EE , pBREV71-1095-EG , pBREV71-02363-KE , pBREV71-75-Yamagata-EQ , respectively . The mutations were introduced into the plasmids by site directed mutagenesis using PCR . The primers used for mutagenesis are provided in Table S3 . The plasmids and viruses with mutations at VP1-98 and/or VP1-145 were named as shown in Figure 1A . We generated viruses from infectious viral cDNA clones as described previously [44] . Briefly , RNA transcripts of EV71 mutants were obtained using a MEGAscript T7 kit ( Life technologies ) or RiboMAX large scale RNA production system-T7 ( Promega ) with linearized DNA of the infectious EV71 clones as the template . RNA transcripts were transfected into a monolayer of RD cells in six-well plates using a Lipofectamine 2000 reagent ( Life technologies ) or 2 mg ml−1 of polyethyleneimine “MAX” ( MW 40 , 000 ) ( Polysciences ) [59] , followed by incubation at 37°C . The medium was replaced with fresh medium 4 h after transfection . The transfected cells and supernatants were freeze-thawed three times at 24 h post-transfection . Before use in experiments , the recovered viruses were amplified once in fresh RD cells , and the sequence of the whole capsid region was confirmed by direct sequencing of RT-PCR products . CHO-K1 cells were used to prepare viruses for the highly sensitive EV71–PSGL-1-binding assay using real-time RT-PCR . CHO-K1 cells were seeded at 2 . 5×105 cells per 2 . 5 ml in a 6-well plate 18 h before transfection . Just before transfection , the medium was replaced with 2 . 5 ml of F-12 nutrient mixture without FBS after washing the cells . Five µg of RNA transcripts was transfected according to the manufacturer's for Lipofectamine 2000 ( Life technologies ) , expect for using 10 µl of 2 mg ml−1 polyethyleneimine “MAX” ( MW 40 , 000 ) ( Polysciences ) instead of Lipofectamine 2000 . The medium was replaced with 1 . 2 ml of fresh medium with 10% FBS 4 h after transfection . The transfected cells and supernatants were freeze-thawed three times at 24 h post-transfection . The supernatant was used for the binding assay . The EV71–PSGL-1-Fc binding assay [12] was performed with minor modification . Briefly , Dynabeads protein G ( Life technologies ) and 1 µg of chimeric Fc proteins were diluted in 300 µl of immunoprecipitation buffer ( 20 mM Tris-Cl , 135 mM NaCl , 1% Triton X-100 , 10% glycerol; pH 7 . 4 ) and incubated for 1 h at 4°C . The beads were washed once . Viruses concentrated by ultracentrifugation ( 0 . 5 µg VP1 protein in SDS-PAGE analysis ) were added and incubated in immunoprecipitation buffer for an additional 1 h . We washed the beads and subjected the immunoprecipitates to 12 . 5% SDS-PAGE . For Western blotting , proteins were transferred onto nitrocellulose membranes and blotted with anti-EV71 VP1 mAb MA105 . Ten µl of Dynabeads protein G ( Life Technologies ) and 0 . 5 µg of chimeric Fc proteins were diluted in 10 µl of immunoprecipitation buffer and 80 µl of F-12 nutrient mixture ( Life technologies ) with 10% FBS and incubated for 1 h at 4°C . The beads were washed once and incubated with 50 µl of the diluted supernatant of CHO-K1 cell culture with 5×107 copies of the EV71 RNA genome , 10 µl of immunoprecipitation buffer , and 40 µl of F-12 nutrient mixture ( Life technologies ) with 10% FBS for 1 h . The beads were washed five times with immunoprecipitation buffer and suspended in 50 µl of DEPC-treated water . The immunoprecipitates were incubated at 95°C for 5 min to release the virion RNA [35] . Real-time RT-PCR was performed as described previously by Johnsson et al . [36] with modifications . Five µl of viral RNA was assayed in a 20 µl reaction mixture using a Power SYBER Green RNA-to-Ct 1-step Kit ( Life technologies ) with primers EnteroFw and EnteroRev ( final 100 nM each ) [36] . The mixtures were subjected to real-time RT-PCR , consisting of a reverse transcription step at 42°C for 30 min followed by 40 cycles of 95°C for 3 s and 60°C for 30 s . The results were analyzed with 7500 Fast Real-Time PCR System ( Life technologies ) . Viral RNA of EV71-1095-EG was used for quantification of copy number . RD cells ( 2×105 cells per 200 µl in a 48-well plate ) were inoculated with viruses at 1 CCID50 per cell for 1 h , washed , and incubated in 400 µl of the medium at 37°C . Jurkat cells ( 4×104 cells ) were inoculated with viruses at 1 CCID50 per cell for 1 h , washed , and incubated in 400 µl of the medium in a 48-well plate at 34°C . For mAb inhibition , the cells were pretreated with 10 µg ml−1 mAb in 100 µl of the medium for 1 h , washed , and maintained with 10 µg ml−1 mAb in 400 µl of the medium . The culture supernatants and infected cells were subjected to three cycles of freeze-thawing before titration . Crystal structures of Protein Data Bank Accession No . 4AED and 3VBS were used for presumed EV71-PB and non-PB , respectively . Molecular graphics and analyses were performed with the UCSF Chimera package [60] . We carried out all infection assays in triplicate and compared the mean viral titers using Student's t-test ( two-tailed ) . P values of <0 . 01 were considered statistically significant . | Enterovirus 71 ( EV71 ) commonly causes mild febrile illness in children ( hand , foot , and mouth disease ) , but some patients suffer severe neurologic disease and death . Recent outbreaks in the Asia-Pacific region have caused thousands of deaths , making EV71 a major public health concern . Some EV71 strains bind to P-selectin glycoprotein ligand-1 ( PSGL-1 ) and infect immune cells , but others do not . We previously found that EV71 binds the PSGL-1 N-terminus , and that binding depends on tyrosine sulfation of the N-terminus , but the viral factors that control interaction with PSGL-1 have not been identified . In our present work we present evidence that a single amino acid , residue 145 of the viral capsid protein ( VP1-145 ) , determines whether a virus binds or does not bind PSGL-1 , and that it functions by influencing the orientation of a nearby lysine residue ( VP1-244 ) on the virus surface . We propose that VP1-145 controls virus tropism by changing the accessibility of the positively-charged lysine side chain of VP1-244 to the negatively charged , sulfated N-terminus of PSGL-1 . Our results shed new light on virus-receptor interaction , and EV71 tropism for PSGL-1-expressing leukocytes . | [
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"dise... | 2013 | Enterovirus 71 Binding to PSGL-1 on Leukocytes: VP1-145 Acts as a Molecular Switch to Control Receptor Interaction |
The temporal expression and secretion of distinct members of a family of virulence-associated cathepsin L cysteine peptidases ( FhCL ) correlates with the entry and migration of the helminth pathogen Fasciola hepatica in the host . Thus , infective larvae traversing the gut wall secrete cathepsin L3 ( FhCL3 ) , liver migrating juvenile parasites secrete both FhCL1 and FhCL2 while the mature bile duct parasites , which are obligate blood feeders , secrete predominantly FhCL1 but also FhCL2 . Here we show that FhCL1 , FhCL2 and FhCL3 exhibit differences in their kinetic parameters towards a range of peptide substrates . Uniquely , FhCL2 and FhCL3 readily cleave substrates with Pro in the P2 position and peptide substrates mimicking the repeating Gly-Pro-Xaa motifs that occur within the primary sequence of collagen . FhCL1 , FhCL2 and FhCL3 hydrolysed native type I and II collagen at neutral pH but while FhCL1 cleaved only non-collagenous ( NC , non-Gly-X-Y ) domains FhCL2 and FhCL3 exhibited collagenase activity by cleaving at multiple sites within the α1 and α2 triple helix regions ( Col domains ) . Molecular simulations created for FhCL1 , FhCL2 and FhCL3 complexed to various seven-residue peptides supports the idea that Trp67 and Tyr67 in the S2 subsite of the active sites of FhCL3 and FhCL2 , respectively , are critical to conferring the unique collagenase-like activity to these enzymes by accommodating either Gly or Pro residues at P2 in the substrate . The data also suggests that FhCL3 accommodates hydroxyproline ( Hyp ) -Gly at P3-P2 better than FhCL2 explaining the observed greater ability of FhCL3 to digest type I and II collagens compared to FhCL2 and why these enzymes cleave at different positions within the Col domains . These studies further our understanding of how this helminth parasite regulates peptidase expression to ensure infection , migration and establishment in host tissues .
Papain-like cysteine peptidases , including cathepsins B and L , are ubiquitously secreted extracorporeally by helminth parasites of human and veterinary importance where they perform many important roles that are critical to the development and survival of the parasite within the mammalian host [1] . These roles include penetration and migration through host tissues [2] , catabolism of host proteins to peptides and amino acids [3] , [4] , and modulation of the host immune response by cleaving immunoglobulin [5] , [6] or by altering the activity of immune effector cells [7] . Accordingly , cathepsin peptidases are leading targets for novel anti-parasitic drugs and vaccines that block their function [8] , [9] . Fasciola hepatica is the causative agent of liver fluke disease ( fasciolosis ) of domestic animals in regions with temperate climates . Although traditionally regarded as a disease of livestock , fasciolosis is now recognised as an important emerging foodborne zoonotic disease in rural areas of South America ( particularly Bolivia , Peru and Equador ) , Egypt and Iran [10] . It is estimated that over 2 . 4 million people are infected with F . hepatica worldwide and around 91 million are at risk of infection [11] . To infect their mammalian hosts , F . hepatica larvae , which are ingested with vegetation contaminated with dormant cysts ( metacercariae ) , penetrate the intestinal wall , enter the liver capsule and migrate through the parenchyma before invading into the bile ducts [12] . To facilitate this tissue migration , Fasciola secrete various members of a multigenic family of cathepsin L peptidases that exhibit overlapping but complementary substrate specificities and together cleave host macromolecules very efficiently [13] , [14] . In fact , the ability of Fasciola to infect and adapt to a wide range of host species has been attributed to the effectiveness of this proteolytic machinery [14] , [15] . Phylogenetic analyses have shown that the Fasciola cathepsin L gene family expanded by a series of gene duplications followed by divergence which gave rise to three clades expressed by tissue-migrating and adult worms ( Clades 1 , 2 , and 5 ) and two clades specific to the early infective juvenile stage ( Clades 3 and 4 ) [13] , [14] . Consistent with these observations , our proteomics analysis identified representative enzymes from Clades 1 , 2 and 5 , but not from Clades 3 and 4 , in the secretory products of adult F . hepatica [14] . More recently , we showed that the temporal expression and secretion of the specific cathepsin L clades correlated with the migration of the parasite through host issues; members of cathepsin L clade 3 ( FhCL3 ) are secreted by Fasciola infective larvae and effected penetration of the host intestinal wall while clades 1 , 2 and 5 ( FhCL1 , FhCL2 and FhCL5 ) peptidases are secreted by the immature liver-stage flukes and adult worms and function in preparing a migratory path through the liver and in the acquisition of nutrient by degrading host blood and tissue components . While clade 4 ( FhCL4 ) peptidases are expressed by infective larvae they do not seem to be secreted and , therefore , may play an intracellular house-keeping function [13] , [16] . Recent transcriptomic analyses of juvenile and adult stages have confirmed these observations [17] , [18] . The secreted Fasciola cathepsins are produced in specialised gastrodermal cells which line the parasites's gut and are packaged in secretory vesicles before being extruded into the gut lumen from where they are released into host tissues [19] . The peptidases can efficiently degrade a range of host macromolecules including haemoglobin , immunoglobulin and interstitial matrix proteins such as fibronectin and laminin [3] , [4] , [20]–[22] . Notably , however , studies in our laboratory using functionally-active recombinant enzymes have shown that FhCL2 and FhCL3 exhibit an unusual ability to cleave native collagen [22] , [23] . This is of relevance because collagenase-like activity is restricted to very few enzymes ( e . g . bacterial collagenases , matrix metalloproteinases and human cathepsin K ) and , therefore , the evolution and maintenance of such an activity in Fasciola suggests that it is essential to the parasite's ability to degrade the connective tissue matrix of the organs through which it migrates . The active site of papain-like cysteine peptidases is relatively short , and while consisting of four subsites ( S2-S1-S1′-S2′ ) with additional binding areas ( S4-S3 and S3′ ) the specificity of substrate binding is principally governed by the residues that make up the S2 subsite [24] , [25] . This S2 site forms a deep pocket capable of holding the P2 amino acid of the substrate and positioning the scissile bond into the S1 subsite for cleavage . In Carica papaya papain ( PDB ID: 9PAP ) , the S2 subsite is composed of residues occupying positions 67 , 68 , 133 , 157 , 160 and 205 . An analysis of these residues in the various cathepsin L clades clearly demonstrates divergence within the S2 subsite , in particular at the three positions that have the greatest influence on P2 binding i . e . at residues 67 , 157 and 205 [14] , [15] , [23] . For FhCL2 , the collagenolytic activity has been attributed to the presence of a particular residue , Tyr69 , within the enzyme's S2 substrate binding site which is also found in human cathepsin K , the only mammalian cathepsin with the ability to cleave within the covalently-linked triple helices , of Col domains of native collagen [26] , [27] . The S2 Tyr69 is also suggested to allow both enzymes to cleave macromolecular and dipeptide substrates with a Pro residue in the P2 position [22] , [28] . More recently we showed that in FhCL3 , this position is occupied by a larger Trp residue , a feature shared only with a ginger rhizome peptidase [29] which is also capable of cleaving collagen . Notably the S3 subsite of both FhCL3 and the plant enzyme are quite shallow , an observation that led us to advance the idea that the specificity of these enzymes might be restricted [23] . Our laboratory recently determined the three-dimensional structure of one of the major cathepsin L peptidases of adult F . hepatica , FhCL1 [22] . Similar to other cathepsins , the enzyme is composed of two domains ( R and L ) at the juncture of which is a cleft that forms the substrate-binding site and contains the enzyme catalytic machinery . Super-imposition of the alpha carbons of FhCL1 with cysteine peptidases from plants ( e . g . papain , PDB ID 9PAP ) and mammals ( e . g . human cathepsin L PDB ID 1CJL ) yields an r . m . s . deviation in the range 0 . 78 Å to 1 . 085 Å which is indicative of the very high conservation of the overall fold and shape that exists amongst all members of the papain family of cysteine peptidases [30] . Since the FhCL1 structure and fold can be described as practically identical to all other cathepsin L-like peptidases its scaffold can be exploited as a ‘prototype’ to investigate the role of critical amino acids within the S2 subsite in substrate binding ( Table 1 ) , particularly those of FhCL2 and FhCL3 , whose primary structures are 78% and 70% identical to FhCL1 , respectively . In the present study , we investigated and compared the substrate specificity of active recombinant forms of FhCL1 , FhCL2 and FhCL3 with specific emphasis on their ability to degrade native collagen . This activity was interpreted by obtaining the enzymatic kinetic parameters ( Km , kcat , and kcat/Km ) of these enzymes on a range of peptide substrates and binding kinetics for specific inhibitory compounds . Furthermore , using mass spectrometry we mapped the cleavage sites of native collagen I and derived peptides , adding evidence for differential activities between FhCL2 and FhCL3 . Finally , using our FhCL1 crystal structure as a template we created molecular dynamics simulations to explain how the active sites of these enzymes accommodate collagen-like substrates and endow them with this unusual collagenolytic activity . Our study provides biochemical and structural insights into the molecular mechanism of tissue invasion by these important parasitic helminths .
Z-Phe-Arg-NHMec , Z-Leu-Arg-NHMec , Z-Val-Val-Arg-NHMec , Tos-Gly-Pro-Arg-NMec , Tos-Gly-Pro-Lys-NMec , Boc-Ala-Gly-Pro-Arg-NMec , Boc-Val-Leu-Lys-NMec , Boc-Val-Pro-Arg-NMec , Z-Phe-Ala-CHN2 , Z-Gly-Pro-Gly-Gly-Pro-Ala and Z-Gly-Pro-Leu-Gly-Pro were obtained from Bachem ( St . Helens , UK ) . Cathepsin K inhibitor II was purchased from BD Biosciences ( Sydney , Australia ) . E-64 , DTT , EDTA and bovine nasal septum collagen type II were obtained from Sigma-Aldrich ( Sydney , Australia ) . Calf skin collagen type I was purchased from Calbiochem . Pichia pastoris strain X33 was obtained from Invitrogen ( San Diego , CA , USA ) . Ni-NTA agarose and columns were obtained from Qiagen ( Australia ) . Pre-cast NuPage 4–12% Bis-Tris gels and pre-stained molecular weight markers were purchased from Invitrogen ( Australia ) . Recombinant F . hepatica procathepsin L1 , L2 and L3 ( FhCL1 , FhCL2 and FhCL3 ) were produced in yeast as previously described [22] , [23] . Briefly , P . pastoris ( for FhCL1 and FhCL2 expression ) and P . angusta ( for FhCL3 expression ) yeast transformants were cultured in 500 ml BMGY broth , buffered to pH 8 . 0 , in 5 L baffled flasks at 30°C until an OD600 of 2–6 was reached . Cells were harvested by centrifugation at 2000× g for 5 min and protein expression induced by resuspending in 100 ml BMMY broth , buffered at pH 6 . 0 containing 1% methanol . Recombinant proteins were affinity purified from yeast using Ni-NTA-agarose . Recombinant propeptidases were dialysed against phosphate buffered saline ( PBS ) and stored at −20°C . The 37 kDa cathepsin L zymogens were autocatalytically activated and processed to 24 . 5 kDa mature enzymes by incubation for 2 h at 37°C in 0 . 1 M sodium citrate buffer ( pH 5 . 0 ) containing 2 mM DTT and 2 . 5 mM EDTA . The mixture was then dialysed against PBS , pH 7 . 3 . The proportion of functionally active recombinant protein in these preparations was determined by titration against E-64 . Initial rates of hydrolysis of the fluorogenic peptide substrates shown in table 2 were monitored by the release of the fluorogenic leaving group , NHMec , at an excitation wavelength of 380 nm and an emission wavelength of 460 nm using a Bio-Tek KC4 microfluorometer . kcat and Km values were determined using nonlinear regression analysis . Initial rates were obtained at 37°C over a range of substrate concentrations spanning Km values ( 0 . 2–200 µM ) and at fixed enzyme concentrations ( 0 . 5–5 nM ) . Assays were performed in 100 mM sodium phosphate buffer ( pH 6 . 0 ) containing 1 mM DTT and 1 mM EDTA . Rate constants for the inactivation of the Fasciola enzymes by Z-Phe-Ala-CHN2 and cathepsin K inhibitor II were determined from progress curves in the presence of substrate as previously described [22] . Calf skin collagen type I and bovine nasal septum collagen type II ( solubilised in 0 . 2 M acetic acid at a concentration of 2 mg/ml ) were dialysed for two days against 0 . 1 M sodium acetate ( pH 5 . 5 ) or PBS ( pH 7 . 0 ) . Digestion reactions contained 10 µg of dialysed collagen substrates , 1 mM DTT and 1 mM EDTA and 2 µM activated FhCL1 , FhCL2 or FhCL3 in a final volume of 100 µl of one of the above buffers at 28°C . For collagen type 1 , reactions were performed for 3 h ( pH 5 . 5 ) or 20 h ( pH 7 . 0 ) whilst collagen type II was digested over 13–18 h . All reactions were stopped by the addition of 10 µM E-64 . Digests were analyzed on reducing 4–12% NuPage Bis-Tris gels and visualised by staining with Flamingo fluorescent stain ( Bio-Rad ) . For digestion of collagen-like peptide substrates , 20 µg of Z-Gly-Pro-Leu-Gly-Pro and Z-Gly-Pro-Gly-Gly-Pro-Ala in DMSO were incubated with FhCL2 or FhCL3 ( 15 µM ) in 100 mM sodium acetate buffer , pH 4 . 5 , containing 1 mM EDTA and 2 µM DTT for 30 min at 37°C . Digestion reactions were halted by the addition of 10 µM E-64 . Recombinant FhCL2 and FhCL3 were removed from collagen type I digests using Ni-NTA agarose . The reactions were then spun at 13 , 000 rpm for 15 min to remove particulates and were concentrated to a final volume of 15 µl using a Concentrator 5301 ( Eppendorf ) . Using an Eksigent AS-1 autosampler connected to a Tempo nanoLC system ( Eksigent , USA ) , 10 µL of the sample was loaded at 20 µl/min with MS buffer A ( 2% acetonitrile+0 . 2% formic acid ) onto a C8 trap column ( Michrom , USA ) . After washing the trap for three minutes , the peptides were washed off the trap at 300 nL/min onto an IntegraFrit column ( 75 µm×100 mm ) packed with ProteoPep II C18 resin ( New Objective , Woburn , MA ) . Peptides were eluted from the column and into the source of a QSTAR Elite hybrid quadrupole-time-of-flight mass spectrometer ( AB Sciex ) using the following program: 5–50% MS buffer B ( 98% acetonitrile+0 . 2% formic acid ) over 15 minutes , 50–80% MS buffer B over 5 minutes , 80% MS buffer B for 2 minutes , 80–5% for 3 min . The eluting peptides were ionised with a 75 µm ID emitter tip that tapered to 15 µm ( New Objective ) at 2300 V . An Intelligent Data Acquisition ( IDA ) experiment was performed , with a mass range of 375–1500 Da continuously scanned for peptides of charge state 2+–5+ with an intensity of more than 30 counts/s . Selected peptides were fragmented and the product ion fragment masses measured over a mass range of 50–1500 Da . The mass of the precursor peptide was then excluded for 15 seconds . Peak list files generated by MSX ( Infochromics ) were exported to a local PEAKS Studio v5 . 0 ( Bioinformatics Solutions Inc . ) search engine for protein database searching . MS/MS data was used to search a custom-made database containing only bovine collagen sequences . The enzyme specificity was set to “no enzyme” and propionamide ( acrylamide ) modification of cysteines was used as a fixed parameter and oxidation of methionines was set as a variable protein modification . The mass tolerance was set at 100 ppm for precursor ions and 0 . 2 Da for fragment ions . Only 1 missed cleavage was allowed . Matched peptides achieving a score >60% were accepted during PEAKs searches [16] . The matching peptides were then mapped onto the primary amino acid sequence of bovine collagen to identify FhCL2 and FhCL3 cleavage sites and to plot P2 residue preference for each enzyme . For collagen-like peptide substrates , digests were concentrated and analysed by MS/MS as described above with the following modifications . The mass range of 150–600 Da was scanned for peptides of charge state 2+ with an intensity of more than 100 counts/s . Selected peptides were fragmented and the product ion fragment masses measured over a mass range of 50–600 Da . The mass of the precursor peptide was then excluded for 120 seconds . An inclusion list describing all possible substrate ions that could be produced by enzymatic cleavage of the peptide substrates was generated and programmed into the Analyst acquisition software . The resulting data files were manually interrogated to determine the presence of peptide ions described in the inclusion list . The MS/MS spectra of those peptides were de novo sequenced for b and y ion fragments describing the peptide substrate's sequence to a mass accuracy of approximately 0 . 2 Da . For the MD simulations , starting coordinates for F . hepatica cathepsin L were taken from the 1 . 4 Å resolution crystal structure of a FhCL1 mutant zymogen , in which the active site Cys was replaced by Gly ( [22]; PDB 2O6X ) . The prosegment ( residues 1–100 ) was removed and the active site Gly mutation reversed to the wild type Cys . Initial coordinates for a template peptide substrate ( Ala-Leu-Ala-Leu-Pro ) were derived from X-ray structures of inhibitors bound to human cathepsin K ( [31]; PDB 1NLJ ) and bovine cathepsin B ( [32]; PDB 1SP4 ) after structural alignment with FhCL1 . This initial peptide was altered to Ala-Leu-Arg-Asn-Ala using the mutate function in Swiss-PdbViewer ( [33]; http://spdbv . vital-it . ch/ ) and then minimized while bound to the wild-type FhCL1 using the equilibration protocol described below . The equilibrated peptide was then extended by one Ala residue at its N- and C-termini using the coordinate generation function in the psfgen program [34] , and then re-equilibrated . The resultant peptide ( ligand A ) was used to generate all other substrate starting coordinates by using the mutate function in Swiss-PdbViewer . Mutations to FhCL1 were similarly generated using Swiss-PdbViewer . Rotamers for mutated enzyme and substrate side-chains were chosen by visual inspection and using the rotamer score provided in Swiss-PdbViewer . The N-terminal residue of the substrate was acetylated and the C-terminus N-methylamidated . Each complex was optimally oriented to minimize cell volume [35] and solvated in a truncated octahedral periodic cell with a minimum of 20 Å between periodic images of the protein . The system was neutralized with sodium ions . MD simulations were carried out with NAMD 2 . 6 [34] using the CHARMM27 force field with φ/ψ cross-term map corrections [36] . Parameters for Hyp were from Veld and Stevens [37] . Water molecules were simulated with the TIP3P model [38] . Simulation conditions were maintained at 1 . 0 atm constant pressure by the Nosé-Hoover Langevin piston method [39] , [40] and at 310 K constant temperature by Langevin dynamics with a damping coefficient at 5 ps−1 . The time step used for the simulations was 1 . 5 fs . A cutoff of 12 Å , with a switching function between 10 and 12 Å , was used for short-range non-bonded interactions . Long-range electrostatic interactions were computed using the particle mesh Ewald method [41] with a grid density of approximately 1/Å . A multiple time-stepping algorithm was used with interactions involving covalent bonds and short-range non-bonded interactions computed every time step , while long-range electrostatic forces were computed every two time steps . SHAKE [42] and SETTLE [43] were applied to constrain the lengths of all bonds that involve hydrogen . The solvated starting structure was minimized using conjugate gradient minimization to a 0 . 5 kcal/ ( mol·Å ) r . m . s . gradient with all enzyme heavy atoms fixed , with the exception of side-chain atoms of mutated residues , which were unrestrained . In addition , in this phase of the equilibration , ligand atoms were not fixed and harmonic positional constraints of 100 kcal/ ( mol·Å2 ) force constant were placed on the Cα atoms of ligand residues 3–5 ( P2 , P1 and P1′ ) . The unrestrained atoms were then further minimized during a 50 ps molecular dynamics run at 310 K . This starting model was then minimized with harmonic positional constraints on the NCαCO backbone of the protein and Cα atoms of ligand residues 3–5 . A 100 kcal/ ( mol·Å2 ) force constant was used to minimise the system to a 0 . 5 kcal/ ( mol·Å ) r . m . s . gradient . The constraints were gradually removed by subsequent minimizations to a 0 . 1 kcal/ ( mol·Å ) r . m . s . gradient , scaling the initial force constants by factors of 0 . 5 , 0 . 15 , 0 . 05 , and 0 . The unrestrained minimized structure was then heated from 50 K to 310 K in steps of 25 K using velocity reassignment during a 30 ps molecular dynamics run . The equilibrated system was then used for production runs with no restraints . All systems were run for 12 ns . All simulations remained stable to completion . For analysis , the distance between the sulphur atom of the active Cys residue and the scissile backbone carbonyl carbon of the substrate ( S-C distance ) was recorded every 50 time-steps ( 0 . 075 ps ) ; trajectory coordinates were recorded every 1000 time-steps ( 1 . 5 ps ) . The free energy of binding of the peptide ligand to the peptidase contains an enthalpic and an entropic contribution . Free energy analysis of the production trajectories employed the single-trajectory MM/PBSA method combined with a determination of the change in configurational entropy using the harmonic approximation of normal-mode analysis [44] , [45] . Snapshots from the MD trajectory , stripped of water and counterions , were analysed . The enthalpy of binding is composed of the change in the molecular mechanics free energy upon complex formation , and the solvated free energy contribution . The molecular mechanics free energy difference was calculated using the SANDER module in AMBER 9 [46] , with no cutoff for the non-bonded energies and the AMBER ff03 force field to describe the protein and peptide ligands [47] . Compatible parameters for Hyp were not available and binding energies for ligand F were not calculated . The AMBER PBSA module was used for the evaluation of the electrostatic free energy of solvation . A grid density of 3/Å was employed for the cubic lattice , the internal and external dielectric constants were set to 1 and 80 , respectively , and 1000 linear iterations were performed . The non-polar solvation free energy was calculated from the solvent accessible surface area using the MSMS program [48] , with a probe radius of 1 . 4 Å , the surface tension set to 0 . 00542 kcal/ ( mol·Å2 ) , and the off-set to 0 . 92 kcal/mol·m . The changes in configurational entropy upon ligand association were estimated by an all-atom normal-mode analysis performed with the AMBER NMODE module . Prior to the normal mode calculations , the complex , receptor , and ligand were subjected to minimization with a distance dependent dielectric constant 4r and convergence tolerance tighter than a root-mean-squared gradient of drms 10−4 kcal/ ( mol·Å ) . Entropy and enthalpy calculations on all peptidase-ligand systems are performed separately and averaged over equally spaced snapshots , extracted over the final 4 . 005 ns of the production phase . The mean of the binding free enthalpies and entropies of all the snapshots were computed and then summed to obtain the binding free energy . For the enthalpy calculations , snapshots were taken every 10 . 5 ps ( 381 frames ) , for the entropy calculations , snapshots were taken every 190 . 5 ps ( 21 frames ) . VMD [49] was used to prepare the initial simulation system and analyse trajectories . Structural figures were prepared with PyMol [50] . Simulaid ( http://atlas . physbio . mssm . edu/~mezei/ ) was used in the preparation of the truncated octahedral cell [35] and to convert the NAMD dcd format MD coordinate trajectories to AMBER format for the MM/PBSA analysis .
Functionally active recombinant forms of the major cathepsin L peptidases of F . hepatica , FhCL1 , FhCL2 and FhCL3 , were expressed in yeast and isolated to homogeneity as previously described [22] , [23] . To compare their biochemical substrate specificity the kinetic parameters ( Km , kcat , and kcat/Km ) for each enzyme was determined against a range of small fluorogenic peptide ( predominantly tripeptide ) substrates ( Table 2 ) . FhCL1 most efficiently cleaved substrates containing hydrophobic residues at the P2 position such as the dipeptides Z-Leu-Arg-NHMec ( kcat/Km 1 , 492 , 354 M−1 s−1 ) , Z-Phe-Arg-NHMec ( kcat/Km 64 , 912 M−1 s−1 ) and tripeptide Boc-Val-Leu-Lys-NHMec ( kcat/Km 54 , 266 M−1 s−1 ) . In contrast , tripeptide substrates containing Pro at the P2 position , including Tos-Gly-Pro-Arg-NHMec ( kcat/Km 671 M−1 s−1 ) , Boc-Ala-Gly-Pro-Arg-NHMec ( kcat/Km 673 M−1 s−1 ) , Tos-Gly-Pro-Lys-NHMec ( kcat/Km 612 M−1 s−1 ) and Boc-Val-Pro-Arg ( kcat/Km 478 M−1 s−1 ) , were cleaved relatively poorly ( Table 2 ) . In comparison to FhCL1 , substrates with Phe and Leu in the P2 position were much less effectively cleaved by FhCL2 and even less so by FhCL3 . The kcat/Km values for FhCL2 and FhCL3 against Z-Phe-Arg-NHMec were 6- and 65-fold lower , respectively , than that observed for FhCL1 . Similarly , the kcat/Km values for Z-Leu-Arg-NHMec were 3 . 5- and 66-fold lower than FhCL1 for FhCL2 and FhCL3 respectively . By contrast , FhCL2 and FhCL3 cleaved Pro-containing substrates much more readily than FhCL1 with kcat/Km values of 18 , 559 M−1 s−1 ( 28-fold greater , FhCL2 ) and 95 , 774 M−1 s−1 ( 142-fold increase , FhCL3 ) for Tos-Gly-Pro-Arg-NHMec; 58 , 027 M−1 s−1 ( 86-fold increase , FhCL2 ) and 60 , 763 M−1 s−1 ( 90-fold increase , FhCL3 ) for Boc-Ala-Gly-Pro-Arg-NHMec; 13 , 746 M−1 s−1 ( 22-fold increase , FhCL2 ) and 36 , 419 M−1 s−1 ( 60-fold increase , FhCL3 ) for Tos-Gly-Pro-Lys-NHMec and 21 , 193 M−1 s−1 ( 44-fold increase , FhCL2 ) and 1 , 564 M−1 s−1 ( 3-fold increase , FhCL3 ) for Boc-Val-Pro-Arg-NHMec ( Table 2 ) . Collectively , these data highlight significant differences in the substrate specificity of the three major F . hepatica cathepsin L peptidases . More specifically , the data demonstrates that FhCL3 prefers a bulky Pro residue in the P2 position of substrates over hydrophobic residues such as Leu or Phe , while FhCL2 can readily accept Pro despite preferring hydrophobic moieties at P2 , and FhCL1 has an inverse preference to FhCL3 . Peptidyl diazomethyl ketones are irreversible inhibitors of cysteine peptidases [51] . Changes in rates of inactivation by these inhibitors have highlighted different specificities at subsites of cysteine peptidases such as cathepsin L and cathepsin B [52] . In this study , we measured the rates of inactivation of FhCL1 , FhCL2 and FhCL3 by the cathepsin inhibitor Z-Phe-Ala-CHN2 . Both FhCL1 and FhCL2 were rapidly inactivated by Z-Phe-Ala-CHN2 with the rate of inactivation of FhCL1 being almost 2-fold higher than that of FhCL2 ( Table 3 ) . This is in accordance with our previous data [22] and demonstrates that FhCL1 accommodates hydrophobic P2 residues more effectively than FhCL2 . In contrast , the rate of inactivation of FhCL3 by Z-Phe-Ala-CHN2 was 20-fold times lower , showing that Z-Phe-Ala-CHN2 is a poor inhibitor of FhCL3 ( Table 3 ) . This is in agreement with our kinetic substrate data using peptidyl fluorogenic substrates ( Table 2 ) that revealed the poor capacity of FhCL3 to accommodate hydrophobic residues in the P2 position . The inhibitor known as cathepsin K Inhibitor II ( Z-LNHNHCONHNHLF-Boc , CKII ) is a potent time-dependent inhibitor of human cathepsin K; its selectivity for this enzyme is largely because of the effectiveness by which Leu occupies the S2 subsite [53] . FhCL1 and FhCL2 were both potently inhibited by cathepsin K inhibitor II with Ki values of 0 . 63 nM and 0 . 46 nM respectively . In contrast , CKII was 14-fold less effective against FhCL3 ( Ki 336 nM ) compared to FhCL1 and 20-fold less effective compared to FhCL2 ( Table 3 ) . The data are consistent with the kinetic data for hydrolysis of peptidyl fluorogenic substrates as both FhCL1 and FhCL2 had high kcat/Km values for Z-Leu-Arg-NHMec whereas that of FhCL3 against this substrate was much lower ( Table 2 ) . The α chains of collagens are woven together to form triple helical , or Col , regions of collagen . These are flanked by non-collagenous , or non-helical , regions termed NC domains Type I and type II collagens are most abundant in nature and are the major components of vertebrate connective tissue . They share ∼70% primary sequence identity and are composed largely of repeating Gly-X-Y motifs [27] . FhCL1 , FhCL2 and FhCL3 effectively degraded type I collagen at pH 5 . 5 which induces a denaturation of the protein's helical Col structure . However , FhCL1 was much less able to degrade type I collagen at pH 7 . 0 , where its native structure is preserved , and its activity was limited to the β and γ chains of the NC domains leaving the α1 and α2 chains of the Col domain intact ( Fig . 1A ) . By contrast , both FhCL2 and FhCL3 degraded native collagen at pH 7 . 0 and cleaved efficiently within the Col helical structures as revealed by the breakdown of the α1 and α2 chains ( Fig . 1A ) . To determine the relative activity of FhCL1 , FhCL2 and FhCL3 for collagen type I , digests were performed at pH 7 . 0 over a time course ( up to 18 h ) at 28°C ( Fig . 1B ) . Only FhCL3 was capable of completely digesting collagen type I after 18 h incubation in these conditions . FhCL2 digested collagen α chains to a lesser extent than FhCL3 while FhCL1 only digested the β11 and β12 dimers but not the collagen α chains ( Fig . 1B ) . Similarly , only FhCL3 was capable of degrading type II collagen whilst FhCL2 displayed much less activity against this substrate at pH 7 . 0 ( Fig . 1C ) . FhCL1 was unable to cleave within the tightly wound type II collagen helices under these conditions ( Fig . 1C ) . To identify the cleavage sites for FhCL2 and FhCL3 within collagen type I α1 and α2 chains , the 18 h reaction aliquots ( shown in Fig . 1B ) were analysed by tandem mass spectrometry to determine the masses and sequence identities of the resulting hydrolytic products . Liberated peptides were mapped onto the primary amino acid sequence of bovine collagen to identify the cleavage sites of the F . hepatica peptidases ( Fig . 2 ) . FhCL2 cleaved collagen type I at 43 sites within the α1 chain and 26 sites within the α2 chain while FhCL3 cleaved at 24 sites within the α1 chain and 24 sites within the α2 chain . Strikingly , only three of these cleavage sites were shared between FhCL2 and FhCL3 , all of which occurred in the α1 chains ( Fig . 2 ) . We examined the frequency of each amino acid in the P1 , P2 and P3 position of the proteolytic cleavage sites identified in the collagen digests described above to determine preferences for binding their respective active site S1 , S2 and S3 subsites ( Fig . 3 ) . While substrate residues present at the P2 position from the scissile bond interact with the S2 subsite of the active site of papain-like cysteine peptidases are considered most critical in determining the efficiency by which the P1-P1′ bond is cleaved [54] , the binding of these residues are influenced by residues in the P3 positions . Consistent with our previous findings using positional scanning of synthetic combinatorial libraries the P1 position can be occupied by many different amino acids without a strong preference [22] . However , specificity is observed in the P2 position; Gly was most commonly found in the P2 position of the FhCL2 cleavages ( 27% ) , and this was followed by Leu ( 21% ) and Pro ( 18% ) ( Fig . 3 ) . By contrast , FhCL3 displayed a highly specific preference for Gly at the P2 position ( 44% of all cleavages ) with a weak preference for all other amino acids including Leu and Pro in this position ( 3% for both residues , Fig . 3 ) . The P3 and P4 positions were occupied by a wide range of amino acids . To further investigate the cleavage of collagen by FhCL2 and FhCL3 , the ability of both enzymes to cleave two small peptide substrates , Z-Gly-Pro-Leu-Gly-Pro and Z-Gly-Pro-Gly-Gly-Pro-Ala , mimicking the repeating Gly-X-Y motifs ( where X is often Pro ) that occur within the collagen primary sequence was followed by tandem mass spectrometry . The presence of several peptides matching hydrolytic cleavage products showed that FhCL2 and FhCL3 were able to digest both substrates ( Fig . 4 ) . The cleavage pattern of peptide Z-Gly-Pro-Leu-Gly-Pro was identical for both FhCL2 and FhCL3 . However , while FhCL2 cleaved the peptide Z-Gly-Pro-Gly-Gly-Pro-Ala at three sites ( with Gly or Pro in the P2 position ) , FhCL3 was unable to cleave at one of these three sites where Pro occupied the P2 position ( Fig . 4 ) . In order to delineate the molecular basis of the ability of FhCL2 and FhCL3 to digest collagen , our recently determined crystal structure of FhCL1 was used as the starting point for a computational analysis of ligand binding . Complexes of FhCL1 , with variations to key residues involved in substrate binding ( summarised in Table 4 ) , bound to different seven-residue peptides ( Table 5 ) , were analysed by performing MD simulations . Using the simulation trajectories , free energies of binding of the peptide substrates were calculated using the well-established MM-PBSA method [44] , [45] . In addition , the distances between the nucleophilic sulphur atom of the active site Cys residue and the backbone carbonyl carbon atom of the scissile peptide bond , were examined over the course of the simulations . Since higher frequencies of close approach of these atoms would likely correlate with higher frequencies of formation of the transition state of the hydrolysis reaction this measure gives an indication of how well the substrate fits into the binding cleft and how readily it is cleaved [55] . Fig . 5 illustrates the critical residues of the active site investigated and their disposition in FhCL1 relative to the bound peptide substrate ligand A ( AALR*NAA , shown as an example , asterisk represents position of scissile bond ) and in FhCL2 bound to ligand C ( AGPR*NAA ) . Table 5 presents the results of the binding energy calculations , as well as the average nucleophilic sulphur-scissile carbon ( S-C ) distances for the various peptidase-ligand complexes simulated . Fig . S1 illustrates the regions of the peptidase that contact the ligand during the FhCL1 ligand A simulation . The MD simulations indicate that , for wildtype FhCL1 , activity is greatest for substrates with Leu at P2 and that Arg is favoured at P1 ( consistent with our substrate and inhibitor binding kinetics shown in Table 2 and 3 ) . Thus , the results for the FhCL1-ligand A complex ( Table 4; Fig . 5A ) are taken as a benchmark against which the other results are compared . The free energy of ligand binding is related to the dissociation constant Kd by the formula ΔG = −RT ln Kd . Thus , the calculated binding energy for the FhCL1-ligand A complex of 10 . 83 kcal/mol corresponds to a Kd of 22 . 9 nM , while the approximate level of error in the free energy calculations of 1 kcal/mol corresponds to a 5-fold difference in Kd . Where differences between calculated binding energies are greater than the error bounds , the calculations are taken to predict differences in binding affinities . The calculations for ligand A ( AALR*NAA ) , which has Leu at P2 , thus discriminate between binding affinities for FhCL2 and FhCL3 , predicting an approximately 5–10 fold difference in Kd , which correlates well with the inhibition constants determined for the CKII inhibitor ( Table 3 ) , which also has Leu at P2 . The calculations also agree with the experimental data in suggesting reduced activity of FhCL1 against a ligand with Pro at P2 ( ligand B , AAPR*NAA ) compared to Leu ( ligand A ) . However , they also predict that for ligand B , FhCL1 has a higher binding affinity than FhCL2 and equal or greater activity than FhCL3 . When ligand B is altered such that Gly is substituted for Ala at P3 ( ligand C , AGPR*NAA ) , the binding affinities for the peptidases with FhCL2 or FhCL3 S2 subsites show a marked increase over those for ligand B . Thus , the data suggest that the collagenolytic activity of FhCL2 and FhCL3 may not be due simply to the P2 Pro-S2 subsite interaction , and that Gly at ligand residue P3 is also important , consistent with our earlier suggestions [23] . This inference is consistent with our previously reported experiments using combinatorial libraries [22] that showed FhCL2 , strongly favoured a Gly at P3 , and with our present data using native collagen digestion which indicated that Gly is favoured at P3 for both FhCL2 and FhCL3 . Analysis of our collagen digest revealed that of the 11 cleavage sites for FhCL2 and FhCL3 ( Tyr and Trp at position 67 , respectively ) containing Pro at P2 ( seven for FhCL2 and four for FhCL3 ) , 8 had Gly at P3 . Given their similar active site residues to FhCL2 and FhCL3 we also analysed previous studies with human cathepsin K [56] and ginger rhizome GP2 [29] ( also possess Tyr and Trp at position 67 , respectively ) and observed that of the 12 peptidase cleavage sites within native collagen type I containing Pro at P2 ( eight for cathepsin K and four for GP2 ) , 10 had Gly at P3 . Examination of the simulation trajectories suggests that Gly ( that lacks a side-chain ) at P3 , would offer minimal steric interference with the large active site Tyr or Trp side-chain at position 67 in FhCL2 and FhCL3 , respectively , allowing the Tyr or Trp ring to form a “lid” over the ligand's P2 Pro ring , helping to sequester it in the S2 subsite ( Fig . 6 ) . Analysis of the cleavage sites within native type I collagen show that both FhCL2 and FhCL3 have a strong preference for Gly at P2 , most particularly for the latter enzyme ( Figs . 2 and 3 ) . To investigate the molecular basis of this preference , simulations were performed using ligands with Gly at P2 ( ligands D and E ) . The simulations with Gly at P2 generally showed markedly greater S-C distances than were observed in the complexes with Leu or Pro at P2 ( Table 5 ) . This supports the idea that the P2-S2 interaction has a strong influence on the S-C interaction . The binding affinity of collagen-like ligand D ( which has Ala at P3 , PAGP*AGP ) is substantially higher for FhCL2 compared wildtype FhCL1 , but when in complex with FhCL3 , ligand D essentially disengages . However , when ligand D is altered such that Leu occurs at P3 ( ligand E , PLGP*AGP ) , binding affinity to FhCL3 is restored but greatly reduced in the complex with FhCL2 . These results further support the idea that the interaction of ligand residue P3 with the peptidase is a significant factor in ligand binding , and possibly of greater importance when Gly is at P2 . Collagens comprise polypeptide chains containing the repeating triplet sequence Gly-Pro-Y where 4-hydroxyproline ( Hyp ) commonly occupies the Y position [57] . Thus , simulations of complexes with a ligand containing Hyp at P3 and P1′ and Gly at P2 ( ligand F , PPGP*PGP ) were performed . For the FhCL2 variant , the ligand began to disengage from the peptidase whilst for FhCL3 the ligand remained closely bound . Although the binding affinity for the FhCL3-ligand F complex was not calculated , the average S-C distance was much lower than for the other complexes with Gly at P2 ( Table 5 ) . Moreover , the plot of the S-C distance frequencies showed high frequencies of very close approach for the FhCL3-ligand F complex ( Fig . 7 ) . These data suggest that FhCL3 is able to digest collagen with Hyp-Gly at P3-P2 whereas FhCL2 cannot . This may explain why we observed a greater ability of FhCL3 to digest type I and II collagen compared to FhCL2 ( Fig . 1 ) . The FhCL3-ligand F simulation trajectories revealed that the side-chain of Trp 67 occupies the FhCL3 S2 subsite and sits against the peptide backbone of the ligand P2 Gly ( Fig . 6A ) . This may stop solvent from entering the S2 subsite and interacting with the ligand P2 Gly , thus “sealing” the ligand in the enzyme's binding cleft . A similar disposition of the Trp 67 side-chain was also observed in the FhCL3-ligand E simulations . Tyr at position 67 in FhCL2 behaves in a similar manner to the Trp 67 of FhCL3 when binding ligands with Gly at P2 . Thus , Tyr 67 occupied the S2 subsite cleft and contacted the peptide backbone of the ligand P2 Gly in the FhCL2-ligand D complex ( Fig . 6B ) . The position of the Tyr side-chain was further stabilised by a hydrogen bond between its hydroxyl oxygen and the backbone oxygen of residue 157 . F . hepatica has evolved a repertoire of cathepsin L peptidases as a result of gene duplication and diversification that exhibit subtle but distinct substrate specificities [1] , [8] , [14] , [15] . The expression of different members of this peptidase family is temporally regulated suggesting that they perform precise functions at different stages of the parasites' development [13] . This idea is supported by our previous data showing that the predominant enzyme , FhCL1 , secreted by the mature adult parasites , which are obligate blood-feeders , is adapted to the degradation of host haemoglobin; the S2 subsite of the FhCL1 active site , which contributes mostly to substrate binding , readily accommodates P2 residues such as Leu , Ala , Val and Phe that together represent >40% of the residues present in haemoglobin [3] . FhCL1 does not readily accept Pro into the S2 subsite as shown in this and other [22] , [28] studies and thus it's activity against type I and II collagens observed here was restricted to the non-collagenous , NC , domains . By contrast , both FhCL2 and FhCL3 have evolved to accommodate Pro in the S2 subsite of their active sites; this property has been attributed to the presence of Tyr and Trp , respectively , at position 67 within the S2 subsite of these enzymes , a position that is occupied by Leu in FhCL1 [1] , [14] , [15] , [22] , [23] . In this study , our computational data show that Tyr and Trp at position 67 have the ability to function in distinct ways to accommodate either Gly or Pro residues at P2 and explains why FhCL2 and FhCL3 have an ability to degrade the Gly-X-Y containing Col helices of collagen . The results are also in accordance with our previous suggestion that the interaction of substrate residue P3 with the peptidase is a significant factor in substrate binding , in particular when Gly or Pro is at P3 [23] . This is also the case when we compare FhCL3 specificity towards synthetic peptides; Pro is readily accepted in P2 only when Gly is at P3 ( Tos-Gly-Pro-Arg-NHMec , Tos-Gly-Pro-Lys-NHMec and Boc-Ala-Gly-Pro-Arg-NHMec ) but not when Val is at P3 ( Boc-Val-Pro-Arg-NHMec ) ( Table 2 ) . Therefore , the collagenolytic activity of FhCL2 and FhCL3 is not due simply to the P2 Pro-S2 subsite interaction , and with Pro at P2 , Gly at residue P3 is critical . A comparison of the cleavage sites of FhCL2 , FhCL3 , human cathepsin K [56] and ginger rhizome GP2 [29] revealed that many of their cleavage sites within collagen where Pro is at P2 , a Gly is present at P3 . A P3 Gly , which lacks a side-chain , offers minimal steric interference with the large active site Tyr or Trp side-chain at position 67 , and allows the Tyr or Trp ring to form a “lid” over the ligand's P2 Pro ring , helping to sequester it in the S2 subsite ( Fig . 6 ) . However , we observed that FhCL3 digested type I and II collagens more efficiently compared to FhCL2 and that these two enzymes cleave at mostly different sites ( see Figs . 1 and 2 ) . The computational data indicates that this may be , in part , because FhCL3 binds substrates containing a P3 and P1′ Hyp much tighter than FhCL2 . A difference between these two enzymes was also observed using a peptide substrate that mimics the Gly-X-Y repeat in the collagen Col domain , Z-Gly-Pro-Gly-Gly-Pro-Ala; FhCL2 cleaves at three sites with Gly or Pro in the P2 position , whereas FhCL3 was unable to cleave at one of these three sites despite having Pro occupying the P2 position and Gly at the P1 position . This result contrasts with our data using fluorogenic peptide substrates which showed that FhCL3 cleaved the tripeptides Tos-Gly-Pro-Arg-NHMec and Tos-Gly-Pro-Lys-NHMec with 5- and 3-fold better efficiency , respectively , than FhCL2 . On the other hand , the two enzymes exhibited equal efficiency for the substrate Boc-Ala-Gly-Pro-Arg-NHMec . The influence of P4 and P′ regions of the peptides on substrate binding in these two enzymes need greater attention in future studies when suitable reagents become available . Notwithstanding , it is clear that the modification within the active site of FhCL2 and FhCL3 ( Tyr or Trp at position 67 ) has subtly altered the substrate specificity of the two enzymes such that they exhibit different substrate profiles without compromising their unique ability to degrade host native collagen . FhCL3 is expressed by the invasive stage of F . hepatica which must quickly penetrate the wall of the intestine to enter its host [1] , [13] . RNAi-mediated knockdown experiments have demonstrated that the secretion of this peptidase and a cathepsin B cysteine peptidase by these invasive parasites is critical to invasion of the intestinal tissue [2] . Once the intestine has been traversed expression of these enzymes is switched off and the parasite up-regulates expression and secretion of FhCL1 and FhCL2 which are required to facilitate tunnelling through the liver mass and feeding on host tissue ( the parasite undergoes rapid growth at this stage ) [16] . The collagenolytic activity of FhCL3 and FhCL2 is important in degrading the extracellular matrix of the tissues through which this parasite moves . While collagenase activity has been demonstrated in ginger rhizome cysteine peptidases [29] , [58] , only one other animal cysteine peptidase , human cathepsin K which functions in bone re-modelling [59] , possesses collagenase activity . Accordingly , the evolution of this activity in F . hepatica must represent an important step in the development of a parasitic way of life . | Fasciola hepatica is a helminth parasite that causes liver fluke disease ( fasciolosis ) in domestic animals ( sheep and cattle ) and humans worldwide . In order to infect their mammalian hosts , F . hepatica larvae must penetrate and traverse the intestinal wall of the duodenum , move through the peritoneum and penetrate the liver . After migrating through the liver , causing extensive tissue damage , the parasites move to their final niche in the bile ducts where they mature and feed on host haemoglobin to support the production of eggs . To achieve these tasks , F . hepatica secretes a number of distinct cathepsin L cysteine peptidases ( FhCL ) . Thus , the infective larvae that penetrate the host gut secrete cathepsin L3 ( FhCL3 ) , the migrating liver-stage juvenile parasites secrete both FhCL1 and FhCL2 while mature bile duct parasites that feed on host blood secrete predominantly FhCL1 but also FhCL2 . Here we show that the major cathepsin L peptidases secreted by F . hepatica ( FhCL1 , FhCL2 and FhCL3 ) display differential ability to degrade host collagen ( an important component of host tissues ) and investigate this phenomenon at the molecular level . | [
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"... | 2011 | Collagenolytic Activities of the Major Secreted Cathepsin L Peptidases Involved in the Virulence of the Helminth Pathogen, Fasciola hepatica |
The evolution of enzymes affects how well a species can adapt to new environmental conditions . During enzyme evolution , certain aspects of molecular function are conserved while other aspects can vary . Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved , while those that vary may indicate functions that are more easily changed or that are no longer required . In analogy to the study of conservation patterns in enzyme sequences and structures , we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies . This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies . Specifically , we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two . Across the 42 superfamilies that were analyzed , substantial variation was found in how much of the conserved substructure is reacting , suggesting that superfamilies may not be easily grouped into discrete and separable categories . Instead , our results suggest that many superfamilies may need to be treated individually for analyses of evolution , function prediction , and guiding enzyme engineering strategies . Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures , thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering . Because the method is automated , it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized and uncharacterized enzyme superfamilies .
The molecular functions of enzymes result from a complex evolutionary interplay between environmental constraints , requirements for organismal fitness , and the functional malleability of a particular enzyme scaffold . Within these constraints , existing enzymes are recruited during evolution to perform new or modified functions while often maintaining some aspects of the ancestral function [1]–[3] . Consequently , among contemporary enzymes we observe groups of evolutionarily related enzymes that share some aspects of molecular function and differ in others . The most divergent groups of evolutionarily related enzymes that still share aspects of function are called superfamilies . Within a superfamily , we define a family as a set of proteins that perform the same overall catalytic reaction in the same way . Why are some aspects of function shared and others allowed to change ? By examining which aspects of function are shared among contemporary enzymes , we can gain insight into the requirements and constraints that govern this evolutionary process . The focus of most studies of enzyme evolution has been the examination of conservation in sequence and structure . The data available to conduct such studies is enormous and still increasing due to the multiplicity of ongoing genomic and metagenomic sequencing efforts [4] . In tandem with the growth of sequence and structural data , a large number of new and sophisticated tools have been developed to improve our ability to identify the divergent members of superfamilies , allowing us to analyze patterns of conservation in sequence and structure that shed light on how enzyme functions have evolved and diversified ( for some examples , see [5]–[7] ) . But such studies only capture aspects of enzyme evolution that can be inferred from the machinery that enables enzymatic catalysis , the enzymes themselves . Far fewer studies have focused on the substrates and products of these reactions , with most of these focused on the requirements of metabolism [8] , [9] . In this work , our goal is to understand the details of how enzymes function and evolve by studying the conservation and variation in their substrates and products . In doing so , we aim for a more extensive view of enzyme evolution in order to improve our abilities to annotate enzymes of unknown function and to infer common aspects of function for superfamilies that have not yet been characterized . The value of any analysis of the evolution of enzyme function depends on how we describe enzyme function , with respect to both the detailed molecular functions of individual enzymes and the properties of function shared across diverse members of enzyme superfamilies . Previous approaches to study enzyme evolution range from detailed manual analyses of small numbers of related enzyme families and superfamilies to automated analyses of many superfamilies . The former have often included not only analyses of sequences and structures but also comparisons of the substrates and reaction mechanisms of the constituent enzymes . These studies have been useful for annotating new sequences and structures and for generating and testing hypotheses about patterns of enzyme evolution ( see [10]–[14] for examples ) . However , because of the expert knowledge required and their time-intensive nature , these types of analyses are not feasible for large numbers of superfamilies . Other semi-automated efforts have contributed to our understanding of enzyme evolution and data from these analyses have been made available in a number of online resources that include the Structure-Function Linkage Database [15] , MACiE [16] , the Catalytic Site Atlas [17] , and EzCatDB [18] . Automated analyses more directly comparable to the large-scale and automated study described here [19]–[21] have used enzyme classification systems , like the Enzyme Commission ( EC ) system [22] , to represent functional properties and determine what properties are conserved . The EC system represents a large proportion of known enzyme reactions , classifying each enzyme with a hierarchical set of four numbers that uniquely identify a reaction , and is easy to use for large-scale analyses . However , this system , developed before analyses of enzyme evolution were common , does not provide a detailed description of enzyme function or substrates at the atomic level [23] . Moreover , the EC classification of function often does not correspond with either the aspects of function that are conserved or those that can change during evolution . These issues make this system unsuitable for evaluating how enzyme function evolves , especially when evolutionary relationships are distant [24] . For enzymes , the Gene Ontology ( GO ) system's [25] molecular function classifications , also often used to describe and analyze function , largely recapitulate the EC system . More similar to the work reported here , several groups have analyzed enzyme relationships and evolution using substrate and reaction similarities [26]–[28] . Although these similarity metrics are useful , especially for clustering enzymes by their substrate similarities , they are not informative about what specific aspects of function are conserved , a specific goal of this work . Here , we use graph isomorphism analyses to compare substrates of enzymes from 42 superfamilies to identify specific aspects of function conserved within each superfamily . We also use comparisons of substrates and their corresponding products to determine whether and how much of the conserved substructure is involved in the reaction . This comparison of substrates and products is similar to an analysis performed for a previous study with a different purpose , to predict EC numbers [29] . To simplify the interpretation of results across the multiple superfamilies in this study , only enzymes comprised of single domains and that catalyze unimolecular reactions were investigated . Automation of the analysis allows us to describe overall trends in functional conservation and variation across a large number of superfamilies . A descriptive representation of conserved enzyme molecular functions using chemical structures and SMILES strings [30] , [31] is also provided . This representation should be useful for annotating new members of superfamilies discovered in sequencing projects and for characterizing new superfamilies .
Results are presented for 42 superfamilies from the Structural Classification of Proteins ( SCOP ) database [32] . These superfamilies meet the following criteria: ( 1 ) they consist of only single-domain enzymes that ( 2 ) perform only unimolecular reactions ( or reactions with two substrates , of which one is water ) , and ( 3 ) the superfamilies include at least two different reactions ( representing at least two different E . C . numbers ) for which substrate and product information are available in the enzyme database BRENDA [33] . Sufficient data were available in BRENDA ( the third criterion ) for 46 . 2% of the superfamilies meeting the first two criteria . These 42 superfamilies include representatives of six of the seven SCOP fold classes; the only fold class not represented is the membrane proteins class . The enzymes in these 42 superfamilies represent a substantial proportion of the diversity of enzyme function , covering 25 . 4% of EC classes defined by the first two digits ( subclasses ) and 18 . 7% of EC classes defined by the first three digits ( sub-subclasses ) . Conservation patterns were examined using only substrates and products as the data available in BRENDA were not sufficient to consider other aspects of reaction conservation , such as transition states and intermediates . Our goal was to determine the molecular features that the substrates of a superfamily share and whether the shared features are involved in the reactions catalyzed by that superfamily . Thus , for each superfamily , we identified the conserved substructure , defined as the set of bonds and their connected atoms that are present in all substrates of the superfamily ( Figure 1A ) . These conserved substructures for the 42 superfamilies in our dataset are shown in Figure 2 . Additional information about the diversity and conservation of functions in these superfamilies is provided in a hyperlinked table in the supplementary information online ( Table S1 ) . Moreover , for each enzyme's substrate ( s ) , we found the reacting substructure by determining what atoms and bonds change between the substrate and the product ( Figure 1B ) . For each enzyme , we then determined whether the conserved substructure overlaps with the reacting substructure and by how much . This overlap was quantified by calculating the fraction of the conserved substructure that is reacting ( fc ) ( Figure 1C , Table S2 ) and the fraction of the reacting substructure that is conserved ( fr ) ( Figure 1D , Table S2 ) . Results for these measures of overlap are presented with respect to both the number of atoms and the number of bonds . For a given superfamily , the average fc and fr calculated using atoms often differ from the values obtained using bonds ( Table S2 ) . This difference arises because the number of bonds is frequently not proportional to the number of atoms in molecular structures ( e . g . , one bond consists of two atoms while three atoms can be connected by three bonds; a cyclic structure will have a different number of bonds compared to non-cyclic structure with the same number of atoms ) . In addition , different types of reactions vary in the ratio of atoms and bonds that are involved in the reaction ( e . g . , a lyase may break one bond involving two atoms while an intramolecular transferase may involve one bond and three atoms ) . Because both are valid measures of substructure size , both are provided in this report . The distribution of average fc for the set of superfamilies ( Figure 3A ) indicates that there is a continuum among the superfamilies in how much of the conserved substructure is reacting , with superfamilies ranging from having little to having most of the conserved substructure participating in the reaction . This trend is observed regardless of whether we use atoms or bonds in our calculations of average fc . The results also show that all superfamilies with a conserved substructure have an average fc above zero , indicating that at least part of the conserved substructure is involved in the reaction . Only one superfamily in our study set , the superfamily defined by SCOP as the metallo-dependent hydrolase superfamily , also known as the amidohydrolase superfamily [34] , [35] , has substrates so diverse that they do not share a common substructure of even a single conserved bond . Detailed analysis of the superfamily , including analysis of differences in the overall functions , how active site motifs are used for catalysis , and other factors such as metal ion dependence , suggests that this group may be more properly considered as multiple superfamilies ( Brown and Babbitt , in preparation ) . Plotting fr against fc illustrates the distribution of superfamilies ( Figure 3B ) across different patterns of overlap ( Figure 3C ) in the reacting and conserved substructures . For simplicity , only the data calculated using atoms is provided in Figure 3B . The values for each superfamily , calculated using both atoms and bonds , are provided in Table S2 . The different regions in Figure 3B are intended merely to orient the reader to the range of variation across multiple superfamilies rather than to infer distinct categories implying fundamental differences between the superfamilies in different regions . To determine whether there are differences in how a conserved substructure is used within a single superfamily , the variation of fc within each superfamily was also evaluated ( Table S2 ) . Most superfamilies have little variation in how much of the conserved substructure is reacting ( Figure 4A ) . However , there are a few superfamilies with substantial variation in fc . We also evaluated the level of variation in which part of a superfamily's conserved substructure is used among the different reactions by calculating the average overlap in reacting and conserved substructures ( or ∩ c ) of every pair of substrates in the superfamily . A flatter distribution and more variation was observed among the superfamilies for the average or ∩ c ( Figure 4B ) than for the standard deviation of fc . The superfamilies that rank highest both in variation in fc and or ∩ c include the carbon-nitrogen hydrolase , metalloproteases ( “zincins” ) ( catalytic domain ) , and the thioesterase/thiol ester dehydrase-isomerase superfamilies . Superfamilies that have low variation in fc and or ∩ c include the HD-domain/PDEase-like , dUTPase-like , and carbohydrate phosphatase superfamilies . From these examples of superfamilies with high and low variation in fc and or ∩ c , we observe that the superfamilies with high variation tend to have smaller conserved substructures while superfamilies with low variation tend to have larger conserved substructures , though the correlation is not perfect . The superfamilies in the low variation group have phosphate groups in the conserved substructure . These tendencies may arise because different superfamilies and different types of reactions have different propensities for variation and conservation through evolution . Alternatively , variation in how different superfamilies are defined in SCOP may lead to some of the variation observed among these superfamilies . We also note that the set of reactions surveyed in this work represents only a subset of enzyme superfamilies , making it difficult to definitively address these hypotheses and questions . More extensive analyses will be required to confirm and further explore these initial observations . As new superfamily members are characterized , modifications of these substructure conservation patterns may be required . To provide updates of this information , work is underway to incorporate this information into a searchable resource within our Structure-Function Linkage Database ( http://sfld . rbvi . ucsf . edu/ ) [15] . Additional data generated in this study , including reacting substructures and how they overlap with conserved substructures for individual superfamily members , are available from the authors upon request . As described below , our method can also be used to determine conserved functional characteristics for superfamilies that have not yet been characterized . Programs and scripts required to perform these analyses are also available upon request .
By automating the analysis of enzyme substrates and reactions , the methodology introduced in this work facilitates the analysis of previously unstudied enzyme superfamilies . This effort contrasts with previous analyses of enzyme superfamilies to determine patterns of functional conservation that have been highly labor-intensive , involving extensive manual analysis of reactions and literature-based curation of functional properties ( see the SFLD , http://sfld . rbvi . ucsf . edu/ , for examples ) . The substructures conserved among the substrates of all members of a superfamily ( Figure 2 ) provide annotation information that describes how function has been conserved in each of these superfamilies . The certainty of these superfamily annotations will depend , however , on how well the range of substrates in each superfamily has been sampled . Thorough substrate sampling may be especially critical for complex superfamilies that include many different catalytic functions . While we have used all available reaction information in our analyses , the sampling of superfamily reactions may still be incomplete . As new reactions are discovered through the sequencing of new genomes and metagenomes , these results can be updated and improved . Despite these limitations , the characterization of superfamily-conserved substructures presented here facilitates the annotation of individual sequences on a large scale , helping to address the need for new strategies for automated function annotation . This issue has become more pressing as the number of sequenced genomes increases and the era of metagenomics moves into high gear [41] . Sequences that can be classified into a superfamily but not into a specific family can be annotated with the substructure common to all characterized members . In these cases , often found in complex superfamilies exhibiting broad diversity in enzyme function , this may be the only level at which accurate annotation can be achieved , as insufficient information may be available to support annotation of a specific reaction or substrate specificity . While substructure-based annotation does not by itself suggest a specific enzyme function , this information can be used as a starting point for additional analyses to determine specific function . For example , many structures have been solved through structural genomics efforts , but their functions remain unknown [42] . We have compiled a list of structures that have been classified into the SCOP superfamilies analyzed in this study , but have unknown functions . These structures , many of them from structural genomics projects , can be at least minimally annotated with the substructure identified here as conserved across that superfamily , illustrated by the examples given in Figure 5 ( see Table S3 for the complete list ) . Using this information , characteristics of ligands likely to be bound or turned over by these proteins can be inferred , providing guidance for biochemical studies to determine specificity . These data also provide information about classes of small molecules that may be useful for co-crystallization trials to aid in solving the structures of these proteins or to capture them in functionally relevant conformations . The variation found within superfamilies presents a caveat to be considered when using these substructures for function annotation . While most of the superfamilies analyzed here have conserved substructures that are used consistently among the different superfamily members ( Figure 4 ) , there are a few superfamilies that have significant variation in the degree to which the conserved substructure is used in the reactions . These superfamilies can be expected to be more difficult cases for function prediction since their variability makes it more difficult to determine conserved aspects of function . In contrast , superfamilies with less variation in the degree to which the conserved substructure is used in the reaction are expected to be more straightforward cases for function prediction . Understanding the patterns of functional conservation associated with the evolution of functionally diverse enzyme superfamilies can provide useful information for guiding enzyme engineering experiments in the laboratory [43] . Using as a starting template for design or engineering an enzyme that already “knows” how to perform a critical partial reaction or how to bind a required substrate substructure ensures that some of the machinery required to perform a desired function is already in place . Although still daunting , the task then simplifies to modifying the enzyme to bind and turn over a new substrate that contains the substructure consistent with the underlying capabilities of the superfamily . As a corollary , aspects of function that have been conserved in all members of a divergent superfamily may be difficult to modify by in vitro engineering [43] , [44] . Using such a strategy in a proof-of-concept study , two members of the enolase superfamily were successfully engineered to perform the reaction of a third superfamily member [45] . As shown in Figure 6 , the superfamily-conserved substructure and the partial reaction associated with that substructure were not changed in these experiments . Rather , engineering the template proteins to perform the target reaction involved changing each to accommodate binding the part of the substrate that is unique to the new reaction desired . To allow for generalization of this approach , our analysis provides for all of the superfamilies that we investigated 1 ) the parts of an enzyme's substrate and reaction that are not conserved among related enzymes , which , provided they can be associated with regions of a target structure that interact with them , may point to structural features amenable to engineering , and 2 ) the parts of the substrates that are conserved across all members of a superfamily , which may point to regions of the structure that may not be easily changed without loss of function or stability [46] . In this study , requirements for a sufficiently large sample of enzyme reactions for a comprehensive analysis restricted us to using only substrates and products . However , enzyme substrates can undergo intermediate changes during catalysis that are not adequately captured by looking only at substrates and products . In some reactions , such as those in the enolase superfamily [47] , some portions of the substrate change and revert back to their original configuration during the reaction; these types of transformations are undetectable in the study described here . The enolase superfamily represents a well-characterized example of chemistry-conserved evolution . However , because our analysis does not currently detect such substrate changes , the average fc ( atoms ) for the enolase superfamily is 0 . 31 and the average fc ( bonds ) for the enolase superfamily is 0 . 34 , which places this superfamily in the middle of the distribution among our superfamilies for these measures of overlap . Being able to detect the full extent to which structures change during a reaction would provide a better picture of substructure conservation in superfamilies like the enolase superfamily . But this will require compilation of additional data to capture all of the partial reactions involved in a given overall reaction , including structures of reaction intermediates . Emerging data resources , such as MACiE [16] and the SFLD [15] , currently seek to catalog information about reaction steps and mechanisms . However , because this process is labor-intensive and often hampered by disagreement or ambiguity in the literature regarding the specific mechanisms of some reactions , these data resources are not yet sufficiently populated to support such broader analyses . As these types of resources grow , we are optimistic that the information required to analyze reaction mechanisms more fully will become increasingly available . Although it is beyond the scope of this study , correlating the conservation patterns we see in enzyme substrates with the conservation patterns in the sequence and structures of the enzymes themselves would also be a valuable extension for these analyses . Finally , recent progress has been made in using in silico docking of small molecules to enzyme structures to infer molecular function . In one such study , a library of high-energy reaction intermediates was generated and used to predict substrate specificity of enzymes in the amidohydrolase superfamily [48] . As these methodologies are further developed , incorporation of predicted reaction intermediates into substructure analysis could improve prediction of substructures that are reacting . In addition to benefiting from such recent advances in docking , the type of analysis presented here may in turn be used to improve applications of docking to predicting substrate specificity in enzymes . Several such studies have recently focused on predicting functional specificity in the enolase [49] , [50] and amidohydrolase [51] superfamilies using knowledge about conserved substrate substructures from earlier analyses [15] , [52] to construct focused ligand libraries for docking . We expect that the set of conserved substructures generated by our analysis can be used similarly to guide the construction of chemical libraries of ligands to improve prediction of substrate specificity in other superfamilies . This study presents an automated method for analysis of superfamilies to determine the conserved aspects of their functions , represented by patterns of substrate conservation . Our results show that superfamilies do not fall into discrete and easily separable categories describing how their functions may have evolved . Rather , the conserved substructures determined in this analysis define superfamily-specific conservation patterns . These results enable precise prediction of functional characteristics at the superfamily level for complex superfamilies whose members perform many different but related reactions , even when the evidence is insufficient to support more specific annotations of overall reaction and substrate specificity . For applications in enzyme engineering , we expect that the identification of the aspects of function that have been most and least conserved during natural evolution will provide guidance for identifying the structural elements of a target scaffold that are most and least amenable to modification , thereby informing engineering strategies for improved success .
For our analyses , we used a subset of superfamilies from SCOP , a database of manually classified protein superfamilies , filtered based on criteria chosen to be most informative about enzyme evolution at high levels of functional divergence . We included only superfamilies of single-domain enzymes with significant functional information in SCOPEC , a subset of SCOP with verified EC numbers , and in BRENDA , the most comprehensive database of enzyme experimental results . Although many enzymes and proteins function as multi-domain units , the nature and organization of which can affect the specificity and regulation of enzymes [53] , for this study , we chose to use only single-domain enzymes as this allowed us to clearly assign a single function to one domain . We included examples of enzymes known to have multiple structural domains only when the composite acts as a single functional unit ( e . g . , the enolase superfamily ) . To ensure that the members of each superfamily were sufficiently divergent in function to analyze conservation of their substructures , only superfamilies annotated with at least two different EC numbers were investigated . Compared to unimolecular reactions , bimolecular reactions have considerably more complex chemical and kinetic mechanisms for how substrates interact with the enzyme's catalytic site ( i . e . , in what order different substrates bind ) . Because these variations would have greatly complicated the analysis , we excluded superfamilies with any reactions that were not unimolecular . Using the top level of the EC annotation , superfamilies were selected in which all the characterized members belong to any one of the following classes: hydrolases ( EC numbers 3 . x . x . x ) , lyases ( EC numbers 4 . x . x . x ) , and isomerases ( EC numbers 5 . x . x . x ) . Experimentally verified substrate and product data were taken from the licensed version of the BRENDA database ( release 6 . 2 ) [54] . Reactions were excluded in which ( 1 ) the product ( s ) had more than five ( non-hydrogen ) atoms more than the substrate or ( 2 ) substrates and products both had three or fewer ( non-hydrogen ) atoms . Reactions in the first category are likely to be erroneous because they are not properly balanced . Reactions in the second category are unlikely to be informative for the analysis because they contain so few atoms . A “conserved substructure” ( Figure 1A ) contains the maximal sets of bonds in a substrate that are present in all the substrates of a superfamily , plus their adjacent atoms . In all our analyses , we considered only bonds consisting of two atoms , neither of which is a hydrogen . The “unconserved substructure” is the set of bonds in a substrate that are not in the conserved substructure , plus their adjacent atoms . An atom can be in both the conserved and unconserved substructure if it is adjacent to both a bond in the conserved substructure and a bond in the unconserved substructure . A “reacting substructure” ( Figure 1B ) consists of the bonds in a substrate that are not present in the product , their adjacent atoms , and any atoms that become connected in new bonds in the product . In the case of a racemization reaction , in which the chirality of an atom center changes , the reacting substructure is defined as including the chiral atom that changes in the reaction , the four adjacent bonds and their adjacent atoms . The “nonreacting substructure” is the set of bonds in a substrate that are also present in the product and their adjacent atoms . An atom can be in both the reacting and nonreacting substructure if it is adjacent to both a bond in the reacting substructure and a bond in the nonreacting substructure . The substrate substructure conserved among all characterized members of each superfamily was calculated using the maximal common substructure ( MCS ) algorithm implemented in the Chemistry Development Kit ( CDK ) [55] , an open source Java toolkit for manipulating small molecules . The molecules are represented as graphs in which the nodes represent atoms and the edges represent bonds . Each node is labeled with an atom type and each edge is labeled with the two atom types of the connected atoms and the bond order . This algorithm finds , for a pair of molecules , the maximum common substructure ( MCS ) present in both molecules . We extended this to find the MCS for the set of all known substrates for a superfamily . In this initial analysis , we treated different atoms as dissimilar as long as the element type was different and bonds as different when the bond order and the two pairs of connected atoms were not identical . The only exception to this rule was made for phosphate and sulfate groups , which we treated as similar in the substrate conservation analyses . Our code allowed for the possibility of multiple unconnected MCSs by representing them as an unconnected graph with each connected portion corresponding to one MCS . Although some of the pairwise MCSs contain multiple unconnected subgraphs , none of the superfamily-conserved substructures contain such multiple unconnected MCSs . Finally , each substrate has a unique unconserved substructure defined as the set of edges not present in the conserved substructure and the atoms adjacent to these edges . For each enzymatic reaction in which both the substrate and its corresponding product ( s ) are known , we calculated the non-reacting substructure by finding the MCS between the substrate and the product ( s ) . The reacting substructure is the set of edges in the substrate that are not present in the product , plus the atoms adjacent to these edges . The reacting substructure also includes atoms that form new bonds in the product . To quantify the overlap between the reacting and conserved substructures , for each reaction in our dataset , we calculate fc ( Figure 1C ) , the fraction of the conserved substructure that is reacting and fr ( Figure 1D ) , the fraction of the reacting substructure that is conserved . The values for fc and fr are calculated in two ways , using atoms or bonds , and the results for both are reported as they provide different but useful views of the data . fc for bonds is determined by dividing the number of bonds that are in both the conserved and the reacting substructures ( r ∩ c ) by the number of bonds in only the conserved substructure . fc for atoms is determined similarly , using the number of atoms instead of bonds . Likewise , fr for bonds is determined by dividing the number of bonds that are in both the conserved and the reacting substructures by the number of bonds in only the reacting substructure; this value was also calculated using atoms . For each enzyme in the BRENDA database , there may be multiple substrates with corresponding reactions that have been characterized . For these cases , the values of fc and fr were obtained by averaging all the substrates of each enzyme and then these values were averaged for all the enzymes in each superfamily . We also determined the standard deviation in fc and fr for the enzymes of each superfamily . To determine whether the same part of the superfamily-conserved substructure was used in the different reactions of the superfamily , every pair of reactions was analyzed in each of the superfamilies in our dataset . Each reaction has a substrate substructure that is both conserved and reacting ( r ∩ c ) . For each pair of reactions , we calculated how much overlap is observed among the two ( r ∩ c ) substructures and normalized each of these overlaps by the smallest ( r ∩ c ) of each pair . The resulting measure of overlap ( or ∩ c ) was then averaged over every pair of reactions in each superfamily . | Enzymes are biological molecules essential for catalyzing the chemical reactions in living systems , allowing organisms to convert nutrients into usable forms and convert harmful or unneeded molecules into forms that can be reused or excreted . During enzyme evolution , enzymes maintain the ability to perform some aspects of their function while other aspects change to accommodate changing environmental conditions . In analogy to studies of enzyme evolution focused on conservation of sequence and structural motifs , we have examined a large number of enzyme superfamilies using a new computational analysis of patterns of substrate conservation . The results provide a more nuanced picture of enzyme evolution than obtained either by detailed small-scale studies or by large-scale studies that have provided only general descriptions of function and substrate similarity . The superfamilies in our set fall along the entire spectrum from the conserved substructure being mostly reacting to mostly nonreacting , with most superfamilies falling in the intermediate range . This view of enzyme evolution suggests more complex patterns of functional divergence than those that have been proposed by previous theories of enzyme evolution . The method has been automated to facilitate large-scale annotation of enzymes discovered in sequencing and structural genomics projects . | [
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] | 2008 | Evolutionarily Conserved Substrate Substructures for Automated Annotation of Enzyme Superfamilies |
Miwi , a member of the Argonaute family , is required for initiating spermiogenesis; however , the mechanisms that regulate the expression of the Miwi gene remain unknown . By mutation analysis and transgenic models , we identified a 303 bp proximal promoter region of the mouse Miwi gene , which controls specific expression from midpachytene spermatocytes to round spermatids during meiosis . We characterized the binding sites of transcription factors NF-Y ( Nuclear Factor Y ) and USF ( Upstream Stimulatory Factor ) within the core promoter and found that both factors specifically bind to and activate the Miwi promoter . Methylation profiling of three CpG islands within the proximal promoter reveals a markedly inverse correlation between the methylation status of the CpG islands and germ cell type–specific expression of Miwi . CpG methylation at the USF–binding site within the E2 box in the promoter inhibits the binding of USF . Transgenic Miwi-EGFP and endogenous Miwi reveal a subcellular co-localization pattern in the germ cells of the Miwi-EGFP transgenic mouse . Furthermore , the DNA methylation profile of the Miwi promoter–driven transgene is consistent with that of the endogenous Miwi promoter , indicating that Miwi transgene is epigenetically modified through methylation in vivo to ensure its spatio-temporal expression . Our findings suggest that USF controls Miwi expression from midpachytene spermatocytes to round spermatids through methylation-mediated regulation . This work identifies an epigenetic regulation mechanism for the spatio-temporal expression of mouse Miwi during spermatogenesis .
Spermatogenesis is a complex process consisting of two types of cell division , mitosis and meiosis . Meiosis is initiated in B spermatogonia , eventually leading to the production of spermatozoa . Spermatozoa generated in the testis enter the epididymis and undergo maturation processes necessary for them to gain motility and become capable of fertilization . During spermatogenesis , differential gene expression is orderly and accurately regulated at the transcriptional level to ensure differentiation of germ cells [1] . Male germ cells in the adult mouse exhibit a highly distinct methylation pattern , and both de novo methylation and demethylation occur during spermatogenesis [2] , [3] . DNA methylation of postmigratory germ cell-specific genes is also observed in both premigratory germ cells and somatic cells [4] . These methylation modifications in germ cells are important for accurate spermatogenesis . Another salient feature of gene expression regulation during spermatogenesis is translational delay or repression [5] , [6] , [7] . After transcription , some mRNAs are assembled in ribonucleoprotein particles , which contain RNA-binding proteins , export proteins and processing factors . Transcripts are then transported through nuclear pore complexes into the cytoplasm , where they join in formation of the chromatoid body ( CB ) for both storage and processing after meiosis [8] . Evidence is accumulating that microRNAs ( miRNAs ) can also reduce translation [9] . miRNAs likely mediate translational repression by decreasing the rate of translation initiation . Miwi , a PIWI subfamily member of Argonaute protein family , is characterized by conserved PAZ and Piwi domains for RNA-binding , and required for initiating spermiogenesis [10] . In mice , there are three Piwi members ( Miwi , Mili/Piwil2 and Miwi2/Piwil4 ) . Miwi interacts directly with piwi-interacting RNAs ( piRNAs ) [11] , [12] , [13] , and is required for the expression of not only piRNAs but also a subset of miRNAs [14] . Miwi is a cytoplasmic protein expressed specifically in the testis from midpachytene spermatocytes to round spermatids [10] , [15] . Particularly , Miwi is present throughout the cytoplasm in spermatocytes , and gradually concentrates in the CB in round spermatids [14] , [16] . A complex formation between Miwi , Mili and Tdrd1 is critical for the integrated subcellular localizations of these proteins [17] . The Piwi interactome has revealed that arginine methylated Miwi can interact with multiple tudor domain containing proteins [18] , [19] . Tudor proteins recognize methyl-arginine of Piwi proteins , driving the localization of Piwi proteins to the CB [19] . It has been suggested that Tudor proteins regulate biological functions of Piwi proteins via specific association with methylation modifications of the Piwi arginine [20] . Despite the wealth of information , expression regulation mechanisms of the gene Miwi remain poorly understood . In this study , we have identified the functional promoter of the mouse Miwi . Miwi-EGFP transgenic mice reveal that a 303 bp core promoter of the mouse Miwi gene directs specific expression during meiosis . Notably , transcription factors NF-Y ( Nuclear Factor-Y ) and USFs ( Upstream Stimulatory Factors ) bind to and activate the promoter of Miwi . Methylation analysis of the CpG islands in the Miwi promoter reveals that CpG methylation at the USF-binding site within the E2 box inhibits the binding of USFs . In addition , both transgenic Miwi-EGFP and endogenous Miwi reveal a co-localization pattern in Miwi-EGFP transgenic mouse testis . Furthermore , the promoter region of Miwi-EGFP transgene in transgenic mice is epigenetically modified in vivo as that of the endogenous Miwi . These results suggest an epigenetic regulation mechanism for the spatio-temporal expression of the mouse Miwi during spermatogenesis .
To identify the promoter region and regulatory elements of mouse Miwi , a series of deletions of the potential promoter were introduced upstream of the luciferase gene based on the prediction of CpG islands . Luciferase activity was determined in both GC-1 and COS7 cells . Promoter analysis has revealed that the 5′ flanking sequence from −106 to −92 is important for its transcriptional activity ( Figure 1a , 1b ) . Because the Miwi gene lacks a TATA box , we identified putative transcription elements using the TFSEARCH software ( http://www . cbrc . jp/research/db/TFSEARCH . html ) . A CCAAT box ( −97∼−93 ) and two E boxes ( −107∼−102 , −82∼−77 ) were observed within the core promoter region from −106 to −92 , which are putative binding sites of transcription factors NF-Y and USF respectively . To functionally determine the importance of these elements , site-directed mutagenesis was performed using wild type pGL3-5M4 construct as a template . The mutated CCAAT box or E2 box showed obvious decreases in promoter activity , as compared to the wild type pGL3-5M4 construct , especially the E2 mutant , where very little transcription was observed ( Figure 1c ) . Meanwhile , mutations for E1 box had no effect on transcription activity . The results indicate that both the CCAAT box and the E2 boxes are important for Miwi promoter activity . To determine binding of transcription factors NF-Y and USF to the CCAAT and E2 boxes of the promoter respectively , electrophoretic mobility shift assays were carried out using nuclear extracts prepared from mouse testis and double stranded oligonucleotides . Incubation of a CCAAT probe spanning −102/−80 with nuclear extract gave rise to the formation of a DNA–protein complex . The addition of an excessive amount of unlabelled oligo DNA , but not the mutated CCAAT box , could compete with this binding ( Figure 2a ) . Furthermore , addition of anti-NFYa , anti-NFYb or anti-NFYc antibody to the binding reaction caused the disappearance of the specific DNA/protein complex , and the appearance of the super-shift band ( Figure 2a ) . These results show that the CCAAT box located from −97/−93 of the Miwi promoter region is capable of binding with transcription factors NF-Ya , NF-Yb and NF-Yc in vitro . With use of nuclear extracts prepared from mouse testis and oligo spanning −102/−71 bearing the CCAAT and E2 boxes , two specific DNA/protein complexes were observed ( Figure 2b ) . Both of the complexes could be competed by the addition of a 50-fold molar excess of unlabelled oligo DNA , but not by the mutated CCAAT box or E2 box , or mutant of both sites . Furthermore , the addition of anti-NF-Ya antibody to the binding reaction generated a super-shift band , and the addition of anti-USF1 or anti-USF2 antibody caused the disappearance of the DNA/protein complex ( Figure 2b ) . These results indicate that both transcription factors USF1 and USF2 bind to the E2 box located from −82/−77 of the Miwi promoter region in vitro . To determine whether NF-Y and USF bind to the mouse Miwi promoter in vivo , we performed chromatin immunoprecipitation ( ChIP ) analysis . As shown in Figure 2c , a 218 bp DNA fragment was amplified from the precipitates of anti-NF-Ya , anti-USF1 and anti-USF2 in testis , but it was not amplified from liver ( control tissue ) ( Figure 2c ) . The amplified fragments were confirmed by sequencing . To further rule out the possibility that the Miwi promoter region precipitated by anti-NF-Y , anti-USF1 and anti-USF2 is due to non-specific binding of these antibodies , an additional PCR amplification of a distinct genomic region ( exon 6 ) was performed on all of the precipitated chromatin DNAs . No band from the precipitate by anti-NFY , anti-USF1 or anti-USF2 antibody was observed . These results indicate that NF-Y , USF1 and USF2 specifically bind to the Miwi promoter region in vivo . To further investigate the role of transcription factors NF-Y and USF in activation of the Miwi promoter , both GC-1 and COS7 cells were co-transfected with luciferase reporter driven by Miwi promoter or the mutant for CCAAT or E2 sites with expression plasmid for NF-Ya/b/c or USF1/2 respectively . The luciferase activity increased when co-transfected with NF-Ya/b/c or USF1/2 ( Figure 3a , 3d ) . However , the activities in the mutants for CCAAT or E2 sites were not upregulated ( Figure 3b , 3e ) . Replacing the NF-Ya/b/c or USF1/2 expression plasmid with relevant dominant-negative constructs ( NF-YAm29 and A-USF ) resulted in a clear reduction of luciferase activity in a dosage-dependent manner ( Figure 3c , 3f ) . NF-YAm29 has a three amino acid substitutions in the C-terminal region of the NF-Ya , which impairs DNA binding activity and acts as a dominant-negative mutant by sequestering the NF-Yb/Yc subunits into a defective complex [21] . A-USF contains the USF heterodimerization domain but lacks the USF-specific region , which is required for transcriptional activation [22] , [23] . The results suggest that NF-Y and USF activate the Miwi promoter . We detected three CpG islands that span positions −466 to −240 , −215 to +17 , and −7 to +199 around the 2 kb promoter region using the MethPrimer program ( Figure 4a ) . To determine the methylation status of the three CpGs islands , we performed bisulfite sequencing analysis using DNA isolated from spermatogonia , pachytene spermatocytes , round spermatids , epididymal spermatozoa , Sertoli cells and liver tissue . The CpG islands from bisulfite-treated DNA were amplified and five randomly selected clones were subjected to sequencing . Methylation analysis of the three CpG islands in the Miwi promoter revealed a differential methylation profile of CpGs among these cell types ( Figure 4b ) . The CpG islands were always hypermethylated in the spermatogonia , Sertoli cells and liver tissue where Miwi was not expressed , while the CpG islands were unmethylated in the pachytene spermatocytes and round spermatids where Miwi was expressed . The CpG islands were also unmethylated in the epididymal spermatozoa , where Miwi was not expressed . From the methylation profile of CpG islands in the Miwi promoter , we noted an inverse association between the methylation status of the CpG dinucleotide ( −80 ) within the E2 box and the Miwi expression pattern . To examine the importance of CpG methylation within the E2 box for Miwi expression , we performed EMSAs using a methylated double-stranded oligonucleotide , in which the cytosine residue in the CpG dinucleotide was methylated with M . SssI ( CpG Methyltransferase ) in vitro . Incubation of nuclear extract from mouse testis with a wild-type oligo probe resulted in the formation of a protein/DNA complex . This complex could be competed with an excessive amount of unlabelled oligo DNA . However , the addition of 50-excess or 100-excess of unlabelled methylated oligo DNA or E2 mutant did not affect the complex formation ( Figure 4c ) . Our previous ChIP experiment in testis and liver showed that USF1/2 could bind to the E2 box only in testis ( Figure 2 ) , which is consistent with the methylation status at the CpG site within the E2 box . These results suggest that CpG methylation at the USF-binding site within the E2 box inhibits the binding of USF . To examine the functional role of the core promoter of Miwi during spermatogenesis , we have made Miwi-drived EGFP transgenic mice . The Miwi promoter sequence spanning from −122 to +181 was cloned into the pEGFP-1 vector . A 1 . 3 kb fragment digested with both HindIII and AflII was purified for the production of transgenic mice ( Figure 5a ) . Western blot analysis revealed that Miwi-drived EGFP was specifically expressed in testis of transgenic founders ( Figure 5b ) . To determine whether transgenic Miwi-EGFP and endogenous Miwi are co-expressed in the seminiferous tubules of transgenic mouse testis , we first examined the subcellular localization by immunofluorescence and confocal microscopy . Transgenic Miwi-EGFP and endogenous Miwi were nearly co-localized in the cytoplasm of spermatocytes and round spermatids , particularly in round spermatids , both Miwi and EGFP concentrate in the chromatoid body ( Figure 6 ) . The cell types expressing EGFP in the seminiferous tubules of transgenic mice were further confirmed by immunochemical localization analysis in both transgenic and wild mice ( Figure 7 ) . These results suggest that EGFP transgene expression , driven by the 303 bp Miwi promoter , is germline-specific during spermatogenesis . Other promoters ( e . g . Dazl and H1t ) have also been observed to drive reporter expression in a germ-cell specific manner [24] , [25] . To verify whether the DNA methylation profile of the Miwi promoter driven transgene matches that of the endogenous Miwi promoter , we performed flow cytometric analysis of male germ cells from the testis of three Miwi-EGFP heterozygous transgenic mice . Around 10% of cells were EGFP-positive in the transgenic mice ( Figure 8a , 8b ) , while male germ cells from wild-type mice did not contain EGFP-positive cells ( Figure 8c , 8d ) . To compare the methylation status of both EGFP-positive and negative cells in the transgenic mice testis , both types of cells were separated and collected from three heterozygous transgenic Miwi-EGFP mice . We then performed bisulfate sequencing analysis using DNA isolated from both EGFP-positive and negative cells . Sixty randomly selected clones were subjected to sequencing . Biased methylation status was observed between EGFP-positive cells and negative cells from the testes of Miwi-EGFP transgenic mice . Methylation frequency for EGFP-negative cells was 90–100% , however the frequency for EGFP-positive cells was lower than 30% . Further , in the CpG positions −105 and −80 ( key USF binding site ) , the methylation frequency for EGFP-negative cells was 93% and 96% respectively , while the CpG dinucleotides in both positions of the EGFP-positive cells were un-methylated ( Figure 8e , 8f ) . These results show that the DNA methylation profile of the Miwi promoter driven transgene is completely consistent with that of the endogenous Miwi promoter and further demonstrate that USF and demethylation of its binding site regulate male germline-specific expression of Miwi gene .
To produce functional sperm , male germ stem cells gradually lose their stem cell potential and initiate differentiation to become highly specialized spermatozoa . During the differentiation process , accurate spatio-temporal expression regulation of a variety of genes is important to germ cell fate . The current study reveals a clear methylation regulatory mechanism of the Miwi gene during spermatogenesis; CpG methylation inhibits the binding of the USF , thereby repressing Miwi expression . When demethylation occurs , the transcription factor USF binds to and activates the Miwi promoter . Importantly , methylation-mediated regulation of Miwi by transcription factor USF occurs in a small region of the Miwi core promoter , which controls germline-specific expression of Miwi in testis . Thus , we present an epigenetic regulation mechanism for the spatio-temporal expression of mouse Miwi , which is driven by transcription factor USF during spermatogenesis . The present study has contributed to our understanding of how ubiquitous protein USF regulates testis-specific expression of the Miwi gene in a methylation-dependent manner . Both USF1 and USF2 are ubiquitous proteins characterized by highly conserved C-terminal basic helix-loop-helix and leucine zipper domains responsible for their dimerization and DNA binding activities [23] , [26] . A number of genes have an E-box in their promoter , and the consensus sequence CANNTG , is known as the binding site for USFs to regulate transcription [27] , [28] . For example , Mitf activates transcription through binding to the E-box element on the regulatory sequences of three germ genes ( dazl , dnd and vasa ) in medaka [29] . Although expressed ubiquitously , USFs appear to be involved in regulation of developmental and tissue-specific expression of other target genes such as surfactant protein A , steroidogenic factor 1 , chipmunk hibernation-specific HP-27 , transcription factor homeobox B4 , carboxyl ester lipase and Prolyl-4-hydroxylase ( I ) [30] , [31] , [32] , [33] , [34] , [35] . Our results show that USF is required for the specific expression of the Miwi gene . This USF regulation is methylation-dependent , which ensures developmental and cell type-specific expression of Miwi during meiosis of male germ cells . The methylation of genomic DNA at CpG dinucleotides is a major epigenetic modification of the genome [36] and contributes to general transcriptional suppression , either by blocking the binding of transcription factors to their CpG-containing binding sites [31] or by recruiting methyl-CpG-binding proteins , which in turn recruit chromatin-remodeling machinery to induce the formation of a repressive chromatin structure [37] . Promoter methylation is associated with down-regulation of gene expression during spermatogenesis [38] , [39] , [40] . Miwi is a gene expressed specifically in the testis from midpachytene spermatocytes to round spermatids . To address how Miwi gene is regulated during spermatogenesis , we used bisulfite genomic sequencing to profile CpG methylation within the proximal promoter in cells where the gene is transcribed or silenced . We indeed observed an obvious correlation between Miwi gene expression and CpG methylation status in concert with differentiation of germ cell types . Importantly , we observed a key binding site for transcription factor USF , which plays a vital role in regulation of Miwi expression in a demethylation dependent manner . A selective demethylation and a consequent remethylation late in germ cell development have also been observed in several other testis-specific genes such as ALF and Pgk2 [41] , [42] , [43] . Taken together , these data support that methylation modification is a key regulation mechanism for spermatogenesis . In mammals , both DNA methylation and demethylation events occur during germ cell development and differentiation . Molecular mechanisms of regulating these processes remain largely unknown . There are five methyltransferases ( DNMTs ) , DNMT1 , DNMT2 , DNMT3a , DNMT3b and DNMT3L [44] , and three of them DNMT1 , DNMT3a , and DNMT3b are involved in methylation in vivo . It has been proposed that DNMT3a and DNMT3b may contribute differentially to the establishment and/or maintenance of methylation patterns in male germ cells [45] , [46] . Understanding of mammalian DNA demethylation represents a great challenge . There are probably two kinds of molecular mechanisms of DNA demethylation in mammals [47] , passive demethylation occuring by blocking methylation of newly synthesized DNA during DNA replication , and active demethylation which is demethylation process is initiated by the same enzymes that establish the methylation , DNMT3A and DNMT3B [48] , [49] . The question remains , as to how the demethylation of such an important E2 cis-element is regulated during spermatogenesis . Further study on the mechanism will provide new insight into mammalian spermatogenesis .
Transcription factor binding sites were predicted in the genomic DNA sequence of mouse Miwi using the TFsearch program ( http://www . cbrc . jp/research/db/TFSEARCH . html ) with a threshold score of 85 and Vertebrate Matrix . CpG islands in Miwi promoter were predicted by MethPrimer ( http://www . urogene . org/methprimer/index1 . html ) with observed/expected ratio >0 . 6 and percent C+G>50% . GC-1 ( a mouse germ-cell line at a stage between a type B spermatogonia and primary spermatocyte , has the capacity to differentiate into spermatids within the seminiferous tubules . ) and COS7 ( African Green Monkey SV40-transformed kidney fibroblast cell line ) cells ( for comparison , both germ-line and non germ-line cells were chosen ) were maintained in high glucose Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum , and plated in 48-well plates and transfected using 2 µl Lipofectamine 2000 ( Invitrogen , Carlsbad , CA , USA ) in each well . Plasmid DNAs were prepared in a dam- and dcm- E . coli strain ( SCS110 ) . Each deletion construct ( pGL3-5M1 , pGL3-5M2 , pGL3-5M3 , pGL3-5M4 , pGL3-5M5 , pGL3-5M6 , pGL3-5M7 and pGL3-5M8 ) was transfected at 0 . 4 µg along with 10 ng/well pRT-TK ( an internal control ) . For co-transfection luciferase assays , 0 . 2 µg of Miwi promoter construct pGL3-5M4 or mutated constructs , and 0 . 2 µg transcription factor expression vectors or corresponding dominant negative plasmid were co-transfected into cells . Empty vector pCX was added to equalize the final DNA content among each well . Luciferase activities were measured 24 h after transfection using the dual-luciferase reporter assay system ( Promega , Madison , WI , USA ) and a Modulus Single Tube Multimode Reader ( Turner Biosystems , Sunnyvale , CA , USA ) based on the manufacturer's protocol . Assays were performed in triplicates and expressed as means ± S . D . Spermatogonia , pachytene spermatocytes , round spermatids , and Sertoli cells were isolated using the method described by Mays-Hoopes et al . [50] . Briefly , after the tunica albunigea was removed , the seminiferous tubules of adult testis of KunMing mice were treated with collagenase and washed to release the Leydig cells as well as interstitial cells . 0 . 25% trypsin treatment for 20 min at 37°C was followed by pipetting to release the cells . After fixed by the high citrate fixative method , the cells were ready for fluorescence-activated cell sorting . For separation and collection of the EGFP-positive and negative cells from the testis of Miwi-EGFP transgenic mice , the seminiferous tubules were treated with trypsin and pipetted to release the cells . The cells were then analyzed on a MoFlo-XDP High-performance cell sorter ( Beckman Coulter , Inc . , Fullerton , CA , USA ) . Excitation of GFP was at 488 nm . Sorted cell populations were collected by centrifugation for 3 min at 3000 g and resuspended in PBS . Sodium bisulfite treatment of genomic DNA was performed as follows . Briefly , 2 µg of genomic DNA was denatured with 2 M NaOH for 15 min , then treated with 3 M sodium bisulfate ( pH 5 . 0 ) ( Sigma-Aldrich , St . Louis , MO , USA ) and 10 mM hydroquinone at 50°C for 18 h . DNA was purified using a Wizar DNA Clean-up kit ( Sigma-Aldrich , St Louis , MO , USA ) , and NaOH was added to 0 . 3M . Bisulfite treated DNA , which was then precipitated with ethanol and resuspended in 20 µl sterile distilled water . Oligonucleotides corresponding to the CCAAT and E boxes of the Miwi promoter were synthesized and annealed into double strands . Radiolabeled probes were generated by incubation of 250 ng annealed oligonucleotides with 20 µCi [γ-32P] dATP in the presence of T4 Polynucleotide Kinase ( Promega , Madison , WI , USA ) for 1 h at 37°C , and were subsequently separated from free nucleotide using G-50 column purification ( Amersham Biosciences , Uppsala , Sweden ) . Testis nuclear extract was then incubated at room temperature for 30 min with a 100 , 000 dpm radiolabeled probe and 1 µg poly ( dI-dC ) in 10 mmol/l Tris-HCl , pH 7 . 5 , 50 mmol/l NaCl , 1 mmol/l dithiothreitol , 1 mmol/l EDTA , and 5% glycerol . For supershift experiments , binding reactions were subsequently incubated with 5 µg of antibody for 30 min at room temperature . For competition experiments , the unlabeled competitor oligos ( in 50-fold molar excess ) were added together with the probe at the start of the incubation . Samples were resolved on 5% polyacrylamide gels in 0 . 5% TBE running buffer at 10 V/cm for 2 h . The dried gel was exposed to a phosphorimager cassette and scanned with typhoon 9200 instrument ( GE-Healthcare , Amersham bioscience , Uppsala , Sweden ) . Mouse testis and livers were chopped into small pieces with a scalpel in cold phosphate-buffered saline ( PBS ) and cross-linked in 1% formaldehyde-PBS for 15 min with constant shaking . The tissue was rinsed in cold PBS and homogenized with a Dounce homogenizer in 1 ml cold cell lysis buffer ( 10 mM Tris-Cl , pH 8 . 0 , 10 mM NaCl , 3 mM MgCl2 , 0 . 5% NP-40 ) supplemented with protease inhibitors ( Roche Diagnostics Ltd , Mannheim , Germany ) . Cells were incubated at 4°C for 5 min to allow the release of nuclei . Nuclei were sedimented by centrifugation at 13 , 000 g for 5 min . After lysis in the buffer ( 1% sodium dodecyl sulfate [SDS] , 5 mM EDTA , 50 mM Tris-Cl , pH 8 . 1 ) , sonication was performed with a Sonic Dismembrator model 100 sonicator ( Fisher Scientific , Inc . , Pittsburgh , PA , USA ) . After centrifugation , the supernatant chromatin was immunoprecipitated with no antibody ( beads only ) , preimmuno IgG ( IgG ) , anti-NF-Ya ( Abcam Inc . , Cambridge , CA , USA ) , anti-USF1 ( Santa Cruz Biotech , CA , USA ) or anti-USF2 ( Santa Cruz ) , together with Protein G PLUS-Agarose ( Santa Cruz ) respectively . DNA isolated from the immunoprecipitated complex was amplified by PCR with primers flanking the CCAAT/E box binding sites or control primers . The PCR products were cloned into T-easy vector ( Promega , Madison , WI , USA ) and sequenced . A 303 bp fragment of mouse Miwi promoter ranging from −122 to +181 was amplified from genomic DNA of the KunMing mouse using primers designed with an EcoRI recognition site at the 5′ end of the forward primer and a BamHI recognition site on the 5′ end of the reverse primer . PCR fragments were subcloned into EcoRI/BamHI-digested pEGFP-1 vector . A 1 . 3 kb fragment digested with both HindIII and AflII was separated by agarose gel electrophoresis , dissolved in buffer of 5 mM Tris and 0 . 1 mM EDTA at 1 ng/µl . The purified DNA was microinjected into pronuclei of fertilized eggs from the KunMing mice on an inverted microscope ( Leica DM IRB ) ( Leica Microscopy Systems Ltd , Wetzlar , Germany ) equipped with a Leica manually operated micromanipulator ( Leica ) attached to a micro-injection system ( Narishige , Tokyo , Japan ) . The injected eggs were transferred into the oviduct of day 1 pseudopregnant recipients . Transgenic animals were identified by PCR screening of genomic DNA isolated from tail with a pair of primers in the transgenic fragment and GFP checking of testis sections under fluorescent microscopy ( Leica ) . Two independent transgenic lines have been analyzed in this study . Homozygotes were identified by crossing experiments and 100% presence of transgene in offsprings . Mouse testes were embedded in OCT medium ( Tissue Tek , Miles , Elkhart , IN , USA ) and cut into a series of 6 µm sections with a cryostat ( Leica , Bensheim , Germany ) . To determine immunocytochemical localization , the sections were fixed with methanol for 20 min at −20°C and then permeabilized with 0 . 1% Triton X-100 in PBS for 30 min . Then sections were treated with 5% BSA for 20 min at room temperature and incubated with anti-Miwi ( G82 ) ( Cell Signaling Technology Inc . , Danvers , MA , USA ) or anti-GFP ( D5 . 1 ) ( Cell Signaling Technology ) . Then SABC and DAB were used for color visualization according to the manufacturer's instructions ( Boster Company , China ) . For immunofluorescence analysis , after sections were fixed with methanol for 20 min at −20°C , permeabilized with 0 . 1% Triton X-100 in PBS for 30 min , and treated with 5% BSA for 20 min at room temperature , the sections were incubated at 4°C overnight in 1∶50 anti-Miwi ( Cell Signaling Technology ) . After washing 3 times with PBS , 1∶100 dilution of the corresponding DylightTM 594-conjugated secondary antibody ( Jackson ImmunoResearch Laboratories , Inc . , West Grove , PA , USA ) was applied at 37°C for 1 hour . The sections were washed in PBS for 5 times . After blocked again at room temperature for 20 min in 5% BSA , the sections were incubated at 4°C overnight in 1∶50 anti-GFP ( Abcam ) . The corresponding FITC-conjugated secondary antibody ( 1∶100 dilution ) ( Proteintech Group , Chicago , IL , USA ) were then incubated at 37°C for 1 hour . The nuclei were counterstained with Hoechst33258 . Images were taken by confocal fluorescence microscopy ( FV1000 , Olympus , Tokyo , Japan ) . | Germ cell differentiation is a key process in the formation of functional spermatozoa . Despite the wealth of information about gene expression patterns and regulations important for this process , it is not clear how spatio-temporal expression of the key factor Miwi during spermatogenesis is controlled . We have characterized the functional promoter of the mouse Miwi gene . Transgenic mice harboring EGFP under the Miwi core promoter containing just the functional CCAAT box and E2 box were generated and demonstrated that it can direct germ cell–specific expression . We further identified the transcription factors NF-Y and USF1/2 as activators of Miwi gene expression , through their binding to the CCAAT box and E-box/E2 site of the Miwi promoter , respectively . A CpG dinucleotide just located within the USF binding site is responsible for mediating methylation-dependent silencing of the Miwi gene . Our findings provide new insight into an epigenetic regulation mechanism for the spatio-temporal expression of the mouse Miwi during spermatogenesis . | [
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] | 2012 | DNA Demethylation and USF Regulate the Meiosis-Specific Expression of the Mouse Miwi |
It is widely accepted in eukaryotes that the cleavage furrow only initiates after mitosis completion . In fission yeast , cytokinesis requires the synthesis of a septum tightly coupled to cleavage furrow ingression . The current cytokinesis model establishes that simultaneous septation and furrow ingression only initiate after spindle breakage and mitosis exit . Thus , this model considers that although Cdk1 is inactivated at early-anaphase , septation onset requires the long elapsed time until mitosis completion and full activation of the Hippo-like SIN pathway . Here , we studied the precise timing of septation onset regarding mitosis by exploiting both the septum-specific detection with the fluorochrome calcofluor and the high-resolution electron microscopy during anaphase and telophase . Contrarily to the existing model , we found that both septum and cleavage furrow start to ingress at early anaphase B , long before spindle breakage , with a slow ingression rate during anaphase B , and greatly increasing after telophase onset . This shows that mitosis and cleavage furrow ingression are not concatenated but simultaneous events in fission yeast . We found that the timing of septation during early anaphase correlates with the cell size and is regulated by the corresponding levels of SIN Etd1 and Rho1 . Cdk1 inactivation was directly required for timely septation in early anaphase . Strikingly the reduced SIN activity present after Cdk1 loss was enough to trigger septation by immediately inducing the medial recruitment of the SIN kinase complex Sid2-Mob1 . On the other hand , septation onset did not depend on the SIN asymmetry establishment , which is considered a hallmark for SIN activation . These results recalibrate the timing of key cytokinetic events in fission yeast; and unveil a size-dependent control mechanism that synchronizes simultaneous nuclei separation with septum and cleavage furrow ingression to safeguard the proper chromosome segregation during cell division .
The division of a cell into two genetically identical daughter cells requires the accurate coordination of events such as the entry into mitosis , chromosome segregation , and cytokinesis . Thus , precise timing of the late mitotic events is critical for faithful chromosome segregation and genome integrity . The events occurring during mitosis exit follow a strictly defined order . First , chromosome arms reach a state of maximum condensation as the spindle extends to pull chromosomes apart . Next , at the end of anaphase B the chromosomes decondense , the spindle breaks down , and cleavage furrow ingression begins [1–3] . Progression through the cell cycle is controlled by fluctuations in the activity of cyclin-dependent kinase ( Cdk ) complexes , which are dependent upon periodic expression of cyclin subunits . At anaphase B onset , cyclin is degraded by the anaphase-promoting complex which allows anaphase B progression and the exit from mitosis [4] . Cdk1 is considered a global inhibitor of cytokinesis , which depends on the conclusion of the preceding mitosis [5 , 6] . Thus , the expression of a non-degradable cyclin B in fission yeast blocks the cells in anaphase B and abolishes cytokinesis [7] . In fungi the spindle pole bodies ( SPBs ) are functionally analogous to centrosomes . The septation initiation network ( SIN ) signals from the cytoplasmic face of the SPB to activate both septum and cleavage furrow ingression after telophase onset [8] . SIN equivalents are conserved in the yeast MEN and metazoans Hippo pathways [8 , 9] . SIN signaling requires activation of the GTPase Spg1 [10] . Byr4 and Cdc16 form a two-component GTPase-activating protein complex , which restrains septation in interphase by keeping Spg1 in the inactive GDP-bound state [11 , 12] . Upon entry into mitosis , phosphorylation of Byr4 by Cdk1 facilitate Byr4-Cdc16 removal from the SPBs [13] , allowing Spg1 to become active at both SPBs [12 , 14] . Then active Spg1-GTP recruits Cdc7 kinase to both SPBs in early mitosis [14] . However , further activation of the SIN during mitosis is blocked by Cdk1 activity . At anaphase B onset , the loss of Cdk1 activity allows the increase of SIN activity [15] , being Spg1 inactivated by Byr4-Cdc16 at one of the two SPBs , and Cdc7 disappearing from that inactive SPB [12 , 16] . The levels of Cdc7 in the active SPB gradually increase and peak at the end of anaphase B , in a process that depends on the SIN Etd1 [17 , 18] . The fact that Etd1 localizes to the cortex and cytoplasm but not to the SPB led to propose that after the spindle is fully elongated , the release of Etd1 from the pole cortex completes SIN activation probably by binding to cytoplasmic Spg1 [18 , 19] . Maximal SIN activation at the end of anaphase B causes the relocation of NDR-family kinase Sid2 from the SPB to the division site , where it presumably triggers septum synthesis and furrow ingression by activating the conserved GTPase Rho1 [9 , 20 , 21] . Cytokinesis in fungi requires the membrane invagination combined with the closure of a conserved actomyosin ring ( AR ) and the synthesis of a special wall structure named division septum [22 , 23] . Fission yeast cytokinesis is divided into four consecutive steps: 1 ) an equatorial band of nodes is established before entry into mitosis; 2 ) the nodes condense into a compact AR in anaphase A; 3 ) the compacted AR maturates acquiring new proteins during anaphase B; and 4 ) septation begins and the AR starts to close after the fully elongated spindle breaks down [24 , 25] . The septum is mainly composed of polysaccharide chains of α- and β-glucans , displaying a three-layered structure with a central disk called the primary septum ( PS ) and flanked by the secondary septum on each side [23] . The septum is built by at least three essential glucan synthases ( GSs ) . Bgs1/Cps1 is responsible for the linear-β ( 1 , 3 ) glucan ( L-BG ) synthesis of the PS [26] , and Bgs4 and Ags1/Mok1 cooperate to form the secondary septum . In addition , Bgs4 couples AR closure with PS ingression , while Ags1 confers to the PS the mechanical strength required for a gradual and safe cell separation [27 , 28] . L-BG is exclusively detected in the PS structure , where this polysaccharide specifically interacts with and has high affinity to the fluorochrome calcofluor white ( CW ) [26] . Therefore , CW has shown to be a valuable tool to precisely visualize cytokinesis both temporally and spatially [27 , 28] . In this study , CW was used to examine the timing of septum synthesis start with respect to mitosis in fission yeast . Contrary to the general belief that septum synthesis initiates after telophase onset , we have determined by fluorescence and electron microscopy that both septum and cleavage furrow start to ingress at early anaphase B , occurring long before the chromosome masses reach the cell tips and the spindle breaks down at the start of telophase . In addition , we describe that septation presents two distinct ingression rates: very slow during anaphase B and much faster after telophase onset . We also found that the timing of septation initiation with regard to mitosis scales with the cell size in an Etd1 and Rho1 dependent manner , and depends on Cdk1 inactivation , which allows the SIN activation and septation onset during early anaphase B . Taken together; our findings reveal the existence of a size-dependent control mechanism that helps to coordinate simultaneous chromosome mass separation with septum and cleavage furrow ingression , recalibrating the timing and regulation of crucial events of fission yeast cytokinesis .
Based on the visual detection of AR closure by standard fluorescence microscopy , it is considered that AR closure and septation initiate simultaneously after spindle disassembly at the onset of telophase [25] . Here the onset of septum synthesis in fission yeast has been investigated by visualizing the emergence of the PS fluorescence through specific CW staining with respect to all possible mitotic events . Table 1 , Table 2 and S1 Table encapsulate the complete time intervals between septum synthesis initiation and the mitotic events defined in Fig 1A of the main strains and growth conditions analyzed in this study . To precisely detect possible alterations in the time of septation onset ( blue arrowhead indicates the time immediately before the first PS detection , Fig 1B and 1C ) , and to avoid the variations that might occur in the timing with respect to distant mitotic events , the anaphase B onset was considered as time zero ( green arrowhead , Fig 1B and 1C ) . This event was the closest and most accurate for measuring the elapsed time until septation onset . When required , other events were considered as time zero as specified in text and figures . The emergence of PS was examined in different wild-type backgrounds to ensure the obtained data was reproducible . Labeling of certain ring proteins can compromise the AR function [29 , 30] . Thus , to avoid artifacts in the timing of septation onset , the analyzed strains did not contain any tagged ring protein . In all strains the PS was reproducibly detected in early anaphase B , close to the transition to mid-anaphase B ( Fig 1B and 1C , S1 Fig , Table 1 and Table 2 ) and long before the spindle breakage and telophase onset ( red arrowhead , Fig 1B and 1C ) . It is assumed that initial septum deposition is tightly coupled with cleavage furrow ingression . Therefore , to determine if the small septa observed during early anaphase really grew inwards , electron microscopy analysis was performed ( Fig 1D–1F ) . Cells in anaphase B and telophase , as shown by the close or distant nuclei position and by the presence and size of the septum , also unequivocally demonstrated that septum formation occurred in early anaphase B . Importantly , the presence of small PS from 32 to 137 nm in length showed that the nascent septa grow inwards . This indicates that membrane invagination and AR closure at the septum edge have also occurred simultaneously ( Fig 1D–1F ) . In agreement , fluorescence microscopy showed that both CW-stained septum and GFP-Bgs1 ring presented a slow ingression rate during anaphase B , increasing about 3-fold after telophase onset ( red arrowhead ) and coinciding with the visual detection of AR closure ( Fig 1G ) . Thus , the start of septum and cleavage furrow ingression overlaps with the early stages of chromosome mass separation during anaphase B , independent of mitosis conclusion . The newly described start of the septation process overlaps with the spindle elongation , and given that spindle length scales with cell size [31] , the correlation between the timing of septation onset , cell size and anaphase B progression was investigated in detail . Both the time of septation onset and cell length were analyzed in short ( cell cycle wee1-50 and cdc2-3W ) , normal and long cells ( wild-type diploid , cell cycle cdc25-22 and cdc10-119 ) ( Fig 2A–2E and Table 2 ) , and a linear correlation was found between the two ( Fig 2F ) . To determine if the start of septation correlates with a specific stage during anaphase B proportional to the cell size , the spindle length during anaphase B was measured . It was found that the percentage of the spindle length at septation onset as compared to the maximal spindle length at late anaphase B was similar in normal and long cells , but slightly reduced in short cells ( Table 3 ) . However , the percentage of elapsed time at septation onset during anaphase B compared to the total mitosis time was consistently similar in wild-type , long and short cells ( Fig 2G and S1 Table ) . Overall , these results reveal that the timing of septation onset , with respect to anaphase B , scales with cell size , and support the existence of a cell size-dependent control that helps to coordinate septum and furrow formation with anaphase B progression . The timing of septum closure ( brown arrowhead , Fig 3A–3C ) with respect to the cell size was also studied . The elapsed times from either anaphase B onset or septation start to septum closure at telophase were similar in wild-type and long cells , but extended in small cells ( Fig 3D and 3E ) . Interestingly , the elapsed time from telophase onset to septum closure was greatly reduced in long cells and increased in short cells , with respect to that of wild-type cells ( Fig 3E and 3F ) . The close proximity between spindle disassembly and septum closure in long cells suggests that adjustments in the timing of septation onset during anaphase B might be required to avoid the breakage of a persistent mitotic spindle , thus ensuring that chromosomes are safely separated from the division site and the divided nuclei are properly centered in the newly formed cells . The fact that septation starts before mitosis exit and very close in time to the described Cdc2/Cdk1 kinase inactivation at early anaphase B [32] prompted us to evaluate its direct dependence on Cdk1 activity . Thus , the role of cyclin Cdc13 location and decline in the septation onset was examined . Cdc13 localizes to the SPBs and spindle during prophase and metaphase , and moves to the nuclear periphery in anaphase A , where it is presumably degraded when the spindle elongates in anaphase B [33] . Septation onset coincided with the large drop of nuclear Cdc13-GFP fluorescence ( Fig 4A and Fig 4D ) . In short cells the premature septation was coincident with the anaphase B onset and the complete decay of Cdc13-GFP ( Fig 4B and Fig 4D ) . By contrast , long cells showed a slow decline of Cdc13-GFP and coincided with a large delay in the start of septation ( Fig 4C and Fig 4D ) . Therefore , the timing of Cdk1 downregulation is dependent on the cell size like the timing of septation onset . To test if septation onset specifically depends on Cdk1 inactivation we first analyzed the effect of a non-degradable Cdc13 in the timing of septation onset ( S2 Fig ) . Expression of endogenous non-degradable Cdc13 [34] caused an anaphase B blockage as previously reported with an inducible version [7] . Two types of cells arrested in anaphase B were observed: Type 1 cells exhibited a considerably delayed septation and mild or non-apparent defects in chromosome segregation , and Type 2 cells exhibited blocked septation and persistent trailing chromosomes and/or failed chromosome segregation ( S2A and S2B Fig ) . Then we analyzed the timing of septation onset in cells with Cdk1 specifically inactivated at early mitosis . For this purpose , the analogue-sensitive cdc2-asM17 mutant was used [35] . Specific Cdk1 inactivation by incubating cdc2-asM17 cells with 10 μM 1-NP-PP1 immediately after their entry into mitosis , led to a premature septation onset ( Fig 4E and 4F ) . These data show that Cdk1 inactivation is required for the immediate and accurate activation of septation during early anaphase B . To further understand the mechanisms that trigger the start of septation during early anaphase B , we examined if the described requirements for septation during telophase are also required in early anaphase B . The chronological localization as a stable ring of the main cytokinetic components regarding septation start was examined . All the analyzed AR proteins sequentially coalesced during the 9 minutes prior to septation onset ( S3A Fig and S3C Fig ) , while Bgs1 was detected as a stable ring 30 seconds before septation onset . Other GSs stably localized after septation start , Ags1 during late anaphase B and Bgs4 at telophase ( S3B and S3C Fig ) , in agreement with the proposed role of Bgs1 and/or the PS determining the location of the rest of GSs [29] . As expected , the presence of a defective AR led to delayed septation onset ( S3D and S3E Fig , Table 1 and Table 3 ) . These results indicate that the AR present at early anaphase B is mature and ready to induce Bgs1 recruitment and immediate septation onset . It is assumed that maximal SIN activity at late anaphase B triggers septation by promoting Sid2-Mob1 kinase complex relocation from the SPB to the AR [20 , 36 , 37] . We found that both Sid2-GFP and Mob1-GFP relocated much earlier than described to the cell middle , coinciding with the first detection of PS ( Fig 5A , S3A Fig and S3C Fig ) and suggesting that the SIN triggers septation before its full activation at late anaphase . Previous studies proposed that Bgs1 displacement to the cell middle requires the SIN signaling [38–40] . In contrast , it has been shown that Ags1 displacement to the cell middle does not require the SIN [27] . Thus , the GSs localization in the absence of SIN was reexamined . It was found that all GSs appeared as a broad medial band after the transient formation of the AR but never concentrated as a ring in SIN-defective cdc11-119 cells ( S4 Fig ) . These data show that proper localization of GSs as a ring depends on both SIN and AR , as previously described for Ags1 [27] , and suggest that the SIN might trigger septation by maintaining the AR and regulating GSs functions . Next , the role of SIN signaling PS formation at early anaphase B was analyzed . sid2-250 mutant cells display reduced SIN even at the permissive temperature [18 , 41] . We found that septation started significantly later in sid2-250 cells ( Fig 5B , Table 1 and Table 3 ) , indicating the Sid2 dependence for septation start at early anaphase B , and that the still low level of SIN activity at early anaphase B is enough to trigger Sid2-Mob1 relocation and septation onset . Cdc7 kinase levels at the SPBs are used as an indirect assessment of SIN activity [14] . Also , Cdc7 disappearance from one SPB is considered a hallmark for SIN signaling [42] . Therefore , the role of the SIN asymmetry was examined; detecting that septation start coincided with the total Cdc7 loss from one SPB ( Fig 5C and S2 Table ) . It has been described that SIN asymmetry scales with cell size [43] and as expected , the timing of symmetric SIN increased in long cdc25-22 cells , as did the timing of septation , matching the Cdc7 asymmetry ( Fig 5D and S2 Table ) . To determine if the asymmetric SIN is required for the timely activation of septum synthesis , csc2Δ cells deprived of the SIN-inhibitory phosphatase complex ( SIP ) , which is required for SIN asymmetry in anaphase B , were analyzed [44] . As expected , the SIN remained symmetric through anaphase B and telophase . However , the timing of PS detection was unaffected and occurred just after the SIN reached a maximum in both SPBs in early anaphase B ( Fig 5E and S2 Table ) . Likewise , SIN asymmetry and septation onset did not correlate , either when the symmetry was extended at higher temperature , or when septation onset was delayed by a partially defective Bgs1 function ( S5 Fig and S2 Table ) . All these observations indicate that the timely septation onset does not depend on the establishment of SIN asymmetry . The timing of septation onset depends on Cdk1 inactivation and the ensuing level of SIN activity during early anaphase B , and all three depend on the cell size . Etd1 is the only known SIN activator that localizes to cortex and cytoplasm at both the cell middle and tips , and its function is critical for SIN signaling [17–19 , 43] . Thus cell middle-localized Etd1 should be the best SIN candidate to change its concentration and therefore the SIN activity with the cell size . In agreement , the activation of septum synthesis in cells with displaced SPBs and with Etd1 normally localized in the cell tips and middle did not depend on the proximity of the Cdc7-active SPB to the cell tip ( S6A and S6B Fig ) . In addition , the Etd1 localization in medial cortex and cytoplasm did not depend on the nucleus position but on the AR and ensuing septum position ( S6C Fig ) , as previously described [17] . In order to know the mechanisms that regulate the SIN to trigger septation during early anaphase B depending on the cell size , the location and levels of GFP-Etd1 at the onset of septation were investigated . At anaphase B start , Etd1 localized to both cytoplasm and cortex of the cell middle ( arrow , Fig 6A ) and cell tips . Interestingly , PS synthesis initiated when the Etd1 levels started to increase in the cell middle cytoplasm , while gradually disappeared from the cell cortex ( Fig 6A ) . Etd1 was not observed either like a defined ring or in the septum membrane throughout anaphase B , instead it concentrated along the membrane of advanced septa at telophase ( Fig 6A and S7 Fig ) , supporting the idea that Etd1 signals SIN activation from the cytoplasm surrounding the SPBs . Like septation onset , the SIN activity during early anaphase B depends on the cell size [43] . In agreement , the increase of Etd1 levels in the cell middle was delayed in long cdc25-22 cells , and the increase start coincided with the septation onset ( Fig 6B ) . To see if Etd1 levels in the cell middle regulate the start of septation , the timing of septation relative to different levels of Etd1 , including increased ( induced 41X-etd1+ ) and reduced Etd1 levels ( repressed 81X-etd1+ ) , was examined . Septation was initiated considerably earlier in Etd1-overproducing cells and was greatly delayed in Etd1-depleted cells ( Fig 6C ) . These results show that Etd1 levels regulate septation onset , which is likely activated by the increase of cell middle Etd1 in early anaphase B . Finally , to test whether the cortex-localized Etd1 is required for septation onset , the timing of septation onset was analyzed in cells expressing a functional Etd1 version that is absent from the cortex and exclusively localizes in the cytoplasm [19] . Similar to wild-type Etd1 , the overproduction of GFP-Etd1-Δ9 greatly advanced the start of PS synthesis ( Fig 6D and 6E ) . These results indicate that the start of septation exclusively depends on cytoplasmic Etd1 . SIN presumably triggers septum synthesis by regulating the activity of the GTPase Rho1 [21] , which is the regulatory subunit of the GS [45] . In agreement , we observed that Rho1 overproduction ( induced 3X-rho1+ ) advanced the septation start ( Fig 6F ) . Altogether , these results indicate that the precise initiation of septation relative to the cell size depends on the levels of cytoplasmic Etd1 and Rho1 , and suggest that both are part of a molecular mechanism coordinating septum and furrow formation with cell size during anaphase B . Spindle elongation is also required for SIN signaling during anaphase B [18] . Therefore we tested whether the spindle is required for timely septation onset by examining the cells with the MT-depolymerizing drug carbendazim ( MBC , Fig 7A ) . To avoid a delay caused by activation of the spindle assembly checkpoint , a strain lacking Mad2 was used [46] . Septation onset was not affected in MBC-untreated mad2Δ cells and coincided with the large decline of Cdc13 cyclin ( Fig 7B and Fig 7F ) . Surprisingly , the spindle absence caused by MBC treatment induced a highly premature septation , while the cells still maintaining high levels of Cdc13 , and with the PS initiation uncoupled ( CW fluorescence remains weak and small ) from the ensuing septum ingression ( CW fluorescence starts to increase ) ( Fig 7C and Fig 7F ) . These data suggest that septation is negatively regulated by the early-mitosis spindle preventing a premature Cdk1-independent septation activation before chromosome mass separation . Coinciding with the assembly of the mitotic spindle at prophase onset , the SPBs are specifically embedded in the nuclear envelope until the anaphase B onset , where both SPBs are extruded to the cytoplasm [47] . Given that the forces exerted by the elongating spindle might be required for both the SPBs insertion in the membrane and the nuclear envelope reinforcement [48] , to test if the advanced septation onset in the absence of spindle could be caused by an altered SPB localization , cells carrying nucleoporin Cut11-GFP were analyzed ( S8A Fig ) . Nucleoporin Cut11 specifically localizes to the SPBs but only when embedded at the nuclear envelope from prophase to anaphase B onset [49] . In control cells the start of both PS synthesis and septum ingression was coupled and it was coincident with the SPBs exit and Cut11 loss ( S8A Fig ) . However , during premature onset of PS synthesis without spindle , Cut11 always localized to the SPBs . Interestingly , septum ingression was uncoupled and delayed from PS synthesis onset until the loss of SPB-located Cut11 ( S8A Fig ) . These results suggest that the SPBs play a role in the septation onset and that nuclear envelope-embedded SPBs might help to prevent the start of septum ingression in early mitosis . The spindle regulation of cytokinesis might also depend on the nucleus/SPBs position , regarding the division site and therefore the Etd1 medial localization . Thus , septation in cells devoid of MT and with the nucleus displaced to the pole , both before and after division site selection , was examined ( Fig 7A2 and Fig 7A3 ) [50] . When the nucleus , division site and Etd1 were displaced in the absence of MT , septation advanced to a similar timing as that of nucleus-centered MBC-treated cells ( Fig 7D , Fig 7F and S8B Fig ) . In contrast , when the nucleus was displaced , but the division site and Etd1 remained in the middle , septation was noticeably delayed ( Fig 7E , Fig 7F and S8B Fig ) . These results indicate that the nucleus position near the division site and Etd1 is required for the premature septation onset caused by the absence of spindle . Finally , to test if the advanced septation of MBC-treated cells depended on the spindle assembly checkpoint and/or the SIN signaling , the timing of septation was analyzed in wild-type and sid2-250 cells treated with MBC . Wild-type cells exhibited the same premature and uncoupled activation of septum synthesis as that of MBC-treated mad2Δ cells without spindle ( Fig 8A and Fig 8C ) . Contrary , the uncoupled timings of septum synthesis and septum ingression in the presence of MBC were restored to wild-type levels in sid2-250 cells with reduced SIN ( Fig 8B and 8C ) . Globally , these results show that the spindle elongation is required for the proper timely activation of septum deposition by negatively regulating the SIN signaling during early mitosis .
The current cytokinesis model establishes that full SIN activation at late anaphase B triggers septation and AR closure onset after spindle breakage in telophase [9 , 25] . However , analysis of cells stained with CW , which specifically binds to the septum L-BG [26] , uncovered that PS synthesis begins at early anaphase B , long before the chromosome masses arrive at the cell tips and the spindle breaks down . Electron microscopy of early anaphase B cells revealed the presence of nascent PS growing inwards , which implies membrane invagination and the closure of the AR located to the new septum edge . In agreement , here we show that the septum is able to slowly ingress during anaphase B . Interestingly , the rate of septum ingression accelerates by 3-fold at telophase onset , which is coincident with the spindle disassembly , the complete activation of the SIN , and the stabilization of Ags1 and Bgs4 GSs in the septum edge ( Fig 8D ) . Given that these GSs are responsible for the synthesis of the major septum polysaccharides α ( 1 , 3 ) glucan and branched-β ( 1 , 3 ) glucan [27 , 28] , their presence at the division site might be the cause of the increased rate of septum synthesis . The SIN is already active in early mitosis [51–53] , bringing into question why it would be kept from activating septation until telophase . The SIN displays two separable states during mitosis [43] . The ‘early’ state depends on Plo1 activity , and is characterized by the weak and unstable Cdc7 association with both SPBs . At early anaphase B the SIN changes to the ‘late’ state that is Plo1 independent and characterized by the increase of activity and asymmetric Cdc7 localization . This SIN ‘late’ state requires Cdk1 inactivation and scales with cell size [15 , 43 , 54] . Additionally , in this “late” stage there is a peak in Sid2 activity that coincides with the establishment of asymmetric SIN signaling [42] , and that is also required for Etd1 translocation from the cell cortex to the cytoplasm during anaphase B [17 , 18] . The transition between SIN states coincides temporally with the new timing of septation onset described in this work . Hence , the current model of cytokinesis activation was reevaluated ( Fig 8D ) . After Cdk1 inactivation at early anaphase B , activation of the ‘late’ state reflects the SIN signaling for septum deposition by inducing Sid2 relocation to the division site . Strikingly , cells lacking the function of SIP complex , which is required for SIN asymmetry establishment [44] , showed that this asymmetry is dispensable for the timely start of septation , but it could serve , together with the asymmetric Etd1 localization as an early preparatory event for the correct SIN inactivation at the end of cytokinesis [18 , 44] . Our study indicates that timely activation of septum synthesis requires an increase of Etd1 in the cell middle . In agreement , we found that the nucleus position with respect to the division site and cell middle Etd1 regulates the activation of septum synthesis . An intriguing hypothesis is that the early anaphase B spindle provides an equatorial zone free of chromosomes and a minimal spacing between dividing nuclei . Thus , in the absence of spindle , the SPBs would be overexposed to Etd1 in the cell middle , which might induce an excessive activation of the SIN and consequently , a premature septation . Similarly , in bacteria the nucleoid occlusion inhibitory system is required to prevent FtsZ-ring assembly and inappropriate cytokinesis without chromosome segregation [55] . In metazoans the specific length of the metaphase or early anaphase spindle grants the minimal space required by astral MT to induce the cleavage furrow onset [56] . However , the role of spindle and astral MT in the regulation of furrow ingression is controversial , with opposing results among the different organisms [57–59] . The absence of spindle could also alter SIN signaling through the function of the γ-tubulin complex , which might be important for the function and organization of the SPBs . This complex localizes to the SPB outer and inner faces , and is responsible for MT nucleation and anchorage [47 , 60] . Thus , the spindle absence in the presence of MBC could also affect the function and/or location of the γ-tubulin complex , leading to changes in the spatial and temporal location of SIN kinases at the SPBs during early mitosis and thus , causing premature SIN signaling and septation start in a Cdk1-independent manner . In agreement , cells expressing a mutant version of the γ-tubulin complex also display premature septation with Cdc13 still present in the nucleus , and it was proposed that the SPB-associated γ-tubulin complex is required to inhibit premature SIN activation until Cdc13 is degraded [61] . Cells have different mechanisms to ensure adequate chromosome segregation in the daughter cells . Structural mechanisms of mitosis and cell division such as spindle elongation rate , spindle length , nuclear size [31 , 56 , 62] and AR closure rate [63 , 64] , as well as cell cycle signaling and transition to the SIN ‘late’ state [43 , 65] , scale in a size-dependent manner . Here we show a size-dependent scalability of septation start , coordinating cytokinesis with anaphase B and cell size . Thus , timely septation depends on Cdk1 inactivation and Etd1 levels in the cell middle . Presently it is unknown how the release of Etd1 from the cell middle cortex affects its function , although there are evidences that strongly suggest that Etd1 regulates SIN signaling by interacting with cytoplasmic Spg1 . Consistently , here we show that moderate overproduction of an Etd1 version that exclusively localizes to the cytoplasm leads to advanced septation start . Thus , the fact that Etd1 presumably activates the SIN from the cytoplasm near the SPBs is consistent with the Etd1 function being modulated in a size-dependent manner . It is generally believed that septum and cleavage furrow , as determined by fluorescence microscopy , start to ingress after telophase onset [1–3 , 25] . However , our study has established the precise size-dependent activation of septum synthesis and furrow formation during early anaphase B in fission yeast , providing new insights into the mechanisms that coordinate cytokinesis with mitosis to safeguard the chromosome segregation and proper nucleus placement in the newly developing daughter cells .
The Schizosaccharomyces pombe strains used in this study are listed in S3 Table . Strains GFP-bgs1+ ( 1723 ) , GFP-bgs4+ ( 2365 ) , and ags1+-GFP ( 3166 ) contain the bgs1Δ::ura4+ , bgs4Δ::ura4+ and ags1Δ 3’UTRags1+::ags13704-7233:ura4+ deletions and an integrated copy of SmaI-cut pJK-GFP-12A-bgs1+ , StuI-cut pJK-GFP-12A-bgs4+ and AgeI-cut pJK-ags11-6267-12A-GFP-12A ( leu1+ selection ) , which direct their integrations at the SmaI site of the bgs1+ promoter sequence ( nt -748 ) adjacent to bgs1Δ::ura4+ , the StuI site of the bgs4+ promoter sequence ( nt -1320 ) adjacent to bgs4Δ::ura4+ and the AgeI site of the ags1+ coding sequence ( nt 6025 ) in ags1Δ 3’UTRags1+::ags13704-7233:ura4+ , respectively . 2xGFP-bgs4+ strain 2285 was made as GFP-bgs4+ strain , and contains an integrated copy of StuI-cut pJK-2xGFP-12A-bgs4+ ( leu1+ selection ) , which directs its integration at the StuI site adjacent to bgs4Δ::ura4+ , at position -1320 of the bgs4+ promoter sequence . Likewise , 2xRFP-bgs1+ strain 1781 contains an integrated copy of SmaI-cut pJK-tdTom-12A-bgs1+ ( tdTomato RFP variant , [66] ) at position -748 of the bgs1+ promoter sequence . Strains 5727 and 5728 were constructed by transforming the strain cdc13Δ::ura4+ Pnmt1+-45-cdc13+:sup3-5 ( sup3-5 selection marker recues ade6-704 auxotrophy ) [67] with an additional copy of either pJK-cdc13+ or pJK-cdc13des2 ( leu1+ selection , native Pcdc13+ promoter in both plasmids ) respectively , and single-copy integrants were isolated . Next , strains 5735 and 5736 were made by a genetic cross between the strains described above and hht1+-RFP:KanMX6 ( 5657 ) , followed by random spore analysis selecting against the corresponding parental auxotrophies . Strains 5727 and 5735 exhibited wild-type phenotype either in the absence ( -T , 45-cdc13+ and additional cdc13+ induced ) or in the presence ( +T , 45-cdc13+ repressed and additional cdc13+ induced ) of thiamine . Strains 5728 and 5736 exhibited wild-type phenotype in the absence ( -T , 45-cdc13+ and cdc13des2 induced ) , whereas in the presence of thiamine ( +T , 45-cdc13+ repressed and cdc13des2 induced ) the cells arrested in anaphase B , as described before for cells expressing cdc13des2 from an inducible promoter [7] . Western blot analysis of the strains grown in the presence of thiamine ( +T , 45-cdc13+ repressed and additional cdc13+ or cdc13des2 induced ) showed similar Cdc13 or Cdc13des2 protein levels . Standard S . pombe rich ( YES ) and minimal ( EMM ) media , and genetic manipulations were used [68] . EMM with the appropriate supplements was used for strains expressing genes under the control of the thiamine-repressible Pnmt1+ promoter . For repression experiments , early log-phase cells grown in EMM were diluted in the same medium and 20 μg ml-1 thiamine was added . SPA medium was used for genetic crosses and mutant strains were selected by tetrad dissection , random spore dissection or random spore analysis methods . Cell growth was monitored by measuring the A600 of early log-phase cell cultures . Carbendazim or methyl 2-benzimidazolecarbamate ( MBC , Sigma-Aldrich ) was used at 50 μg ml-1 final concentration ( from a stock of 5 mg ml-1 in DMSO , stored at -20°C ) . 4-Amino-1-tert-butyl-3- ( 1’-naphthylmethyl ) pyrazolo[3 , 4-d]pyrimidine ( 1-NP-PP1 , from a stock of 1 mM in DMSO , stored at -20°C , Toronto Research Chemicals Inc ) was added to the media at the concentration of 1 μM to arrest the cells in G2 phase or 10 μM to quickly inactivate Cdc2 kinase during early mitosis . Plasmids pJK-GFP-12A-bgs1+ , pJK-GFP-12A-bgs4+ and pJK-ags11-6267-12A-GFP-12A are the integrative plasmid pJK148 ( leu1+ selection ) with a 9 . 6 kb ApaI-SpeI GFP-12A-bgs1+ , 9 . 6 kb PstI-NheI GFP-12A-bgs4+ , and 9 . 9 kb EcoRI-NheI ags11-6267-12A-GFP-12A fragment , respectively . pJK-2xGFP-bgs4+ contains a 10 . 3 kb 2xGFP-12A-bgs4+ fragment with a 1 . 5 kb tandem of two GFP-12A sequences cloned in-frame and separated by a 12-alanine linker to make a more flexible 2xGFP epitope . pJK-tdTom-12A-bgs1+ contains a 10 . 2 kb 2xRFP-12A-bgs1+ fragment with the 1 . 4 kb tandem dimer tdTomato variant of the monomeric mRFP1 protein [66] ( provided by R . Y . Tsien , University of California , La Jolla , CA ) separated by a 12-alanine linker . pJK-cdc13+ and pJK-cdc13des2 [34] are the integrative plasmid pJK148 ( leu1+ selection ) with a 4 . 2 kb KpnI-BamHI cdc13+ and cdc13des2 fragment respectively , containing the 1 . 4 kb coding sequence , a 1 . 9 kb promoter sequence and a 0 . 9 kb terminator sequence . The second destruction box mutant cdc13des2 was built by changing the cdc13+ coding sequence of amino acids RHALDDVSN to AHAADDVSN as described [7] . All DNA manipulations were carried out by established methods [69] . Enzymes were used according to the recommendations of the suppliers . Plasmid DNA was introduced into S . pombe cells by an improved LiAc method [70] . Escherichia coli DH10B was used as host to propagate plasmids by growth in Luria-Bertani medium containing 50 μg ml-1 ampicillin . Calcofluor white ( CW ) labeling for fluorescence images of cell cultures was performed adding directly a solution of CW ( 50 μg ml-1 final concentration , from a stock of 10 mg ml-1 in water ) to early logarithmic phase cells . The CW is a fluorochrome that displays a high affinity for chitin , cellulose and L-BG . The fission yeast cells , which do not have either chitin or cellulose in their cell wall but contain L-BG in their PS , are extremely resistant to CW , growing in the presence of concentrations of up to 1 . 5 mg ml-1 of the dye [26 , 71] . Images were obtained with a Leica DM RXA fluorescence microscope , a PL APO 63×/1 . 32 OIL PH3 objective , a Leica DFC350FX digital camera and Leica CW4000 cytoFISH software . Images were processed with the Adobe Photoshop software . To follow the synthesis of the PS by time-lapse fluorescence microscopy , the cells were stained with a highly reduced concentration of CW ( 5 μg ml-1 ) , which does not disturb the physiology of fission yeast cell growth and cytokinesis [27 , 28] . To avoid unexpected cell effects due to the near-UV light exposure during image acquisition of CW-stained cells , the percentage of light transmission and the exposure time were greatly reduced . Similarly , since time-lapse fluorescence microscopy of multiple z-slices might occasionally alter the timing of septation onset of cells containing certain tagged proteins ( data not depicted ) ; time-lapses of one single medial z-slice were performed . When necessary , multiple z-slices were made under safe condition in which the septum synthesis start and septum ingression were not altered compared to the same processes observed in single z-slice time-lapses . Similarly , to reduce cellular stress , the time-lapse image acquisition was performed in liquid medium-containing chambers with cells growing freely only attached to the surface with a lectin , instead of using agarose pads with embed and immobilize cells . Early logarithmic phase cells ( 0 . 3–0 . 6 ml ) were collected by centrifugation ( 1 , 000 g , 1 min ) and resuspended in 0 . 3 ml of the same growth liquid medium containing CW ( 5 μg ml-1 ) , and placed in a well from a μ-Slide 8 well or a μ-Slide 8 well glass bottom ( 80821-Uncoated and 80827; Ibidi ) previously coated with 5 μl of 1 mg ml-1 soybean lectin ( L1395; Sigma-Aldrich ) as described [27] . Time-lapse experiments were made at 25 , 28 , 32 or 36°C , by acquiring epifluorescence cell images in single planes and 1×1 binning on an inverted microscope ( Olympus IX71 ) equipped with a PlanApo 100x/1 . 40 IX70 objective and a Personal DeltaVision system ( Applied Precision ) . Images were captured using CoolSnap HQ2 monochrome camera ( Photometrics ) and softWoRx 5 . 5 . 0 release 6 imaging software ( Applied Precision ) . Subsequently , time-lapse CW , GFP and RFP images were restored and corrected by 3D Deconvolution ( conservative ratio , 10 iterations and medium noise filtering ) through softWoRx imaging software . Next , images were processed with Image J ( National Institutes of Health ) and Adobe Photoshop software . For time-lapses using maximum projection of SPB Cdc7-GFP and Cut11-GFP , images were obtained in z-stacks of 7 slices at 0 . 4 μm intervals . Then slices were processed with the function stacks and 3D projection of the Image J software . Total fluorescence of Cdc7-GFP and Cdc13-GFP was quantified as described [29] . First , Image J software was used to correct the background fluorescence of each cell series through the Background subtraction from ROI function . Then , the sum of the remaining values of the pixels in the nucleus or SPB area was calculated . Similarly , total fluorescence of GFP-Etd1 in cell pole and middle was quantified as described in [18] by drawing the outlines of the tips and remaining medial region of the cell , and the sum of values of the pixels in each region was obtained with Image J software . Briefly , given that Etd1 spreads along the cell cortex in both poles and middle at the beginning of anaphase B , we followed the GFP-Etd1 intensity in these locations by drawing the corresponding ROIs of the cell poles and middle . The end of the ROI used to measure the cell tip continued with the end of the ROI used to measure the cell middle . Thus , the complete cell intensity was considered the sum of the three ROIs selected in the cell . Early logarithmic phase wild-type cells were fixed with 2% glutaraldehyde EM ( GA; Electron Microscopy Science ) in 50 mM phosphate buffer pH 7 . 2 , 150 mM NaCl ( PBS ) for 2 h at 4°C , post-fixed with 1 . 2% potassium permanganate overnight at 4°C and embedded in Quetol 812 as described [72–74] . Ultrathin sections were stained in 4% uranyl acetate and 0 . 4% lead citrate , and viewed with TEM H-800 ( Hitachi ) operating at 125 kV . | Fission yeast cytokinesis requires the invagination of the equatorial plasma membrane ( cleavage furrow ingression ) coupled to the synthesis of a special wall structure named septum ( septation ) . Despite Cdk1 kinase is inactivated in early anaphase , it is believed that cleavage furrow ingression and septation onset require anaphase progression and mitosis completion , only initiating after the complete activation of the Hippo-like septation initiation network ( SIN ) after telophase onset . Here , we studied the precise timing of septation start with respect to mitosis through specific septum-staining and electron microscopy . We found that septum and cleavage furrow ingression initiate in early anaphase , showing first a slow ingression rate during anaphase B , and increasing to a fast ingression rate after telophase onset . Thus , mitosis and cleavage furrow ingression are not concatenated but simultaneous events in fission yeast . The timing of septation correlated with cell size and depended on the level of cytoplasmic activators like SIN Etd1 and Rho1 . We further analyzed the mitotic mechanisms that control the septation onset during early anaphase . Cdk1 directly regulated the timing of septation onset during early anaphase , and the low SIN activity present after Cdk1 inactivation was enough to trigger septation . Globally , these results recalibrate the timing of the main cytokinetic events of fission yeast and reveal a size-dependent control mechanism that synchronizes simultaneous nuclei separation with septum and cleavage furrow ingression . | [
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"cytoki... | 2018 | Specific detection of fission yeast primary septum reveals septum and cleavage furrow ingression during early anaphase independent of mitosis completion |
With 2 . 5 billion people at risk , dengue is a major emerging disease threat and an escalating public health problem worldwide . Dengue virus causes disease ranging from a self-limiting febrile illness ( dengue fever ) to the potentially fatal dengue hemorrhagic fever/dengue shock syndrome . Severe dengue disease is associated with sub-protective levels of antibody , which exacerbate disease upon re-infection . A dengue vaccine should generate protective immunity without increasing severity of disease . To date , the determinants of vaccine-mediated protection against dengue remain unclear , and additional correlates of protection are urgently needed . Here , mice were immunized with viral replicon particles expressing the dengue envelope protein ectodomain to assess the relative contribution of humoral versus cellular immunity to protection . Vaccination with viral replicon particles provided robust protection against dengue challenge . Vaccine-induced humoral responses had the potential to either protect from or exacerbate dengue disease upon challenge , whereas cellular immune responses were beneficial . This study explores the immunological basis of protection induced by a dengue vaccine and suggests that a safe and efficient vaccine against dengue should trigger both arms of the immune system .
The four serotypes of dengue virus ( DENV1-4 ) are mosquito-borne and cause a spectrum of diseases ranging from a self-limiting flu-like illness ( dengue fever , DF ) to the potentially lethal dengue hemorrhagic fever/dengue shock syndrome ( DHF/DSS ) [1] . DENV is endemic in more than 100 countries [2] and 2 . 5 billion people worldwide are at risk of infection , mostly in tropical and subtropical regions [3] . It is estimated that 390 million cases of DENV infection occur annually , of which 96 million are apparent , 500 , 000 are severe and 20 , 000 are fatal [4] . The more severe disease resulting from DENV infection , DHF/DSS , usually occurs in individuals who have pre-existing dengue-reactive antibodies ( Abs ) , acquired either from a previous infection with a heterologous DENV serotype or by passive transfer from an immune mother in the case of infants [5] . Based on these epidemiological observations , Halstead and colleagues hypothesized that sub-protective levels of DENV-specific Abs may amplify viral infection and thus exacerbate disease , a phenomenon termed antibody-dependent enhancement of infection ( ADE ) [6] , [7] . We and another group have recently confirmed this hypothesis by demonstrating in mice that a sub-protective amount of anti-DENV Abs can turn a mild illness into a lethal disease upon infection with DENV [8] , [9] . The potential risk of ADE represents a major challenge associated with the development of a safe vaccine against DENV [2] . A vaccine that induces sub-protective levels of anti-DENV Abs may not only be inefficient , but also potentially cause ADE-mediated severe dengue disease upon infection . In addition , despite the initial induction of a protective Ab response , the Ab levels could wane and reach ADE-causing concentrations some time after vaccination , as even protective anti-DENV Ab has the ability to cause ADE at lower concentrations [9]–[11] . Detecting neutralizing Ab in vitro may not accurately correlate with protection in vivo , as recently exemplified by the results of the phase IIb clinical trial of the most advanced dengue-vaccine candidate [12] . The vaccine candidate had only limited efficacy despite induction of a balanced neutralizing Ab response to all four serotypes . This infers the involvement of other branches of the immune system in protection against DENV . The role of T cells during re-infection is controversial , and often seen as minor [2] or pathogenic [13] . Accordingly , it is commonly accepted that the primary goal of dengue vaccination should be induction of neutralizing Ab responses , and that vaccine-induced T cell responses likely play only a secondary role in protection . However , there is a substantial lack of knowledge of the immune mechanisms involved in protection during successive DENV infections [14] , [15] . Therefore , a better understanding of the relative role of the humoral versus cellular components of a vaccine-induced immune response to protection against dengue virus infection is urgently needed . The goal of our study was to assess the relative contribution of the humoral and cellular arms of the immune system in protection mediated by a dengue-vaccine candidate . Venezuelan equine encephalitis virus ( VEE ) is an alphavirus that can be used as a vaccine expression vector in which the genes coding for the structural proteins are replaced by one or more transgenes [16] . The resulting viral replicon particles ( VRP ) induce high level expression of the transgenes in a single round of infection , but due to the absence of endogenous structural proteins , do not propagate further in the host [16] . VRP expressing HIV [17] , influenza [18] , or human cytomegalovirus [19] immunogens have been used safely in phase I vaccine trials in humans . VRP coding for DENV membrane ( prM/M ) and envelope ( E ) proteins have been previously used to immunize mice [20] . VRP immunization induced anti-DENV neutralizing Abs and protected suckling mice from lethal intracranial DENV challenge [20] . Recently , White and colleagues demonstrated that immunization with VRPs expressing the DENV-E protein ectodomain ( E85-VRP ) derived from each of the four DENV serotypes induced balanced neutralizing Ab responses to all four serotypes in mice and in macaques , and protected macaques from infection with DENV ( White , unpublished observations ) [21] . In the present study , VRP expressing the DENV2-E protein ectodomain ( DENV2 E85-VRP ) were used to immunize AG129 mice ( type I and II IFN receptor-deficient mice in the 129/Sv genetic background ) , followed by challenge with DENV serotype 2 ( DENV2 ) to assess the relative contribution of the cellular versus humoral components of a protective vaccine-induced immune response against DENV . AG129 were used because DENV replicates to high levels in these mice , and they represent the best-characterized animal model of DENV infection in which DHF/DSS-like disease can be induced . The results obtained using AG129 mice were confirmed in an adoptive transfer system . Wildtype ( WT ) mice were immunized with the DENV2 E85-VRP , and , subsequently , DENV2 E85-VRP primed WT T cells , B cells or serum were transferred into AG129 recipient mice prior to challenge . This transfer system allowed us to assess the contribution of the cellular and humoral components the DENV2 E85-VRP-induced immune response generated in WT mice . Due to their high sensitivity to DENV infection , the AG129 recipient mice served as a stringent challenge model to assess the contribution of the transferred cells or serum from WT mice . Two rounds of immunization with DENV2 E85-VRP efficiently protected AG129 mice from challenge with DENV even when disease-enhancing amounts of Abs were administered at the time of challenge ( ADE conditions ) . Mice were protected as early as five days after the second immunization , and remained protected for at least four weeks after immunization . Short-term protection was mainly mediated by CD8+ T cells , whereas long-term protection relied on CD8+ T cells to different degrees depending on the immunization schedule . These results were confirmed in a series of transfer experiments where T cells , B cells or serum from DENV2 E85-VRP vaccinated WT mice were transferred into AG129 mice before challenge . Transfer of DENV2 E85-VRP-primed WT T cells into naïve AG129 mice reduced viral load upon challenge with DENV . In contrast , transfer of DENV2 E85-VRP-immune serum had the potential to increase viral load . Transfer of DENV2 E85-VRP-primed WT B cells into naïve mice either reduced or increased viral load depending on the number of B cells transferred . These results demonstrate that , taken in isolation and at certain concentrations , the humoral component of a protective vaccine-induced immune response to DENV has the potential to exacerbate dengue disease , whereas in all conditions tested , the cellular immune response reduced viral load . This implies that a safe and protective vaccine against DENV should trigger both the cellular and humoral arms of the immune system rather than relying exclusively on induction of DENV-specific Abs .
To assess whether immunization with DENV2 E85-VRP would protect AG129 mice ( lacking type I and II IFN receptors ) against DENV , AG129 mice were immunized twice with DENV2 E85-VRP prior to challenge with DENV . AG129 were used because they are highly sensitive to DENV infection and can develop DHF/DSS-like lethal disease upon infection , and are the best characterized animal model of DENV infection to date . Using these mice , we have previously demonstrated ADE in vivo and confirmed that even a protective anti-DENV Ab can induce ADE at sub-protective concentrations [9] . The viral strain used for challenge in our study , S221 , is a triple-plaque-purified clone isolated from a mouse-passaged DENV2 strain and has been previously described [22] , [23] . This strain was used to challenge the mice in all our experiments , and for clarity will be referred to as “DENV” throughout the text . We have previously demonstrated that at the S221 challenge dose of 5×108 genomic equivalents ( GE ) , AG129 mice do not develop severe disease , but instead manifest neurological symptoms between day 11–14 after infection [9] , [22] . In contrast , in the presence of sub-neutralizing amounts of anti-DENV Ab 2H2 , antibody-mediated disease enhancement occurs and the same dose of virus ( 5×108 GE ) causes elevated viral RNA titers in the liver on day 3 and lethal DHF/DSS-like disease by day 4–5 [9] . Therefore , 5×108 GE was chosen as the challenge dose for the present study , and we hypothesized that protective vaccination should reduce liver RNA viral titers and prevent neurological symptoms as well as death , whereas potential enhancement would be reflected by elevated liver titers and acute lethal disease around day 4–5 . AG129 mice were immunized with 1×106 infectious units ( IU ) of DENV2 E85-VRP either intraperitoneally ( i . p . ) or intra footpad ( i . f . ) 14 and 5 days prior to challenge with 5×108 GE DENV on day 0 . As non-vaccinated controls , two groups that were not immunized were challenged with 5×108 GE DENV: one group in the presence of 15 µg of exogenous monoclonal anti-DENV Ab 2H2 given i . p . to cause Ab-mediated enhancement of infection ( “ADE group” ) and another group in the presence of 15 µg of C1 . 18 , an isotype control Ab of irrelevant specificity ( “baseline group” ) . Viral RNA in the liver was quantified by qRT-PCR on day 3 ( figure 1A ) and survival was monitored ( figure 1B ) . The liver was chosen because high viral RNA levels in the liver on day 3 correlate with increased severity of disease and decreased survival [9] . Immunization through either the i . p . or i . f . route dramatically reduced viral RNA levels in the liver ( figure 1A ) and prevented death in 80% of immunized animals ( figure 1B ) . As expected from our previous work [9] , the ADE group had approximately 10-fold more viral RNA in the liver on day 3 and its survival was decreased relative to the baseline group . These results demonstrate that DENV2 E85-VRP immunization provides protection against DENV infection and disease , as measured by liver DENV titer and survival , respectively . To start dissecting the immunological basis of this DENV2 E85-VRP-induced protection , the induction of Abs following immunization with DENV2 E85-VRP on day -14 and -5 was assessed in the serum of AG129 mice 1 day before challenge with DENV . Virus-specific serum IgG levels were measured by ELISA on DENV-coated plates ( figure 1C ) . Both i . p . and i . f . immunization induced DENV-specific IgG , but the i . p . route induced a higher IgG response than the i . f . route . The virus-neutralizing capacity of the serum was assessed by plaque reduction neutralization test ( PRNT50 , figure 1D ) and only i . p . immunization induced detectable levels of neutralizing Abs . Therefore , the i . p . immunization route was chosen for subsequent experiments . Next , to investigate whether immunization with DENV2 E85-VRP could also protect from Ab-induced severe dengue disease , AG129 mice were immunized with DENV2 E85-VRP as above . Subsequently , immunized mice were challenged with 5×108 GE DENV in the presence ( or absence ) of exogenous anti-DENV Ab 2H2 . Viral RNA levels were quantified in the liver on day 3 after challenge ( figure 2A ) . DENV2 E85-VRP immunization reduced viral RNA levels to the same extent regardless of the presence or absence of exogenous anti-DENV Ab . As seen in figure 1 and in our previous studies [9] , the viral RNA levels in the liver were about 10-fold higher in the ADE group as compared to the baseline group . To exclude a contribution from non-specific immune responses elicited by the VRP vector , AG129 mice were immunized with VRP expressing GFP ( VRP-GFP ) instead of the DENV-E protein . DENV2 E85-VRP-immunization , but not immunization with the non-specific VRP-GFP , reduced viral load in the liver on day 3 after challenge ( figure 2B ) , demonstrating the specificity of the protection induced by DENV2 E85-VRP immunization . Taken together , these data demonstrate that immunization with DENV2 E85-VRP specifically reduces viral load upon DENV challenge , even in the presence of exogenous Ab ( ADE ) . To assess whether the protective effect of the DENV2 E85-VRP immunization was mediated by serum , 50 µl , 200 µl or 500 µl of serum from AG129 mice immunized 14 and 5 days earlier with 1×106 IU of DENV2 E85-VRP were injected i . v . into naïve AG129 recipient mice one day prior to challenge with 5×108 GE DENV . An additional group of recipients received a total of 1500 µl of DENV2 E85-VRP-immune serum i . v . ( 500 µl on day -3 , -2 and -1 ) . As controls , one group received 1500 µl naïve serum ( 500 µl on day -3 , -2 , -1 ) and two groups received no serum and were challenged either in the absence or presence of exogenous anti-DENV Ab ( baseline and ADE groups ) . Viral RNA was quantified in the liver 3 days after challenge ( figure 3A ) . Viral RNA levels in the liver were significantly higher in all groups that had received DENV2 E85-VRP-immune serum compared to the baseline group . As transfer of naïve serum had no effect on the viral load , we concluded that Ab present in the serum of immunized mice had caused ADE . To confirm these results , 2×107 B cells from AG129 mice immunized with DENV2 E85-VRP 14 and 5 days earlier were adoptively transferred into naïve AG129 recipients one day prior to challenge with DENV . Similar to the ADE control animals , the mice that received DENV2 E85-VRP-primed B cells prior to challenge had viral RNA levels in the liver 3 days after challenge that were significantly higher than baseline ( figure 3B ) . Taken together , these experiments have revealed that although immunization with DENV2 E85-VRP was protective , the presence of either serum or B cells from DENV2 E85-VRP-immunized mice did not reduce viral load upon challenge with DENV , but instead increased viral loads in the liver . Thus far , our results have shown that immunization with DENV2 E85-VRP can protect from DENV challenge , and even prevent Ab-induced lethal dengue disease . With the chosen immunization schedule ( day -14 and -5 ) , immune serum or DENV2 E85-VRP-activated B cells had the potential , if transferred into naïve recipients , to increase viral load in the liver upon challenge . Therefore , we hypothesized that , using this particular schedule and route of immunization , T cells could be responsible for the DENV2 E85-VRP-mediated protection . To assess the contribution of T cells to protection , AG129 mice were immunized with DENV2 E85-VRP as described above , but prior to challenge with DENV , either CD4+ or CD8+ T cells were depleted . A control group was immunized but not depleted , and the baseline and ADE groups were included . As expected from previous experiments , DENV2 E85-VRP-immunization reduced viral RNA levels in the liver 3 days after challenge ( figure 4A ) . CD4+ T cell-depletion had no detrimental effect on control of the liver viral RNA levels , but depletion of CD8+ T cells abrogated the reduction in liver viral RNA levels of the immunized mice . CD8+ T cell depletion also abolished the decrease in liver viral RNA levels observed in the immunized mice after DENV challenge in the presence of exogenous anti-DENV monoclonal Ab 2H2 ( figure 4B ) . The presence of elevated levels of multiple cytokines is a hallmark of severe dengue disease . To determine if cytokine levels were reduced in the immunized mice after challenge with DENV , serum levels of IL-6 and IL-10 were measured 3 days after infection . In addition , CD4+ or CD8+ T cells were depleted prior to challenge in some of the immunized mice ( figure 4C and 4D ) . Serum IL-6 and IL-10 levels were lower in DENV2 E85-VRP-immunized mice than in non-immunized animals , probably due to the viral load reduction observed in immunized mice . Reduced cytokine levels were also observed in the serum of CD4+ T cell-depleted DENV2 E85-VRP-immunized mice . In contrast , levels of IL-6 and IL-10 in the serum of CD8+ T cell-depleted DENV2 E85-VRP-immunized mice were similar to non-immunized mice . Similar results were found for TNF , IFN-γ , IL-1β and KC/GRO ( supplementary figure S1 ) . Collectively , these results show that CD8+ T cells are responsible for the DENV2 E85-VRP-induced protection under this immunization protocol , as measured by reduction in liver viral RNA and serum cytokine levels on day 3 after challenge . To confirm the results obtained with AG129 mice , in which DENV2 E85-VRP-induced immune response was elicited in the absence of type I and II IFN receptors , congenic WT mice were immunized on day -14 and -5 with DENV2 E85-VRP i . p . Splenic T or B cells were isolated from DENV2 E85-VRP-immunized WT mice on day 0 ( MACS negative selection of total T or B cells ) and adoptively transferred into naïve AG129 one day prior to challenge with DENV . The viral RNA was quantified in the liver 3 days after challenge . This experimental setup allowed us to assess how DENV2 E85-VRP-primed WT T or B cells contributed to protection upon DENV infection . AG129 mice were used as DENV-sensitive recipients , thereby allowing us to assess the protective versus pathogenic capacity of the transferred WT T or B cells under stringent challenge conditions . Three different amounts of DENV2 E85-VRP-primed T cells ( or naïve T cells ) from WT mice were transferred into AG129 recipients . All the recipient groups that received DENV2 E85-VRP-primed T cells prior to challenge with DENV had lower levels of viral RNA in the liver 3 days after challenge ( figure 5A , black triangles ) , whereas animals that received naïve WT T cells had viral RNA levels similar to the baseline group ( figure 5A , white triangles ) . These results demonstrate a protective role for the VRP vaccine-induced WT T cells during DENV infection . Three different numbers of DENV2 E85-VRP-primed B cells from WT mice ( 2×108 , 4×107 or 5×106 B cells ) were transferred into naïve AG129 recipients one day prior to challenge . Transfer of 2×108 DENV2 E85-VRP-primed WT B cells reduced the liver day 3 viral RNA levels in 3 out of 4 animals compared to the animals that received no B cells ( baseline ) , but this difference was not statistically significant when the whole group was considered ( figure 5B ) . Transfer of 4×107 DENV2 E85-VRP-primed WT B cells caused a small but significant increase in the viral load in the liver on day 3 , likely via ADE . Transfer of 5×106 B cells had no effect on the viral load , probably due to the low amount of B cells transferred . To verify that the transferred B cells produced virus-specific Abs , DENV2-specific IgG levels were measured by ELISA on DENV-virion-coated plates in serum obtained from recipients 3 days after challenge ( Figure 5C ) . No DENV2-specific IgG was detected in the mice that had not received B cells and were challenged with DENV on day 0 ( either with or without exogenous anti-DENV Ab ) . In the groups that received WT DENV2 E85-VRP-primed B cells one day prior to challenge , the amount of Ab detected 3 days after challenge was proportional to the number of B cells transferred . DENV-specific IgG could not be detected by ELISA in the group that received only 5×106 WT DENV2 E85-VRP-primed B cells , probably due to the low amount of B cells transferred . To confirm the results obtained from transfer of DENV2 E85-VRP-primed WT B cells , we performed a passive transfer experiment using DENV2 E85-VRP-immune serum from WT mice . WT mice were immunized on d-14 and -5 with 1×106 IU of DENV2 E85-VRP i . p . followed by collection of serum on day 0 and transfer of the immune serum into naïve AG129 mice one day prior to challenge with DENV . Viral RNA levels were quantified in the liver 3 days after challenge ( figure 5D ) . Mice that received 200 µl of DENV2 E85-VRP-immune WT serum had elevated DENV RNA levels in the liver compared to mice that received either naïve serum , or no serum ( baseline ) . Next , we assessed the effect of adoptively transferring DENV2 E85-VRP-primed WT T cells together with an enhancing amount of DENV2 E85-VRP-immune WT serum into AG129 recipients . WT mice were immunized with 1×106 IU DENV2 E85-VRP i . p . on days -14 and -5 , and on day 0 serum was collected and total splenic T cells were isolated by negative selection . 200 µl of DENV2 E85-VRP-immune WT serum was transferred with or without 4×107 DENV2 E85-VRP-primed WT T cells into naïve AG129 recipient mice one day prior to challenge with DENV2 . As shown in figure 5E , transfer of serum alone increased viral RNA levels in the liver on day 3 after challenge , but co-transfer of T cells and serum did not increase liver viral RNA levels . Taken together , these data confirm the results obtained from our studies using AG129 mice . Studies with both AG129 and WT mice have demonstrated that DENV2 E85-VRP-immune T cells can reduce viral load , whereas DENV2 E85-VRP-immune serum induced by the day -14 and -5 immunization schedule , if taken in isolation from other components of the immune system , can increase viral load upon challenge . We have shown that DENV2 E85-VRP-immunization confers CD8+ T cell-mediated short-term protection against DENV challenge . To examine whether DENV2 E85-VRP-immunization can confer longer-term protection against DENV challenge , AG129 mice were immunized twice with 1×106 IU of DENV2 E85-VRP i . p . 9 days apart ( as described in all experiments so far ) , and mice were challenged with DENV 33 days after the second immunization . Half of the immunized mice were depleted of CD8+ T cells before challenge . Three days after challenge , viral RNA levels in the liver were significantly lower in the immunized mice compared to the non-immunized baseline group , and CD8-depletion abrogated this decrease in viral load ( figure 6A ) . Thus , DENV2 E85-VRP immunization can provide CD8+ T cell-dependent , long-term protection against DENV , as determined by reduction of liver viral RNA titer 3 days post challenge in AG129 mice . Two immunizations with 1×106 IU DENV2 E85-VRP i . p . 9 days apart provided CD8+ T cell-dependent long-term protection against DENV challenge . However , the 9-day interval between the two immunizations ( as in all experiments so far ) may not be long enough to induce an optimal Ab response . Therefore , we increased the time interval between the immunizations to investigate whether this would modify the relative contribution of cellular and humoral immunity to long-term protection . AG129 mice were given two immunizations with 1×106 IU of DENV2 E85-VRP i . p . 28 or 9 days apart . Mice were challenged with DENV 28 days after the second immunization and in each group , some mice were depleted of their CD8+ or CD4+ T cells prior to challenge . Viral RNA levels were measured in the liver 4 days after challenge ( figure 6B , C ) . Livers were harvested 4 days after challenge instead of 3 ( as in the short-term experiments performed thus far ) because in the long-term experiments , mice are older and heavier , and the viral load increase caused by exogenous anti-DENV Ab ( ADE ) starts to be detectable 4 days post infection ( our unpublished observations ) . Baseline and ADE groups were included as controls . All immunized and CD8+ T cell-competent mice had lower viral RNA levels in the liver on day 4 compared to non-immunized mice ( baseline ) ( figure 6B ) . CD8+ T cell-depleted mice that were immunized 28 days apart also contained lower viral RNA levels than the baseline group , indicating minimal requirement for CD8+ T cells in protection mediated by the 28-day apart immunization protocol , possibly due to further maturation of the Ab response during the extended interval . In contrast , CD8+ T cell-depleted mice that were immunized 9 days apart had similar levels of viral RNA as the baseline mice , indicating a critical role for CD8+ T cells in protection induced by the 9-day apart immunization protocol . This result suggests that reducing the time between immunizations may increase the dependency on CD8+ T cells for protection , possibly due to sub-optimal Ab maturation when the time between immunizations is insufficient . Regardless of the immunization protocol ( 28 or 9 days apart ) , depletion of CD4+ T cells prior to challenge with DENV had only a minimal ( and not significant ) effect on protection ( figure 6C ) . To determine if the different immunization schedules had an influence on the induction of DENV-specific Ab , the serum of mice immunized 28 days ( d-61/-33 ) or 9 days ( d-42/-33 ) apart was analyzed 33 days after the second immunization ( but before challenge with DENV ) . No difference in DENV-specific IgG levels was observed by ELISA ( figure 6D ) , and neutralizing titers were similar as determined by PRNT50 ( figure 6E ) . Titers of DENV-specific IgG1 , IgG2a , IgG2b and IgG3 were also similar between the two immunization protocols ( figure 6F ) , suggesting that other characteristics of the Ab response ( e . g . epitope-specificity , affinity/avidity , and biological activities such as ADCC and complement fixation ) induced by the extended immunization schedule likely account for protection .
In this study , alphavirus VRP expressing the DENV2-E protein ectodomain ( DENV2 E85-VRP ) were used to immunize mice prior to challenge with DENV in order to assess the relative contribution of humoral and cellular immunity in a protective vaccine-induced immune response to DENV . A better understanding of the determinants of protection after dengue vaccination is urgently needed [14] , especially after the recent report of the first phase IIb clinical trial of a tetravalent live-attenuated dengue-vaccine candidate [12] . For reasons that remain unclear , the phase IIb trial showed only limited efficacy of the vaccine despite induction of a balanced Ab response against all four serotypes . Our study begins to address this lack of knowledge by assessing the relative role of the humoral and cellular arms of a protective vaccine-induced immune response . Two immunizations with DENV2 E85-VRP ( 9 days apart , day -14 and -5 ) dramatically reduced the viral RNA levels in the liver 3 days after challenge with DENV; viral RNA was undetectable in some immunized animals . All immunized mice survived at least 20 days post challenge , and 80% survived without showing any sign of disease until the experiment was terminated on day 60 . Immunization with DENV2 E85-VRP reduced the viral load upon challenge even in the presence of sub-protective , enhancing levels of exogenous anti-DENV Ab . A regimen of two immunizations with DENV2 E85-VRP , either 9 or 28 days apart , was protective up to 33 days after the second immunization , the latest time point tested in this study . These findings are remarkable considering that AG129 are highly susceptible to DENV replication , and that a DENV dose as low as 5×104 GE ( approximately 1 PFU ) causes paralysis in 100% of the mice by day 25 post-infection [22] . Thus , immunization with DENV2 E85-VRP confers robust protection and appears to mediate near-sterilizing immunity . T cell depletion and adoptive transfer experiments demonstrated that when mice were challenged 5 days after the second immunization ( immunization on day -14/-5 ) , CD8+ T cells were responsible for reducing the viral load upon challenge . When mice were challenged more than 4 weeks after the second immunization , protection appeared to be dependent on CD8+ T cells in mice immunized 9 days apart , but independent of CD8+ T cells in mice immunized 28 days apart . In all experiments performed , DENV2 E85-VRP-primed T cells were protective as determined by decreased viral RNA levels in the liver and reduced cytokine levels 3 days after challenge . In contrast , transfer of DENV2 E85-VRP-immune serum or DENV2 E85-VRP-immune B cells had the potential to increase liver viral RNA levels . When T cells and serum were co-transferred , no enhancement was observed . The use of AG129 mice ( lacking type I and II IFN receptors ) in this study is not ideal as the mechanisms by which DENV causes disease in these mice may differ relative to WT animals . However , the experiments described in this study would be impossible in WT mice , as , to this day , there are no known DENV strains that replicate and cause disease in WT mice . DENV inhibits IFN signaling in humans but not in mice [24]–[26] , possibly explaining why WT mice are resistant to DENV infection and disease . The AG129 mouse model of DENV infection is the most thoroughly characterized animal model of DHF/DSS-like disease to date . This mouse model recapitulates many key features of the human disease including vascular leakage , cytokine release , high viremia , low platelet counts and elevated hematocrit [8] , [9] . AG129 mice have been used to demonstrate ADE [8] , [9] and to evaluate the therapeutic efficacy of modified antibodies that no longer bind to the Fcγ-receptor [27] . The results obtained by immunization and challenge of AG129 mice were confirmed with transfer experiments in which DENV2 E85-VRP-immunized WT mouse T cells , B cells , or serum were transferred into naïve AG129 recipients prior to challenge with DENV . This experimental approach was chosen to assess under stringent challenge conditions the efficacy of a DENV2 E85-VRP-induced immune response generated in WT ( i . e . IFN receptor competent ) animals . Additionally , the transfer approach allowed us to separate the cellular and the humoral components of a protective vaccine-induced immune response and evaluate their relative contribution to protection versus disease enhancement . The presence of DENV2 E85-VRP-primed WT T cells reduced viral load upon challenge with DENV; whereas WT serum or DENV2 E85-VRP-primed WT B cells had the potential to increase viral RNA levels at certain concentrations . When present in a sufficiently high concentration , DENV2 E85-VRP primed WT B cells reduced viral load . One of the challenges to developing a safe and efficient vaccine against DENV is that a vaccine should simultaneously generate a protective immune response against all 4 DENV serotypes . An unbalanced immune response resulting in sub-protective levels of Ab against one of the serotypes could potentially result in Ab-mediated disease enhancement [2] . In theory , a vaccine-induced Ab response that is initially protective could wane over time and reach levels at which Ab exacerbates disease , as even neutralizing Abs can cause ADE at lower , sub-protective concentrations [9]–[11] . Although passive transfer of serum from DENV2 E85-VRP-immune mice did not reduce viral load upon challenge in our study , in another study , BALB/c females immunized with VRP co-expressing DENV-E- and prM-protein could passively transfer antibodies to their pups , which were subsequently protected from a lethal intracranial challenge with DENV2 strain NGC [20] . It is also important to note that in our long-term experiments , two immunizations with DENV2 E85-VRP 28 days apart reduced DENV viral RNA titer in the liver upon challenge in the absence of CD8+ or CD4+ T cells . This strongly suggests that vaccination with DENV2 E85-VRP can readily induce a protective antibody response in AG129 mice when another immunization schedule is chosen . In our study , T cell responses were beneficial for the host and co-transfer of immune T cells together with immune serum abrogated the immune serum-mediated enhancement . Therefore , we propose that a vaccine triggering both the humoral and the cellular arms of the immune system may be more efficient and safer than a vaccine relying exclusively on the induction of Ab . DENV2 E85-VRP induced a highly protective T cell response although the DENV E-protein is not a major T cell target during natural DENV infection [28]–[30] . A likely explanation for the protective T cell responses elicited by the VRP vaccination is the excellent ability of VRPs to induce CD8+ T cell responses [31] . VRPs efficiently target and activate dendritic cells [32] and are known to induce both humoral and cellular immunity to the encoded transgene [31] . In addition , VRPs not encoding a transgene have potent adjuvant ability when co-injected with antigen [33] and strongly amplify the CD8+ T cell responses to the co-delivered antigen [34] . During a natural infection with DENV , the T cell response to non-structural antigens is dominant [30] , [35] and may outcompete the response to structural proteins . As the DENV2 E85-VRP does not code for DENV non-structural proteins , the T cell response to the E-protein may be higher than what would be expected after natural infection . The relative contribution of CD8+ T cells to the VRP vaccine-mediated protection in our murine model was influenced by the interval between immunizations . In the experiments where mice were challenged four weeks after the second immunization , two doses of DENV2 E85-VRP given 9 days apart protected via the induction of CD8+ T cells , whereas if the doses were given 28 days apart , CD8+ T cells were unnecessary for the vaccine-induced protection . The interval of 9 days between the two immunizations may not induce an Ab response that is sufficient to protect from challenge in the absence of CD8+ T cells . However , the 28-day interval immunization schedule provides protection even in the absence of CD8+ T cells , possibly through a more complete maturation of humoral immunity . This suggests that a robust CD8+ T cell response may be crucial for protection if the Ab response is insufficient and/or inefficient . Both protocols induced similar levels of anti-DENV Ab responses , as measured by ELISA and PRNT50 , but only the 28-day protocol protected from challenge in the absence of CD8+ T cells . Therefore , the presence of neutralizing Abs in the serum before challenge ( as measured by PRNT50 ) did not correlate with protection in vivo . Similarly , immunization with DENV2 E85-VRP via the i . p . or the i . f . routes induced short-term protection when mice were challenged , but only the i . p . route induced neutralizing Ab responses . These results support the emerging notion that measuring neutralizing Abs by PRNT50 may not accurately predict the efficacy of a vaccine against DENV [36]–[38] , and highlights the urgent need for further investigation into the correlates of protection against DENV . Based on our results , the lack of induction of a robust anti-DENV T cell response may be a potential explanation for the recent results of a phase IIb clinical trial of a live attenuated tetravalent dengue vaccine showing low efficacy against DENV2 despite the induction of DENV2-specific neutralizing Abs [12] . The vaccine used in the clinical trial consisted of a yellow fever backbone , and therefore did not contain DENV non-structural proteins , the major targets of CD8+ T cells during natural infection in humans . The cellular responses were possibly skewed towards the yellow fever backbone non-structural proteins . Unlike the VRPs used in our study , which induced a protective cellular response to the E-protein , the live attenuated vaccine used in the phase IIb trial may not have triggered a strong T cell response to the DENV E proteins . The protective T cell responses to DENV E-protein observed in our study may be the direct result of the remarkable ability of the VRPs to trigger CD8+ T cell responses to the encoded transgene . Cellular immunity during DENV infection is often perceived as minimally protective [2] , or potentially pathogenic [13] , although a protective role for T cells has been demonstrated previously in mouse models of DENV infection [35] , [39] . The present study investigated the contribution of different amounts of homologous T cells to protection . Our results showed that the presence of homologous T cells was always beneficial to the host , and did not increase the severity of disease or the viral load . Further studies are now needed to determine whether heterologous T cells behave similarly to homologous T cells , or whether they contribute to pathogenesis . In addition , the mechanisms by which CD8+ T cells mediate protection after DENV2 E85-VRP vaccination need to be clarified . Our model is well suited to investigate the relative contribution of humoral and cellular immunity to protection or pathogenesis after vaccination or during sequential infections with DENV . Our approach makes it possible to isolate serum , B cells and/or T cells and assess their respective roles in vivo , either alone or in combination . However , extrapolation to dengue vaccination in humans should be done with great caution , as is the case with any finding made using an animal model . Our findings thus delineate areas that deserve thorough exploration in future human studies . They also highlight the need to take a comprehensive approach that considers the roles of both humoral and cellular immunity in order to tackle the challenges posed by the development of a dengue vaccine [36] . In summary , despite several dengue vaccine candidates in phase I , II and III clinical trials , little is known about the immunological mechanisms of protection ( or potential enhancement ) after dengue vaccination . This study starts to explore the mechanisms of dengue vaccine-mediated protection or enhancement by examining the relative contribution of the humoral and cellular arms of the immune system during a protective vaccine-induced immune response to DENV . Our results demonstrate that the humoral component of a protective vaccine-induced immune response to DENV had the potential , when taken in isolation from other components of the immune system , to reduce or increase viral load upon challenge , whereas cellular immunity , alone or in combination with humoral immunity , was always beneficial to the host . These findings suggest that the role of T cells in the context of DENV vaccination should not be ignored , and that a safe and efficient vaccine against DENV should ideally trigger both arms of the immune system .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health , the US Public Health Service Policy on Humane Care and Use of Laboratory Animals , and the Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC ) . All experimental procedures were approved and performed according to the guidelines set by the La Jolla Institute for Allergy and Immunology Animal Care and Use Committee ( protocol number AP-28SS1-0809 ) . 129/Sv mice deficient in type I and II interferon receptors ( AG129 , originally obtained from Dr . Skip Virgin at Washington University in St . Louis ) and wild-type 129/Sv mice ( purchased from Taconic ) were housed under SPF conditions at the La Jolla Institute for Allergy and Immunology ( LIAI ) . For all experiments , sex-matched 5 to 6 week-old mice were used . For challenge experiments , mice were infected intravenously ( via the tail vein ) with 5×108 GE of DENV serotype 2 , strain S221 diluted in a total volume of 200 µl PBS with 10% FCS . For survival studies , mice were sacrificed when moribund or at the first signs of paralysis . DENV strain S221 ( serotype 2 ) is a triple plaque-purified clone from the D2S10 quasi-species population [23] . S221 was amplified in C6/36 cells ( purchased from ATCC ) cultured at 28°C in Leibovitz's L-15 medium ( Gibco ) supplemented with penicillin , streptomycin , HEPES , and 10% FBS as previously described [40] . Genomic equivalents ( GE ) were quantified by real-time qRT-PCR as previously described [40] . Based on a standard baby hamster kidney cell ( BHK-21 ) plaque assay , there are approximately 5×104 GE/PFU for S221 . 2H2 is an IgG2a reactive for the prM/M protein of DENV , serotypes 1–4 ( IgG2a anti-DENV1-4 prM ) . 2H2 hybridoma was purchased from ATCC and grown in PFHM-II ( Gibco ) with penicillin ( 100 U/ml ) , streptomycin ( 100 µg/ml ) and 55 µM β-mercaptoethanol in CELLine CL1000 bioreactors ( Wilson Wolf Manufacturing Corporation ) . Antibody was purified using protein G-coupled resin according to the manufacturer's instructions ( Pierce ) , dialyzed against PBS , concentrated , and sterile filtered prior to use in experiments . The purity of Ab preparations was verified by SDS-PAGE and binding to DENV was assessed by ELISA . Protein content was quantified using a BCA protein assay kit ( Pierce ) . Antibody C1 . 18 , mouse IgG2a isotype control , was purchased from BioXCell . Tissues were collected into RNAlater ( Qiagen ) and subsequently homogenized as described previously [40] . RNA was isolated and DENV and relative 18S were quantified using real-time qRT-PCR as described previously [40] . T cell-depleting antibodies 2 . 43 ( IgG2b anti-mouse CD8 ) , GK1 . 5 ( IgG2b anti-mouse CD4 ) and the isotype control LTF2 ( IgG2b ) were purchased from BioXCell . CD8+ or CD4+ T cells were depleted by administering 250 µg of 2 . 43 or GK1 . 5 antibody intraperitoneally in 200 µl total volume in PBS 3 days and 1 day before challenge with virus . Sucrose-purified DENV2 strain S221 was used to coat 96-well plates overnight at 4°C ( 109 GE per well in 50 µl 0 . 1 M NaHCO3 ) . Next , virus on plates was UV-inactivated and plates were blocked with 100 µl Blocker Casein in PBS ( Thermo Scientific , 1 hour , room temperature ) . Blocking solution was flicked off and serum was titrated 1∶3 over 7 titration steps , starting with an initial dilution of 1 in 30 in PBS . Serum dilutions were incubated 1 . 5 hr at room temperature , followed by washing of the plates three times with 0 . 05% ( v/v ) Tween 20 ( Sigma ) in PBS ( Gibco ) . Bound antibody was detected using a 1∶5000 dilution of HRP-conjugated goat anti-mouse IgG , IgG1 , IgG2a , IgG2b or IgG3 antibodies ( Jackson Immunoresearch ) and TMB ( eBioscience ) . Results are reported as a plot of absorption ( OD 450 nm ) versus dilution . Alternatively , in the ELISA measuring the DENV-specific IgG1 , IgG2a , IgG2b , IgG3 , the titer was reported as the greatest dilution with an absorption higher than twice the background absorption . One day prior to infection , a total of 1×105 BHK cells were seeded in each well of standard 24-well plates ( Costar ) in 1 ml of medium ( 1×105 cells/ml cell suspension in MEM supplemented with 10% FCS , 10 mM HEPES Buffer , 100 U/ml Penicillin , 100 µg/ml Streptomycin , all from Gibco ) . On the day of the assay , 20 µl of serum was diluted in 80 µl culture medium and subsequently titrated 1∶2 over 6 titration steps . 50 µl of each serum dilution was mixed with 50 µl of DENV2 ( 7 . 5×107 GE/ml in medium ) and incubated 1 hr at 4°C . Each serum/virus mix ( total volume of 100 µl ) was used to infect BHK cells in one well of the 24-well plates seeded the day before . The medium was carefully removed from the monolayer of cells prior to infection , and 100 µl of the serum/virus mix was applied to the monolayer of cells in each well . For each serum sample , the dilutions tested were therefore 1∶10 , 1∶20 , 1∶40 , 1∶80 , 1∶160 and 1∶320 . Plate were subsequently incubated for 1 hour at 37°C ( with 5% CO2 ) . After 1 hour , the infection medium was removed and each well was overlaid with 1 ml of culture medium ( as described above ) containing 1% ( w/v ) Carboxymethylcellulose ( CMC ) Medium Viscosity ( Sigma ) . Following incubation of the plates for 3 days at 37°C ( with 5% CO2 ) , cells were fixed with 1 ml per well of a 4% buffered formalin solution ( Fisher Scientific ) for 30 minutes at room temperature . Overlay/formalin mix was decanted , the cells were washed 3 times with PBS , and the cell layer was stained with 1% crystal violet ( Fisher Scientific ) in 20% ethanol . Plaques were counted and the highest dilution neutralizing 50% of plaques or more was reported as neutralizing titer ( PRNT50 ) . As a positive control , 10 µg of the DENV-neutralizing monoclonal Ab 4G2 ( IgG2a anti-DENV1-4 E ) was used and titrated 1∶2 like the serum samples . Donor spleens were homogenized through 70 µm strainers and the desired cell populations were isolated by MACS negative selection ( depletion of non-target cells , keeping the desired population untouched ) using the Pan B Cell Isolation Kit or Pan T Cell Isolation Kit II from Miltenyi Biotech . Procedures were performed according to the manufacturer's instructions . Cells were enriched to over 90% purity ( T cells ) and 80% purity ( B cells ) as determined by flow cytometry . Cells were administered intravenously into recipient mice one day prior to challenge with DENV . Cytokine levels in the serum were measured using the mouse pro-inflammatory 7-plex base kit ( IFN-γ , IL-1β , IL-6 , IL-10 , IL-12p70 , KC/GRO/CINC , TNF ) from MDS Meso Scale Discovery according to the manufacturer's instructions . | Dengue virus is an escalating public health threat for over 2 . 5 billion people worldwide . The disease caused by dengue virus ranges from mild ( dengue fever ) to lethal ( dengue hemorrhagic fever , dengue shock syndrome ) . To date , there is no cure or vaccine for dengue . One of the challenges to developing a safe and efficient dengue vaccine is that antibodies , usually induced by vaccines to protect the host from re-infection , can increase the severity of dengue disease if they are not present in sufficient amounts to neutralize the virus . An efficient vaccine is urgently needed to slow down the progression of dengue disease , but little is known about the way the immune system protects the body against dengue re-infection . Using a protective vaccine candidate for dengue , the present study evaluates in mice the relative contribution of T cells and antibodies to protection against dengue . We show that the antibody component of an immune response that is overall protective had the ability , when isolated from the other components of the immune system , to either decrease or increase viral burden , whereas T cells reduced viral burden in all situations tested . Our results suggest that vaccine development efforts should focus on approaches that induce both T cell and antibody responses against dengue virus . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Role of Humoral versus Cellular Responses Induced by a Protective Dengue Vaccine Candidate |
Across phylogeny , glutamate ( Glu ) signaling plays a critical role in regulating neural excitability , thus supporting many complex behaviors . Perturbed synaptic and extrasynaptic Glu homeostasis in the human brain has been implicated in multiple neuropsychiatric and neurodegenerative disorders including Parkinson’s disease , where theories suggest that excitotoxic insults may accelerate a naturally occurring process of dopamine ( DA ) neuron degeneration . In C . elegans , mutation of the glial expressed gene , swip-10 , results in Glu-dependent DA neuron hyperexcitation that leads to elevated DA release , triggering DA signaling-dependent motor paralysis . Here , we demonstrate that swip-10 mutations induce premature and progressive DA neuron degeneration , with light and electron microscopy studies demonstrating the presence of dystrophic dendritic processes , as well as shrunken and/or missing cell soma . As with paralysis , DA neuron degeneration in swip-10 mutants is rescued by glial-specific , but not DA neuron-specific expression of wildtype swip-10 , consistent with a cell non-autonomous mechanism . Genetic studies implicate the vesicular Glu transporter VGLU-3 and the cystine/Glu exchanger homolog AAT-1 as potential sources of Glu signaling supporting DA neuron degeneration . Degeneration can be significantly suppressed by mutations in the Ca2+ permeable Glu receptors , nmr-2 and glr-1 , in genes that support intracellular Ca2+ signaling and Ca2+-dependent proteolysis , as well as genes involved in apoptotic cell death . Our studies suggest that Glu stimulation of nematode DA neurons in early larval stages , without the protective actions of SWIP-10 , contributes to insults that ultimately drive DA neuron degeneration . The swip-10 model may provide an efficient platform for the identification of molecular mechanisms that enhance risk for Parkinson’s disease and/or the identification of agents that can limit neurodegenerative disease progression .
Across phylogeny , the amino acid glutamate ( Glu ) plays multiple , important roles including contributions to protein synthesis , intermediary metabolism , and chemical neurotransmission [1–4] . At neuronal synapses , Glu signals through both metabotropic receptors that initiate G-protein coupled signaling [5–7] as well as ionotropic receptors that flux ions such as Na+ and Ca2+ , altering membrane excitability [5 , 8–10] . Excessive ionotropic Glu signaling in the mammalian brain has been implicated in a variety of brain disorders including addiction , schizophrenia , amyotrophic lateral sclerosis ( ALS ) , and Parkinson’s disease ( PD ) [11–14] , as well as the neuronal death that arises in the context of stroke and glioblastoma [15 , 16] . Acute treatment of neurons with high , non-physiological , levels of Glu can induce signs of cell death within minutes , characterized by intense vacuolization and cell swelling characteristic of necrosis [17–20] . Chronic hyper-activation of neurons by Glu , within physiological limits , can drive apoptotic mediated neural degeneration , particularly if other genetic or environmental risk pathways are engaged [21–23] . Glu activation of Glu receptors can lead to prolonged alterations in intracellular Ca2+ homeostasis , driving Ca2+-dependent proteolysis and activation of apoptotic programs [24] . Although cell autonomous mechanisms remain a focal point for many investigations seeking insights into determinants of neurodegeneration , increasing attention has been given to astrocytic mechanisms that can sustain neuronal viability , in the context of constant Glu stimulation that could otherwise lead to cell death . These mechanisms include the shuttling of metabolic intermediates such as lactate to neurons that can help sustain ATP synthesis [25–27] , the buffering of extracellular ions such as K+ , since excess extracellular K+ due to chronic ion channel activation and Na+/K+ ATPase dysregulation can contribute to excess neuronal activation [26 , 28 , 29] , and the efficient clearance of extracellular Glu that both limits the amplitude of synaptic and extrasynaptic Glu signaling but also Glu-driven neuronal degeneration [26 , 30 , 31] . Astrocytic Glu clearance is mediated by multiple Na+-dependent Glu-transporters of the SLC1 family ( e . g . GLT1/rodents , EAAT2/humans ) that terminate Glu signaling via binding and uptake of Glu in proximity to synaptic release sites [13 , 31 , 32] . A second astrocytic Glu transporter that participates in extracellular Glu homeostasis is xCT ( SLC7A11 ) , the transporter subunit of a dimer that supports intracellular Glu exchange for extracellular cystine . xCT is generally thought to act oppositely to SLC1 transporters , balancing control of extrasynaptic Glu levels with the provision of precursor ( cysteine ) for astrocytic glutathione synthesis [33–36] . Due to their significant impact on synaptic and extrasynaptic Glu homeostasis , Glu transporters and exchangers have been widely studied to determine their contribution to Glu-induced neural degeneration as well as in efforts to manipulate their activity and expression for therapeutic ends [36–38] . For example , Rothstein and coworkers identified β-lactam antibiotics , typified by the cephalosporin-type agent ceftriaxone ( Cef ) , as capable of elevating GLT1 expression in vitro and in vivo , protecting neurons from Glu toxicity , and enhancing longevity in an ALS mouse model [13] . Subsequently , many investigators have demonstrated the neuroprotective activity of Cef administration in rodents [39–41] , with evidence supporting antibiotic modulation of both GLT1 and xCT expression [13 , 36 , 42] , although candidates targeted by the antibiotic in glia to induce transporter expression have , until recently , been unidentified . In a screen for novel genes that control DA signaling in the nematode , C . elegans [43] , we identified a glial-expressed gene , swip-10 , whose mutation induces hyper-excitability of DA neurons and elevates rates of vesicular DA release , culminating in the hyperdopaminergic phenotype , Swimming induced paralysis ( Swip ) [44] . These studies also demonstrated a critical role for Glu signaling in establishing the paralytic phenotype of swip-10 mutants [44] . Swip-10 is conserved across phylogeny , with the unstudied gene , Mblac1 , as the putative mammalian ortholog . Both SWIP-10 and MBLAC1 proteins are metallo β-lactamase domain ( MBD ) -containing proteins [44] , with residues key for metal binding and catalysis conserved across worm and vertebrate proteins . Although the substrate hydrolyzed by SWIP-10/MBLAC1 enzymatic activity is currently unknown , we recently established that MBLAC1 is a specific , high-affinity target for Cef [45] . We presented evidence that Cef binding activity in brain lysates could be totally eliminated by MBLAC1 immunodepletion therefore supporting the hypothesis that MBLAC1 may be the exclusive , non-microbial target of Cef in vivo . These findings also suggest that further study of SWIP-10/MBLAC1 may reveal mechanisms normally engaged to protect neurons from chronically elevated extracellular Glu and a path to the identification of novel neuroprotective agents . A key piece of data lacking in this hypothesis , however , is evidence that loss of SWIP-10/MBLAC1 either induces Glu-dependent neural degeneration or eliminates the neuroprotective actions of Cef . Here , we capitalize on the ease of monitoring the morphology and degeneration of C . elegans DA neurons engineered to stably express green fluorescent protein ( GFP ) to examine a requirement for swip-10 expression in limiting Glu-dependent DA neuron degeneration . We find that swip-10 mutants demonstrate a striking , progressive degeneration of DA neurons that can be suppressed by glial expression of wild type swip-10 and by mutation of Ca2+ permeable Glu receptor mutants . Through our studies , we provide evidence that a cell non-autonomous action of SWIP-10 sustains DA neuron viability in the context of excess Glu signaling and elevations of cytosolic Ca2+ that we hypothesize leads to increased cellular stress and , ultimately , apoptotic cell death . Our findings support SWIP-10 ( and by extension MBLAC1 ) as a key protective agent whose further study may yield important insights into risk factors for progressive neurodegenerative disorders and their treatment .
Given the Glu signaling-dependent , Swimming-induced paralysis ( Swip ) phenotype present in swip-10 mutants [44] , and evidence from the latter study that swip-10 DA neurons are hyper-excitable , as assessed by a cytoplasmic Ca2+ reporter ( GCamp ) , we sought to determine whether these animals might display signs of excitotoxic neural degeneration . We examined the DA neurons of multiple mutant swip-10 alleles crossed to BY250 , a strain that stably expresses the integrated transcriptional fusion pdat-1::GFP ( vtIs7 ) ( Fig 1A ) [46] . We focused our evaluations on CEP DA neurons , and quantitatively evaluated degeneration by three distinct morphological assessments: 1 ) neurite truncations and breaks in GFP-labeled dendrites ( Fig 1B and 1C ) , 2 ) shrunken cell soma ( Fig 1D ) and 3 ) missing cell soma ( Fig 1E ) , as previously described [47 , 48] . From these categories , we also calculated an overall degeneration score where the appearance of any of the components qualifies an animal as displaying CEP degeneration [48] . We found that all three available swip-10 alleles ( vt29 and vt33 from our forward genetic screen , and the larger deletion allele , tm5915 ) exhibited elevations in the degeneration index , relative to wildtype animals ( Fig 1F–1I ) . To further support that mutation of swip-10 induces morphological changes in DA neurons , versus a sequestration or inactivation of cytoplasmic GFP , we corroborated our findings using a DA neuron-targeted , membrane-bound reporter ( pdat-1::myrRFP ) which also yielded evidence of tm5915 DA neurodegeneration ( S1 Fig ) . Interestingly , evaluation of swip-10 impact on C . elegans glia broadly ( marked by the ptr-10 promoter driven myrRFP ) or on CEPsh glia that ensheath CEP DA neurons specifically ( marked by phlh-17::GFP ) failed to reveal evidence for gross morphological changes ( S2 Fig ) . These findings suggest that swip-10 mutation induces a localized , cell non-autonomous effect on the integrity of neighboring DA neurons . To be sure that our fluorescent reporters of DA neuron morphology were faithfully reporting structural changes in DA neurons , we assessed CEP cilia of swip-10 via electron microscopy ( EM ) . Previously , we used this approach to document damage to CEP dendrites in the context of 6-OHDA induced DA neuron degeneration [49] . The tm5915 deletion allele was selected for EM studies of swip-10 induced neural degeneration , though as noted above , all mutants demonstrated comparable degeneration . The morphology of CEP neuronal processes is well characterized at the ultrastructural level [50] , especially the specialized cilium at the tip of the CEP dendrite , which can be visualized in transverse thin sections through the lips of adult C . elegans ( Fig 2A ) [51 , 52] . Using relative position and the defined morphological characteristics of CEP DA neurons , such as the electron dense cuticular branch or nubbin associated with their cilia to anchor the dendrite to the cuticle [52] and the presence of the electron dense clumps of tubule-associated material ( TAM ) previously shown to be characteristic of CEP cilium [51] , we were able to identify multiple anomalies in tm5915 CEP structure . These defects include changes in the size and appearance of the nubbin ( Fig 2B ) , loss or misplacement of TAM and microtubules ( Fig 2C and 2D ) , and the presence of large or small vacuoles in several locations either below or above the axoneme ( Fig 2E and 2F ) . A summary of the swip-10 mutant CEP cilium defects is depicted in Fig 2G . In addition to the defects described above , half of the CEP dendrites of swip-10 mutants were missing any cilium that extended beyond the axoneme . These TEM studies confirm that swip-10 mutation results in striking DA neuron morphological changes . In order to determine whether the DA neuron degeneration observed in swip-10 animals represents a late onset phenomenon and/or might arise from a progressive perturbation across development , we assayed DA neuron degeneration in swip-10 mutants across various post-embryonic ages . We observed that tm5915 animals display time-dependent indications of DA neuron degeneration that are distinct from the changes seen with wildtype animals ( Fig 3D ) . In wildtype animals , signs of DA neuron degeneration are evident only in older , adult animals whereas signs of degeneration are already evident in tm5915 animals by day 1 ( L1 stage ) of larval development ( Fig 3D ) . A breakdown of the components that comprise the overall degeneration score of tm5915 mutants is revealing , where non-uniform patterns are evident across measures . Although we were unable to follow individual DA neuron morphological changes over time , our population findings are suggestive of a progressive form of degeneration at the single neuron level , with dendritic breaks and truncations as earliest signs of degeneration ( Fig 3A ) , followed by the appearance of shrunken soma ( Fig 3B ) , and then by missing soma ( Fig 3C ) . Overall similar patterns are evident with wildtype animals , just appearing much later in life . Together our findings indicate that swip-10 mutation begins to disrupt the health of DA neurons early in development with the appearance of indices of morphological perturbations arising in distal processes that progress to neuronal death . Although the Swip behavior of swip-10 mutants at the L4 stage arises as a consequence of excess DA signaling [43] , this paralysis is a cell non-autonomous consequence of glial , and not DA neuron , expression of swip-10 [44] . To determine whether the degeneration of DA neurons is similarly a consequence of mutation of glial swip-10 , we expressed a full length wild type swip-10 cDNA fused to GFP ( swip-10::GFP ) under control of glial and DA neuron promotors . Fig 4A demonstrates that pan-glial swip-10 expression , as achieved through use of the ptr-10 promoter [53] , robustly rescues DA neuron degeneration of tm5915 animals , comparable to that achieved with a genomic construct that encodes swip-10 and the upstream elements needed to achieve full rescue of Swip [44] . Significant rescue of DA neural degeneration was also achieved with the CEP sheath glia-specific promotor hlh-17 [53] . In contrast , DA neuron specific expression of swip-10 , driving expression with the dat-1 promoter , failed to restore normal morphology . Together , these findings support the conclusion that glial expression of swip-10 is required to maintain the normal viability of DA neurons . Although not explored extensively , we sought to understand whether neural degeneration in swip-10 mutant animals is limited to the DA neurons . We chose to evaluate swip-10 mutant ( tm5915 ) animals bearing reporters to demarcate OLL and BAG neurons . Glutamatergic OLL neurons are similar in location and morphology to dopaminergic CEP neurons , are mechanosensitive like CEP neurons , and share an association with glia ( OLLsh ) that ensheath OLL processes . Carbon-dioxide sensing , glutamatergic BAG neurons are similar in location and morphology to the CEP neurons , although not associated with direct ensheathing or socket glia . We observed degeneration of glutamatergic OLL neurons ( Fig 4B ) but not of BAG neurons ( Fig 4C ) . These findings , along with rescue of DA neurodegeneration through glial re-expression of swip-10 , reinforce a key role for glia in maintaining the viability of C . elegans DA neurons . Mechanisms proposed to support DA neuron degeneration include mishandling of intracellular DA that can form cytotoxic quinones [54 , 55] . Thus , elevations in cytosolic DA that arise with pharmacological blockade of the vesicular monoamine transporter ( VMAT , cat-1 in C . elegans ) by reserpine results in DA neuron degeneration [48] , and a genetic reduction of VMAT2 expression causes progressive DA neuron degeneration in mammals [56] . The degeneration of DA neurons in swip-10 animals does not appear to arise as a consequence of elevations of intracellular DA as disruption of DA synthesis capacity arising from a loss of function mutation in cat-2 , the C . elegans ortholog of tyrosine hydroxylase , the rate-limiting step in DA synthesis , did not alter tm5915 DA neuron degeneration ( S3A Fig ) . Extracellular DA elevations can lead to the formation of toxic adducts with vital cell proteins [57] and our prior studies support excess DA secretion in swip-10 animals [44] . However , loss of extracellular DA clearance capacity achieved via mutation of the presynaptic DA transporter , dat-1 , which triggers Swip , [58] did not induce DA neuron degeneration ( S3B Fig ) . Neural degeneration , more generally , can be triggered by extrinsic or intrinsic activation of cell death genetic programs , first elucidated at a molecular level in C . elegans [59–62] . Additionally , disruptions of vital cellular processes ( e . g . ATP production , membrane permeability , ion gradients or cytoskeletal organization ) by genetically encoded neurotoxins or following exposure to reactive chemical species [63–65] have been shown to lead to the death of neurons . Lastly , excitotoxicity , a form of neurodegeneration with features of both apoptotic and necrotic cell death , is well known in mammalian brain preparations and typically observed in the context of over stimulation of Glu-responsive , ionotropic receptors [10 , 65 , 66] . Our prior findings that DA neurons in swip-10 animals display elevated excitability that is dependent on Glu signaling [44] encouraged our consideration of the latter mechanism of DA neuron degeneration . We therefore quantified DA morphological changes in swip-10 animals bearing loss of function mutations in genes supporting synaptic Glu packaging and Glu signaling , as well as mutations in genes encoding transporters thought to modulate extracellular Glu levels . First , we examined contributions of vesicular Glu transporters ( vGLUTs ) . These proteins are responsible for packaging Glu into synaptic vesicles prior to release [67 , 68] . There are three genes that encode proteins homologous to VGLUTs in C . elegans ( eat-4 , vglu-2 and vglu-3 ) [69–71] with eat-4 being the only one functionally characterized to date [68 , 72] . Loss of individual vGLUTs ( Fig 5A ) had no effect on DA neuron morphology . Interestingly , whereas eat-4 mutation significantly reduced the Swip behavior of swip-10 mutants [44] , the same eat-4 allele failed to blunt the degeneration of DA neurons in tm5915 animals . vglu-2 mutation was also unable to reduce DA neuron degeneration . In contrast , loss of vglu-3 significantly , suppressed DA neuron degeneration ( Fig 5A ) , suggesting a contribution of vesicular Glu signaling , directly or indirectly , to swip-10 DA neuron degeneration . Mammalian glia express multiple Na+-dependent Glu transporters ( GLTs ) of the SLC1 family that support efficient clearance of Glu after release at synapses and their dysfunction figures prominently in investigations of Glu-dependent neuronal injury and death [31] . Additionally , our previous studies [44] demonstrated that mutation of several GLTs ( glt1 , glt3 and glt4 ) conferred DA-dependent Swip . However , we found that mutation of individual glt genes failed to induce DA neuron degeneration ( Fig 5B ) . A second , glial Glu transport system , xCT , regulates extra-synaptic Glu levels , acting as a cystine/Glu exchanger [36] . xCT imports extracellular cystine in exchange for intracellular Glu , and thus altering the expression or activity of this transporter can modulate extracellular Glu levels . xCT is a member of the mammalian heteromeric amino acid transporter ( HAT ) family , for which there are 9 C . elegans homologs , with the highest homology for xCT being to AAT-1 and AAT-3 [73] . To determine whether xCT-like proteins could contribute to DA neuron degeneration , we generated tm5915 double mutants with all available aat mutants . Of the 7 xCT homologs tested , we found that loss of aat-1 uniquely attenuated the DA neuron degeneration of tm5915 ( Fig 5C ) . These findings implicate non-vesicular Glu release as a contributor to swip-10 DA neuron degeneration . To determine if both vesicular Glu release supported by VGLU-3 and transporter-mediated Glu release supported by AAT-1 act in parallel or via a shared pathway to support DA neuron degeneration , we examined DA neuron morphology in an aat-1;vglu-3 double mutant . We found no enhancement of the suppression of the tm5915 degeneration beyond that of the individual mutants ( S4 Fig ) . These findings are consistent with common mechanisms , downstream of extracellular Glu availability through either vesicular or non-vesicular Glu secretion mechanisms , as determinant of the quantitative extent of swip-10 DA neuron degeneration . Post-synaptically in both vertebrates and nematodes , Glu binds and activates ionotropic and metabotropic receptors ( iGluRs and mGluRs , respectively ) [74 , 75] . To further pursue the hypothesis that mutation of swip-10 triggers DA neuron degeneration via excess Glu signaling , we examined DA neuron morphology in tm5915 lines bearing available mutant alleles for the iGluRs and mGluRs . Among the twelve GluR mutants tested , we found that loss of either the NMDA-type iGluR , nmr-2 , or loss of the AMPA-type iGluR , glr-1 [76] , significantly suppressed swip-10 DA neuron degeneration ( Fig 6A ) . Interestingly , these GluRs are distinct from the GluRs previously shown to suppress the paralysis phenotype of swip-10 mutants ( glr-4 , glr-6 and mgl-1 ) [44] . A double mutant of glr-1 and nmr-2 did not further suppress tm5915 degeneration beyond that seen with either mutant alone , suggesting that these receptors support neurodegeneration through a common pathway ( Fig 6B ) . To further substantiate that excess GluR signaling via NMR-2 and GLR-1 could support our swip-10 observations , we selectively overexpressed these receptors in DA neurons and examined CEP neuron morphology . As hypothesized , we detected statistically significant DA neuron degeneration , as compared to non-transgenic lines , similar to that observed in swip-10 mutants ( Fig 6C ) . The evidence presented above of a role of Ca2+-permeant iGluRs [77] in DA neuron degeneration , as well as our prior findings that swip-10 DA neurons demonstrate an exaggerated Ca2+ elevation in response to food contact [44] , suggested to us that DA neuron degeneration in these animals could reflect activation of Ca2+-dependent programs linked to apoptotic and/or necrotic cell death [78–80] . Consistent with this idea , we found that loss of the primary endoplasmic reticulum ( ER ) Ca2+ storage/binding protein , calreticulin ( crt-1 ) protected against swip-10 DA neuron degeneration ( Fig 7A ) . Excessive activation of the Ca2+-activated protease calpain-1 , has been shown to lead to cellular damage , including neurodegeneration , in both mammals and C . elegans [81–83] . In keeping with these findings , a loss of function mutation of clp-1 , the C . elegans calpain-1 ortholog , significantly attenuated the DA neuron degeneration of tm5915 animals ( Fig 7A ) . Together , these results support the hypothesis that inappropriate or excessive elevations of intracellular Ca2+ support swip-10 DA neurodegeneration . In mammals , aberrant excitotoxic Ca2+ signaling can generate reactive oxygen species ( ROS ) leading to activation of cell stress pathways that drive neuronal cell death [84 , 85] . To explore this idea , we inspected swip-10 animals for signs of oxidative stress by monitoring reporter expression from Pgst-4::GFP . The gst-4 gene encodes a glutathione-s-transferase , and is a target for the ROS responsive transcriptional regulator SKN-1 ( C . elegans Nrf2 ortholog ) [86 , 87] . As shown in Fig 7B and 7C , tm5915 animals demonstrate a significant elevation in Pgst-4::GFP expression . As a measure of ER stress , we monitored the transcriptional reporter , Phsp-4:GFP [88 , 89] . Although tm5915 animals did not show indications of basal ER stress with this marker ( Fig 7D and 7E ) , they were more sensitive to the pharmacological ER stressor , tunicamycin , compared to N2 animals ( Fig 7D and 7E ) . Glu-induced excitotoxicity has been reported to arise from multiple mechanisms , including necrosis , autophagy , and apoptosis [90] , processes that also contribute to cell death in the nematode [61] . Cells dying by necrosis exhibit cell swelling and vacuolization [61] , which we do not observe in swip-10 animals ( S5 Fig ) . In contrast , as described in Fig 1 , DA neurons in swip-10 mutants display blebbing or breaks in processes ( Fig 1C and 1F ) and shrunken soma ( Fig 1D and 1G ) , features characteristic of apoptosis [91] . Consistent with this idea , we found that gain of function ced-9 mutant animals [92] and loss of function ced-4 and ced-3 mutants , well-known contributors to programmed cell death [59] , significantly suppressed tm5915 DA neuron degeneration ( Fig 8A ) . Apoptosis in the context of normal developmental programmed cell death is tightly coupled to cell corpse engulfment [93] , with two partially-redundant and parallel pathways involving ced-1/ced-6 [94 , 95] and ced-10 [96] responsible for recognition of dying cells and initiation of cell corpse clearance . Little is known concerning the integration of death and engulfment programs in relation to DA neuron cell death , though Offenburger recently reported contributions from both ced-6 and ced-10 linked engulfment mechanisms in 6-OHDA induced DA neuron degeneration [97] . In contrast , we found that genetic disruption of individual genes associated with ced-1/ced-6 and ced-10 had no effect on measures of swip-10 DA neuron degeneration ( Fig 8B ) . These findings suggest that swip-10 DA neuron degeneration arises from the activation of a cell-autonomous apoptotic pathway , one that draws little observable support from known engulfment mechanisms .
Overall , our findings reveal that loss of glial-expressed swip-10 results in DA neuron degeneration through a process supported by excess Glu signaling through Ca2+-permeant ionotropic Glu receptors and Ca2+-dependent cell death mechanisms that engage apoptotic cell death pathways , as summarized in Fig 9 . Although , we predominantly characterized swip-10 DA neuron viability in gravid ( egg-laying ) adults , time-dependence studies indicate that degeneration is evident by day 2 post-hatching , and increases on all degeneration measures across the lifespan . A predominant display of fragmented or truncated dendrites in young animals versus shrunken or lost soma at later stages ( Fig 3 ) suggests that degeneration in individual neurons is progressive , first emerging as altered neurite structure , followed by engagement of all compartments and eventually resulting in disappearance of some DA neurons altogether . This progressive pattern of degeneration is commonly seen with neurons suffering from energy depletion , that can be triggered by excessive stimulation or through metabolic poisoning [98 , 99] . Most of our observations were obtained with a DA neuron-specific , cytosolic , fluorescent reporter , findings corroborated using a membrane anchored reporter ( Figs 1 and S1 ) . Subsequent studies of swip-10 mutants using electron microscopy to image DA dendrites and cilia provided clear evidence of physical alterations ( Fig 2 ) that we believe reflect the declining health of the DA neurons , versus a direct action of swip-10 or its immediate effectors though further studies are needed . Additional EM studies would also be valuable in investigating the degeneration of swip-10 mutants at the level of the DA neuron cell bodies , axons , and synapses . There are significant technical challenges associated with identifying these cells and processes in the densely populated nerve ring , though future studies that make use of correlated light electron microscopy ( CLEM ) can be envisaged [100] . The discovery that DA neurons degenerate in swip-10 animals was initially surprising as our identification of swip-10 derives from a hyperdopaminergic behavioral phenotype [44] , though we previously demonstrated reduced DA levels in these animals [43] . Since swip-10 DA neurons exhibit increased excitability and tonically-elevated DA secretion rates [44] , we hypothesize that the degenerative process we have characterized likely contributes to a perturbation of mechanisms that insure a tight control over DA release ( e . g . DA autoreceptors ) , along with a diminished capacity for DA clearance , leading to Swip . Alternatively , a degeneration-induced loss of DA signaling capacity could result in a hypersensitivity of postsynaptic DOP-3 DA receptors , such that DA release arising from water immersion then triggers excessive inhibition of motor neurons and Swip . In support of the latter possibility , movement of swip-10 animals on plates reveals a heightened sensitivity to exogenous DA [43] . Having generated evidence for an age-dependent degenerative process impacting the morphology of swip-10 DA neurons , we pursued mechanistic studies through a combination of genetic and imaging techniques . Such approaches have provided for a systematic elucidation of mechanisms underlying both programmed and environmentally-triggered cell death [59 , 101–104] . In addition to the apoptotic pathways that drive programmed cell death during development , molecular determinants of later stage necrotic neuronal death , that arise as a result of the constitutive activity of mutant ion channels [105 , 106] and ligand-gated Glu receptors [107] , have been investigated . A potential role for excess Glu signaling in swip-10 DA neuron degeneration seemed plausible given the contribution of Glu receptors and Glu transporters to Swip reported in our prior study [44] . In this regard , the groups of Driscoll and Mano have provided evidence that necrotic cell death arises with excess Glu signaling that occurs from a combined loss of Glu clearance and a hyperactive , constitutively active form of the alpha subunit of the G-protein , Gs [108–110] . Although swip-10 DA neuron death shares features associated with the degeneration described in Mano’s studies , specifically the contributions from the iGluR , glr-1 ( Fig 6A ) , and the intracellular Ca2+ sequestering protein , crt-1 ( Fig 7A ) , our analysis also reveals a number of differences . Thus , besides a lack of morphological evidence of swollen , vacuolated soma seen in prior studies ( S5 Fig ) , we found no evidence for a contribution to DA neuron degeneration of the adenyl cyclase ortholog , acy-1 , nor could we implicate the autophagy-associated , cell death protein kinase , dapk-1 ( S6 Fig ) [108 , 109] . We also obtained evidence that the damaging effects of swip-10 mutation are quite distinct from those observed with 6-OHDA induced DA neuron cell death . For example , the degeneration of DA neurons that arises within a day following 6-OHDA administration to wildtype worms lacks contributions from genes that participate in programmed cell death mechanisms [49] , whereas , as we discuss below , contributions of these genes are evident in the swip-10 model . Moreover , recent studies indicate that ced-6 and ced-10 dependent engulfment pathways support 6-OHDA induced loss of DA neurons [97] , whereas we found no contribution of these engulfment genes to swip-10 effects ( Fig 8B ) . Moreover , Offenburger and colleagues have reported that 6-OHDA induced DA neuron death is exacerbated by mutation of the Ca2+ chaperone crt-1 whereas we demonstrated crt-1 mutation confers neuroprotection [111] . Together , these findings indicate that the DA neuron degeneration induced by swip-10 mutation is an altogether unique form of neural degeneration as compared to prior glutamatergic and exogenous neurotoxin models . A critical step in defining the mechanism associated with swip-10 DA neuron degeneration is to determine the site ( s ) where wildtype swip-10 expression is required to support normal DA neuron morphology . As with the rescue of DA-dependent Swip behavior [44] , we found that glial swip-10 re-expression , both genomic and cDNA , rescued swip-10 DA neuron degeneration , whereas DA neuron expression of swip-10 was without effect . These findings attest to a cell non-autonomous mode of action and raise the possibility that glial loss of swip-10 may damage the glial cells themselves , rendering them unable to engage in secretory or contact-mediated support for DA neurons . Although non-quantitative , we detected no obvious morphological differences between wildtype and swip-10 glia ( S2 Fig ) , which may indicate that swip-10 expressing glia are deficient in a capacity to provide specific trophic or metabolic support to DA neurons , versus participating in critical cell autonomous mechanisms . Future studies using higher resolution , EM-based methods should be pursued to refine this analysis . Importantly , we obtained rescue of swip-10 DA neuron degeneration with a promoter driving wildtype swip-10 expression in CEP sheath glia . Moreover , degeneration was apparent in OLL neurons that like CEPs are ensheathed by glia but not in BAG neurons , which lack these contacts . These studies reinforce a contribution of glia to the cell non-autonomous actions of swip-10 to sustain neuronal viability and suggest that neurons in close apposition to ensheathing glia may preferentially depend on the activity of swip-10 . Our prior studies [44] assessing Swip behavior in swip-10 mutants provided evidence of perturbed glial control of extracellular Glu that we hypothesized was responsible for the iGluR and mGluR dependence of Swip in these animals . We therefore considered the possibility that perturbed buffering of extracellular Glu by swip-10 glia also underlies DA neuron degeneration . Mammalian literature emphasizes the critical role of glial Glu buffering mechanisms as protective against Glu excitotoxicity . As first described by Olney and colleagues , Glu excitotoxicity derives from excessive synaptic Glu acting on post-synaptic iGluRs [112–114] , a process recapitulated by the actions of iGluR agonists such as kainic acid and ibotenate [115–117] . Moreover , inhibition of Glu transporters and increased extracellular Glu recapitulates the pathological hallmarks of PD in animal models , including DA neuron degeneration [118] . Our findings that mutations in the Ca2+ permeant iGluRs , glr-1 and nmr-2 , protect against swip-10 DA neuron degeneration and that overexpression of these receptors leads to DA neuron degeneration in wildtype animals ( Fig 6C ) provide strong supportive evidence that glial mechanisms dictating the availability of extracellular Glu are likely disrupted in swip-10 animals . Mammalian glia have been reported to modulate extracellular Glu by vesicular release [119] , Glu-permeable channels [120] , synaptic clearance of Glu by Na+-coupled Glu transporters ( GLTs ) [31] , and extrasynaptic Glu buffering by the cystine/Glu exchanger ( xCT ) [34 , 35] . We found that a mutation in the vesicular Glu transporter vglu-3 attenuates swip-10 DA neuron degeneration ( Fig 5A ) . We were surprised that an eat-4 mutation did not contribute to swip-10 induced degeneration , as such a mutation reduced Swip behavior [44] . Although the expression pattern and role for vglu-3 is undetermined , these findings raise the possibility that EAT-4 supports Glu signaling in the neural circuitry that drives DA neuron excitation in response to water , whereas VGLU-3 contributes to Glu release directly onto DA neurons , including SWIP-10 expressing glia , and drives tonic activation of Glu receptors on DA neurons and over time , excitotoxicity . Consistent with this model , distinct Glu receptors support Swip ( GLR-4 , GLR-6 and MGL-1 ) versus DA neurodegeneration ( GLR-1 and NMR-2 ) . Although we did not observe DA neurodegeneration with genetic loss of single GLT orthologs in the nematode ( Fig 5B ) , unlike Swip [44] , this may be due to genetic redundancy among the six GLTs . Indeed , studies by the Driscoll lab demonstrated that loss of one or two GLTs is insufficient to drive Glu-dependent neurodegeneration [121] . In contrast to our inability to implicate specific GLTs , we found that genetic disruption of the xCT related gene , aat-1 , significantly reduced swip-10 DA neuron degeneration ( Fig 5C ) . As with vglu-3 , the expression pattern for aat-1 in the worm is undefined , and thus additional studies are needed to determine site ( s ) of expression that contribute to our results . The effects of aat-1 mutation were not additive with those of vglu-3 , suggesting that both genes act to support DA neurodegeneration through a common mechanism , which we propose is through the control of tonic , extracellular Glu providing tonic excitation of DA neuron expressed Glu receptors . Finally , it is important to note that mammalian xCT is upregulated by the β-lactam antibiotic ceftriaxone [36 , 42 , 122] , which we have shown binds directly to the putative swip-10 ortholog MBLAC1 [45] . Moreover , research , initiated by findings of Rothstein and colleagues [13] , has demonstrated that ceftriaxone is neuroprotective , including in models of DA neuron degeneration [41] . Although not exclusive , Glu-induced neural degeneration often involves activation of Ca2+-permeable NMDA type iGluRs [19 , 123 , 124] and , as noted , our studies demonstrate an important contribution of Ca2+-permeable C . elegans iGluRs , the NMDA-type iGluR , nmr-2 , as well as the AMPA-type iGluR , glr-1 in swip-10 neural degeneration[77] ( Fig 6A ) . Expression profiling data provides evidence that nmr-2 and glr-1 are expressed in DA neurons [125] . Since swip-10 mutant animals with loss of both nmr-2 and glr-1 do not demonstrate enhanced suppression of DA neural degeneration as compared to single receptor mutations ( Fig 6B ) , we suggest that the flux of Ca2+ through one of these receptors is sufficient to increase intracellular Ca2+ sufficiently to initiate downstream signaling pathways that lead , over time , to neurodegeneration . Aberrant intracellular Ca2+ regulation and signaling has been implicated in excitotoxic cell death [126] , with evidence supporting a role for Na+/Ca2+-permeable degenerin/epithelial sodium channels ( DEG/ENaCs ) [127–129] , Ca2+-dependent proteases such as calpain [130 , 131] , and deficiencies in ER Ca2+ buffering [80 , 106] in cell death mechanisms . We found that disrupting ER Ca2+ storage , by mutation of crt-1 , or mutation of the C . elegans calpain ortholog , clp-1 , significantly rescued swip-10 DA neural degeneration ( Fig 7A ) . Ca2+ dysregulation following excessive Glu stimulation has also been shown to engender multiple indications of cell stress including oxidative stress and ER stress [132 , 133] , which swip-10 mutants display . Finally , although acute Glu excitotoxicity has been more typically associated with necrosis [17 , 19] , evidence suggests that chronic dysregulation of Glu signaling and altered intercellular Ca2+ homeostasis can lead to activation of apoptotic pathways [134 , 135] , and a recent study by Anilkumar and colleagues has demonstrated that external factors , such as nutrient availability , determine whether or not excess Glu signaling triggers apoptotic or necrotic cell death ( Anilkumar 2017 ) . Consistent with this idea , genetic disruption of apoptosis in C . elegans [59] significantly reduced the DA neurodegeneration of swip-10 mutants ( Fig 8A ) . The progressive DA neuron degeneration we detect in swip-10 animals supports the occurrence of a chronic insult and thus is in line with our genetic findings of apoptotic program engagement . However , our data suggests that swip-10 involvement of apoptotic cell death associated genes differs from the involvement of these genes in developmental programmed cell death , as loss of genes critical for cell-corpse engulfment during programmed cell death did not alter the levels of swip-10 DA neuron degeneration ( Fig 8B ) . Although lack of a reliance on engulfment genes could be a reflection of the partial redundancy of the two major engulfment pathways , we suspect that these findings are indicative of a slower engagement of apoptotic genes in the swip-10 model . Additionally , the majority of our assays are conducted at a mid-point , with degeneration in progress , to capture various degrees of degeneration in swip-10 animals , it is possible that we have simply not assessed the correct temporal window for engulfment . Although we present clear evidence for a significant role of excess Glu signaling in the degeneration of swip-10 DA neurons , other mechanisms besides changes in extracellular Glu homeostasis are likely to contribute to our observations since Glu homeostasis and signaling mutants afford incomplete suppression of swip-10 DA neurodegeneration . The elucidation of the normal role and genetic pathway for wildtype swip-10 in C . elegans glial cells will likely clarify other contributors to swip-10 induced neural degeneration . For example , mammalian glia have been shown to support neurons by buffering ions such as potassium ( K+ ) and hydrogen ( H+ ) [136] , and by providing metabolic support via lactate , glutathione , and ATP shuttling [26] . Although only limited data speaks to glial-neuronal crosstalk in worms , we suspect that one or more of these mechanisms contribute to the diminished viability of DA neurons in swip-10 animals . As our transcriptional stress reporter data indicate a systemic increase in cellular stress mechanisms ( Fig 7B–7E ) , it seems entirely likely that the perturbations induced by swip-10 mutation extend beyond the deficits observed in CEP ( and OLL ) neuron viability . Since wholesale degeneration is not evident , we suspect that the premature degeneration of DA neurons reflects a more dependent relationship of these cells on glia . The selective loss of nigrostriatal DA neurons in idiopathic PD has been suggested to derive from an intrinsic vulnerability to stress , possibly arising from the reactivity of DA itself , as well as inefficient anti-oxidant protection , ultimately rendering these cells more vulnerable than others to Glu-induced cell death [137] . Since genetic elimination of the capacity to synthesize DA did not reduce swip-10 DA neuron degeneration , we feel it more likely that excess Glu signaling drives degeneration in combination with a parallel loss of glial metabolic or trophic support required by DA neurons . In summary , our findings reveal a previously unreported dependence of DA neurons on C . elegans glia , one that when disrupted leads to neuronal degeneration . DA degeneration triggered by glial loss of swip-10 appears to be progressive and dependent on excess Glu signaling through Ca2+ permeant iGluRs . We propose that these effects lead to perturbed intracellular Ca2+ homeostasis and , progressively , the engagement of apoptotic cell death pathways . Our work adds support to studies in mammals that indicate a critical role of proper glial function in DA neuron viability [138–141] and reveals a new worm model of Glu excitotoxicity , one likely amenable to pharmacological manipulation that could provide insights to novel therapeutics to treat human neurodegenerative disorders .
Strains were maintained as described previously [142] . We thank J . Rand ( Oklahoma Medical Research Foundation ) ; the Caenhorhabditis Genetics Center; Shohei Mitani of the National Bioresource Project at Tokyo Women’s Medical University; and Shai Shaham , Niels Ringstad , and Oliver Hobert for providing the strains used in this work . N2 ( Bristol ) served as our wild-type strain , and unless specified otherwise , we utilized the proposed null allele , TM5915 , of swip-10 [44] . Strains used in this study are enumerated per figure appearance in S1 Table . In all cases , insertion of the DNA fragment of interest and the fidelity of the vector was confirmed by sequencing and all PCRs were performed using KAPA HiFi HotStart ReadyMix ( Kapa Biosystems ) . All constructs resulted in C-terminal cDNA fusion to an unc-54 3’ UTR . For the membrane bound transcriptional reporter , we used overlap PCR [143] and Gibson Assembly ( NewEngland Biolabs ) to subclone the 700bp dat-1 promotor into the myrRFP containing backbone from pptr-10:myrRFP ( gift from Shai Shaham ) to create pRB1349 ( pdat-1:myrRFP ) . For transgenic swip-10cDNA::GFP rescue experiments , DA neuron , pan-glial , and CEPsh glial expression was achieved using the previously described plasmids , pRB1157 , pRB1158 , and pRB1159 , respectively [44] . Genomic full-length swip-10 rescue experiments were conducted as previously described [44] . For the DA neuron-specific Glu receptor experiments , a PCR product ( 20ng/μL ) was amplified by overlap PCR [143] to include the 700bp dat-1 promoter and genomic glr-1 from the ATG start to 2890 of genomic nmr-2 from the ATG start to 2974 fused to unc-54 3’ UTR for injection , along with punc-122:RFP ( 35ng/μL ) and pdat-1:myrRFP ( 35ng/μL ) . Crosses were performed using publicly available , integrated fluorescent reporter strains to mark chromosomes in trans . Single worm PCR was performed to confirm the presence of the indicated mutation . For all deletions , we used a three primer multiplex strategy that produces PCR amplicons with a 100–200 bp difference between N2 and mutant . This method was highly effective in eliminating preferential amplification of a lower-molecular-weight species . In all cases , a synthetic heterozygous control was used to ensure that heterozygous clones could be identified . We identified recombinant lines by PCR genotyping of single worm genomic DNA lysates . All genotyping PCRs were performed with the KAPA Genotyping Kit ( KAPA Biosystems ) . In some cases , alleles were sequenced with sequence-specific primers to verify mutation homozygosity ( GeneHunter and EtonBioscience ) . Confocal microscopy of mutants on the BY250 strain background was performed using a Nikon A1R confocal microscope in the FAU Brain Institute Cell Imaging Core using a 20x or 60x oil-immersion objective and Nikon Elements capture software . Worms were immobilized using 30mM levamisole in M9 on a fresh 2% agarose pad and cover-slipped with a 1mm cover glass before sealing with paraffin wax [144] . The neurodegeneration assay was adapted from a previously described method [145] . In our case , we transferred 20 worms to normal NGM/OP50 plates as L4s and incubated these plates for 48hrs at 19°C until animals reached the gravid adult stage , unless otherwise noted . We then picked 15 worms into 20μL of 30mM levamisole in M9 on slides prepared with a 2% agarose pad . For imaging , we utilized a Zeiss Discovery V12 inverted fluorescent microscope outfitted with a Xenon UV light source and GFP/YFP/RFP filter sets . We used a Zeiss mono FWD 16mm objective lens to visualize Green Fluorescent Protein ( GFP ) containing integrated transgenes , vtIs7[Pdat-1::GFP] , nsIs242[Pgcy-33::GFP] , wgIs328[Pser2prom3::GFP] selectively expressed in DA , BAG , and OLL neurons respectively , allowing us to examine neurodegeneration in a cell-specific manner . For the DA neurons , analysis was limited to CEP neurons , because out of the 8 DA neurons in C . elegans , the 4 CEP neurons display the clearest and most distinct dendritic projections and can be readily identified via both light and electron microscopy ( see below ) . Neurons were examined for the presence of 1 ) breaks in the CEP dendrites 2 ) shrunken or 3 ) missing somas . Worms were counted as displaying degeneration if one or more of these features were present . Normal N2 CEP , BAG , and OLL neurons lacked any of these abnormalities at the gravid adult stage . Total animals with degeneration , shrunken and missing somas , or neurite breaks were calculated for each trial . The percentage of animals exhibiting each morphological trait was determined for graphical analysis . Animals were tested 15 animals/day on 7–9 separate days ( n = 90–135 animals assayed per genotype ) blinded to genotype . N2 and swip-10 mutant animals were raised and maintained at 20°C on E . coli OP50/NGM plates and 2-day adult animals ( fixed 2 days after the L4 stage ) were fixed and embedded for transmission electron microscopy ( TEM ) following a chemical immersion protocol [146 , 147] . Briefly , animals were first cut open in a cacodylate-buffered osmium tetroxide fixative , then en bloc stained in uranyl acetate , and dehydrated and embedded in Spurr resin . Thin sections were collected onto Formvar-coated slot grids and examined on a Philips CM10 electron microscope . Digital images were collected with an Olympus Morada camera on the TEM , and figures were created using Photoshop . All fluorescent stress reporter stains were a generous gift from Dr . Matt Gill ( Scripps Research Institute , Jupiter , FL ) . All stress reporter strains were imaged as gravid adult animals grown at 19°C for 48hrs after transfer to a fresh OP50/NGM plate at the L4 stage . To determine levels of stress we used the transcriptional reporter strains , dvIs19 [pgst-4:GFP] and zcIs4 [phsp-4:GFP] to measure oxidative stress and ER stress respectively . We adapted previously described methods [87 , 88] . Briefly , the overall pgst-4:GFP fluorescence intensity/μm per 15–20 3 day adult swip-10 animals and 15–20 3 day adult N2 animals ( with subtracted background fluorescence per animal ) was determined , and the fold change GFP intensity compared to N2 signal was calculated for all animals assayed from one population and subsequently averaged over 4 independent days ( n = 60–75 animals assayed ) . As a positive control for oxidative stress , we picked 15–30 L4 N2 animals to OP50 plates 2mM paraquat ( Sigma ) mixed with the NGM agar [148] . phsp-4:GFP fluorescence intensity/μm was assayed as described above . To determine susceptibility of swip-10 mutants to ER stress , we transferred 15–30 L4 N2 and swip-10 animals to NGM plates containing 10μg/mL tunicamycin ( Sigma ) [89] . For each of the stress reporters , images were acquired using identical imaging settings across blinded genotypes and drug treatments , via a Nikon A1R confocal microscope in the FAU Brain Institute Cell Imaging Core using a 4x objective and Nikon Elements capture and analysis software . All statistical tests were performed and graphs generated using Prism version 7 . 0 . Data were analyzed by Student’s t-tests , one-way ANOVAs followed by Sudak or Dunnet’s post-hoc tests and two-way ANOVAs , where appropriate . A P < . 05 was taken as evidence of statistical significance in all cases . | Glutamate ( Glu ) is an important signaling molecule used by nerve cells to communicate information , although excessive Glu signaling can overexcite neurons to the point where they degenerate , a phenomenon termed excitotoxicity . Glu induced excitotoxicity has been linked to neurodegeneration arising in the context of stroke , amyotrophic lateral sclerosis ( ALS ) and Parkinson’s disease ( PD ) . Glial cells , that surround neurons , and their processes have been shown to limit Glu-induced excitotoxicity in mammals . Here , we demonstrate that C . elegans glia limit progressive degeneration of dopamine ( DA ) neurons that arises in the context of mutation of the protein , SWIP-10 , and that this degenerative process relies on Glu signaling , altered Ca2+ homeostasis and apoptotic pathway genes . Our findings reveal a novel molecular contributor to glial maintenance of DA neuron viability , provide a genetically-tractable example of Glu-dependent cell death , and encourage further evaluation of SWIP-10 linked pathways for mechanistic insights into neurodegenerative diseases and their treatment . | [
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"s... | 2018 | Glial loss of the metallo β-lactamase domain containing protein, SWIP-10, induces age- and glutamate-signaling dependent, dopamine neuron degeneration |
A primary goal of the recent investment in sequencing is to detect novel genetic associations in health and disease improving the development of treatments and playing a critical role in precision medicine . While this investment has resulted in an enormous total number of sequenced genomes , individual studies of complex traits and diseases are often smaller and underpowered to detect rare variant genetic associations . Existing genetic resources such as the Exome Aggregation Consortium ( >60 , 000 exomes ) and the Genome Aggregation Database ( ~140 , 000 sequenced samples ) have the potential to be used as controls in these studies . Fully utilizing these and other existing sequencing resources may increase power and could be especially useful in studies where resources to sequence additional samples are limited . However , to date , these large , publicly available genetic resources remain underutilized , or even misused , in large part due to the lack of statistical methods that can appropriately use this summary level data . Here , we present a new method to incorporate external controls in case-control analysis called ProxECAT ( Proxy External Controls Association Test ) . ProxECAT estimates enrichment of rare variants within a gene region using internally sequenced cases and external controls . We evaluated ProxECAT in simulations and empirical analyses of obesity cases using both low-depth of coverage ( 7x ) whole-genome sequenced controls and ExAC as controls . We find that ProxECAT maintains the expected type I error rate with increased power as the number of external controls increases . With an accompanying R package , ProxECAT enables the use of publicly available allele frequencies as external controls in case-control analysis .
Recent investments have produced sequence data on millions of people with the number of sequenced individuals continuing to grow . Although large sequencing studies , such as the Trans-Omics for Precision Medicine ( TopMed ) through the National Heart , Lung , and Blood Institute , exist , most sequencing data is gathered and processed in much smaller units of hundreds to thousands of samples . This is especially true in the study of diseases that are not very common but still likely to have a complex or oligogenic genetic architecture . These silos of data mean that most rare-variant association studies of uncommon , complex diseases are underpowered . Zuk et al . suggest that sample sizes in the tens , and perhaps hundreds of thousands are required for adequate power[1] . In addition to increasing the sample size of future studies , fully leveraging existing sequencing resources could increase power considerably and could be vital in scenarios where resources to sequence more samples are limited . Existing genetic resources such as the Exome Aggregation Consortium ( ExAC; >60 , 000 exomes ) [2] and more recently , the Genome Aggregation Database ( gnomAD; ~140 , 000 sequenced samples ) have the potential to be used as controls in studies of complex diseases . However , to date , these large , publicly available genetic resources remain underutilized , or even misused[3] , in large part due to the lack of statistical methods that can appropriately use this summary level data in complex disease studies . In particular , there is a large potential for bias caused by differences in sequencing technology , processing , and read depth[3] . Recently , Lee et al[4] developed iECAT , a method to incorporate publicly available allele frequencies from controls into an existing , unbiased , but underpowered case-control analysis . They found that iECAT controls for bias while increasing power to detect association to a genetic region and can be applied to both single variant analysis and gene region analysis using a SKAT-O framework[5] . iECAT cannot be applied to very rare variants such as singletons or doubletons and requires a set of controls that were sequenced and variant-called in parallel to the cases ( i . e . internal controls ) . Additionally , the type I error rate for iECAT increases as the size of the internal control sample set decreases relative to the internal cases . Thus , there is still the need for methods that can incorporate very rare variants and external controls without the explicit need for large internal control samples . Here we present Proxy External Controls Association Test ( ProxECAT ) , a method to estimate enrichment of rare variants within a gene region using internal cases and external controls . Our method addresses existing gaps such as using singleton and doubleton variants and requiring only external controls . Rare-variant tests in a gene are often limited to variants predicted to have a functional effect on the protein , hence discarding non-functional variants . This can result in greater power[6 , 7] . The development of ProxECAT was motivated by the observation that these discarded variants can be used as a proxy for how well variants within a genetic region are sequenced and called within a sample . ProxECAT is both simple and fast , requiring only allele frequency information , and is thus well suited to use publicly available resources such as ExAC and gnomAD . We evaluate ProxECAT in simulations , and empirical analysis of high depth of coverage ( 80x ) whole-exome sequenced childhood obesity cases ( N = 927 ) using both low-depth of coverage ( 7x ) whole-genome sequenced controls ( N = 3 , 621 ) , and ExAC ( N = 33 , 370 ) . Our method controls the type I error rate in simulations and yields the expected distribution of test statistics in real data settings . Given an accompanying R package , ProxECAT provides a robust and previously unavailable method to use publicly available allele frequencies as external controls in case-control analysis . This increases the utility of existing sequenced datasets to generate hypotheses and further research into the genetic basis of disease .
For a gene region-based test , we consider the following . Let Y denote the disease status , with Y = 1 and Y = 0 for internal case and external control status , respectively . We split the variants into those that are predicted to have a functional genetic impact and those that are not predicted to have a functional impact . We use the latter as the proxy variants . Let , x1f and x1p denote the counts of the functional and proxy rare variant alleles respectively for internal cases and x0f and x0p denote the counts of functional and proxy rare variant alleles respectively for external controls ( Table 1 ) . We model the observed variant minor allele counts in Table 1 as a random sample from four independent Poisson distributions , i . e . , X1f∼Pois ( λ1f ) , X0f∼Pois ( λ0f ) , X1p∼Pois ( λ1p ) , and X0p∼Pois ( λ0p ) . The derivation of the ProxECAT test statistic follows from the null hypothesis in Eq ( 1 ) : H0:λ1fλ1p=λ0fλ0p . ( 1 ) Using the method of Lagrange Multipliers and the constraint as defined by the null hypothesis , we find the maximum likelihood estimates ( MLEs ) of our parameters: λ1f , λ1p , λ0f , λ0p . Details are in S1 Appendix . Our MLEs under the null hypothesis are: λ^1f= ( x1f ) 2+x1fx0f+x1fx1p+x0fx1px1f+x0f+x1p+x0pλ^0f= ( x0f ) 2+x1fx0f+x0fx0p+x1fx0px1f+x0f+x1p+x0pλ^1p= ( x1p ) 2+x1fx1p+x1px0p+x1fx0px1f+x0f+x1p+x0pλ^0p= ( x0p ) 2+x0fx0p+x1px0p+x0fx1px1f+x0f+x1p+x0p . ( 2 ) We use the parameter estimates in the likelihood for the constrained null hypothesis . The MLEs for the unconstrained alternative hypothesis parameters are the variant allele counts for each group ( i . e . λ˜1f=x1f , λ˜0f=x0f , λ˜1p=x1p , λ˜0p=x0p ) . We then complete a likelihood ratio test ( LRT ) as the ratio of the constrained ( null hypothesis ) and unconstrained ( alternative hypothesis ) likelihoods , which , by Wilk’s theorem[8] can be transformed to have a chi-squared distribution with 1-df . It has been shown that functional variants have a lower minor allele frequency ( MAF ) distribution compared to synonymous variants[9] . Further , high-depth of coverage sequencing will detect a higher amount of variation at lower MAFs compared to low-depth of coverage sequencing[9 , 10] . This results in high-depth of coverage sequencing detecting more functional variation relative to synonymous variation compared to low-depth of coverage sequencing . To allow for scenarios where sequencing coverage varies considerably between cases and controls , we weight the observed functional variant minor allele counts . Specifically , we divide the number of minor alleles for functional variants by the median ratio of the number of minor alleles for functional to synonymous variants within cases ( M1 ) and within controls ( M0 ) separately: x1 , weightedf=x1fM1x0 , weightedf=x0fM0 . The weighted functional variant minor allele counts , x1 , weightedf and x0 , weightedf , are used in place of the observed functional variant minor allele counts , x1f and x0f , respectively to estimate the parameters in ( 2 ) . This new test statistic is called ProxECAT-weighted . By assuming a Negative Binomial distribution for the number of minor alleles in a region instead of a Poisson distribution , we extend ProxECAT to incorporate possible over-dispersion . We model the Negative Binomial distribution with the mean , λ , and over-dispersion , η , parameters where the distribution approaches Poisson as η becomes large ( S1 Fig ) . We simulated a variety of confounding scenarios . Case-control confounding represents systematic , genome-wide differences in the number of rare minor alleles observed in cases and controls due to differences in sequencing technologies and pipelines . Gene confounding refers to a gene having a higher or lower number of rare minor alleles than expected based on gene length . Gene confounding can occur in both cases and controls for a variety of reasons including differences in mutation rates , ability to detect variants , and annotation quality . Confounding can also occur when a particular gene region has a different number of rare minor alleles in cases and in controls due to sequencing differences between cases and controls . This confounding is distinct from case-control confounding in that it is isolated to a particular gene region rather than genome-wide . Here , we refer to this confounding as gene confounding only in cases . The simulation scenarios and parameters are presented in Table 2 and Supplemental Table 1 . The case-control LRT ( see Software and Statistical Analysis under Subjects and Methods ) was robust to gene confounding scenarios maintaining the appropriate type I error rate but had an increased type I error rate in the presence of case-control confounding . The case-only LRT maintained appropriate type I error rate in the presence of case-control confounding but was inflated in the presence of gene-confounding . The inflation in the type I error for the case-control LRT and the case-only LRT increased further when both gene and case-control confounding were present . This was especially true for the case-control LRT ( Fig 1 ) . Despite usually being within the 95% confidence interval for type I error , ProxECAT appeared to have a slight , but consistent inflation ( Supplemental Table 2 ) . This minor , but consistent inflation in the type I error rate can be addressed by using a more conservative significance threshold . We found that multiplying the significance level by 0 . 9 works well such that a 0 . 045 significance threshold maintains a 0 . 05 type I error rate , a 0 . 009 significance threshold maintains a 0 . 01 type I error rate , etc . Both the case-control LRT used here and ProxECAT assume a Poisson distribution and had inflated Type I Error rate in the presence of overdispersion ( S3 Table ) . ProxECAT-over , which assumes a Negative Binomial distribution instead of a Poisson distribution , corrects for overdispersion in simulations when the overdispersion parameter is known and overdispersion is not too extreme ( i . e . over-dispersion , η ≥ 5 ) ( S3 Table ) . Case-control LRT had higher power than ProxECAT under scenarios of no case-control confounding and given the same sample size ( S4 Table ) . However , the power of ProxECAT increased as the sample size of the external control set increased eventually reaching higher power than the case-control LRT for the same number of internal sequences ( Fig 1 ) . This increase in power for ProxECAT is due , in part , to being able to sequence more cases with ProxECAT ( N = 1000 ) than with a case-control LRT where sequencing resources need to be split between cases and controls ( here Ncases = 500 and Ncontrols = 500 ) . ProxECAT’s power increased while the type I error stayed the same under confounding scenarios where the number of functional variants in the cases increases ( S4 Table ) . To assess the fit of the Poisson distribution and specifically look for over dispersion , we simulated rare minor alleles assuming a Binomial distribution for each variant and compared these results to the theoretical Poisson distribution for the number of rare minor alleles in a genetic region . No over dispersion was apparent as the sampling mean and variance of the simulated scenarios were similar across different sample sizes , MAFs , and number of minor alleles per gene ( S2 and S3 Figs ) . When the expected number of minor alleles per gene was greater than 20 , the Poisson approximation for the number of minor alleles started to look more continuous . In other words , as the expected number of variants per gene decreased , the Poisson approximation became more discrete and multimodal ( S2 and S3 Figs ) . The theoretical distribution for the number of minor alleles per gene created from simulating genotypes for individual , independent variants from a Binomial distribution was more robust to discretization maintaining a mostly continuous distribution until the expected number of minor alleles per gene was equal to or less than four . We evaluated ProxECAT using 926 cases from the Severe Childhood Onset Obesity Project ( SCOOP ) sample as cases and either 3 , 621 UK10K Cohort or 33 , 370 ExAC non-Finnish Europeans as controls . High-depth of coverage WES SCOOP cases vs . low-depth of coverage WGS UK10K Cohort controls had an inflated distribution of test statistics for the case-control LRT both at the center ( lambda = 1 . 971 ) and in the tail of the distribution . While we did not observe inflation in the tail of the distribution for ProxECAT ( Fig 2 ) , there was a large inflation in the overall distribution of test statistics ( lambda = 3 . 151 ) . We observed a much higher ratio of the number of minor alleles in functional to synonymous variants per gene for the high-depth of coverage cases , median = 3 . 00 , versus the low-depth of coverage controls , median = 1 . 89 ( Table 3 ) . ProxECAT-weighted , which adjusts for this systematic difference in sequencing coverage , resulted in a distribution of observed test statistics that more closely matches the expected distribution ( lambda = 1 . 026 , Fig 2 ) . A large strength of this method is the ability to use allele frequency data directly , rather than individual level allele calls . To assess the ability of this method to use publicly available allele frequency data , we used ExAC allele frequencies as controls for the SCOOP cases . The standard case-control LRT was inflated at both the median , lambda = 1 . 713 , and tail ( Fig 3 ) while our method maintained the expected distribution of test statistics . Because the depth of sequencing coverage is comparable and high for both SCOOP cases and ExAC controls , ProxECAT-weighted produced similar results to the standard , un-weighted test . For both analyses , filtering to very rare variants was essential to avoid inflation in the distribution of observed test-statistics . This can be accomplished using moderate internal frequency filters and an external dataset such as 1000Genomes ( MAF < 1% ) as in the SCOOP vs UK Cohort analysis or using more stringent internal frequency filters ( MAF < 0 . 1% ) and no external dataset as in the SCOOP vs ExAC analysis . Four genes , passing a 0 . 01 level of significance in both the SCOOP vs UK10K Cohort analysis and in the SCOOP vs ExAC analysis , are shown in Table 4 . These results are putative novel obesity candidates meriting further replication . MIB2 may be of particular interest as it is associated with decreased body weight in mice in the International Mouse Phenotyping Consortium ( p-value = 7 . 49*10−10 , http://www . mousephenotype . org/data/genes/MGI:2679684 ) . Additional genes with the smallest p-values are found in S5–S7 Tables . Within the SCOOP vs . ExAC analysis , we completed a sensitivity analysis using three increasingly broad proxy selection strategies of Sequence Ontology terms: ( 1 ) synonymous ( SYN ) ; ( 2 ) predicted low impact rating from Ensembl [11] ( LOW ) ; and ( 3 ) not in our functional category ( NOT FUNC ) . These strategies are nested with LOW Sequence Ontology terms included in NOT FUNC , and SYN Sequence Ontology terms included in both LOW and NOT FUNC . We assessed consistency across the number of alternate alleles and in the distribution of test statistics across the three proxy selection strategies . As expected given the nested nature of the proxy selection strategies , SYN had a smaller number of alternate alleles than either LOW or NOT FUNC and LOW had a smaller number of alternate alleles than NOT FUNC . SYN and LOW proxy selection strategies produced similar numbers of alternate alleles per gene while the correlation was lower for NOT FUNC with either SYN or LOW ( S4 Fig ) . We found similar consistency in the distributions of test statistics between the proxy selection strategies ( S5 Fig ) .
We propose a new method , ProxECAT , to test for enrichment of an accumulation of very rare variant alleles in a gene-region using publicly available external allele frequencies . ProxECAT only requires allele frequencies and uses exclusively external controls enabling the use of large , publicly available datasets such as ExAC and gnomAD . Analyses in simulations and using UK10K Cohort and ExAC as control sets for childhood obesity cases show that ProxECAT keeps the type I error rate and expected distribution of test statistics under control despite differences in sequencing technology and processing . Because ProxECAT uses external controls , additional resources can be devoted to sequencing cases . This results in greater power for ProxECAT compared to the case-control LRT test for the same number of internally sequenced individuals . There are several limitations to the method proposed here . First , ProxECAT has a minor , but consistent inflation in the type I error rate . This limitation is easily addressed by using a more conservative significance threshold . Second , ProxECAT cannot currently include covariates such as sex , and ancestry . Thus , internal cases and external controls should be closely matched by ancestry and , as with any association study , findings will need independent replication preferably using a study where cases and controls are sequenced and processed in parallel . Third , the current approach does not enable internal controls to be analyzed along with external controls . While two analyses can be done in parallel and compared , it would be ideal to incorporate internal and external controls into the same statistical test . We are actively working on extensions to address these limitations . It is important to highlight that research utilizing solely external controls is more susceptible to confounding due to known or unknown factors . Thus , any genes identified using ProxECAT or any method that uses only external controls should be carefully followed up in further validation , replication , and functional studies . ProxECAT provides a robust approach to using allele frequencies from existing , publicly available sequencing data enabling case-control analysis when no or limited internal controls exist . ProxECAT uses the insight that readily available genomic information often discarded from analyses ( here synonymous variation ) can adjust for sizeable confounding due to differences in data generation . In the era of big data , we hope that both this insight and the ProxECAT method will enable additional genetic discoveries and will also motivate future methodological advancements in analyzing data across technologies and platforms .
All tests were implemented using functions from our accompanying R package ProxECAT ( https://github . com/hendriau/ProxECAT ) . Our primary test , which can model both ProxECAT and ProxECAT-weighted , was implemented with the proxecat function and our secondary test modeling over-dispersion was implemented using the proxecat . over function . We also implemented a case-control LRT to test for enrichment of rare , functional variant alleles in cases vs . controls and a case-only LRT similar to that performed by Zhi and Chen in 2012 [12] . The case-only LRT tests for enrichment of rare alleles for functional variants in each gene of interest compared to the genome-wide average number of minor alleles per gene in cases only adjusting for the length of each gene . Unless otherwise specified , we assumed the data follow a Poisson distribution for all LRTs . Within each case-control confounding simulation , we simulated 20 , 000 independent genes under four gene-disease association and gene confounding states . The four distinct gene states are: ( 1 ) association with case status and no gene confounding , ( 2 ) association with case status and gene confounding , ( 3 ) no association with case status and gene confounding , ( 4 ) no association with case status and no gene confounding . The number of rare minor alleles per gene was simulated under a Poisson distribution or an over-dispersed Poisson modeled using a Negative Binomial parameterization using the R functions rpois and rnbinom , respectively . The mu and size parameters in rnbinom represent the mean and over-dispersion , respectively . To assess the fit of the Poisson distribution , we simulated the number of each genotype group for each variant assuming Hardy-Weinberg Equilibrium and a Binomial distribution where p was the MAF . We varied the MAF ( 0 . 0001 , 0 . 0005 , 0 . 001 , 0 . 005 ) , the sample size ( 1000; 10 , 000 ) , and the maximum number of variable variants within the gene region ( 5 , 10 , 20 ) . We then assessed how closely the simulated distributions of the number of minor alleles observed per gene region matched a theoretical Poisson distribution where λ was the mean from each simulation scenario . Whole-exome sequenced ( WES ) cases are from the Severe Childhood Onset Obesity Project ( SCOOP ) cohort[6 , 13] , which is a self-reported UK European subset of the Genetics of Obesity Study ( GOOS ) . GOOS includes individuals with severe early-onset obesity body mass index ( BMI ) standard deviation score ( SDS ) > 3 and age at onset of obesity < 10 years . Leptin deficient individuals ( identified by biochemical measurement ) and those with mutations in the MC4R gene were excluded . We used VerifyBamID ( v1 . 0 ) [14] and a threshold of ≥3% to identify contaminated samples . We computed principal components with the 1000Genomes Phase I integrated call set[9] using EIGENSTRAT v4 . 2[15] to identify non-Europeans , and pairwise identity by descent estimates from PLINK v1 . 07[16] with a threshold of ≥0 . 125 to identify related individuals . Contaminated , non-European , and related samples were removed resulting in 927 SCOOP cases for analysis . Details about sequencing and variant calling for the SCOOP cases , as part of the UK10K exomes can be found elsewhere[17] . All participants gave written informed consent and all methods were performed in accordance with the relevant laboratory/clinical guidelines and regulations . The whole-genome sequenced ( WGS ) controls consist of the UK10K Cohort sample , comprised of two population cohorts: the Avon Longitudinal Study of Parents and Children ( ALSPAC ) and the TwinsUK study from the Department of Twin Research and Genetic Epidemiology at King’s College London ( TwinsUK ) . We used allele frequency data for 3 , 621 individuals that passed sample QC as described elsewhere[17] . We used allele frequency values for the N = 33 , 370 non-Finnish European ( NFE ) group from the ExAC variant site dataset version 1 . 0 ( http://exac . broadinstitute . org/downloads ) [2] . To focus on rare or very rare variants , we limited to variants below a pre-specified MAF threshold in both cases and controls . We used MAF ≤ 1% in the SCOOP cases vs . UK10K cohort controls analysis and MAF ≤ 0 . 1% in the SCOOP vs . ExAC analysis . For the SCOOP cases vs . UK10K controls analysis , we also applied external filtering excluding variants with a MAF > 1% in at least one of the 1000Genomes five primary ancestry groups . Exclusion by 1000Genomes MAF was not possible when using ExAC as 1000Genomes sample are included in the ExAC genotype frequencies . We explored the distribution of test statistics over several thresholds for the minimum number of functional ( xf ) and proxy ( xp ) variants within each gene ( 5 , 10 , and 20 ) . Analysis regions were limited to the intersection of respective target regions for SCOOP vs . UK10K Cohort and for SCOOP vs . ExAC . All variant annotation was applied using the GRCh37 human reference . The Ensembl Variant Effect Predictor ( VEP , http://www . ensembl . org/info/docs/tools/vep/index . html [11] v79 and v90 . 1 ) from Ensembl was used to add variant consequence annotations for SCOOP vs . UK10K Cohort and SCOOP vs . ExAC respectively . We defined functional variation using the following Sequence Ontology terms[18] variant consequences: splice_donor_variant , splice_acceptor_variant , stop_gained , frameshift_variant , stop_lost , initiator_codon_variant , inframe_insertion , inframe_deletion , missense_variant , and protein_altering_variant . Variants were considered synonymous if they had the “synonymous_variant” flag . We defined the LOW proxy group as having a predicted low impact rating from Ensembl , SO terms: splice_region_variant , incomplete_terminal_codon_variant , stop_retained_variant , synonymous_variant . We used quantile-quantile plots ( QQ-plots ) to assess the resulting distribution of test statistics from the real data applications . Specifically , we looked at the middle of the distribution of test statistics as assessed by the lambda value ( i . e . the median of the observed test statistic divided by the median of the expected test statistic ) and the tail of the distribution of test statistics , which we assessed visually . | Recent investments have produced sequence data on millions of people with the number of sequenced individuals continuing to grow . Although large sequencing studies exist , most sequencing data is gathered and processed in much smaller units of hundreds to thousands of samples . These silos of data result in underpowered studies for rare-variant association of complex diseases . Existing genetic resources such as the Exome Aggregation Consortium ( >60 , 000 exomes ) and the Genome Aggregation Database ( ~140 , 000 sequenced samples ) have the potential to be used as controls in rare variant studies of complex diseases and traits . However , to date , these large , publicly available genetic resources remain underutilized , or even misused , in part due to the high potential for bias caused by differences in sequencing technology and processing . Here we present a new method , Proxy External Controls Association Test ( ProxECAT ) , to integrate sequencing data from different , previously incompatible sources . ProxECAT provides a robust approach to using publicly available sequencing data enabling case-control analysis when no or limited internal controls exist . Further , ProxECAT’s motivating insight , that readily available but often discarded information can be used as a proxy to adjust for differences in data generation , may motivate further method development in other big data technologies and platforms . | [
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"... | 2018 | ProxECAT: Proxy External Controls Association Test. A new case-control gene region association test using allele frequencies from public controls |
Tribendimidine is an anthelminthic drug with a broad spectrum of activity . In 2004 the drug was approved by Chinese authorities for human use . The efficacy of tribendimidine against soil-transmitted helminths ( Ascaris lumbricoides , hookworm , and Trichuris trichiura ) has been established , and new laboratory investigations point to activity against cestodes and Strongyloides ratti . In an open-label randomized trial , the safety and efficacy of a single oral dose of albendazole or tribendimidine ( both drugs administered at 200 mg for 5- to 14-year-old children , and 400 mg for individuals ≥15 years ) against soil-transmitted helminths , Strongyloides stercoralis , and Taenia spp . were assessed in a village in Yunnan province , People's Republic of China . The analysis was on a per-protocol basis and the trial is registered with controlled-trials . com ( number ISRCTN01779485 ) . Both albendazole and tribendimidine were highly efficacious against A . lumbricoides and , moderately , against hookworm . The efficacy against T . trichiura was low . Among 57 individuals who received tribendimidine , the prevalence of S . stercoralis was reduced from 19 . 3% to 8 . 8% ( observed cure rate 54 . 5% , p = 0 . 107 ) , and that of Taenia spp . from 26 . 3% to 8 . 8% ( observed cure rate 66 . 7% , p = 0 . 014 ) . Similar prevalence reductions were noted among the 66 albendazole recipients . Taking into account “new” infections discovered at treatment evaluation , which were most likely missed pre-treatment due to the lack of sensitivity of available diagnostic approaches , the difference between the drug-specific net Taenia spp . cure rates was highly significant in favor of tribendimidine ( p = 0 . 001 ) . No significant adverse events of either drug were observed . Our results suggest that single-dose oral tribendimidine can be employed in settings with extensive intestinal polyparasitism , and its efficacy against A . lumbricoides and hookworm was confirmed . The promising results obtained with tribendimidine against S . stercoralis and Taenia spp . warrant further investigations . In a next step , multiple-dose schedules should be evaluated .
There is a growing awareness of the intolerable burden due to the so-called neglected tropical diseases [1] . Hence , new initiatives are underway for their control [2] . For helminth infections , the mainstay of control in high-burden areas rests on regular administration of anthelminthic drugs [1]–[5] . However , only a handful of drugs that have been developed many years ago are available [6] , and there is considerable concern that resistance might develop , e . g . , following repeated exposure of helminths to sub-curative doses . Reduced efficacy of common anthelminthic drugs is a major problem in veterinary medicine already , but for humans , it is of no clinical relevance thus far [7] . Another issue is that in a world where intestinal polyparasitism is common , yet neglected [8]–[12] , and frequently used treatment regimens are only effective against a limited number of helminths [6] , some species which are not effectively controlled by common drugs might increase in relative frequency , e . g . , Strongyloides stercoralis and Taenia spp . High prevalences of soil-transmitted helminths ( Ascaris lumbricoides , hookworm , and Trichuris trichiura ) and , consequently , a high level of intestinal multiparasitism , have recently been described from Nongyang , a settlement in Manguo administrative village , located in southwest Yunnan province , People's Republic of China [13] . Whilst S . stercoralis is endemic in the People's Republic of China [14] , the local epidemiology of this parasite is not well understood . A prevalence of ∼15% was found in Nongyang [15] . Taeniasis and infections with the larval stage of Taenia solium that causes cysticercosis , have been documented throughout the People's Republic of China . Most infections occur in counties inhabited by non-Han nationalities whose traditional diets include the consumption of raw or undercooked meat [14] , [16] . However , there is a paucity of epidemiologic data for taeniasis from the People's Republic of China , at least in the English literature [17] . In a recent cross-sectional survey , we found an egg-prevalence of Taenia spp . of 3 . 5% among 3220 individuals in Eryuan county , northwest Yunnan province [18] . In Nongyang , the prevalence of Taenia spp . was 5 . 1% [13] . The benzimidazoles , i . e . , albendazole and mebendazole , are the most widely used drugs for the control of soil-transmitted helminthiasis [1] , [19] . Both drugs have some effect against S . stercoralis and Taenia spp . , but triple doses are recommended to achieve high cure rates [6] , [20] . The drug of choice for treating S . stercoralis is ivermectin [21] , [22] . Praziquantel and niclosamide are the recommended drugs against Taenia spp . [23]–[26] . Large-scale administration of ivermectin is the cornerstone of control programs targeting filarial infections , most notably onchocerciasis [27] . However , Onchocerca volvulus is not endemic in the People's Republic of China . Additionally , ivermectin is highly efficacious against A . lumbricoides , shows some activity against T . trichiura , but fails to cure hookworm infections . Since hookworms are common in the People's Republic of China , ivermectin is not commonly used for soil-transmitted helminth control in this country . These issues might explain why ivermectin is registered for human use in the People's Republic of China , but not readily available . Ivermectin , however , is produced at large scale for veterinary medicine , the bulk of which is exported . Tribendimidine is an anthelminthic drug that has been registered in the People's Republic of China for use in humans [6] , [28] . Tribendimidine is a symmetrical diamidine derivative of amidantel [28] , its CAS registration number is 115103-15-6 . Used at the current standard dose of 200 mg for children aged 5 to 14 years , and 400 mg for individuals aged ≥15 years , tribendimidine is safe and efficacious against A . lumbricoides , hookworm , and Enterobius vermicularis [28] . It also shows some activity against T . trichiura [29] , [30] , cestodes [28] , and some trematodes [31] , [32] . New research revealed in vitro and in vivo activity of tribendimidine against Strongyloides ratti [33] . The objective of this study was to assess the safety and efficacy of single-dose oral tribendimidine for treating intestinal helminth infections in a rural setting where polyparasitism is common , with a focus on the effects on S . stercoralis and Taenia spp . Comparison is made with single-dose oral albendazole as half of the participants were administered either drug . The primary outcome measures were the reduction of the infection prevalence of intestinal helminths , and the frequency and severity of adverse events . The secondary outcome measure was the Kato-Katz-derived egg count reduction of common soil-transmitted helminths . Multiple stool samples were collected before and after drug administration and examined by different diagnostic tools to enhance the diagnostic sensitivity .
The study was carried out in Nanweng , a village in Menghai county , Xishuangbanna prefecture , Yunnan province , People's Republic of China , from May to July 2007 . Nanweng has 81 resident families and is situated on the slope of a mountain , 1650 m above sea level at 21 . 77 N latitude and 100 . 40 E longitude . The village is exclusively inhabited by the Bulang ethnic group . Its economic basis is provided by the surrounding tea plantations and the more distant , partially irrigated rice and other crop fields . Untreated tap water is delivered to every house but no sanitation facilities are available in the entire village . The leader of the Menghai-based county Center for Disease Control and Prevention ( CDC ) briefed the village authorities about the study . Village leaders then informed the residents who were all invited to participate . Over a 3-week period , 20–30 families were enrolled weekly , and household as well as individual questionnaires that have been used before [15] , [18] were administered . Children below the age of 15 years were assisted by their parents or legal guardians to answer the questions , and infants younger than 2 years were excluded from the study . Participants were asked to provide a large stool sample in pre-labeled collection containers . Filled containers were collected daily and exchanged by empty ones with the goal to obtain 3 stool samples per participant . The evaluation of the treatment efficacy commenced 2 weeks post-treatment and followed the same field procedures . Stool samples were collected over a 2-week period , again aiming to obtain 3 samples per study participant . Stool samples were collected in the village between 07:00 and 09:00 a . m . , transferred to the laboratory in Menghai city , and processed within a maximum of 6 hours of receipt . First , ∼10 g of stool were placed on a gauze which was embedded on a wire mesh in a glass funnel equipped with a sealable rubber tube , the so-called Baermann device [33] . The funnel was then filled with de-ionized water and illuminated from below with an incandescent bulb . Second , for the Koga agar plate test [34] , ∼2 g of stool were placed in the centre of a 9 cm Petri dish with freshly prepared nutrient agar . Third , a single 41 . 7 mg Kato-Katz thick smear [35] was prepared on a microscope slide and helminth eggs were enumerated after a clearing time of 30–60 min . The lowest 45 ml of the liquid in the Baermann funnel were drained after 2 h , centrifuged , and the sediment was examined for S . stercoralis larvae at low magnification ( 40× ) . Koga agar plates were inspected for helminth larvae at similar magnification after a 2-day incubation period at 28°C in a humid chamber . Subsequently , all plates were rinsed with 12 ml sodium acetate–acetic acid–formaline ( SAF ) solution [36] , gently scraped , and the eluent was centrifuged . The sediment was examined for helminth larvae , i . e . , S . stercoralis and hookworms . Helminth larvae were differentiated based on established anatomical criteria , i . e . , considering the buccal cavity and genital primordium of first-stage ( L1 ) S . stercoralis larvae ( buccal cavity: short; genital primordium: prominent ) , and the tip of the tail of third-stage ( L3 ) S . stercoralis larvae ( tip of the tail: cut ) . Samples were considered positive if larvae were detected at any stage and by any test . Helminth eggs in the Baermann and Koga sediment were also noted . Study participants who had submitted at least 1 stool sample were listed according to their identification number in ascending order . Subsequently , a random sequence of 0's and 1's was generated in Excel ( Microsoft Corp . ) by the study coordinator , and aligned with the list of eligible study participants . Individuals matched with a “0” were assigned albendazole , whereas those with a “1” were assigned tribendimidine . Albendazole and tribendimidine were purchased from Shanxi Hanwang Medicine Co . Ltd . ( Han Zhong , People's Republic of China ) and Shandong Xinhua Pharmaceutical Co . Ltd . ( Zibo , People's Republic of China ) , respectively . Drugs were administered as single 200 mg oral dose for children aged 5 to 14 years , and 400 mg for individuals ≥15 years of age . Drugs at the appropriate dosage according to participants' age were packed in identical envelopes labeled with the participant's name only . Hence , our study was an open-label trial , i . e . , participants did not know which drug they received . Participants were asked to avoid alcohol consumption on the day of drug administration . After dinner time , teams of fieldworkers visited the village , met the participants at home and asked them about signs of acute or chronic illness , and alcohol consumption . Women aged ≥14 years were asked about pregnancy . Drugs were handed out together with fresh bottled water to healthy , non-drunk and non-pregnant participants , and drug intake was observed . Those treated were asked to report any potential drug-related signs or symptoms including sleeping troubles to the accompanying medical doctor . The study was approved by the institutional research commission of the Swiss Tropical Institute ( Basel , Switzerland ) . Ethical clearance was obtained from the Ethics Committee of Basel ( EKBB ) , Switzerland ( reference no . 149/07 ) and the Ethics Committee of the National Institute of Parasitic Diseases , Chinese Center for Disease Control and Prevention ( Shanghai , People's Republic of China ) . The trial is registered with controlled-trials . com under the registration number ISRCTN01779485 . The study procedures , potential risks , and benefits were explained to the village leaders . After their consent to perform the study , field workers visited the homes of the selected families where detailed information was provided to all potential participants , and questions were answered . Emphasis was placed on voluntary participation and the option to quit the study at any moment without further obligation . Written confirmation that full information had been provided and individual participation was voluntary ( informed consent ) was obtained from the head of each participating household or a literary substitute ( adult child or relative ) , and this procedure was approved by the above-mentioned ethical committees . A single 200 mg ( for children aged 5 to 14 years ) , or 400 mg ( individuals aged ≥15 years ) oral dose of albendazole was offered to those participants who were not eligible for randomization because they had failed to provide any stool sample . The assessment of their health status and the treatment procedures followed the same protocol as for study participants , but the treatment outcome was not assessed . Locally-used remedies for Taenia spp . infection and ivermectin for treating S . stercoralis at the standard dose ( 200 µg/kg ) were provided at the end-of-study follow-up . Finally , albendazole was provided to the village authorities for later distribution to untreated inhabitants and participants who still harbored active infections . The target sample size was 130 individuals , based on the following assumptions: Prevalence of S . stercoralis: 20%; efficacy of tribendimidine and albendazole against S . stercoralis: 85% ( similar to ivermectin at standard dosage [37] ) and 0% , respectively , with a confidence level of 95% and a power of 80% . The questionnaire data were double-entered and cross-checked in EpiData version 3 . 1 ( EpiData Association; Odense , Denmark ) . The laboratory data were examined for internal consistency , and merged with the questionnaire data . Statistical analysis was done with STATA version 9 . 2 ( StataCorp; College Station , USA ) . Our final study cohort consisted of individuals aged ≥5 years who had submitted at least 2 stool samples at baseline , did not suffer from any chronic or acute illness , had not drunk alcohol on the day of drug administration , for women were not pregnant , had taken the randomly assigned drug , and had again at least 2 stool samples examined at the end-of-study survey . Thus , statistical analyses only considered treated individuals with complete diagnostic data records , stratified by drug ( see Figure 1 , end points of the right arm ) . Multiple stool readings at baseline and follow-up were required in order to boost diagnostic sensitivity [13] , [15] . The infection status was determined based on the pooled results from the different diagnostic methods ( i . e . , soil-transmitted helminths and Taenia spp . : all tests; S . stercoralis: Baermann and Koga agar plate tests ) . Pearson's χ2-test and Fisher's exact test , as appropriate , were used to assess the association between infection and demographic variables . Treatment outcomes by drug and the differences between albendazole and tribendimidine were explored by calculating drug-specific prevalence reductions , and analyzing the difference between the observed cure rates ( 2-sample test of proportions ) . The infection intensity for the common soil-transmitted helminths was determined based on the quantitative Kato-Katz thick smear readings , multiplied by a factor of 24 . Infection intensities for individual study participants were obtained by calculating the arithmetic mean of the available egg counts . The arithmetic mean of the averaged egg counts among the infected yielded a summery measure of infection intensity among the infected population . The individual infection intensities were subsequently stratified according to the classification proposed by the World Health Organization ( WHO ) [38] . The effects of albendazole and tribendimidine on the infection intensities in the respective groups were analyzed using a paired t-test , and drug-specific infection intensity reductions were compared by a 2-sample test of proportions .
We counted 294 family members , aged above 2 years , in the 81 resident families . Another 60 individuals were recorded in the village registry but they had either left the village , were younger than 2 years , or refused to answer the questionnaire . Figure 1 shows that 106 ( 36% ) of the eligible participants provided none ( n = 57 ) , or only a single ( n = 49 ) stool sample of sufficient quantity to perform all diagnostic tests . The remaining 188 individuals ( 64% ) were 5 to 87-year-old and among them , 17 could not be treated due to pregnancy ( n = 8 ) , ill health ( n = 5 ) or other reasons ( n = 4 ) . The randomization of the 171 participants with complete parasitological baseline data who were eligible for treatment resulted in equal-sized groups for single-dose oral tribendimidine or albendazole administration , but stool sample submission at baseline and actual treatment rates were somewhat lower among tribendimidine recipients . Of the 91 participants treated with albendazole , 2 to 3 sufficiently large stool samples were available from 66 individuals ( 73% ) at the end-of-study follow-up . A similar stool sample submission rate was observed for the 80 tribendimidine recipients ( 71% ) . The final cohort consisted of 123 individuals who received either albendazole ( n = 66 ) or tribendimidine ( n = 57 ) , and submitted at least 4 ( 2 pre- plus 2 post-treatment ) sufficiently large stool samples to carry out the full range of diagnostic tests . While drop-out rates were similar for males and females ( 42 . 2% versus 41 . 5% ) , full participation ranged from 21% for 10 to 14-year-olds to 58% for those aged ≥40 years ( data not shown ) . Considering the joint results of the 2 to 3 Kato-Katz , Koga agar plate and Baermann tests , our final cohort showed high prevalences of T . trichiura ( 87 . 8% ) , hookworm ( 74 . 8% ) , and A . lumbricoides ( 72 . 4% ) . The prevalences of Taenia spp . and S . stercoralis were 26 . 0% and 17 . 9% , respectively . Table 1 shows that Taenia spp . infections were significantly more prevalent among males ( 33 . 9% ) than females ( 18 . 0% , χ2 = 4 . 01 , degrees of freedom ( df ) = 1 , p = 0 . 045 ) and increased with age , albeit not significantly ( p = 0 . 163 ) . An increase with age was also noted in the prevalence of S . stercoralis infections . No S . stercoralis were diagnosed among pre-school children and students as opposed to farmers ( p = 0 . 121 ) ; Taenia spp . was also more common among farmers ( 27 . 3% ) than pre-school children and students ( 8 . 3% , p = 0 . 292 ) . Illiterates had a lower A . lumbricoides prevalence than those with formal education ( 50 . 0% vs . 79 . 2% , χ2 = 9 . 32 , df = 1 , p = 0 . 002 ) , reflecting the generally lower prevalence among older age groups . Table 1 also summarizes the infection intensities for the common soil-transmitted helminths according to the Kato-Katz results . While most hookworm and T . trichiura infections were of light intensity , a considerable number of moderate and a few heavy infections were noted for A . lumbricoides . As detailed in Table 2 , single-dose oral albendazole and tribendimidine significantly reduced the prevalence of A . lumbricoides ( albendazole: from 75 . 8% to 0; tribendimidine: from 68 . 4% to 5 . 3%; both p<0 . 001 ) , and hookworm ( albendazole: from 69 . 7% to 21 . 2%; tribendimidine: from 80 . 7% to 38 . 6%; both p<0 . 001 ) . Whilst the difference between the drug-specific cure rates against A . lumbricoides showed borderline significance in favor of albendazole , there was no difference in the efficacy of the two drugs against hookworm . Although neither albendazole nor tribendimidine resulted in a significant reduction in the prevalence of T . trichiura , single-dose oral albendazole was significantly more efficacious than tribendimidine in curing T . trichiura ( p = 0 . 014 ) . Single-dose albendazole and tribendimidine significantly reduced the arithmetic mean egg counts among those who were infected at baseline ( all p<0 . 05 ) except for tribendimidine administered to individuals with T . trichiura ( p = 0 . 136 ) . Both drugs decreased the mean egg counts of A . lumbricoides and hookworm to a similar extent , but mean T . trichiura egg counts declined significantly more following albendazole than tribendimidine treatment ( p = 0 . 005 ) . Single-dose albendazole and tribendimidine resulted in prevalence reductions of S . stercoralis of 6 . 1% and 10 . 5% , respectively , which was not statistically significant ( both p>0 . 05 ) . The prevalence of Taenia spp . was reduced from 25 . 8% to 10 . 6% after albendazole administration ( p = 0 . 024 ) , whilst among the tribendimidine recipients , the prevalence was lowered from 26 . 3% to 8 . 8% ( p = 0 . 014 ) . Table 2 also shows that after administration of albendazole , S . stercoralis larvae could still be found among 7 of the previously 11 infected individuals ( observed cure rate: 36 . 4% ) . Among those treated with tribendimidine , 6 out of 11 individuals were larvae-free ( observed cure rate: 54 . 5%; difference: 18 . 1% , p = 0 . 394 ) . The baseline Taenia spp . prevalence of 25 . 8% and 26 . 3% among those given albendazole and tribendimidine was reduced by 58 . 8% and 66 . 7% , respectively ( difference: 7 . 9% , p = 0 . 645 ) . Taking into account S . stercoralis and Taenia spp . infections that had only been recognized at treatment evaluation ( these infections were most likely missed pre-treatment due to the lack of diagnostic sensitivity of the available tests ) , the efficacy of the drugs was lower ( Table 3 ) . In both treatment groups , S . stercoralis was diagnosed in 2 individuals previously declared uninfected . Hence , the net cure rate was 18 . 2% for albendazole and 36 . 4% for tribendimidine recipients ( difference: 18 . 2% , p = 0 . 338 ) . The number of “new” Taenia spp . infections in the albendazole group diagnosed during follow-up equaled the number of recoveries ( n = 10 ) , resulting in a zero overall cure rate . Among tribendimidine recipients , only 2 additional Taenia spp . infections were found; the prevalence reduction showed borderline significance ( −14 . 0% , p = 0 . 058 ) . The net cure rate of Taenia spp . in the tribendimidine recipients was 53 . 3% , significantly different from the albendazole group ( difference: 53 . 3% , p = 0 . 001 ) . No adverse events were mentioned by participants treated with single-dose oral albendazole . In the tribendimidine group , an 87-year-old woman reported mild sleeping disorders , headache , dizziness , and gastrointestinal symptoms , including a single episode of vomiting . This subject had a light infection with T . trichiura and hookworm at baseline . Upon treatment evaluation , the participant did not submit any further stool samples , and hence was excluded from the final cohort .
To our knowledge , this is the first investigation assessing the safety and efficacy of single-dose tribendimidine for treating S . stercoralis and Taenia spp . infections , and the first clinically-monitored use of tribendimidine in a setting with high rates of intestinal multiparasitism . Indeed , infections with one of the three main soil-transmitted helminths were found in 72 . 4–87 . 8% of the study participants , and only 4 of the 123 individuals in our final cohort ( 3 . 3% ) harbored none of these helminths . The prevalence of S . stercoralis and Taenia spp . at baseline was 17 . 9% and 26 . 0% , respectively . Our study was an open-label trial , comparing a single oral dose of albendazole with that of tribendimidine , with both drugs administered at either 200 mg or 400 mg according to participants' age . The final study cohort comprised less than 50% of those initially contacted . Whilst the cohort had a similar sex distribution than the total population of Nanweng village , it was considerably biased toward older age groups . We screened multiple stool samples for intestinal helminths and randomly assigned the participants to either albendazole or tribendimidine . Treatment outcome was assessed 2 to 4 weeks after dosing using multiple stool samples and a diversity of diagnostic approaches . The prevalence of S . stercoralis was not significantly reduced by either albendazole or tribendimidine , and no significant drug-specific difference was observed . Among individuals infected with Taenia spp . at baseline , the observed cure rates of 58 . 8% for albendazole and 66 . 7% for tribendimidine showed statistical significance ( both p<0 . 05 ) . During follow-up , however , additional infections were found , mainly Taenia spp . among those who had received albendazole , and the difference between the drug-specific overall cure rates was 53 . 3% ( p = 0 . 001 ) . The observed cestocidal effect of tribendimidine , for the time being , should rather be regarded as an indication of a possible activity than as a proof-of-concept . This is due to the obvious diagnostic challenges encountered in field-based clinical studies on large cestodes . In the current trial , albendazole generally performed slightly better than tribendimidine in curing common soil-transmitted helminth infections . The most notable difference was seen with T . trichiura , confirming earlier observations that single-dose albendazole is somewhat more efficacious than single-dose tribendimidine against this nematode [6] . The observed cure rates against hookworm and T . trichiura following single-dose albendazole are rather low compared to the results of a recent meta-analysis of this and other WHO-recommended anthelminthics commonly used against common soil-transmitted helminth infections [39] . We speculate that this observation is rather reflecting the rigorous diagnostic approach employed than an unusually low susceptibility of local hookworm and T . trichiura to albendazole . For example , hookworm infections could not only be detected by the widely used Kato-Katz technique , but also by the Koga agar plate method . However , the low cure rates observed in this study should also be seen as a warning sign and call for monitoring of drug efficacy and the potential emergence of drug resistance [38] . The inclusion of only 123 individuals who met our sample submission requirements into the final study cohort reduced the reported compliance rate but increased the reliability of the results due to the increased overall sensitivity of the employed diagnostic methods [15] . Indeed , a lower prevalence was found among those 175 participants who had at least 1 stool sample analyzed , but the drug-specific efficacies were similar ( data not shown ) . The discovery of notable numbers of infections among those who were deemed negative before treatment can be explained by at least 2 mechanisms , or a combination thereof . First , it is conceivable that the baseline evaluation fell within the prepatent period of recent infections . Second , it is well known that parasitological diagnosis of both S . stercoralis [40] and Taenia spp . [25] , [41] lacks sensitivity . For S . stercoralis , the main remedy for this challenge is screening of multiple stool specimens [21] , whilst for Taenia spp . , sensitive coproantigen enzyme-linked immunosorbent assay ( ELISA ) tests provide valuable alternatives [23] , [25] , [26] , [41] . The current diagnostic ‘gold’ standard to confirm treatment success in taeniasis is the recovery of the tapeworm scolex . Alternatively , the absence of proglottids from stools and underwear over a period of 3 months also provides solid proof of cure . However , such extensive observation is usually only feasible in hospital settings . Re-infection after treatment can almost certainly be excluded for Taenia spp . , and it is rather unlikely for S . stercoralis . We speculate that in our study , the limited sensitivity of the diagnostic tools was more significant since the 3 to 5-week period between baseline and follow-up investigation is rather short for any notable level of re-infections . We are confident that our results are valid despite the imperfect sensitivity of the employed diagnostic tests , not least due to our rigorous sampling effort . This assumption is supported by the following observations . For S . stercoralis , the numbers of “new” infections at follow-up was similar in both treatment groups ( both n = 2 ) , thus reducing the observed cure rate but not affecting the overall conclusion that both drugs exhibit some effect at the employed dosage . A mathematical model [42] for the prediction of “true” prevalence further suggested an underestimation of the S . stercoralis prevalence by the employed procedures within the range actually observed in the present trial [15] . After a study involving extensive stool sample collection and analysis by the Baermann technique , Dreyer and co-workers [43] suggested that at least 4 stool samples need to be collected to accurately assess the S . stercoralis infection status , and that only those with at least 2 positive test results should be included in clinical drug trials . In our study , we only included those individuals who had at least 2 stool specimens examined with 2 different diagnostic approaches . Thus , at least 4 , and ideally 6 , results were available to judge the infection status of the participants both before and after drug administration . Among the 22 S . stercoralis positives at baseline , 6 , 6 , 3 , 5 , 1 and 1 individuals had 1 , 2 , 3 , 4 , 5 and 6 positive test results , respectively . The 10 arguably cured individuals had 1 ( n = 3 ) , 2 ( n = 3 ) , 3 ( n = 2 ) and 4 ( n = 2 ) positive baseline test results . Our findings indicate that participants with only 1 or 2 positive tests at baseline were not more likely to be considered cured at treatment evaluation than those with multiple positive tests . However , infections were still found in all participants with 5 or 6 positive tests at baseline . The four individuals who were only found to be infected at treatment evaluation then had 1 , 1 , 2 and 3 positive test results . Finally , the observed activity of albendazole against Taenia spp . among those who were found to be infected at baseline has to be put into perspective with the high number of “newly” detected infections at treatment evaluation . Among tribendimidine recipients , only few additional Taenia spp . infections were found , indeed indicating that single-dose tribendimidine , but not albendazole , might have some effect against Taenia spp . Unfortunately , the eggs of T . saginata , T . solium , and T . asiatica cannot be readily distinguished microscopically [23] , [25] . Hence , we are not in a position to determine their relative frequency in our study population . However , the reported and observed diets suggest that the locally dominant species is T . solium or possibly T . asiatica since Bulang favor raw pork over raw beef . As a next step , the efficacy of multiple-dose tribendimidine could be assessed as our results indicate some , albeit currently unsatisfactory effect of this drug against S . stercoralis and Taenia spp . In future studies with a focus on these 2 parasites rather than the common soil-transmitted helminths , the reference drug should be praziquantel or niclosamide for Taenia spp . , and ivermectin for S . stercoralis . Alternatively , triple-dose albendazole might be used as reference treatment [20] . When further investigating the efficacy of tribendimidine against large cestodes , including Taenia spp . in humans , we propose to treat a small group of confirmed taeniasis cases who agreed to submit multiple stool samples and observe proglottids in their stools and underwear over extended time periods . Infections with A . lumbricoides and hookworm are the main targets for single-dose mass chemotherapy using albendazole or mebendazole . Discussions are underway in the People's Republic of China for the larger-scale use of tribendimidine . Efficacy of the latter drug on other intestinal parasites would be of considerable public health significance , which is explained by the geographic overlap of different helminth infections , including S . stercoralis and Taenia spp . Treatment of individuals with multiple species parasite infections , including S . stercoralis and Taenia spp . , is likely to occur . Hence , there is a pressing need to determine the most efficacious tribendimidine treatment regimen for S . stercoralis and Taenia spp . since the exposure of the parasites to sub-curative doses exacerbates the risk of resistance development . Therefore , pharmacovigilance needs to also cover non-target parasites to assure timely detection of emerging resistance . | More than a billion people are infected with intestinal worms and , in the developing world , many individuals harbor several kinds of worms concurrently . There are only a handful of drugs available for treatment , and drug efficacy varies according to the worm species . We compared the efficacy of a single oral dose of tribendimidine , a new broad-spectrum worm drug from China , with the standard drug albendazole for the treatment of hookworm , large roundworm ( Ascaris lumbricoides ) , whipworm ( Trichuris trichiura ) and , for the first time , Strongyloides stercoralis and tapeworm ( Taenia spp . ) . Our single-blind randomized trial was conducted in a village in Yunnan province , southwest China . Both drugs showed high efficacy against A . lumbricoides and a moderate efficacy against hookworm . Among 57 tribendimidine recipients , the prevalence of S . stercoralis was reduced from 19 . 3% to 8 . 8% , and that of Taenia spp . from 26 . 3% to 8 . 8% . Similar prevalence reductions were noted among the 66 albendazole recipients . Taking into account additional infections only discovered at treatment evaluation , the difference between the drug-specific Taenia spp . net cure rates was highly significant in favor of tribendimidine . In view of our promising results , multiple-dose schedules with tribendimidine against S . stercoralis and Taenia spp . should be evaluated next . | [
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"... | 2008 | Tribendimidine and Albendazole for Treating Soil-Transmitted Helminths, Strongyloides stercoralis and Taenia spp.: Open-Label Randomized Trial |
The number of cattle herds placed under movement restrictions in Great Britain ( GB ) due to the suspected presence of bovine tuberculosis ( bTB ) has progressively increased over the past 25 years despite an intensive and costly test-and-slaughter control program . Around 38% of herds that clear movement restrictions experience a recurrent incident ( breakdown ) within 24 months , suggesting that infection may be persisting within herds . Reactivity to tuberculin , the basis of diagnostic testing , is dependent on the time from infection . Thus , testing efficiency varies between outbreaks , depending on weight of transmission and cannot be directly estimated . In this paper , we use Approximate Bayesian Computation ( ABC ) to parameterize two within-herd transmission models within a rigorous inferential framework . Previous within-herd models of bTB have relied on ad-hoc methods of parameterization and used a single model structure ( SORI ) where animals are assumed to become detectable by testing before they become infectious . We study such a conventional within-herd model of bTB and an alternative model , motivated by recent animal challenge studies , where there is no period of epidemiological latency before animals become infectious ( SOR ) . Under both models we estimate that cattle-to-cattle transmission rates are non-linearly density dependent . The basic reproductive ratio for our conventional within-herd model , estimated for scenarios with no statutory controls , increases from 1 . 5 ( 0 . 26–4 . 9; 95% CI ) in a herd of 30 cattle up to 4 . 9 ( 0 . 99–14 . 0 ) in a herd of 400 . Under this model we estimate that 50% ( 33–67 ) of recurrent breakdowns in Britain can be attributed to infection missed by tuberculin testing . However this figure falls to 24% ( 11–42 ) of recurrent breakdowns under our alternative model . Under both models the estimated extrinsic force of infection increases with the burden of missed infection . Hence , improved herd-level testing is unlikely to reduce recurrence unless this extrinsic infectious pressure is simultaneously addressed .
The number of cattle herds in Great Britain ( GB ) placed under movement restrictions due to the suspected presence of bovine tuberculosis ( bTB ) has progressively increased over the past 25 years [1] . This increase in the rate of so-called “breakdown” herds is despite an intensive and costly test-and-slaughter control program [2] . Recent studies have focused on estimating the contributions of cattle movements and wildlife transmission to incidence [1] , [3]–[8] as measured by the rate of new breakdowns . However , less attention has been paid to quantifying the dynamics of transmission within herds , even though this is arguably the most data-rich unit within the wider ecology of M . bovis . Previous history of disease within a herd is an important predictor of breakdown [5] , [8] , [9] , with 38% of herds that clear movement restrictions experiencing a recurrent incident within 24 months [10] . This high rate of recurrence suggests that infection may be persisting within herds in the face of repeated testing . In GB and internationally , detection and clearance of herds is dependent on variants of the imperfect tuberculin skin test . In GB and Ireland this takes the form of a single intra-dermal comparative cervical tuberculin ( SICCT ) [11] test . Infection missed by SICCT testing is likely to be contributing to recurrence within herds . However , the lack of a gold-standard diagnostic test for bovine tuberculosis means that the efficiency of the SICCT test is poorly characterized and the contribution of missing infection to recurrence cannot be easily assessed . Furthermore , reactivity to the SICCT test is dependent on the time-from-infection [12] , most often characterized [13] , [14] as an occult period where animals are infected but not yet detectable . As a consequence , the efficiency of testing within a herd depends not only on the characteristics of the diagnostic test , but also on the competing timescales of transmission and the frequency of testing . Within-herd models of bTB have been developed to address this problem with a view to informing government policy [13] , [14] . However , extant models have been based on ad-hoc parameterizations informed by disparate experimental studies and expert opinion . There exists considerable uncertainty in the assumed values of key parameters , in particular the occult period , the scaling of transmission rates with herd size [15] , [16] and the duration of latency between infection and infectiousness . To address this uncertainty , we propose a novel basis for parameterization of within-herd models using measures of stochastic persistence [17] as a metric for Approximate Bayesian Computation ( ABC ) [18]–[19] . Persistence measures have proven to be a powerful probe on which to parameterize models of childhood infectious diseases [20]–[23] . A successful approach has been to assume that the intrinsic rate of transmission within a population is rapid compared to the combined rate of transmission from sources extrinsic to the local population . This time-scale separation allows us to model local populations independently . Extrinsic routes of transmission are modeled through a generalized infectious pressure [20]–[22] . Comparatively less theoretical attention has been paid to modeling the persistence of managed endemic diseases . For chronic diseases , such as bTB , demographic turnover of the population is the only natural mechanism acting to clear infection from populations . In this context persistence can be used as an indirect measure of the efficiency of diagnostic testing . In this study we model the within-herd persistence of bTB as a balance between three key processes: the infectious pressure acting to introduce infection into the herd from extrinsic sources , the rate of cattle-to-cattle transmission within the herd and the rate of removal of infection through testing and demographic turnover . Herds are considered as isolated populations loosely connected to a reservoir of infection modeled as an infectious pressure . We are therefore not concerned with modeling the routes of introduction to the herd – which may be through movements of infected animals or contact with wildlife reservoir populations . Instead we focus on the processes of transmission within a herd with relation to the detection and resolution of breakdowns . We do so using two mechanistic models of within-herd transmission that we parameterize using routinely collected epidemiological data . We finally apply our parameterized models to estimate the hidden burden of infection and its implications for control of bTB in Great Britain .
The probability of extinction within epidemic models is dependent on the past history of infection within the population [24] . Alternative empirical measures of persistence , that capture different aspects of the transmission dynamics , can be constructed depending on how we condition on the past history of infection [21] . For bTB , infection missed during a given test is likely to contribute to the probability of the herd failing subsequent tests . Contingent on the natural time-scale of transmission and the scheduling of testing , missing infection may act to prolong the duration of breakdowns and/or increase the probability of a recurrent breakdown . We therefore quantify within-herd persistence through two competing measures related to the duration and the rate of recurrence of breakdowns . The duration of breakdowns is captured by the probability that breakdowns are prolonged [25] , defined as lasting longer than 240 days . Recurrence is captured through the probability of a breakdown recurring [10] within a fixed time horizon of 6 , 12 and 24 months after the end of a breakdown . Bovine tuberculosis is a statutory infectious disease . Incidence and testing data are routinely collected by the Animal Health and Veterinary Laboratories Agency ( AHVLA ) and collated within the VetNet database . It is not feasible or desirable to model the full complexity of the British testing regime within a herd level model . In addition to the schedule of statutory surveillance testing , there exists a diverse range of auxiliary tests including pre-movement testing , contiguous tests and tests trigged by epidemiological investigations of “at risk” premises . As a consequence there is considerable variability in the duration of time that infection can spread unobserved within herds before detection . In an attempt to control for this uncertainty we restrict our current analysis to new breakdowns with start dates between 2003–2005 that were detected through routine surveillance , either through the slaughterhouse or by the detection of reactors at a routine or whole herd test ( tests classified as ‘VE-WHT’ , ‘VE-WHT2’ , ‘VE-RHT’ or ‘VE-SLH’ ) . We choose to restrict our study to the period 2003–2005 due to systematic changes to the testing system surrounding the 2001 foot-and-mouth disease epidemic [5] and a later increase in use of the gamma-interferon test [26] . Although discretionary use of the gamma interferon test increased after the end of our study period , this does not appear to have impacted upon persistence and our model still provides an equally good fit to target measures taken from more recent data ( Figures S7 , S11 ) . The cessation of testing during the 2001 foot-and-mouth epidemic artificially increased the duration of time that herds were kept under movement restrictions , delayed the scheduling of routine surveillance tests and was associated with an increase in incidence and spread of bTB to new areas [5] . Given the slow rate of transmission of bTB , the perturbative effect of this disruption to testing is likely to have continued for many years . However , our interest is primarily in the sequence of transmission and testing after the disclosure of a breakdown . Disruptions to testing prior to the disclosure of infection will increase the duration of time that infection was able to transmit silently within our study herds . However , as this variation is included within the empirical testing distributions used within our model ( Figure S1 , Dataset S1 ) we do not expect it to affect our results . Of 10 , 174 breakdowns recorded within our study period , 3 , 456 ( 34% ) breakdowns match our criteria for inclusion . Restricting our analyses to this sub-population has an important advantage . The scheduling of surveillance tests in GB is based on the local incidence ( Figure 1 ) , that determines the so-called parish testing interval ( PTI ) . The duration of time that infection may have remained undisclosed within our sub-population of herds is strongly constrained by the herd's local PTI . After detection of infection , regardless of the disclosing test type , all herds must undergo the same statutory sequence of testing . We therefore do not believe that our inclusion criteria impacts upon the generality of our inference for rates of within-herd transmission and the efficiency of surveillance . Of the remaining breakdowns the majority are either recurrent breakdowns ( 2 , 102; 21% ) initiated by a follow-up ‘VE-6M’ or ‘VE-12M’ test or breakdowns that started with a so-called “inconclusive” reactor ( 2 , 032; 20% ) . Inconclusive reactors ( IRs ) demonstrate a response to the SICCT that is close to the cut-off value defining a reactor . IRs do not necessarily trigger a breakdown but require the animal , rather than the herd , to be retested at an interval of 60 days . The population of IRs will be composed of both false reactor and truly infected animals and cannot be rationally treated within our model framework , requiring us to omit these herds from our analysis . The remaining 25% of breakdowns were initiated through a mixture of contiguous testing of affected premises and contact tracing . The persistence of bTB has previously been demonstrated to scale with herd size [27]; a known risk factor for both prolongation [25] of breakdowns in GB and recurrence in Irish herds [28] . We extend these analyses to quantify the relationship of our two persistence measures with herd size . The size of a cattle herd varies dynamically , even over the course of a breakdown . We therefore define herd size as the maximum herd size over the breakdown . The distribution of herd sizes is right skewed with 90% of breakdowns having herd sizes less than or equal to 360 cattle ( Figure S4 ) . The scarcity of herd sizes larger than this limits our ability to measure the relationship with persistence [27] . We therefore finally restrict our study population to breakdowns with a herd size of less than or equal to 360 cattle , leaving us with a final study population of 3 , 094 breakdowns . These 3 , 094 breakdowns were then binned into 6 groups with histogram mid-points of [30 , 90 , 150 , 210 , 270 , 330] . We further stratify these herds by the parish testing interval ( PTI ) and confirmation status of breakdowns to produce empirical distributions of persistence ( Figure 2 , Figures S6 , S10 ) . This classification is motivated by the systematic differences in testing for herds within different PTIs and after confirmation . Confirmed ( recently re-classified as Officially TB-Free Status Withdrawn or OTF-Withdrawn ) breakdowns are required to pass an additional clear test at a more strict ( severe ) interpretation of the SICCT test ( Figure 1B ) . The severe interpretation increases the sensitivity of the SICCT test at the expense of reducing the specificity . Confirmation is triggered by the discovery of reactor animals with visible lesions and/or culture of M . bovis . The proportion of prolonged and recurrent breakdowns both scale with herd size , but demonstrate distinct relationships with respect to both confirmation status and the local background risk of infection as measured by PTI . These empirical relationships are consistent with previous analyses suggesting that confirmation is associated with an increase in the duration of breakdowns [25] , but has negligible impact on recurrence [10] . In contrast to the consistency of the duration of breakdowns across all areas there is a marked increase in the rate of recurrence with local risk as measured by PTI . These differential relationships of persistence with herd size are the basis on which we set out to infer the within-herd transmission dynamics of bTB . We consider the persistence of bTB to be a product of the non-linear interaction of both the disease and testing dynamics . Heuristically , our model can therefore be considered as having two interacting dynamic components: an epidemic model that describes transmission within and into the herd and a testing model that models the sequence of tests and removal of reactors . We estimate the parameters of our model ( Table 1 ) directly from our target measures of persistence using a sequential Monte Carlo implementation of Approximate Bayesian Computation ( ABC-SMC ) [18] , [19] . This framework is designed to bypass the calculation of computationally infeasible likelihood functions and instead generates distributions of parameters for which model outputs are consistent with the data according to a set of pre-defined goodness-of-fit metrics . The period of latency between infection and infectiousness is a key epidemiological parameter that sets the time-scale between subsequent epidemic generations . Given the chronic , progressive nature of bTB , models have conventionally assumed long epidemiological latent periods of ∼6–20 months [13] , [14] . However , animal challenge studies have suggested that bacterial shedding , and therefore transmission , may occur over shorter time-scales of ∼30 days [29] . In order to explore these two scenarios of latency , we fitted two models using vague ( uniform ) prior assumptions ( Table 2 ) , one based on the conventional model structure assumed for bTB [13] , [14] ( SORI , Table 3 ) and an alternative model allowing for “early” infectiousness ( SOR , Table 4 ) . In the SORI model cattle are assumed to be infectious ( I ) only after passing through two latent stages: an occult stage ( O ) where animals are infected , but not detectable by SICCT testing and a reactive stage ( R ) where animals are ‘reactive’ to the SICCT test but not yet infectious . The SOR model decouples this relationship between epidemiological latency and reactivity to the SICCT test . Animals are assumed to be potentially infectious from both the occult ( O ) and reactive ( R ) classes , dispensing with the infectious class ( I ) . Both models provide comparable fits to the empirical target distributions ( Figure 2 , Figures S6 , S10 ) despite remarkably different estimates for the duration of the occult period and the epidemiological period of latency between infection and infectiousness ( Figures S5 , S9 ) . The occult period is estimated to last ∼1 . 8 days ( 0 . 0–7 . 7 days , 95% CI ) for the SOR model and ∼275 days ( 24–517 , 95% CI ) for the SORI model . Whereas infectiousness is implicitly assumed to begin with infection under the SOR model , the period of epidemiological latency estimated under the SORI model is more than twice that of previously assumed values [13] , [14] with a point estimate of 406 days ( 116–827 , 95% CI ) . The occult period estimated from the SORI model is an order of magnitude larger than the range of 8–65 days observed in animal challenge studies [12] , [30] , [31] . Placing a more informative prior ( uniform on the range 0–128 days ) on the occult period for the SORI model has no appreciable impact on the fidelity of the model fit and reduces the estimated occult period to a median point estimate of ∼28 days ( 1–119 days , 95% CI ) . We therefore select the SORI model fit with the more informative prior for comparison with the alternative SOR model within this paper . Both models estimate that the rate of cattle-to-cattle transmission within a herd increases , non-linearly , with herd size . The potential for transmission within a herd can be characterized by the basic reproductive ratio R0 , defined as the expected number of secondary cases within a herd of size N on the introduction of a single infectious individual . Within the range of our study population our ( median ) point estimate of R0 from the SORI model increases from 1 . 5 ( 0 . 26–4 . 9; 95% CI ) in a herd of size 30 up to 4 . 9 ( 0 . 99–14 . 0; 95% CI ) in a herd of 400 cattle ( Figure 3A ) . Estimates from the SOR model are smaller , but with overlapping credible intervals , increasing from 0 . 52 ( 0 . 1–1 . 6 , 95% CI ) in a herd of size 30 up to 3 . 6 ( 0 . 73–8 . 85 , 95% CI ) in a herd of size 400 ( Figure 3A ) . As a consequence , both models predict that the efficiency of control will also scale with herd size . The SOR and SORI models provide contrasting estimates of the efficiency of SICCT testing in Great Britain . The SOR model estimates a true median SICCT test sensitivity of 66% ( 52–80% , 95% CI ) at the standard interpretation , rising to 72% ( 56–88% , 95% CI ) under the severe interpretation . Estimates of true sensitivity from the more traditional SORI model are far lower , at 36% ( 24–51% , 95% CI ) for the standard interpretation rising to 48% ( 34–69% , 95% CI ) for the severe interpretation ( Figure 3B ) . However , these model estimates are relative to the true infection status of animals . Given the lack of a gold standard diagnostic test for bTB , such information is not available in real populations . Rather , out of necessity , sensitivity of diagnostic tests for bTB is routinely measured relative to the presence of visible lesions and/or culture . The limitations of such comparative measures of sensitivity are aptly illustrated by comparison of our estimates from the SOR and SORI models . Despite the differences in the true sensitivity between the two models , the effective test sensitivities relative to visible lesions are indistinguishable and considerably higher than the true values ( Figure 3B ) . As a consequence the diverse sensitivity estimates from the SOR and SORI models are nonetheless both consistent with published estimates of the sensitivity of SICCT relative to visible lesions of up to 93 . 5% at the severe interpretation [11] . In order to quantify the efficiency of control we introduce a new measure - the infectious burden . We define infectious burden as the probability that at least one infected animal remains within a herd after movement restrictions are lifted . By simulating our within-herd models using the distribution of herd sizes from our study population we can generate predictive distributions for the infectious burden at the national level ( Figure 3 C , D ) . Once more , the SOR and SORI models provide contrasting views of the efficiency of control . Under the SOR model we estimate that 8% ( 3–17%; 95% CI ) of breakdowns will have an infectious burden when they clear restrictions , with a median of 1 ( 1–3; 95% CI ) infectious animal remaining in these herds . Under the SORI model this estimate increases to 21% ( 12–33%; 95% CI ) of breakdowns with an infectious burden when they clear restrictions , with a median of 1 ( 1–4; 95% CI ) infectious animal remaining in these herds . We apply our fitted models to predict how different herd-level interventions may affect the resolution of breakdowns ( Figure 4 ) . Specifically we consider two treatments: application of a ‘perfect test’ and eliminating the extrinsic infectious pressure through ‘perfect isolation’ . In the SOR model , recurrence is driven almost completely by re-introduction , with ‘perfect isolation’ having the potential to eliminate recurrence completely in some herds ( Figure 4B ) . Perfect isolation is predicted to be less effective at reducing recurrence in herds with a low extrinsic rate of re-introduction ( i . e . small herds in low incidence areas ) . Although these herds are predicted to have a lower infectious burden ( Figures S8 , S12 ) , when they do experience a recurrent breakdown it is more likely to be caused by infection missed by SICCT testing than re-introduction . Under the SORI model there is a similar relationship in the response to ‘perfect isolation’ , except that a greater proportion of recurrence is attributable to persistence of infection . At the national level , averaging over our study population of herds once again , we estimate that 50% ( 33–67 , 95% CI ) of recurrent breakdowns are attributable to persistence within the SORI model , compared to 24% ( 11–42 , 95% CI ) under the SOR model . However , eliminating this hidden burden of infection is not sufficient to eliminate recurrence if the extrinsic infectious pressure acting on herds is not simultaneously addressed . Under both models a ‘perfect test’ with 100% sensitivity , specificity and no occult period fails to improve the probability of recurrence in high incidence areas ( Figure 4 ) . Although the perfect test reduces the duration of breakdowns , it can also detect infection within herds more quickly . In the short term , rates of recurrence will therefore increase in high incidence areas and such a perfect test would only be of benefit for low incidence ( PTI 4 ) breakdowns where the rate of re-introduction is sufficiently low . This counter-intuitive result demonstrates an important limitation of our approach . Our herd-level model does not distinguish between movements to slaughter or to other herds , so the infectious burden output from our model may potentially be contributing to the extrinsic rate of transmission that drives recurrence in our herd-level model . Both of our within-herd models can equally well fit the empirical patterns of persistence of bTB despite very different predictions for the level of the infectious burden . However , such a difference would place very different weights on the importance of cattle movements in network models of herd-to-herd transmission . Recent analyses of the between-herd transmission of the disease in GB have simplified , or ignored these within-herd dynamics of transmission [6] , [7] .
A fundamental challenge in epidemiological modeling concerns identifying the appropriate level of model complexity required to understand the dynamics of transmission and form a rational basis for policy development . Tuberculosis has been described as an infectious disease with a period of latency ranging from one day to a lifetime [32] . However , this uncertainty surrounding the progression of disease in individuals is rarely considered as a part of epidemiological modeling studies . In this study we have demonstrated how assumptions concerning the relative timing of infectiousness and reactivity to tuberculin profoundly impacts upon the estimated efficiency of SICCT testing . Both the SOR and SORI models are equally well supported by the population level data used in this study , despite very different estimates for the efficiency of testing . This suggests that persistence measures alone are insufficient to distinguish the true burden of infection and points to experimental studies that could resolve this uncertainty . Neither model identifies , without the support of informative priors , an occult period within the range observed from animal challenge studies [12] , [30] , [31] . However , if there is a relationship between infectious dose and the duration of latency , estimates from challenge studies must also be treated with caution . Given the importance of this parameter in determining the hidden burden of infection , further research is required to clarify the relationship between infectiousness and sensitivity to diagnostic tests . Our modeling suggests that transmission of bTB ‘early’ in infection necessitates a lower level of persistence of infection than predicted by traditional ( SORI ) transmission models . However , evidence for such ‘early’ transmission comes from animal challenge studies [29] , [33] and has not been verified under natural transmission conditions . Our modeling emphasizes the critical importance of understanding how the pattern of bacterial shedding in naturally infected animals changes over time . Both models estimate that the rate of cattle-to-cattle transmission in GB herds is non-linearly density dependent . This result has immediate importance for the formulation of bTB policy at the herd level , suggesting that additional controls may need to be targeted towards larger herds . Our models suggest that the key to addressing the ongoing spread of bTB lies with reducing the rate of transmission into herds . The central question remains as to whether this requires management of the reservoir of infection in wildlife populations , or simply improved surveillance and diagnostic testing to reduce the movement of undisclosed infection between herds . We have shown that stochastic persistence measures can provide insights into the efficiency of control measures for managed populations . However , the interpretation of these patterns of persistence requires a modeling approach that simultaneously accounts for the dynamics of control as well as the intrinsic dynamics of disease transmission . In the case of bTB , the dynamics of infection at the individual level have a profound impact on the estimated burden of infection missed by testing . It is therefore imperative to improve our understanding of the , still mysterious , life history of infection of bTB in individual cattle .
Within-herd transmission of bTB is modeled using the standard compartmental approach where animals are classified only by their epidemiological status . We consider two alternative models ( SORI and SOR ) corresponding to different assumptions concerning the relationship between latency , transmission and reactivity to the SICCT skin test . In the traditional SORI model for bTB , occult ( O ) and reactive ( R ) animals are infected but not yet infectious , differing only in their response to the SICCT test . Infectious animals ( I ) are assumed to be both infectious and detectable by the SICCT test with the same efficiency as reactive animals . In the SOR model occult ( O ) and reactive ( R ) animals are both assumed to be potentially infectious eliminating the need for the infectious class ( I ) . Both epidemic models are implemented as stochastic Markov chains in continuous time and can be defined by the allowed transitions between the four state variables: Susceptible ( S ) , Occult ( O ) , Reactive ( R ) and Infectious ( I ) ( Tables 3 , 4 ) . A per capita turnover rate μ is sampled from an empirical distribution for each simulation ( Figure S3 ) . A constant target herd size ( ) is maintained by balancing a constant per-capita removal rate ( ) with a fixed import rate of . Herd size therefore fluctuates , with an instantaneous herd size of . The extrinsic infectious pressure ( ) is the only parameter to vary with the parish testing interval ( PTI ) taking unique values for PTI 1 , 2 and 4 ( , , ) . The sequence of tests before , during and after a breakdown is simulated by a model where the timing of tests and number of animals to be tested changes dynamically according to the state-variables of the epidemic model and the outcome of individual animal tests . Model simulations are initialised with the entire herd in the susceptible compartment ( S , O , R , I ) = ( N , 0 , 0 , 0 ) . The model is then simulated forward , piecewise , between the dynamically scheduled tests before , during and for 5 years following the end of the first breakdown , or until a recurrent breakdown is triggered . The sequence of decisions following the outcome of herd tests is summarized in Figure 1B . Simulations begin with the herd undergoing routine surveillance through slaughterhouse inspection and whole herd tests ( classified as RHT or WHT ) at 1 , 2 or 4 yearly intervals ( described below ) . Detection of a reactor animal triggers a breakdown . The herd then enters a sequence of short interval tests ( SIT ) . Unconfirmed breakdowns end after a single clear test at the standard interpretation , while confirmed breakdowns must clear two tests – one at severe interpretation and the second at standard interpretation . Two follow-up tests , one six months after the end of a breakdown ( VE-6M ) and one 12 months later ( VE-12M ) are then scheduled . The time between all tests associated with a breakdown ( SIT , VE-6M , VE-12M ) are sampled from empirical distributions ( Figure S1 , Dataset S1 ) . The duration of time between routine tests is also sampled from an empirical distribution ( with separate distributions for PTI 1 , 2 and 4 ) to account for the additional variation in the time to detection that is a consequence of delays in testing and the transition of herds between different parish testing intervals ( Figure S1 , Dataset S1 ) . Breakdowns are triggered by the detection of a reactor , either due to the presence of infected animals in the herd or the generation of a false positive test result . Nominally , we simulate the full sequence of tests until either of these events occurs with the proportion of false-positive breakdowns determined by the relative values of the specificity ( ) and the infectious pressure ( ) . In practice , and to increase the speed of simulations , this can be pre-calculated by explicitly calculating the probability of a false positive breakdown occurring between periods where there are no infectious animals within the herd . Breakdowns can also be triggered by routine slaughterhouse surveillance that is modeled as a fixed probability ( ) that removals from the O , R and I compartments will be detected . Breakdowns triggered by slaughterhouse surveillance are treated as confirmed breakdowns , with the first whole herd test carried out under the severe interpretation . The application of herd tests in GB can be modeled by simulating three basic processes 1 ) the number of animals to test 2 ) the number of reactor animals detected at the standard and severe interpretations of the skin test and 3 ) the confirmation process . For tests associated directly with a breakdown ( SIT , VE-6M , VE-12M ) the whole herd is tested . However , there is more variation in the type of test , and numbers of animals tested , in PTI 2 and 4 herds . We simulate this process by choosing the test type – either a whole herd test or a routine herd test – at random according to the proportion of tests recorded within the parish testing interval of the simulated herd ( Figure S2 , Dataset S1 ) . Whole herd tests specify that all bovines older than 6 weeks should be tested . We simulate this requirement by approximating the instantaneous proportion of the herd ineligible for testing to be ( 6/52 ) μ/N , where μ is the per-capita turnover of the herd . The number of animals tested with a WHT ( X ) is then sampled from a binomial distribution: There is greater variability in which non-breeding animals are tested during a routine herd test ( RHT ) , and therefore in the proportion of the herd tested . In order to account for this we sample the proportion from a Cauchy distribution fitted to the empirical distribution from VetNet data by maximum likelihood ( Figure S2 ) with scale parameter 0 . 0932 and shift parameter 0 . 494 ( to 3 s . f . ) . The number of animals tested with a RHT ( X ) is then sampled from a binomial distribution as before with: The outcome of diagnostic tests within our model is determined by the set of parameters defining the sensitivity and specificity of the SICCT test at both the standard and severe interpretations ( Table 1 ) . In the field , the classification of reactors is based on cut-off values for the difference in reaction between avian and bovine tuberculin , with the cut-off value for a reactor changing with the severe and standard interpretation . As a consequence , tests can , and are , re-interpreted at the severe interpretation following the confirmation of a reactor animal after slaughter . In order to model the process of confirmation in a consistent fashion we must simulate the test outcome for each individual animal in the herd separately to ensure that the number of reactors at the severe interpretation is strictly greater than or equal to the number at the standard interpretation . Given X animals to test we sample them randomly ( and uniformly ) from each of the model compartments ( S , O , R , I ) to generate the number of animals from each compartment that are tested ( XS , XO , XR , XI ) . For each ( XS , XO , XR , XI ) we sample a uniform random number and use the value to simulate the number of reactor animals at the standard ( Standard Reactors ) and severe ( Severe Reactors ) interpretations: For each XS: if ( ) : Standard Reactors +1; if ( ) : Severe Reactors +1 For each Xo: if ( ) : Standard Reactors +1; Z+1 if ( ) : Severe Reactors +1 For each XR: if ( ) : Standard Reactors +1; Z+1 if ( ) : Severe Reactors +1 For each XI: if ( ) : Standard Reactors +1; Z+1 if ( ) : Severe Reactors +1 We must also keep track of the number of true reactors ( Z ) in order to simulate the number of confirmed reactors ( C ) : Provided that the breakdown has not been previously confirmed and C = 0 , then all reactors at the standard interpretation are removed from the herd . Otherwise , if the number of confirmed reactors , then the breakdown status is switched to confirmed ( requiring an additional severe interpretation clear test to move back to the standard interpretation ) and the reactors at the severe interpretation ( including those from the current test ) are removed . We use the ABC-SMC algorithm described in Toni et al . [19] . In essence the method replaces the calculation of a likelihood function with an approximation based on matching model simulations to the observed data using a set of goodness-of-fit metrics ( in this case corresponding to a set of key epidemiological target measures ) . These target measures can be combined to produce a single metric ( described in the next section ) . For a given set of parameters , a series of stochastic simulations from the model are produced using the algorithm described in the previous section . A simulation is said to “match” the observed data if the corresponding ( simulated ) metric value lies below a pre-determined threshold . A Monte Carlo estimate of the probability of matching can then be used as an approximation to the likelihood in an SMC framework [18] , [19] . As the tolerance applied to the metric is reduced , the approximate ( posterior ) distribution should in principle converge towards the ( true ) posterior distribution . The algorithm begins by generating 10 , 000 particles - each “particle” corresponding to a set of model parameters – from a set of uniform proposal distributions ( Table 2 ) . For each particle we produce a binary Monte Carlo estimate for the probability of matching . This information is combined with the prior distributions for the parameters to produce a set of weights across the whole population of particles . The algorithm then proceeds through a series of repeated steps , whereby the population of particles is re-sampled from the previous weighted population and then each particle perturbed according to a set perturbation kernel . A new set of weights is then generated in a similar manner to before . The tolerance controlling the matching is reduced at each step until the predictive distributions from the simulated model generate an acceptable agreement with the target epidemiological measures . All parameters are log transformed and the perturbation kernel is uniform for each parameter ( on this scale ) , where is the range of the marginal distribution for parameter from the previous SMC round . At each successive round the threshold was reduced semi-automatically to the median value of the metric from the previous round . Heuristically the ABC-SMC procedure can be thought of as using goodness-of-fit criteria to inform the shape of the approximate posterior distributions , rather than the likelihood function . Uniform prior distributions are applied to individual parameters and combinations of parameters to constrain their values to biologically relevant ranges . Probabilities are constrained to be in the interval [0 , 1] and rates are constrained to be positive . The sensitivity and specificity of the SICCT test are constrained to increase and decrease respectively under the severe interpretation and the probability of confirmation of reactors under routine slaughterhouse surveillance is assumed to be less than the probability of confirmation of reactors . All prior assumptions are intended to be uninformative , apart from the occult period for the SORI model and the upper bound ( 0 . 0003 ) placed on pFP . This value , equivalent to a lower bound on the specificity of the skin test at the standard definition of 99 . 97% , was obtained by calculating the value of pFP required to explain all of the unconfirmed breakdowns within VetNet data given the total number of animal tests . Table 5 summarizes the target measures used to build our ABC metric . For each measure there are 6 empirical targets , by 2 values of confirmation status , by 3 values of PTI to give 36 target distributions/probabilities for each epidemiological measure . All of the target epidemiological measures for our final ABC-SMC scheme can be expressed either as probabilities or as ( binned ) probability distributions . This motivated the choice of an ABC metric based on the relative entropy , also known as the Kullback-Leibler divergence [34] , that measures the distance between a proposed probability distribution ( p ) and a reference distribution ( q ) : Two properties of the relative entropy should be noted: firstly , the relative entropy is asymmetric to the choice of reference distribution , with . We choose to remove the potential ambiguity introduced through the choice of reference distribution by using a symmetrical metric ( ) : Secondly the relative entropy is undefined if any of the elements of the reference distribution qi = 0 . We numerically approximate the distribution of each of our target measures through histograms , with bin-sizes chosen to capture the range of observed values within VetNet data . Where we are free to choose appropriate bin sizes for the empirical distributions ( q ) such that we avoid any empty bins , we cannot ensure the same for the proposed distributions ( p ) generated from model simulations . To ensure that our metric is always defined , we add 1 to every bin of our empirical and simulated histograms . For each proposed set of parameters ( particle ) we simulated a fixed number of realizations of the model ( 500 ) at the midpoint of each of the 6 herd-size histogram bins [30 , 90 , 150 , 210 , 270 , 330] for PTI 1 , 2 and 4 and generate a set of j proposed distributions ( ) for each corresponding target measure ( ) . We use a superscript to indicate the jth distribution with elements indexed by i . We calculate the distance between the proposed and target distributions using the symmetric metric ( ) introduced above . The maximum value of this metric will increase with the number of histogram bins associated with that target distribution . In order to ensure that the overall metric places a more equal weight on each epidemiological measure we weight the contributions from each target measure proportionally to the total number of bins forming that measure ( ) ( Table 5 ) : | Epidemic models are commonly used to assess the impact of alternative management strategies . The efficacy of controls is typically assumed from “expert opinion” rather than estimated from data . Managed endemic diseases such as bovine tuberculosis offer the potential to estimate the efficiency of control directly from epidemiological data . Our methodology constitutes a shift in the level of statistical rigor applied to “policy” models and offers insights into the epidemiology of Bovine tuberculosis in Great Britain . bTB continues to persist and spread relentlessly in Britain , despite extensive testing and control programs . Cattle farmers question the efficacy of cattle controls , blaming the badger wildlife reservoir . Contrary to much public perception , we demonstrate the importance of cattle-to-cattle transmission , especially in larger herds . We estimate that in the worst case scenario up to 21% of herds may be harboring infection after they clear restrictions . However , we also estimate that there is a high rate of re-introduction of infection into herds , particularly in high incidence areas . Eliminating the hidden burden of infection alone is unlikely to be sufficient to prevent recurrent breakdowns . Rather , the high rate of external infection , both through cattle movements and environmental sources , must be addressed if recurrence is to be reduced . | [
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] | 2012 | Estimating the Hidden Burden of Bovine Tuberculosis in Great Britain |
Feedback delays are a major challenge for any controlled process , and yet we are able to easily control limb movements with speed and grace . A popular hypothesis suggests that the brain largely mitigates the impact of feedback delays ( ∼50 ms ) by regulating the limb intrinsic visco-elastic properties ( or impedance ) with muscle co-contraction , which generates forces proportional to changes in joint angle and velocity with zero delay . Although attractive , this hypothesis is often based on estimates of limb impedance that include neural feedback , and therefore describe the entire motor system . In addition , this approach does not systematically take into account that muscles exhibit high intrinsic impedance only for small perturbations ( short-range impedance ) . As a consequence , it remains unclear how the nervous system handles large perturbations , as well as disturbances encountered during movement when short-range impedance cannot contribute . We address this issue by comparing feedback responses to load pulses applied to the elbow of human subjects with theoretical simulations . After validating the model parameters , we show that the ability of humans to generate fast and accurate corrective movements is compatible with a control strategy based on state estimation . We also highlight the merits of delay-uncompensated robust control , which can mitigate the impact of internal model errors , but at the cost of slowing feedback corrections . We speculate that the puzzling observation of presynaptic inhibition of peripheral afferents in the spinal cord at movement onset helps to counter the destabilizing transition from high muscle impedance during posture to low muscle impedance during movement .
The presence of sensory and motor delays in any control process can lead to highly unstable behavior [1] . Impressively , humans ( and other animals ) are able to make rapid corrective responses even with sensorimotor delays on the order of 50 ms [2] , [3] . Several hypotheses have been formulated , each making distinct predictions about how the nervous system handles sensorimotor delays . One common view argues that the brain exploits the spring-like properties of muscle to stabilize the body during motor control , commonly termed impedance control [4] . In this framework , the brain controls the state of the peripheral motor apparatus in such a way that the intrinsic biomechanical properties of the limb restore a force proportional to changes in joint angle ( stiffness ) and velocity ( viscosity ) with zero delay [4]–[9] . In order to avoid ambiguous terminology , we will use impedance to refer to muscle's intrinsic visco-elastic properties , therefore excluding motor responses mediated by neural feedback [4] , [10] , [11] . It is important to stress that there is some confusion in the literature relative to the definition of impedance control . Many studies include not only the stiffness related to muscle activation , but implicitly also neural feedback as a factor contributing to limb impedance [5] , [7] , [8] , [12]–[18] . This is because these studies use estimates of joint stiffness and viscosity based on perturbation responses that last >200 ms [12] , and thus depend on neural feedback including the short-latency ( ∼20 ms–50 ms ) , long-latency ( ∼50 ms–100 ms ) and early voluntary responses ( >100 ms ) . This methodology is now questionable given recent observations on the sophistication of long-latency and early voluntary responses [2] . Also , long-latency responses are known to involve cortical and cerebellar circuits involved in voluntary control [19] , [20] . Thus , estimates of limb impedance based on motor responses beyond ∼50 ms include essentially the entire motor system , peripheral and central . Using our definition of muscle impedance , it is clear that the conventional perturbation technique does not provide estimates of the intrinsic muscles properties . Thus it is important to re-evaluate the contribution of muscles' intrinsic impedance independent of neural feedback in order to better understand how the nervous system counters perturbations during motor control . A challenge in modeling the stiffness properties of muscle is that their properties vary with changes in muscle length: in vivo studies highlight relatively high stiffness for small perturbations corresponding to less than only a few degrees of joint motion ( short-range stiffness , [21] , [22] ) , whereas larger perturbations must rely on relatively low stiffness properties associated with the muscle's force-length/velocity curves [21]–[24] . Taking these limitations into account , it remains unclear how the brain generates fast and stable feedback responses to external disturbances , in particular when perturbations exceed the short-range impedance . To address this issue , we first illustrate how changes in muscle impedance dramatically alter the capabilities of muscles' intrinsic properties to oppose external disturbances , such that stable corrections for small disturbances abruptly switch to slow and oscillatory responses following the transition from high to low impedance that occurs beyond the short-range . Next , we characterize the performance of healthy humans instructed to counter moderate-sized perturbations , highlighting the ability of humans to make very rapid and stable motor corrections . Finally , we investigate whether different feedback control mechanisms can generate human-like corrective responses , considering long-latency delays ( ∼50 ms ) and intrinsic joint impedance observed beyond the short-range stiffness . We show that the model including a state estimator was the best candidate to reproduce fast motor responses of humans following abrupt perturbations inducing large motor errors . Essentially , participants were able to increase their feedback gains without altering the kinematics of corrective movements , which we show is the signature of state estimators . We also suggest that impedance control of muscle can be beneficial during postural control against small perturbations . Beyond the short-range stiffness , our data and simulations suggest that fast and stable feedback control requires internal models and state estimation to compensate for low impedance and sensorimotor delays .
Muscles perturbed in vivo display high-impedance over a short range , a property commonly referred to as short-range stiffness [21] , [22] , [25] . Beyond this short range , the intrinsic impedance of muscle drops dramatically , and depends on its force-length and force-velocity properties [23] , [24] . Data from the cat soleus muscle suggest that the short-range impedance corresponds to ∼1 mm of muscle stretch ( Figure 1A , schematic redrawn of Figure 2 from [22] ) , which corresponds to ∼2 . 6% of its fascicles length [23] . Transposed to human elbow muscles ( see Methods ) , these numbers suggest that the elbow joint exhibits high intrinsic impedance when changes in angle are less than 5 deg in amplitude ( see also [26] ) . The transition from high to low impedance has a direct impact on corrective trajectories generated by intrinsic muscle properties [4] . Indeed , transient perturbations inducing changes in joint angles >5 degrees dramatically reduce the potential contribution of muscle intrinsic impedance to the corrective response . Figure 1 B displays the simulated perturbation-related changes in joint angle following application of small- ( gray ) and medium-sized ( black ) perturbations . For these simulations , we considered both elastic and viscous terms to describe the short-range intrinsic properties , and therefore refer to it as short-range impedance ( see also Methods ) . The values of the exemplar perturbations in Figure 1 B were chosen so that the motion either maintained muscles within its short range ( high impedance ) , or exceeded 5 degrees , transitioning muscle to low impedance . Observe the important difference in joint trajectories induced by the change in muscle visco-elastic properties . The peak-to-peak change in joint angle and time to first zero-crossing are markedly altered following the transition from high to low impedance . These two variables are plotted as a function of pulse magnitude in Figure 1C to further illustrate the bifurcation in kinematics parameters resulting from the transition from high to low muscle impedance . This emergent consequence of changes in muscle intrinsic properties on joint motion clearly emphasizes the need for central compensation for biomechanical features of the motor system . Thus , impedance control may provide stability when perturbations induce small amounts of joint motion . Against larger disturbances , low muscle impedance would generate slow and oscillatory corrections clearly incompatible with human motor behaviour . The following sections present human motor responses to perturbations and address how the nervous system may handle the low muscle impedance along with the additional problem related to temporal delays in sensorimotor transmission . In order to develop control models , we must first estimate parameters that best capture the visco-elastic properties of the limbs . First , it is clear that the values associated with high , short-range stiffness cannot be considered to reflect the intrinsic joint impedance for perturbations in our Main Experiment , as the perturbation-related motion was substantially greater than 5 degrees ( range from Main Experiment was 8 to 20 degrees ) . Estimates provided in the literature based on hand forces following perturbations typically depend on reflexes [5] , [7] , [8] , [12]–[15] , [28] , and as a consequence do not represent the intrinsic impedance of the joint . For example , muscle impedance values commonly used in the literature ( K = 16 Nm/rad and G = 2 . 4 Nms/rad , [29] ) predict ∼5 degrees of maximum joint displacement , even without considering any contribution from neural feedback . This displacement is significantly lower than the experimentally observed joint displacement in the 600 ms condition ( 14 . 5±3 . 7 degrees ) , and even lower than observed for the 300 ms in our Main Experiment ( 11 . 6±3 . 2 deg , t-test between participants' individual means and theoretical maximum displacement , t ( 14 ) = 5 . 79 , P<0 . 001 ) . In light of these limitations , we used force-length and force-velocity curves to estimate the intrinsic impedance of the joints . Based on the literature , we estimated muscle stiffness ( K ) to be 1 . 61 Nm/rad and muscle viscosity ( G ) to be 0 . 14 Nms/rad ( see Methods for details about the derivation ) . One can identify if these values are reasonable by using the data from our Main Experiment , as described below . The following analysis estimates the set of plausible muscle stiffness and viscosity terms based on comparisons between perturbation-related motion and simulations of a passive single joint with intrinsic visco-elastic properties varied across simulations ( see Methods ) . First , it is clear that estimates of muscle impedance cannot generate less elbow displacement than we observed in our 600 ms condition , because motion in this condition still includes some contribution of participants' neural feedback ( Figure 7A ) . We used the measured joint displacement at 150 ms from our human subjects in the moderate temporal condition ( θ600 ) and computed the set of values of K and G that predict a displacement of the elbow joint at 150 ms equal to θ600 . The boundary between the values of K and G predicting a joint displacement θ ( t ) at 150 ms greater or lesser than θ600 is an upper bound on the intrinsic joint impedance . This boundary delineates the regions C and B in the parameter space represented in Figure 7 C ( gray lines ) . Note that the commonly used values in the literature for muscle impedance lie outside of the range displayed in the diagram ( K = 16 Nm/rad and G = 2 . 4 Nms/rad [29] ) . Second , a closer estimate of a reasonable set of muscle stiffness and viscocity terms is obtained by comparing changes in the patterns of elbow motion with changes in perturbation-related EMG between the two time constraints . In the following analysis , perturbation-evoked changes in EMG are used to quantify the effect of the feedback response on the joint kinematics and extrapolate the theoretical motion of the passive joint due to muscles intrinsic properties only . We related changes in joint angle across timing conditions ( Figure 7A , δA ) to the corresponding changes in muscle response ( Figure 7A , δR ) . The ratio δA/δR was used to extrapolate the joint displacement at 150 ms corresponding to the intrinsic impedance ( Figure 7A and B , θI ) . We used the measured joint displacement at 150 ms from our human subjects in the moderate temporal condition ( θ600 ) and the estimated joint displacement corresponding to the intrinsic properties ( θI , open dot in Fig . 7B ) to determine set of acceptable values for K and G based on simulations ( thick dashed line in Fig . 7C ) . We then calculated the set of K and G values predicting a joint displacement at 150 ms equal to θI , which represents the best estimates for participants involved in the main experiment . The corresponding set represents the boundary between the regions A and B of the parameter space ( Figure 6 C , black dashed lines ) . Observe that our estimates obtained independently ( Equations 6 , black dot ) are in perfect agreement with the values of K and G that generate a joint displacement at 150 ms equal to θI . As in any model , there are several free parameters that can be difficult to estimate ( moment arm , activation level , PCSA , fascicle length and normalizing constant ) . Although we based our estimates on measured muscle properties to the best of our abilities ( see Methods ) , it is clear that each value is subject to experimental measurement error . We calculated how errors in each parameter would impact the estimates of joint impedance by varying them up to ±25% . The worst-case relative change in K and G was between 50% and 160% of the initial values . These possible variations are reported in Figure 7 C with a gray rectangle . Such a range of uncertainty resulting from model parameter errors is still clearly confined within the regions A and B of the parameter space presented in Figure 7 . The purpose of the following analysis is to determine which control model can explain the ability of human subjects to perform fast and stable feedback control given low impedance and the presence of sensorimotor delays . We consider a linear model of the elbow joint coupled with a first order low-pass filter representing muscle dynamics . The equations of motion are: ( 1 ) ( 2 ) where θ is the joint angle ( dots represent time derivative ) , I [Kgm2] is the forearm inertia , K [Nm/rad] and G [Nms/rad] are the elastic and viscous components of the intrinsic stiffness , T is the controlled torque , τ is the time constant of the muscle and u is the control variable . A first candidate control model is a direct mapping of the state of the system ( represented by x ) delayed by δt = 50 ms , compatible with long-latency delays ( see Methods ) . This control model can be written as: ( 3 ) where C is a row vector of feedback gains . We analyze three candidate controllers from this class of models , each controller being represented by a row vector of feedback gains C . The first controller ( C1 ) minimizes the spectral abscissa , which is the rightmost eigenvalue of the closed loop control system . The spectral abscissa is directly related to the exponential decay of the joint motion towards the equilibrium following a perturbation [30] , which in theory guarantees the fastest corrective movement towards the target . This controller corresponds to relatively low feedback gains ( Figure 8 , blue trace , C1 = [−2 . 03 −1 . 07 −0 . 58] ) . This seems counter-intuitive since this controller should present the fastest exponential decay following perturbations . A closer look indicates that this is indeed the case , as this controller does not generate any oscillation in the corrective response , resulting in a fast decay of the angle , velocity and torque towards 0 following the perturbation . The return time obtained with this controller was 585 ms . Although the performance of this controller is good for moderately fast return times , we are interested to generate corrective responses with return times <300 ms . Reducing the return times can only be obtained at the cost of tolerating oscillations , provided that they remain within the virtual target bounds . This is illustrated with the second controller minimizing the return times ( red trace , C2 = [−10 . 81 −2 . 37 −0 . 98] ) . The return time for this controller was 260 ms . However , it presented oscillations that exceeded those generated by human subjects ( Figure 8 A , inset ) . It is also important to realize that this controller was quite sensitive to the presence of noise in the process , a common feature of biological motor systems [31]–[33] . Indeed , injecting small amounts of noise in the simulations substantially altered its performance as illustrated in Figure 8 B ( dashed red histogram , average return time >350 ms ) . Observe that the oscillations do not vanish on average , as they are not due to noise but to the controller spectral properties . Thus , although the performance of this control candidate is good in the absence of any source of noise , we may question its relevance as a model for human behaviour on the basis that its sensitivity to process noise impedes consistent success in the fastest temporal condition . In theory , it is possible to limit the impact of process noise by using robust control design ( black trace , C3 = [−10 . 92 −2 . 4 −1 . 22] ) , which minimizes the controller sensitivity to perturbations and uncertainties in the model parameters [30] , [34] . Indeed , with similar amounts of sensorimotor noise , the robust controller generates a distribution of return times that is narrower than the distribution obtained with the controller designed to minimize return times ( Figure 8 B ) . However , improving the robustness is achieved to the detriment of control performance [35] , as illustrated by the shift in return times towards greater values ( average return time = 0 . 33 s , Figure 8 B ) . To summarize , increasing the feedback gains of delay-uncompensated controllers ( as for C2 and C3 in comparison with C1 ) reduced the return times but generated oscillations . Any attempt to increase the feedback gains to match human performance generated oscillations that exceeded the target bounds , and we were not able to find any stable delay-uncompensated controller generating consistent return times <300 ms in the presence of sensorimotor noise . In contrast , it was much easier to reproduce participants' data with simulations based on state estimation , without any apparent limitations on the feedback gains ( Figure 8 A , right and 8 B ) . This class of control models differs from Equation 3 in that the feedback gains are applied to an estimation of the present state of the system represented by given by a Kalman filter: ( 4 ) For these controllers , increasing the feedback gains simultaneously reduced the maximum joint angle as well as the target overshoot , which was the signature of participants' successful trials ( Figure 2 ) . Interestingly , the high feedback gains used with the state estimator generated unstable control when applied directly to delayed sensory input . This result highlights the advantage of state estimation to generate fast and stable feedback control , and corroborates our previous findings regarding the influence of state estimation on rapid feedback responses to perturbations [36] . Thus , the controller based on state estimation performs better than the tested delay-uncompensated feedback controllers . However , using internal models is prone to errors in the presence of model errors , causing an inherent trade-off between control performance and robustness dependent upon the accuracy of the internal model [30] . We observed the consequences of this control principle by varying the joint inertia while maintaining constant the robust controller ( C3 ) , and the model-based controller ( including feedback gains and Kalman gains ) . Varying the inertia by ±10% only induced small changes in return times obtained with the robust controller ( <10% on average ) , in agreement with the fact that it is in theory the least sensitive to small changes in model parameters . In contrast , similar variations induced more than 20% increase in return times when using estimation-based control . The performance of the controller minimizing the return times was also quite sensitive to changes in inertia ( >30% increase in return times when inertia decreases by 10% ) . Simulations further indicated that the robust controller started to perform better than the model-based controller if inertia changed by ≥15% . Thus , robust control is clearly a good candidate in the presence of model uncertainty when internal models of dynamics are not sufficiently accurate to ensure fast and stable control . Finally , the simulations allow us to quantify the relative contribution of intrinsic impedance to the total torque produced against the external perturbation . In the slowest temporal condition , the peak elastic and viscous torques represent 23% and 18% , respectively , of the controlled torque generated by the feedback response . It is worth noting that the intrinsic elastic and viscous torques do not reach their peak values at the same time , nor at the peak resultant torque . The contribution of the passive torques represents 22% of the peak resultant torque . The relative contribution of the joint intrinsic stiffness is reduced in the fastest temporal condition because the feedback response limits the perturbation-related motion . In this condition , the intrinsic stiffness and viscosity represent 9 . 6% and 9 . 4% of the peak controlled torque respectively , and their combined action contributes to 10% of the peak resultant torque .
We show that participants are able to generate very fast corrective movements following mechanical perturbations without substantial oscillations and while using only small increases in pre-perturbation activity . Based on anthropometric data , observed perturbation-related motion , muscle recordings and a full muscle model [23] , [24] , we derived a linear model of the elbow joint with realistic feedback delays and intrinsic impedance . With these parameters , we found that state estimation is an easy and effective way to permit fast corrective movements ( return times <300 ms ) . Estimating the joint stiffness and viscosity was central to our analyses because it critically influences the predictions of each control model . The conventional technique for estimating joint stiffness is with servo-controlled perturbations to extract the relationship between the hand displacement and the restored force [5] , [7] , [12] , [13] , [15] , [28] . One shortcoming of this technique is that the restored force measured over ∼200 ms includes the contribution of short-latency ( >20 ms ) , long-latency ( >50 ms ) and early voluntary feedback responses ( >100 ms ) [3] , [37]–[39] . Thus , this approach provides estimates that include not only the intrinsic mechanical impedance of the joint , but also neural feedback . We estimated joint stiffness and viscosity from the mechanical properties of muscle . Our sensitivity analysis may not fully capture estimation errors that result from using a linear muscle model . However , some ignored non-linear features ( such as plateaus in force-velocity curves and the presence of elasticity in the tendon , see Methods ) would result in estimates of the muscles visco-elastic properties that are even smaller than our first-order approximations . It is clear that future studies would be useful to explore the limitations of these linear approximations to address whether the basic results presented in this paper remain valid in general . We wish to emphasize that we do not reject the presence of peripheral joint impedance . We show that short-range impedance can contribute substantially against transient perturbations during postural control . However , short-range impedance cannot counter perturbations during movement or when abrupt perturbations induce large motor errors . Thus , what we question is the contribution of joint intrinsic impedance during movement and feedback responses to moderate-sized disturbances , given that perturbation-related motion quickly overcomes the short-range stiffness ( in ∼60 ms in the main experiment ) . Previous work also reported that the intrinsic components of muscles only provide limited contribution to force feedback in comparison with neural feedback [40] . Further , short-range impedance cannot contribute during voluntary movements , as it is only present in static conditions . These observations indicate that feed-forward regulation of intrinsic joint impedance , suggested as the first level of sensorimotor control hierarchy [9] , [41] , may not play a substantial role during voluntary control . This is puzzling because co-contraction is often observed as a spontaneous strategy [42]–[44] . We also found a significant ( small ) increase in baseline activity across timing conditions . However , higher levels of joint stiffness and viscosity can only be obtained by increasing baseline activation substantially ( see the Control Experiment ) , yet nobody chose this strategy despite the failure rate . Why then , in some conditions , does the brain use co-contraction ? Perhaps co-contraction is mostly beneficial to counter small disturbances by exploiting the short-range stiffness . In order to understand the role of co-contraction , one must consider not only its contribution peripherally , but also its contribution centrally to feedback processing . It has been shown that perturbations applied with higher background levels of muscle activity lead to larger short-latency stretch responses [45]–[47] ( see also the Control experiment ) . This gain-scaling quality of the short-latency spinal reflex is likely due to the size recruitment principle of motoneurons , whereby the motor units are recruited in order of their strength [48] . Thus , by increasing the level of baseline activation , spinal feedback can recruit stronger motor units with faster contraction times , increasing the gain of spinal feedback and hastening the corrective response . Like the short-range stiffness , it is worth pointing out that spinal gain-scaling is potentially deleterious as the short-latency stretch response lacks most of the sophisticated capabilities present in long-latency responses [2] . In fact , this increase in gain is transient , as it disappears within 100 ms after the perturbation , during the long-latency time period [47] . In general , our data do not allow us to make any definitive statement regarding how neural circuits express model-based control , and it is clearly possible that the spinal cord is engaged in addition to supra-spinal and cortical contributions . However , we provide empirical evidence that participants did not strongly engage short-latency spinal responses in this task , as measured response onsets occurred at ∼50 ms on average . The control experiment also revealed that the spontaneous increase in co-activation observed in the main experiment was very small in comparison with the range in which the benefits of co-contraction are apparent . Thus , although the short-latency spinal stretch reflex generates a faster muscle response , it was clearly not exploited by participants . It should be noted that this strategy may have depended on the task instruction . We focused on timing constraints because return times are directly affected by the boundedness of the set of stabilizing delayed-feedback controllers , making the limitations of such controllers easier to observe . Other tasks for which stability and model-based control are less critical ( such as shooting through a target ) may allow other strategies such as more recruitment of intrinsic impedance and short-latency feedback . The present results on the limited contribution of short latency spinal reflexes appears to be at odds with the classic studies by Nichols and Houk [40] that highlight spinal reflexes in decerebrate cats can compensate for changes in muscle stiffness . However , this study demonstrated that this spinal-based compensation was only possible with sufficient background muscle activity . At low levels of background muscle activity , spinal reflexes were insufficient to counter the change in muscle stiffness properties [40] , consistent with the observations in the present study . State estimation has been exploited to characterize unperturbed reaching during which the brain may rely on internal predictions from forward models and efference copy of motor commands [49]–[52] . Although this hypothesis is firmly established in the context of voluntary movements , evidence of estimation underlying rapid feedback responses has remained elusive . Previous studies provide indirect evidence for state estimation driving feedback control , without dissociating the rapid update based on sensory feedback about the perturbation from the prediction of the effects of the motor commands [53]–[56] . Recently we showed that internal priors about the perturbation profiles were engaged in the long-latency response before sufficient sensory information was collected to accurately respond to the perturbation [36] . This previous study showed that long-latency responses were not purely dependent upon sensory feedback; instead these responses were compatible with a rapid update of the estimate state of the limb using internal knowledge of the perturbation profiles . The present paper highlights the benefits of this state estimation to provide rapid feedback control following perturbations ( and more generally during voluntary control ) . However , our simulations also highlight potential strategies for delay-uncompensated feedback control to provide relatively fast corrective responses . In particular , robust control is beneficial to reduce the impact of errors in model parameters , but at the cost of greater return times that can impede task success . Robust control may also provide important insight on the role of Golgi tendon organs ( GTO ) . This sensory organ provides feedback about the muscle force [57] , [58] , but its role remains controversial . Interestingly , the robust controller has higher absolute torque feedback than the other delay-uncompensated controllers . This results from the fact that maximizing the stability radius requires that the closed-loop control system is as far as possible from the unstable bounds , and the system becomes quickly unstable with non-negative torque feedback . Thus , increasing negative torque feedback improves the system's stability margin , but also slows down corrective feedback . This theoretical feature of robust control is compatible with the regulatory action of the GTO [57] . More generally , the inherent trade-off between performance and robustness , previously reported in a bimanual task [59] , is likely an important feature of online feedback control that requires further study . It is possible that biological motor systems select control solutions that achieve performance or robustness depending on the quality and reliability of body and environment's internal models of dynamics . Our results have important implications for motor learning and adaptation given the link between feedback control and motor learning [60] . Indeed , previous studies addressing adaptive changes in movement control have predominantly focused on trial-by-trial adjustment of the descending commands [11] , [29] , [61]–[64] . This approach is partially supported by the fact that several modeling studies have considered very high impedance values [29] , [54] , [65]–[67] , or have ascribed them to short-latency spinal pathways [53] , [54] , which in both cases substantially overestimates the actual properties of the limb ( see Figure 7C ) . Thus , simulations obtained with such models predict that online corrections for motor errors encountered while moving in a novel dynamical environment are handled by the intrinsic properties of the limbs or by high-gain short-latency reflexes . It seems important to re-evaluate this aspect of motor learning theory and re-explore the mechanisms underlying online feedback control while exposed to unknown dynamics . Robust control is again a good candidate model to capture motor strategies during early exposure to unknown dynamics , giving place to a greater reliance on internal models following the acquisition of motor skills . In theory , it is also possible to adjust internal models within a single movement with rapid reverberating loops mapping motor errors into model updates [68] . We expect that future work on motor learning will shed light on how the motor system handles model errors during online feedback control . The rapid drop in muscle stiffness beyond a short range of motion may explain the presence of pre-synaptic inhibition of direct spinal feedback during movement [69] . There is an extensive literature highlighting that a group of GABA inhibitory interneurons in the spinal cord form synapses on the axons of sensory afferents that terminate onto motor neurons and interneurons [70] . These GABA interneurons generate presynaptic inhibition on sensory afferents during the transition from posture to movement [70] , [71] . Pre-synaptic inhibition effectively reduces the gain of sensory feedback at the level of the spinal cord , and it has been presumed that this is necessary to extract task-relevant information about movement [70] , [72] . Although plausible , it is unclear why information about self-generated motion is irrelevant to the central nervous system , nor how presynaptic inhibition on synapses between sensory afferents and motoneurons relate to sensory processing . We believe that pre-synaptic inhibition may also have a functional role for motor function . Indeed , limb movement also results in muscle switching from high to low impedance [73] . Thus , it is possible that pre-synaptic inhibition reflects a relative shift in the contribution of spinal feedback . During postural control , the motor system can exploit short-range impedance of muscles and relatively elevated spinal gains . However , during movement when muscle possesses low stiffness , direct spinal feedback is reduced and the central nervous system exploits to a greater extent on internal models and state estimation that is expressed in long-latency motor responses . How these mechanisms , purely spinal and supra-spinal , interact is not straightforward . However , this question appears to be at the core of how the nervous system maintains stable interactions with the environment . In the present study , we emphasized that upper limb stability is threatened by state-dependent muscle mechanics as well as sensorimotor delays . Stability issues also arise when interacting with intrinsically unstable environments , such as when one manipulates non-rigid objects or when stepping on unsteady ground . Using a paradigm that can reproduce such situations , Lawrence and colleagues [74] recently showed that humans displayed consistent capabilities to stabilize finger or lower-limb forces against unstable springs across healthy and clinical populations . Altogether , these observations suggest that the interaction between peripheral and central mechanisms is likely a core challenge for the nervous system in most tasks . Simple hierarchical models have been suggested [9] , [41] , [75] , but this view still leaves open the problem of central compensation for state-dependent properties of the lower level of the hierarchy such as the transition from high to low impedance as well as task-dependent spinal feedback . Alternatively , the brain may selectively rely on short-range control or model-based control depending on the task or on the perturbation-related motion . In this framework , it is possible that pre-synaptic inhibition regulates spinal sensorimotor gains to complement muscles biomechanics and partially compensate for changes in their properties across postural control and movement tasks .
The Queen's University Research Ethics Board approved the experimental protocol and participants gave written informed consent following standard procedures . A total of 16 healthy volunteers ( 11 males ) between 19 and 33 yrs of age took part in this study . Fifteen participants performed the main experiment . Five of them also performed the control experiment . One participant was tested for the control experiment only . The elbow angle , velocity and the activity of the major muscles spanning the elbow joint were sampled at 1 kHz . Position signals were digitally low-pass filtered with a dual pass 4th order Butterwoth filter with 50 Hz cutoff frequency . The activity of elbow muscles was collected with surface electrodes ( DE-2 . 1 , Delsys , Boston , MA ) attached over the muscle belly after light abrasion of the skin with alcohol . We collected the activity of bi- and mono-articular elbow flexors and extensors ( brachioradialis , biceps , triceps lateralis and triceps long ) . Muscle recordings were digitally band-pass filtered with a dual-pass , 4th order Butterworth filter ( band-pass 10–400 Hz ) , rectified and averaged across trials . Normalization was performed relative to the activity evoked by a 2 Nm background load when maintaining postural control at the start target ( 90 degrees of elbow angle ) . We present the ensemble-averaged activity as we found qualitatively similar behaviour across muscles . We extracted the maximum elbow angle following the perturbation and the maximum target overshoot when returning to the goal . The maximum target overshoot was computed relative to the centre of the goal target . We also extracted the return times , defined as the time when the elbow angle returned within ±1 . 5 deg of the initial angle , corresponding to a virtual goal target of 3 degrees centered on the start position . Trial success was determined offline based on the return time of each individual trial . Comparisons of parameters from individual trials across conditions were performed for each participant independently with a non-parametric Wilcoxon ranksum test . Group comparisons across conditions were based on paired t-tests performed on each participants' individual means . The control experiment addressed the onset of divergence between perturbation-related changes in joint angles across pre-activation conditions . The onset of divergence across trials was computed for each participant based on time series of ROC areas following procedures described earlier [78] . We also addressed the onset of divergence across participants . For this analysis , a time series of paired t-tests was preferred in order to mitigate the impact of inter-participants variability . For each analysis ( ROC on individual trials or running t-tests across participants ) , we extracted the divergence onset as the last chance-crossing time . The estimate of joint intrinsic stiffness and viscosity is based on the static force-length/velocity curves as described in [23] , [24] . The normalized fascicle tension ( F ) is a function of the normalized muscle activation level ( a ) , length ( L ) and velocity ( V ) . The normalized tension must be multiplied by a constant proportional to the physiological cross-sectional area to estimate the total muscle force ( S ) , and by the muscle moment arm to estimate the muscle torque ( d ) . Thus , the resultant muscle torque is given by: ( 5 ) The intrinsic joint impedance ( Equation 1 ) is the ratio between changes in the joint torque and the changes in joint angle or velocity . Thus , a first approximation , the intrinsic elastic ( K , [Nm/rad] ) and viscous ( G , [Nms/rad] ) component of the muscle impedance is given by computing the derivative of the muscle torque with respect to muscle length or velocity as follows ( p0 = [a0 , L0 , V0]T is the parameter vector around which the derivatives were computed ) : ( 6 ) The numerical values used to compute K and G were either measured or taken from the literature . We used d = 4 cm for the moment arm [79] , [80] . The physiological cross-sectional areas ( PCSA ) and muscle fascicle lengths were measured on human muscles samples from 9 cadavers following standard techniques ( see Supporting Information ) [81] . We considered the sum of PCSA over muscle groups ( flexors or extensors ) , and averaged it across groups and individuals . The average PCSA across human muscle samples was 16 . 38 cm2 , which must be multiplied by the maximum tension generated per square centimeter ( 31 . 8 N/cm2 , [23] ) , which gives S = 520 . 9 N . Muscles fascicle length was also measured for each muscle group , and averaged across samples ( L = 13 . 38 cm ) to calculate the relationship between changes in joint angle and changes in normalized length and velocity . We added 0 . 05 Nms/rad to the constant G to account for joint friction independent from muscle dynamics . To estimate the level of activation a0 , we calculated the activation needed to produce 2 Nm joint torque according to Equation 5 , and used the fraction corresponding to the pre-perturbation activity measured in the 300 ms condition averaged across participants ( 54% of the activity evoked by 2 Nm background load ) . The other values used in Equation 6 were L0 = 1 , V0 = = 0 and θ0 = 90 deg . With these parameters , we obtained K = 1 . 61 Nm/rad and G = 0 . 14 Nms/rad . The short-range stiffness was obtained by estimating the slope for force-length relationship based on Rack and Westbury [22] ( Figure 2 in this reference , ∼7 N/mm ) , and scaling this number to human muscles properties . This computation gave us an elastic stiffness 9 . 4 times greater than the value obtained from the static force-length curve ( ∼15 Nm/rad , see also [17] ) . The viscosity was scaled by the same factor to generate the simulations with short-range impedance presented in Figure 1 . We validated these parameters by comparing simulations of a passive joint following perturbation pulses . The motion was generated by considering a system corresponding to Equation 1 with T = 0 . We varied the parameters K and G across simulations and compared the perturbation-related motion with participants' data from the main experiment . This approach allowed us to derive sets of values for K and G representing upper bounds and admissible combinations given participants' data ( Figure 7 ) . The equations of motion and control models were defined in Equations 1–4 . The system inertia was estimated based on average anthropometric data [82] and on the robot linkage mass and geometry ( I = 0 . 11 Kgm2 ) . The time constant of the muscle model ( Equation 2 ) is τ = 66 ms , compatible with first order approximation of muscle dynamics [24] . Sensorimotor delays were measured as the time when muscle responses exceed 2 standard deviation of their baseline activity . Individual onset times were averaged across muscles and participants ( Figure 9 , 50 . 5 ms±5 . 5 ms , mean ± SD across participants ) . This value corresponds to long-latency delays typically observed in absence of muscle pre-loading [2] . One limitation of our approach is to consider a linear model of muscles visco-elastic properties , which is clearly a crude approximation given the non-linearity of muscles biomechanics [24] . Two important points can be examined: first we ignored the contribution of heterogeneous connecting tissues acting in series with contractile tissues in the quantification of the overall elastic component of muscles . Second , we ignored that the force-velocity relationship rapidly plateaus for moderate joint velocities [23] . Observe these two modeling choices yield an overestimation of the parameters K and G . Indeed , our approach effectively considers that tendons have infinite stiffness and therefore likely overestimate the actual elastic force beyond the short range . This approximation is partially justified by the fact that tendon stiffness was reported to be much greater than the stiffness of the contractile elements [83] . Similarly , considering a constant viscosity across all velocities overestimates the true ( time varying ) muscle viscosity resulting from the non-linear force-velocity curve . Identification techniques are sometimes used in the literature to quantify the contribution from intrinsic and reflexive components of joint torque following a perturbation [16] , [17] . Although the values of stiffness can clearly vary across joints and muscles due to different properties , several estimates published with this technique seem extremely high ( >100 Nm/rad ) , and almost certainly non physiological . One potential problem with this approach is that it is often based on fitting procedures , and the conditioning of the fit is not systematically verified . As a result the fitting procedure may by extremely sensitive to model errors and data variability . An important question for future studies is to investigate the origin of disagreement between this approach and ours . We considered three candidate controllers corresponding to Equation 3 , minimizing the spectral abscissa , the sensitivity to parameters error ( robust control ) and the return times . The eigenvalues of the closed-loop system were computed with the freely available Matlab package DDE-BIFTOOL [84] . Each optimization procedure was based on first order evaluation of the sensitivity of the objective function ( spectral abscissa , stability radius or return time ) relative to the feedback gains [30] . The second class of control models corresponding to Equation 5 is based on an estimation of the state of the system [51] , [85] , [86] . The cost-function used for this model penalizes position errors ( θt≠0 ) and motor costs ( u ) as follows: ( 7 ) where N is the time horizon ( >2 sec , 5 ms time steps ) , w and rt ( 1≤t≤N-1 ) are parameters adjusted to get return times <600 ms ( w = 0 . 01 , rt = 10−4 , rN = 0 ) . We followed the procedures fully described earlier to derive the optimal Kalman gains and control gains ( C in Equation 4 ) , while taking the feedback delays into account [87] , [88] . We varied w to increase the feedback gains until the simulated trajectories matched participants' return times . Varying w to match participants' behaviour was the only fitting procedure used in this study . The noise parameters ( additive and signal dependent ) were identical for each model simulation and were not fitted to participants' data . We verified that the variability across simulations was lesser than participants' trial-to-trial variability , which ensures conservative conclusions . All other parameters were measured experimentally or taken from the literature . Our model assumes that the brain receives feedback about the joint position , velocity and joint torque . However , previous work emphasizes that information about the joint acceleration is also encoded in the discharge rate of muscle spindles [89] . Observe that the differential equation describing joint dynamics ( Equations 1 and 2 ) can be transformed into its canonical form in which the joint acceleration becomes a state variable as follows: ( 8 ) Thus , the systems considering torque or acceleration derivatives ( Equations 2 or 8 ) are equivalent in the sense that similar control inputs generate the same motion . We verified that the predictions obtained with Equation 8 gave the same results as those obtained based on Equations 1 and 2 . | Recent studies have investigated how the brain generates purposeful feedback responses to perturbations during motor control . One hypothesis suggests that the brain exploits the spring-like properties of muscles to counter perturbations . However , muscles exhibit high mechanical impedance only against small perturbations during posture , which questions the general contribution of intrinsic muscle impedance for feedback control . Alternatively , the brain may directly map sensory data into motor commands without compensating for sensorimotor delays , which is known to limit control performance . A third hypothesis suggests that neural activity following an external disturbance estimates the current state of the limb to generate a motor response . We used a perturbation paradigm where healthy participants were instructed to respond to perturbations within an extremely short time window . Comparing participants' performances with a model considering intrinsic joint impedance and conduction delays revealed that the case of state estimation was the best candidate control model to explain very fast corrective response of humans . This study emphasizes that model-based control can generate human-like rapid and stable feedback responses given low muscle stiffness and sensorimotor delays . | [
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"syste... | 2014 | Beyond Muscles Stiffness: Importance of State-Estimation to Account for Very Fast Motor Corrections |
Rhadinoviruses establish chronic infections of clinical and economic importance . Several show respiratory transmission and cause lung pathologies . We used Murid Herpesvirus-4 ( MuHV-4 ) to understand how rhadinovirus lung infection might work . A primary epithelial or B cell infection often is assumed . MuHV-4 targeted instead alveolar macrophages , and their depletion reduced markedly host entry . While host entry was efficient , alveolar macrophages lacked heparan - an important rhadinovirus binding target - and were infected poorly ex vivo . In situ analysis revealed that virions bound initially not to macrophages but to heparan+ type 1 alveolar epithelial cells ( AECs ) . Although epithelial cell lines endocytose MuHV-4 readily in vitro , AECs did not . Rather bound virions were acquired by macrophages; epithelial infection occurred only later . Thus , host entry was co-operative - virion binding to epithelial cells licensed macrophage infection , and this in turn licensed AEC infection . An antibody block of epithelial cell binding failed to block host entry: opsonization provided merely another route to macrophages . By contrast an antibody block of membrane fusion was effective . Therefore co-operative infection extended viral tropism beyond the normal paradigm of a target cell infected readily in vitro; and macrophage involvement in host entry required neutralization to act down-stream of cell binding .
The γ-herpesviruses chronically infect most mammals and cause disease in humans [1] and economically important ungulates [2] . Vaccines have failed to prevent infection . In part this reflects that host entry is ill-understood . Oral entry is often assumed , because the archetypal Epstein-Barr virus ( EBV ) causes acute tonsillitis . However clinical presentation occurs at least 1 month after EBV acquisition [3] and correlates better with peak salivary shedding . Thus , it may reflect host exit rather than entry , and because virus shedding involves reactivation from circulating B cells [4] entry and exit routes need not coincide . Lymphocryptoviruses such as EBV are known only in primates; Rhadinoviruses such as the Kaposi’s Sarcoma-associated Herpesvirus ( KSHV ) are more widespread [5] . KSHV in nasal secretions [6] , and pulmonary Kaposi’s Sarcoma complicating AIDS [7] , suggest respiratory infection . Consistent with this idea , Ovine herpesvirus-2 and Alcelaphine herpesvirus-1 show salivary and nasal shedding , and infect in nebulized form [8]; Equid γHV-5 causes respiratory disease [9] and can be isolated from the respiratory tract [10]; and Bovine herpesvirus-4 colonizes the respiratory tract [11] , and persists in calves after intranasal ( i . n . ) inoculation [12] . MuHV-4 ( strain MHV-68 ) [13 , 14] allows rhadinovirus infection to be analysed in mice . Like EBV and KSHV , it is B cell tropic . MuHV-4 infects readily mice i . n . , and infects orally only if it spills into the respiratory tract [15] . When given i . n . under anesthesia it infects the lungs [16]; without anesthesia it infects the upper respiratory tract [17]; and it transmits sexually from female mice to co-caged males [18] . How MuHV-4 transmits in its natural host of yellow-necked mice [19] is unknown . Analysis of wood mice identified DNA from MuHV-4 or a related virus in lungs [20] , and lung inoculation is used widely for experimental infections . Therefore MuHV-4 is suited to analyzing this mode of host entry . A key unknown is the first infected cell type . Lungs contain type 1 AECs , which comprise 95% of the alveolar surface area; type 2 AECs , which secrete surfactant; macrophages , which phagocytose inhaled debris [21]; and some lymphocytes . By 5 days after i . n . inoculation , MuHV-4 antigens are detectable in type 1 AECs and large mononuclear cells [16] . DNA of i . n . -inoculated , replication-deficient MuHV-4 has been detected by PCR of B cells from lung homogenates [22 , 23] , suggesting that they are a primary infection target . However MuHV-4 normally binds to heparan , which B cells express poorly [24 , 25] , and shows a post-binding block to B cell infection that is not overcome until virions pass through myeloid cells [26] . PCR detects replication-deficient MuHV-4 DNA also in association with B cells recovered from splenic homogenates after intraperitoneal ( i . p . ) inoculation [22 , 23] , when in situ analysis shows no evidence of B cell infection [27] . Therefore the PCR signals associated with lung B cells might reflect adsorbed inoculum debris rather than infection . Any understanding of MuHV-4 infection must encompass its dependence on heparan for cell binding [24] , a characteristic common to many rhadinoviruses [28–30] . The MuHV-4 gp70 and gH/gL bind to heparan [31 , 32] , and gp150 further inhibits heparan-independent binding until heparan is engaged [24] . Thus , gL-gp70- MuHV-4 infects poorly both in vitro and in vivo , unless gp150 is disrupted also [33] . The Bovine Herpesvirus-4 gp150 homolog , gp180 , is functionally similar [34] . While most transformed epithelial cells express abundant heparan , most in vivo epithelia do so only basolaterally [35] . Thus , heparan-dependent host entry by incoming , apical virions is not necessarily straightforward . The olfactory neuroepithelium , which we have analysed previously [17] , unusually expresses both basolateral and apical heparan . The genital epithelium lacks apical heparan [36]; entry here may depend on epithelial trauma , as MuHV-4-infected cells are seen underneath rather than in the stratified squamous epithelium [18] . We asked which cell type MuHV-4 targets in the lungs and how this relates to heparan binding . Our aim was to understand better rhadinovirus entry into new hosts and so establish a rational basis for infection control .
Four days after i . n . MuHV-4 inoculation , viral antigens were expressed in morphologically typical type 1 AECs , whose extensive cytoplasmic projections line the lung air spaces ( Fig . 1A ) . This tropism was confirmed by co-staining lungs for viral antigens and the type 1 AEC marker podoplanin ( PDP ) [37] ( Fig . 1B ) —at 4 days post-inoculation >50% of viral antigen+ cells were PDP+ ( Fig . 1C ) . The morphologically distinct remainder expressed the macrophage / granulocyte marker CD68 [38–40] . However at 1 day post-inoculation viral antigens were confined to CD68+ cells . Therefore while infection spread to type 1 AECs , it appeared to start in macrophages or granulocytes . MuHV-4-immune sera recognize mainly lytic antigens . As much early lung infection is latent [41] , we tracked host entry more comprehensively via lytic cycle-independent [42] eGFP expression from a viral HCMV IE1 promoter ( Fig . 2A ) . At 1 day post-inoculation , 238/238 eGFP+ cells on lung sections of 3 mice were CD68+ . None was PDP+ . MuHV-4 rendered incapable of uncomplemented lytic spread by disruption of its ORF50 transactivator ( ORF50- ) showed a similar eGFP distribution: 78/78 eGFP+ cells on lung sections from 5 mice expressed CD68 , and none expressed PDP ( Fig . 2B ) . Therefore CD68+ cells were direct infection targets . More than 80% of eGFP+ cells expressed also the tissue macrophage marker F4/80 [39] and the alveolar macrophage / dendritic cell marker CD11c [38 , 40] ( Fig . 2C ) . Macrophage populations are heterogeneous and often not clearly demarcated [38] , but combined CD11c , CD68 and F4/80 expression argued that most infected cells were alveolar macrophages . The lack of HCMV IE1-driven eGFP expression in type 1 AECs at day 1 post-inoculation established that they were uninfected rather than just slow to initiate lytic infection . We saw also no eGFP expression in B cells at day 1 by either eGFP+ wild-type ( WT , >100 eGFP+ cells counted from 3 mice ) or eGFP+ORF50- MuHV-4 ( >50 eGFP+ cells counted from 3 mice ) ( S1 Fig ) . Nor did MuHV-4 with EF1α promoter-driven eGFP expression show a day 1 infection of B220+ lung B cells or type 1 AECs ( S1 Fig , >100 eGFP+ cells counted from 3 mice ) . Therefore incoming virions targeted selectively alveolar macrophages . Viral lytic gene expression suggests but does not prove lytic replication . To determine how alveolar macrophage infection contributed to virus production we infected lysM-cre mice with MuHV-4 in which cre-mediated recombination switches fluorochrome expression from mCherry+ ( red ) to eGFP+ ( green ) ( MHV-RG ) [26] . LysM-cre mice express cre from the myeloid lysozyme locus [43] . To identify cre+ lung cells we crossed them with a reporter strain in which cre activates Zsgreen expression from a widely expressed promoter [44] . PDP+ cells lacked Zsgreen ( S2 Fig ) . CD68+ cells expressed Zsgreen in an endosomal distribution . Type 2 AECs ( surfactant protein C precursor+ , CD68- , MAC-2- ) also expressed Zsgreen—in a uniform rather than endosomal distribution . However in C57BL/6 mice given HCMV-IE1-eGFP+ MuHV-4 they remained eGFP- ( S3 Fig ) and so were not infected . Thioglycollate-induced Gr-1+ peritoneal exudate neutrophils of lysM-cre x Zsgreen mice were Zsgreen+ , consistent with published data [43] , but lung cells expressing neutrophil markers ( Gr-1 or myeloperoxidase ) were not ( S2 Fig ) . They also lacked eGFP expression by HCMV-IE1-eGFP+ MuHV-4 . Therefore we interpreted MHV-RG switching in lysM-cre mice as an infection of alveolar macrophages . Some switching in lung dendritic cells was possible , but they express little lysM [43] or CD68 [39] , so there was no other indication that dendritic cells were a significant target for incoming virions . The MHV-RG recovered from lysM-cre lungs at 1 day post-inoculation was >96% eGFP+ , indicating that essentially all of it had passed through an alveolar macrophage ( Fig . 3A ) . Surprisingly the MHV-RG recovered after 4 days showed only 85% switching . This reduction in % switched virus with time was consistent and statistically significant ( Fig . 3B ) , suggesting that virions could also enter the lungs without infecting a macrophage , but that virus production was then delayed . We confirmed myeloid infection as the predominant route by infecting CD11c-cre mice ( Fig . 3C ) , as alveolar macrophages ( and dendritic cells ) are CD11c+ [38] . Again the virus recovered from lungs after 1 day was >96% eGFP+ , and that recovered after 4 days was >85% eGFP+ . The prominent viral lytic antigen expression in type 1 AECs at day 4 post-inoculation ( Fig . 1 ) but not day 1 implied that they receive and amplify virus from alveolar macrophages . Their lack of HCMV IE1-driven or EF1α-driven eGFP expression at day 1 ( Figs . 2 , S1 ) excluded acute infection . Day 1 MHV-RG-infected lung sections ( Fig . 3D ) confirmed this: >95% of fluorescent cells were morphologically typical alveolar macrophages and PDP- . Of these 92 . 6 ± 4 . 3% were eGFP+ ( mean ± SD of counts from 8 mice ) , confirming efficient viral fluorochrome switching in infected myeloid cells . However at day 4 , when 63 . 7% of fluorescent cells were PDP+ , 48 . 5 ± 10 . 6% of these were eGFP-mCherry+ ( mean ± SD of counts from 8 mice ) . Thus , not all type 1 AEC infection was down-stream of myeloid cell infection . And while no eGFP+ cells were PDP+ 1 day after i . n . eGFP+ORF50- MuHV-4 inoculation of C57BL/6 mice ( Fig . 2 ) , 35 . 5 ± 29 . 7% of eGFP+ cells were PDP+ after 5 days ( mean ± SD , 6 sections from 3 mice ) ( S4 Fig ) . Therefore a delayed type 1 AEC infection without prior macrophage infection seemed to account for the reduction in % virus switching between days 1 and 4 in Fig . 3A-3C . Either virions eventually penetrated AECs , or macrophages licenced AEC infection without becoming infected themselves , for example by recycling virions from early endosomes to exosomes and releasing them in a licensed form [45] . Most lung macrophages express CD169 [46] , so we used CD169-DTR mice [47] to test their importance for host entry ( Fig . 4 ) . Mice were given diphtheria toxin i . p . for 2 days , then given eGFP+ ORF50- MuHV-4 i . n . for 1 day . Staining lungs for CD169 , CD68 , F4/80 and CD206 ( Fig . 4A ) showed that CD169 expression was ablated completely . CD206 and F4/80 expression were ablated >90% , consistent with most lung macrophages expressing CD169 . EGFP+ cell numbers in toxin-treated mice were <10% those of untreated controls ( Fig . 4B ) . CD68+ cells were still seen , but they were larger than in undepleted mice and lacked other alveolar macrophage markers . Such cells were not evident in undepleted infected mice , and so seemed likely to be bone marrow-derived monocytes or granulocytes , recruited in response to alveolar macrophage ablation . They were clearly poor infection targets . Therefore alveolar macrophages played a crucial role in virus acquisition , for which lineage-distinct bone marrow-derived myeloid cells [48] did not substitute . Host entry via lung macrophages was unexpected , as MuHV-4 infects the lungs efficiently [49] and myeloid cells poorly [26] . The cell-free virions used for in vivo infection poorly infected ex vivo lung macrophages , peritoneal macrophages or the monocyte line RAW-264 ( Fig . 5A ) . Virus stocks containing cell debris infected macrophages with reasonable efficiency . However cell-associated virus infections would occur only after host entry . Efficient alveolar macrophage infection by cell-free virions evidently depended on their normal tissue context . MuHV-4 that lacks heparan binding due to gp70 and gL disruption infects mice i . n . 100-fold less well than the WT , unless its gp150 , which inhibits heparan-independent binding , is disrupted too [33] . This result implies that host entry requires heparan engagement . We confirmed it by pre-incubating WT virions with soluble heparin ( Fig . 5B ) . Seroconversion by virus-specific serum IgG ELISA—the the most sensitive measure of low dose infection in immunocompetent mice [33]—was reduced 10–100-fold by the heparin treatment . Pre-incubating virions with heparin also reduced NMuMG epithelial cell infection 100-fold ( Fig . 5C ) . However , while low dose heparin inhibited RAW-264 monocyte infection better than NMuMG cell infection—presumably because RAW-264 cells have little heparan for virion binding to start with [26]—there was also a heparin-resistant component of infection , such that the maximal inhibition was only 4-fold . Therefore the efficiency of lung infection and its susceptibility to inhibition by heparin both suggested an initial interaction with epithelial cells rather than macrophages . To identify heparan+ cells in the lungs we stained sections with mAb 10E4 , which recognizes sulfated heparan [50] ( Fig . 6A ) , and with mAb NAH46 , which recognizes non-sulfated heparan [51] ( Fig . 6B ) . Staining by both mAbs co-localized with PDP ( type 1 AECs ) and not with CD68 ( alveolar macrophages ) . To distinguish apical from basolateral heparan , we co-stained sections for the alveolar basement membrane component collagen IV ( Fig . 6C ) . Sulfated heparan ( mAb 10E4 ) co-localized completely with collagen IV and so was just basolateral . Unsulfated heparan ( mAb NAH46 ) also co-localized largely with collagen IV . However there were also NAH46+ surfaces abutting the air spaces that lacked collagen IV . Therefore specifically unsulfated heparan was accessible to incoming virions on the apical AEC surface . One day after WT MuHV-4 inoculation , viral antigens were evident as isolated dots—presumably virions . Of these 94 . 8 ± 3 . 9% associated with PDP+ type 1 AECs ( >300 dots counted on 6 sections from 3 mice ) . By contrast strong cellular staining—presumably lytic infection—was confined ( 78/78 cells ) to CD68+ macrophages ( Fig . 7A ) . Therefore incoming virions bound to type 1 AECs but infected macrophages . Similarly 94 . 0 ± 5 . 1% of inhaled replication-deficient MuHV-4 virions associated with PDP+ cells ( ORF50- , Fig . 7B , >300 dots on 6 sections from 6 mice ) . In mice given WT eGFP+ MuHV-4 , 109/113 lytically infected cells ( 96 . 5% ) were eGFP+ and 109/140 eGFP+ cells ( 77 . 9% ) were strongly lytic antigen+ ( 5 sections from 3 mice ) ( Fig . 7C ) . Therefore eGFP expression identified most lytically infected cells , plus eGFP+lytic antigen- cells that were presumably latently infected . Only 8 . 7 ± 5 . 9% of antigen+ dots ( virions ) were associated with eGFP+ cells . Therefore most of the cells binding input virions remained uninfected . In mice given eGFP+ORF50- MuHV-4 , the lack of new lytic infection allowed us to identify diffusely antigen+ cells as those accumulating input virions ( Fig . 7D ) . 32/35 antigen+ cells ( 91 . 4% ) were eGFP+ , and 32/46 eGFP+ cells ( 69 . 6% ) were antigen+ ( 8 sections from 4 mice ) . The greater number of eGFP+ cells reflected presumably that detectable eGFP expression could come from a single infecting virion , but only cells accumulating multiple virions would be detectably antigen+ . Nonetheless antigen accumulation and eGFP expression showed a significant correlation ( p<0 . 0001 by Fisher's exact test ) . By contrast only 7 . 1 ± 3 . 8% of extracellular virions ( isolated antigen+ dots ) were associated with eGFP+ cells . Therefore type 1 AECs bound most input virions , but alveolar macrophages then accumulated the virions and became infected , consistent with their function of collecting particulate alveolar antigens [52] . MuHV-4 infection is endocytic [53] and virion antigens did not accumulate inside PDP+ cells . Therefore poor virion endocytosis seemed a likely explanation for type 1 AEC non-infection . Consistent with this idea , i . n . Herpes simplex virus type 1 , which binds to heparan but penetrates most cells at the plasma membrane [54] , showed abundant early type 1 AEC infection without marked macrophage involvement ( S5 Fig ) . The γ-herpesviruses transmit from immune carriers and elicit salivary antibody responses [55] . Therefore shed virions may carry glycoprotein-specific antibodies . Also virions entering immune hosts or vaccinees could acquire antibodies before reaching mucosal surfaces . Therefore how bound antibodies affect host entry is important to understand . In vitro , immune sera inhibit MuHV-4 epithelial and fibroblast infections , but fail to inhibit and even enhance myeloid infection due to productive IgG Fc receptor ( FcR ) -mediated uptake [56] . This reflects that sera neutralize in vitro mainly by blocking cell binding , for which FcR binding can substitute . We tested 2 MuHV-4 glycoprotein-specific mAbs for their effect on lung infection: 8F10 , which binds to gH/gL and blocks its interaction with heparan [57] , and SC-9E8 , which binds near the gB fusion loops and blocks membrane fusion [58] ( Fig . 8 ) . In vitro both mAbs inhibited fibroblast infection , but while SC-9E8 inhibited RAW-264 monocyte infection 8F10 enhanced it ( Fig . 8A ) . Enhancement reflects that FcR binding improves significantly cell-free MuHV-4 attachment to RAW-264 cells [56] . In vivo virion capture via type 1 AECs was efficient already , so here mAb 8F10 simply failed to neutralize ( Fig . 8B , 8C ) . By contrast mAb SC-9E8 , which neutralizes independent of cell binding [58] , was effective at blocking host entry . Most transformed epithelial cell lines display both sulfated and unsulfated heparan and are bound by both gp70 and gH/gL [33] . However only gH/gL binds well to unsulfated heparan ( 10E4-NAH46+ ) [17] , as displayed by the apical alveolar epithelium ( Fig . 6 ) . Accordingly , an additional block of gp70 heparan binding by mAb LT-6E8 [33] did not affect host entry ( Fig . 8C ) . To understand better the in vivo fate of 8F10-opsonized virions , we exposed WT and ORF50- eGFP+ MuHV-4 to mAb 8F10 or not , gave them i . n . , and 1 day later stained lung sections for eGFP expression ( Fig . 8D ) . Pre-incubation with 8F10 increased significantly the number of eGFP+ lung macrophages . Epithelial infection was unaffected , consistent with this occurring via macrophages rather than directly . Pre-incubating WT MuHV-4 with mAb 8F10 or not and staining lungs for virion antigens 5h after i . n . inoculation ( Fig . 9 ) showed that 8F10 reduced significantly the virion binding to PDP+ cells without compromising their accumulation in CD68+ cells . Therefore blocking cell binding with antibody was insufficient to block host entry , because it merely diverted virions to where they were already going: macrophages .
How rhadinoviruses first infect new hosts has been little explored . Even for lymphocryptoviruses there is considerable uncertainty . In vitro analyses have focussed on epithelial cell and lymphocyte infections . However MuHV-4 , which enters the upper respiratory tract via olfactory neurons , entered the lungs via alveolar macrophages . Most entry models envisage a target cell with a specific binding receptor that is infected readily ex vivo . Alveolar macrophages lacked the key initiator of MuHV-4 binding—heparan—and were infected poorly ex vivo . These data suggest a new paradigm , in which virions with a sophisticated fusion machinery can exploit normal host pathways for entry . One such pathway appeared to be particle scavenging by alveolar macrophages; another was opsonization-dependent endocytosis . Thus , in vivo neutralization had to act post-binding . As immune sera neutralize MuHV-4 mainly by blocking cell binding [56] , this explains their limited capacity to block host entry [59] . In vivo cell diversity and interaction provide a dimension lacking from most cell lines . Co-operative infections are one reflection of this and are a recurring theme in γ-herpesvirus host colonization: EBV can infect epithelial cells by transfer from B cells [60] , and MuHV-4 infects olfactory sustentacular cells via binding to adjacent neurons [17] . The homeostatic interactions of macrophage with other cell types [61] make them prime candidates to participate in co-operative infections . Here , epithelial heparan binding licensed virions for macrophage infection , and interaction with macrophages in turn licensed epithelial infection . How might epithelial cell binding license macrophage infection ? The MuHV-4 gp150 promotes virion release from heparan-deficient surfaces by inhibiting heparan-independent binding [24] . ( Inhibition of EBV epithelial cell infection by gp350 [60] may analogously promote virion release . ) Thus , initiating MuHV-4 infection requires heparan for binding and to engage gp150 . A lack of heparan normally limits macrophage infection by non-opsonized , cell-free virions . If heparan is provided , or if the need for heparan is bypassed by gp150 disruption or by virion opsonization , then infection works well [26] . Therefore type 1 AECs licensed macrophage infection by their apical heparan capturing virions and engaging gp150 . How might virion interaction with myeloid cells license epithelial infection ? The next step in MuHV-4 entry after binding is endocytosis . Virions lacking gL show an endocytic defect that can be reproduced by gH/gL-directed neutralization , implying that gH/gL engages a pro-endocytic ligand [57] . ( This ligand is not heparan as distinct mAbs block gH/gL heparan binding and virion endocytosis . ) Thus , a paucity of pro-endocytic ligands may strand heparan-engaged virions on type 1 AECs . ( By contrast the olfactory epithelium shows no barrier to virion uptake after binding [17 , 26] . ) The MuHV-4 gB and gH change conformation between endocytosis and fusion; fusion itself involves further changes [53 , 62] . Myeloid-derived virions display constitutively the intermediate , post-endocytic forms of gB and gH and overcome a post-binding block to B cell infection [26] . We envisage that they overcome the post-binding block to type 1 AEC infection in the same way . The tropism-associated gB and gH conformation changes occur in early endosomes , whereas fusion occurs in late endosomes [63] . Therefore virions sorted from myeloid early endosomes to exosomes could be released in a tropism-switched form without a need for myeloid infection . This would explain how follicular dendritic cells transfer MuHV-4 from marginal zone to follicular B cells without becoming infected [27] . A similar cycling through alveolar macrophage endosomes would explain delayed type 1 AEC infection without prior macrophage infection . Myeloid cells have roles in host colonization by several lymphotropic viruses [64 , 65] including the human MuHV-4 ortholog , KSHV [66 , 67] . Although MuHV-4 myeloid infection is sparse at steady state [68] , inefficient ex vivo and not prominent in disease states , it is important for host entry , lymph node colonization [69] and systemic spread [27] . Myeloid cells engulf environmental antigens at many mucosal surfaces [70–72] and this can be extensive [73] . Therefore myeloid contributions to human γ-herpesvirus infections may be under-estimated . The amount of antibody attached to shed virions is unknown , but salivary antibody has clear potential to affect tropism [74 , 75] . Membrane fusion blocks or strong IgA responses might block host entry , because all infection requires fusion and mucosal IgA has a strong outward flux . However MuHV-4 elicits little post-binding neutralization [76] and little IgA [77]; antibody protects by effector recruitment [78] and poorly blocks host entry [59] . MuHV-4 is adapted to B cell physiology like EBV [79] but also to myeloid physiology , which encompasses the capture , transport and transfer of environmental antigens to B cells . Such adaptation is unlikely to be unique . Therefore γ-herpesvirus infection control strategies must consider not just the defensive functions of myeloid cells , but also their potential vulnerabilities .
C57BL/6J , BALB/c ( Harlan UK or Animal Resources Centre , WA ) , LysM-cre [43] ( Jackson Laboratories ) , Ai6-ZSgreen1 [44] , CD11c-cre [80] and CD169-DTR mice [47] were bred in the Department of Pathology , Cambridge or the Herston Medical Research Centre , Queensland . For infection , 6–12 week old mice were anesthetized with isoflurane and virus ( 30μl ) pipetted onto the nares , from where it was inhaled . For luciferase imaging , mice were injected i . p . with 2mg D-luciferin , anesthetised with isoflurane and monitored for light emission with a charge-coupled device camera ( IVIS lumina , Caliper Life Sciences ) . CD169-DTR mice ( CD169+/DTR ) were depleted of CD169+ cells by i . p . diphtheria toxin ( 2μg / mouse ) ( Sigma-Aldrich ) . Animal experiments were approved by the Cambridge University ethical review board , the UK Home Office ( Project Licence 80/2538 ) , and the University of Queensland animal ethics committee . BHK-21 cells ( ATCC CCL-10 ) , RAW-264 ( ATCC TIB-71 ) , 3T3–50 [15] , NMuMG ( ATCC CRL-1636 ) and 3T3-cre cells [24] were grown in Dulbecco’s Modified Eagle’s Medium with 2mM glutamine , 100IU/ml penicillin , 100μg/ml streptomycin , and 10% fetal calf serum ( PAA laboratories ) . Macrophages were recovered from mice by post-mortem bronchial or peritoneal lavage , then adhered to tissue culture plastic ( 1h , 37°C ) and washed in medium to remove contaminating B cells . The remaining cells were >95% CD68+ . All viruses were derived from a BAC-cloned MuHV-4 genome [81] with an HCMV IE1 promoter-driven eGFP expression cassette . In some experiments this loxP-flanked cassette was retained to identify infected cells by eGFP expression; for fluorochrome-switching [26] , luciferase-expressing [15] and EF1α promoter driven eGFP viruses [82] it was removed by passage in 3T3-cre cells . ORF50- MuHV-4 was grown and titered in 3T3-ORF50 cells , with ORF50 expression induced by doxycycline ( 1μg/ml ) . Herpes simplex virus type-1 ( HSV-1 ) expressing eGFP from an HCMV IE1 promoter was grown and titered on BHK-21 cells [83] . MuHV-4 was recovered from infected cell supernatants by ultracentrifugation ( 13 , 000 x g , 2h ) . Any cell debris was removed by low speed centrifugation ( 500 x g , 5min ) and filtration ( 0 . 45μm ) . HSV-1 was recovered from infected cells by low speed centrifugation ( 500 x g , 10min ) then sonicated ( 3 x 1min ) . Cell-associated MuHV-4 was prepared as for HSV-1 by centrifugation and sonication of infected BHK-21 cells . All virus stocks were titered by plaque assay and stored at-70°C . Infectious virus was measured by plaque assay [24]: dilutions of virus stocks or organ homogenates were incubated with BHK-21 cells ( 2h , 37°C ) , overlaid with 0 . 3% carboxymethylcellulose , cultured for 2 ( HSV-1 ) or 4 days ( MuHV-4 ) , then fixed ( 4% formaldehyde ) and stained ( 0 . 1% toluidine blue ) for plaque counting . To assay fluorochrome switching by MHV-RG , plaque assays were performed at limiting dilution in 96 well plates ( 12–24 wells per dilution ) , and plaques scored as red or green fluorescent under ultraviolet illumination after 4 days . To assay infection by eGFP expression , cells were infected with eGFP+ virus ( 2h , 37°C ) , cultured overnight ( 18h , 37°C ) in the presence of phosphonoacetic acid ( 100μg/ml ) to prevent further spread , trypsinized , washed x2 in PBS , and analysed for eGFP expression with a FACS Calibur ( BD Biosciences ) . To assay neutralization , viruses were incubated ( 1h , 37°C ) with dilutions of mAbs 8F10 ( anti-gH/gL , IgG2a ) , SC-9E8 ( anti-gB , IgG2a ) , or as a negative control 28 . 14 . 8 ( anti-H2Db , IgG2a , American Type Culture Collection HB-27 ) , then assayed for infectivity as above . Organs were fixed in 1% formaldehyde / 10mM sodium periodate / 75mM L-lysine ( 24h , 4°C ) , equilibrated in 30% sucrose ( 18h , 4°C ) , then frozen in OCT . 9μm sections were air dried ( 1h , 23°C ) , blocked with 0 . 3% Triton X-100 / 5% normal goat serum ( 1h , 23°C ) , then incubated ( 18h , 4°C ) with combinations of primary antibodies to eGFP ( rabbit pAb , Abcam ) , mCherry ( rabbit pAb , Badrilla ) , B220 ( rat mAb RA3–6B2 , Abcam ) , CD11c ( hamster mAb N418 , Abcam ) , CD169 ( rat mAb 3D6 . 112 , Abcam ) , CD206 ( rat mAb MR5D3 , Santa Cruz ) , unsulfated heparan ( mouse mAb NAH46 , Seikagaku Corporation ) , sulfated heparan ( mouse mAb 10E4 , Seikagaku Corporation ) , CD68 ( rat mAb FA-11 , Biolegend ) , PDP ( goat pAb , R&D Systems ) , surfactant protein C precursor ( goat pAb , Santa Cruz Biotechnology ) , MAC-2 ( rat mAb M3/38 , eBioscience ) , GR-1 ( rat mAb RB6–8C5 , R&D Systems ) , myeloperoxidase ( goat pAb , R&D Systems ) , F4/80 ( rat mAb CI:A3–1 , Serotec ) , collagen IV ( rabbit pAb , Abcam ) , HSV-1 ( rabbit pAb , Abcam ) , and MuHV-4 ( rabbit pAb ) . After incubation with primary antibodies ( 16h , 23°C ) , sections were washed x3 in PBS , incubated ( 1h , 23°C ) with Alexa633-conjugated goat anti-rat IgG pAb , streptavidin-conjugated Alexa568 and Alexa488- or 568-conjugated goat anti-rabbit IgG pAb ( Invitrogen ) , washed x3 in PBS , and mounted in Prolong Gold + DAPI . Fluorescence was visualised with a Leica TCS SP5 or Zeiss LSM 510 confocal microscope or a Nikon epifluorescence microscope , and analysed with ImageJ . MuHV-4 virions were disrupted with 0 . 05% Triton-X100 in 50mM sodium carbonate pH = 8 . 5 , and coated ( 18h , 4°C ) onto Maxisorp ELISA plates ( Nalge Nunc ) . The plates were washed x3 in PBS / 0 . 1% Tween-20 , blocked with 2% bovine serum albumin in PBS / 0 . 1% Tween-20 , incubated with 3-fold dilutions of serum from MuHV-4-exposed mice ( 1h , 23°C ) , washed x4 in PBS / 0 . 1% Tween-20 , incubated ( 1h , 23°C ) with alkaline phosphatase-conjugated goat anti-mouse IgG-Fc pAb ( Sigma Chemical Co . ) , washed x5 , and developed with nitrophenylphosphate substrate ( Sigma ) . Absorbance was read at 405nm ( Biorad ) , and the presence of a MuHV-4-specific response determined by comparison with sera from age-matched , uninfected controls . | All viral infections start with host entry . Entry into cells is studied widely in isolated cultures; entry into live hosts is more complicated and less well understood: our tissues have specific anatomical structures and our cells differ markedly from most cultured cells in size , shape and behaviour . The respiratory tract is a common site of virus infection . Size dictates where inhaled particles come to rest , and virus-sized particles can reach the lungs . Rhadinoviruses chronically infect both humans and economically important animals , and cause lung disease . We used a well-characterized murine example to determine how a rhadinovirus enters the lungs . At its peak , infection was prominent in epithelial cells lining the lung air spaces . However it started in macrophages , which normally clear the lungs of inhaled debris . Only epithelial cells expressed the molecules required for virus binding , but only macrophages internalized virus particles after binding; infection involved interaction between these different cell types . Blocking epithelial infection with an antibody did not stop host entry because attached antibodies increase virus uptake by lung macrophages; but an antibody that blocks macrophage infection was effective . Thus , understanding how rhadinovirus infections work in normal tissues provided important information for their control . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Rhadinovirus Host Entry by Co-operative Infection |
Higher plants adapt their growth to high temperature by a dramatic change in plant architecture . It has been shown that the transcriptional regulator phytochrome-interacting factor 4 ( PIF4 ) and the phytohormone auxin are involved in the regulation of high temperature–induced hypocotyl elongation in Arabidopsis . Here we report that PIF4 regulates high temperature–induced hypocotyl elongation through direct activation of the auxin biosynthetic gene YUCCA8 ( YUC8 ) . We show that high temperature co-upregulates the transcript abundance of PIF4 and YUC8 . PIF4–dependency of high temperature–mediated induction of YUC8 expression as well as auxin biosynthesis , together with the finding that overexpression of PIF4 leads to increased expression of YUC8 and elevated free IAA levels in planta , suggests a possibility that PIF4 directly activates YUC8 expression . Indeed , gel shift and chromatin immunoprecipitation experiments demonstrate that PIF4 associates with the G-box–containing promoter region of YUC8 . Transient expression assay in Nicotiana benthamiana leaves support that PIF4 directly activates YUC8 expression in vivo . Significantly , we show that the yuc8 mutation can largely suppress the long-hypocotyl phenotype of PIF4–overexpression plants and also can reduce high temperature–induced hypocotyl elongation . Genetic analyses reveal that the shy2-2 mutation , which harbors a stabilized mutant form of the IAA3 protein and therefore is defective in high temperature–induced hypocotyl elongation , largely suppresses the long-hypocotyl phenotype of PIF4–overexpression plants . Taken together , our results illuminate a molecular framework by which the PIF4 transcriptional regulator integrates its action into the auxin pathway through activating the expression of specific auxin biosynthetic gene . These studies advance our understanding on the molecular mechanism underlying high temperature–induced adaptation in plant architecture .
Higher plants continually sense environmental conditions to adapt their growth and development . To a large extent , this is achieved through integrating environmental cues into the growth-regulating hormonal pathways . Exposure of Arabidopsis thaliana plants to high temperature ( 29°C ) results in dramatic plant architecture changes including rapid hypocotyl elongation , leaf hyponasty , and early flowering [1]–[4] . High temperature-induced hypocotyl elongation of Arabidopsis plants provides an ideal model system to investigate the regulatory mechanisms underlying adaptive growth of plants to their ever-changing environments . Among the endogenous cues involved in the regulation of high temperature-induced hypocotyl elongation is the plant hormone auxin [3] . An early observation revealed a correlation between high temperature-induced hypocotyl elongation and high temperature-induced elevation of endogenous free indole-3-acetic acid ( IAA ) levels [3] . Genetic analyses found that high temperature-induced hypocotyl elongation is sharply reduced in Arabidopsis mutants defective in auxin biosynthesis , transport or signaling [3] . Together , these data attribute an essential role of the auxin pathway in mediating high temperature-induced hypocotyl elongation . It is long-recognized that auxin has profound effects on plant growth and development . A combination of physiological , biochemical , pharmacological and molecular genetic studies provide an ever-growing body of insights on our understanding of the auxin biosynthesis pathway [5] , [6] . It is generally believed that , IAA , the main auxin in higher plants , can be synthesized from tryptophan ( Trp ) -dependent and -independent pathways [5] . Among the best-characterized enzymes involved in the Trp-dependent auxin biosynthetic pathway are the YUCCA ( YUC ) family of flavin-containing monooxygenases [5] , [7]–[9] and the TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS1/TRYPTOPHAN AMINOTRANSFERASE-RELATED ( TAA1/TAR ) family of aminotransferases [5] , [10] , [11] . A wealth of genetic evidence indicated that , while inactivating members of the YUC family genes causes dramatic developmental defects [8] , [9] , overexpression of the YUC family genes leads to auxin overproduction and long hypocotyl phenotype in Arabidopsis [7] . Although mutation of TAA1 or its close homologs ( TAR genes ) leads to developmental defects similar to those of the yuc mutants [10] , [11] , overexpression of TAA1/TAR does not cause obvious developmental phenotype , suggesting that TAA1/TAR probably do not catalyze a rate-limiting step in IAA biosynthesis [10] , [11] . Interestingly , recent studies provide evidence that TAA1/TARs and YUCs may act in a common linear biosynthetic pathway for auxin production [6] , [12] , [13] . In addition to auxin , a family of phytochrome-interacting factors ( PIFs ) , which encode basic helix-loop-helix ( bHLH ) transcription factors , have been shown to be central integrators of versatile environmental and hormonal signals during plant adaptive growth [14] , [15] . Among the PIF family of transcriptional regulators , a selective function of PIF4 in high temperature-induced hypocotyl elongation has recently been reported [1] , [16] . These studies revealed that high temperature induced a rapid elevation of PIF4 transcript levels and that the pif4 mutant largely lost the robust enhancement of hypocotyl elongation induced by high temperature [1] . In the context that both the transcription factor PIF4 and the phytohormone auxin are required for high temperature-induced hypocotyl elongation , a fascinating hypothesis is that PIF4 may directly link the auxin pathway in regulating plant adaptation growth to high temperature . We provide here evidence that , in response to high temperature , PIF4 directly activates YUC8 expression and thus elevates endogenous free IAA levels . We also show that the SHY2/IAA3 protein is a downstream component of the PIF4-auxin signaling pathway in regulating high temperature-induced hypocotyl elongation . Our results exemplify how a transcriptional regulator integrates environmental cues with endogenous hormonal signaling to mediate specialized developmental changes in regulating plant adaptive growth .
It has been shown that high temperature activates the expression of the transcription factor PIF4 [1] , and elevates endogenous free IAA levels [3] in Arabidopsis . To explore the possible molecular linkage between PIF4 and the auxin pathway in regulating high temperature-mediated adaptation growth , we examined high temperature-induced expression of PIF4 and the YUCCA ( YUC ) family of auxin biosynthetic genes [5] . Consistent with previous reports [1] , , when wild type ( WT ) seedlings grown at 22°C for 6 days were transferred to 29°C in continuous light over a 24 h time course , PIF4 transcript abundance was transiently elevated to a peak level at 3 h after transfer ( Figure 1A ) . Correlating with an increased expression of PIF4 , high temperature also markedly increased transcript abundance of YUC8 with a peak at 3 h in WT seedlings ( Figure 1B ) . Closer observation with a narrower range of time points revealed that high temperature-mediated induction of YUC8 expression occurred generally later than that of PIF4 ( Figure S1 ) . Parallel experiments indicated that high temperature did not upregulate the expression of other YUCCA family genes tested ( Figure S2 ) . We then compared high temperature-induced YUC8 expression between WT and the pif4 mutant , which has been shown to be defective in high temperature-induced adaptations in plant architecture ( Figure 1B ) . As shown in Figure 1B , the basal expression levels of YUC8 were already low in pif4 seedlings and , significantly , high temperature-induced upregulation of YUC8 expression was largely abolished in this mutant , indicating that the function of PIF4 is important for the basal- and high temperature-induced expression of YUC8 . The pif4 mutation impairs high temperature-induced upregulation of YUC8 expression suggests that this mutation may also affect high temperature-induced elevation of free IAA levels . To test this , we compared high temperature-induced elevation of free IAA levels in WT and pif4 seedlings . For these experiments , we grew seedlings at 22°C or 29°C in continuous light for 6 days and collected hypocotyls for IAA measurement . Consistent with a previous report [3] , high temperature increased free IAA levels of WT seedlings by around 50% ( Figure 1C ) . As expected , high temperature-induced elevation of free IAA levels was abolished in the pif4 mutant ( Figure 1C ) , indicating that PIF4 is also required for high temperature-induced elevation of auxin biosynthesis . Together , these results suggest that PIF4 and YUC8 may function in linking temperature and auxin pathway in regulating hypocotyl elongation . As a first step to test the possibility that PIF4 may directly regulate YUC8 expression during high temperature-induced adaptation growth , we examined YUC8 expression in transgenic plants overexpressing PIF4 ( 35S-PIF4 ) . Like the reported yucca mutants which contain increased endogenous auxin levels [7] , 35S-PIF4 plants show a long hypocotyl phenotype that resembles high temperature-grown WT seedlings ( Figure S3 ) . As revealed by quantitative reverse transcription-polymerase chain reaction ( qRT-PCR ) assays , the expression of YUC8 ( Figure 2A ) , but not that of TAA1 ( Figure S4 ) , was substantially increased in 35S-PIF4 seedlings as compared to WT . We also generated PIF4-overexpression plants ( pMDC7:PIF4 ) using the chemical inducible vector pMDC7 [17] . In the presence of the chemical inducer estradiol , pMDC7:PIF4 seedlings show increased expression of PIF4 ( Figure 2B ) and display a long hypocotyl phenotype like 35S-PIF4 seedlings ( Figure S5 ) . As expected , YUC8 expression was considerably elevated following estradiol induction ( Figure 2C ) . Consistently , measurement of auxin revealed that the free IAA levels in 35S-PIF4 plants were increased by 50% as compared to those in WT plants ( Figure 2D ) . In line with increased free IAA levels in 35S-PIF4 plants , the expression of the auxin responsive DR5:GUS , a widely used reporter of auxin response , was clearly enhanced in the basal region of 35S-PIF4 hypocotyls ( Figure S6 ) . These data together indicate that overexpression of PIF4 leads to increased expression of the auxin biosynthetic gene YUC8 and , as a result , elevated endogenous free IAA levels in planta . Three lines of evidence support a scenario that the PIF4 transcription factor may directly regulate YUC8 expression during high temperature-induced adaptation growth . First , underlying high temperature-induced hypocotyl elongation , high temperature upregulates the expression of PIF4 in a similar fasion to that of YUC8 . Second , high temperature-induced upregulation of YUC8 expression requires the function of PIF4 . Third , overexpression of PIF4 leads to increased expression of YUC8 and elevated free IAA levels in planta . Given that PIF4 specifically binds to a core DNA G-box motif ( CACGTG ) of its target gene promoters [18] , we searched for the presence of G-box motifs in the promoter regions of the 11 YUC family genes present in the Arabidopsis genome . As shown in Figure 3A , G-box motifs were found not only in the promoter of YUC8 , whose expression was significantly induced by high temperature ( Figure 1 ) , but also in the promoters of YUC5 , YUC9 and YUC10 , whose expression was not or slightly induced by high temperature ( Figure S2 ) . To test the idea that PIF4 may actually bind to the G-box-containing regions of these YUC genes , we performed chromatin immuno-precipitaiton ( ChIP ) assays using a previously reported transgenic line expressing a fusion of PIF4 to the haemagglutinin ( HA ) antigen ( PIF4-HA ) [19] and anti-HA antibody ( Abcam ) . PCR amplification of the promoter regions of the four YUC genes showed that PIF4-HA specifically bound to the G-box-containing promoter region of YUC8 , but not to the G-box-containing promoter regions of YUC5 , YUC9 and YUC10 ( Figure 3B ) . These results suggest that PIF4 associates with the G-box DNA motifs in the promoter region of YUC8 in vivo . Further evidence supporting this conclusion came from electrophoretic mobility-shift assays ( EMSA ) using PIF4 protein expressed in vitro . As shown in Figure 3C , PIF4 bound to the G-box-containing DNA fragments present in the promoter region of YUC8 and , this binding could be effectively competed by the addition of excess amount of unlabeled G-box-containing DNA probes ( Figure 3C ) . As a control , we showed that DNA probes containing a mutated G-box motif ( CACGGG ) failed to compete the binding of PIF4 to the G-box-containing DNA fragments ( Figure 3C ) . Together , these results support that the PIF4 transcription factor regulates YUC8 expression by directly binding to its promoter region . Next , using the well-established transient expression assay of Nicotiana benthamiana leaves , we verified the activation effect of PIF4 on the expression of a reporter containing the YUC8 promoter fused with the firefly luciferase ( LUC ) gene . When the pYUC8:LUC reporter was infiltrated into N . benthamiana , the LUC activity could be detected at lower level ( Figure 4A , B ) . Coexpression of pYUC8:LUC with the 35S:PIF4 construct led to an obvious induction in luminescence intensity ( Figure 4A , 4B ) , suggesting that ectopic expression of PIF4 can activate pYUC8:LUC expression in this transient expression assay . In a parallel experiment , pYUC8 ( mut ) :LUC , in which the two G-boxes of the YUC8 promoter were deleted and fused with LUC , togehter with 35S:PIF4 were co-infiltrated into N . benthamiana leaves . As shown in Figure 4 , the activation effect of PIF4 on pYUC8 ( mut ) :LUC expression was abolished . Together , our transient expression assays in N . benthamiana leaves confirmed that PIF4 directly activates YUC8 expression in vivo . To determine the genetic relationship between PIF4 and YUC8 , we identified a yuc8 mutant ( SALK_096110 ) which harbors a T-DNA insertion that markedly reduced the expression levels of the YUC8 gene ( Figure S7 ) . We show that the yuc8 mutant is defective in high temperature-induced hypocotyl growth ( Figure 5A ) . We then introduced the above-described 35S-PIF4 construct into the genetic background of the yuc8 mutant through genetic crossing . As shown in Figure 5B , the yuc8 mutation substantially suppressed the long-hypocotyl phenotype of the 35S-PIF4 plants , supporting that YUC8 acts genetically downstream of PIF4 in regulating high temperature-induced hypocotyl elongation . Several elegant observations have demonstrated the involvement of PIF4 and auxin in regulating adaptive growth of plants to high temperature [1] , [16] . Our data presented here further revealed that , through directly activating of the YUC8 expression , PIF4 integrates its action into the auxin pathway in regulating high temperature-mediated hypocotyl elongation . To further identify auxin signaling components involved in this process , we employed a genetic approach to search for auxin-related mutations that can suppress the long-hypocotyl phenotype of the 35S-PIF4 plants . It has been shown that the shy2-2 mutant , which harbors a stabilized mutant form of the SHY2/IAA3 protein , displays a short hypocotyl phenotype [20] , suggesting a role of SHY2/IAA3 in regulating auxin-mediated hypocotyl growth . We showed that shy2-2 seedlings are defective in high temperature-induced hypocotyl growth ( Figure S3 ) . Importantly , like the auxin signaling mutant axr1-12 ( Figure S8 ) , shy2-2 genetically suppressed the long-hypocotyl phenotype of 35S-PIF4 ( Figure 6 ) . In contrast , other gain-of-function mutations in different IAA proteins [21]–[24] , including slr-1 ( contains a gain-of-function mutation in IAA14 ) , axr2-1 ( contains a gain-of-function mutation in IAA7 ) , axr5-1 ( contains a gain-of-function mutation in IAA1 ) and iaa28-1 , did not affect hypocotyl elongation in response to high temperature and failed to suppress the long-hypocotyl phenotype of 35S-PIF4 seedlings ( Figure S9 ) . These results demonstrate that the auxin signaling repressor SHY2/IAA3 is selectively involved in high temperature-induced hypocotyl growth .
In this study , we discovered that , as a molecular integrator , the PIF4 transcription factor links high temperature to the auxin pathway in regulating high temperature-induced hypocotyl elongation . Several lines of evidence support this finding: First , underlying the long-standing observation that high temperature induces a dramatic elongation of the hypocotyl , we showed that high temperature triggers an elevation of the transcript abundance of both PIF4 and YUC8 ( Figure 1 ) . Second , high temperature-induced upregulation of YUC8 expression largely depends on the function of PIF4 ( Figure 1 ) . Third , overexpression of PIF4 leads to increased expression of YUC8 and elevated endogenous free IAA levels ( Figure 2 ) . Fourth , as revealed by ChIP and EMSA assays , PIF4 specifically binds to a core DNA G-box motif ( CACGTG ) present in the promoter of the YUC8 gene ( Figure 3 ) . Fifth , transactivation assays in N . benthamiana leaves support that PIF4 stimulates the activity of the YUC8 promoter fused with a reporter ( Figure 4 ) . Finally , the yuc8 mutation , which is defective in high temperature-induced hypocotyl elongation , is able to partially suppress the long-hypocotyl phenotype of the 35S-PIF4 plants ( Figure 5 ) . Together , these data support that , PIF4 selectively activates the expression of the auxin biosynthetic gene YUC8 , thus integrates high temperature to the auxin pathway in regulating adaptive hypocotyl growth . It is worthy of note that the yuc8 mutant still retains some response to high temperature in hypocotyl elongation and that this mutation fails to completely suppress the long-hypocotyl phenotype of 35S-PIF4 plants ( Figure 5 ) . A plausible explanation for this is that the yuc8 mutant used in this study shows reduced , but not loss of , YUC8 expression ( Figure S7 ) . Alternatively , we could not rule out the possibility that PIF4 may activate auxin biosynthetic genes other than YUC8 , which act weakly in PIF4-mediated hypocotyl growth in response to high temperature . A very recent report hints that the PIF4 transcription factor could target TAA1 [29] , which acts genetically upstream of the YUC family genes in IAA production [12] , [13] . Considering that overexpression of TAA1 does not lead to any obvious developmental phenotype [11] , [12] and that TAA1 and YUCs act in a common linear biosynthetic pathway for auxin production [6] , [12] , [13] , it is reasonable to propose that TAA1 acts together with other auxin biosynthesis genes such as YUC8 to mediate high temperature-induced and PIF4-mediated hypocotyl elongation . However , our gene expression analyses reveal that overexpression of PIF4 alone fails to elevate TAA1 transcription ( Figure S4 ) . PIF4-mediated activation of YUC8 expression in response to high temperature exemplifies a mechanism by which environmental cues manipulate auxin , the key endogenous modulator of plant architecture . Another known physiological process in which both PIF4 and auxin are involved is shade avoidance syndrome ( SAS ) , plant adaptive growth responses to the light signal [14] , [30] , [31] . PIF4 is therefore emerging as a molecular “hub” to integrate both temperature and light signals to regulate plant architecture remodeling [14] . Accumulating evidence reveals that , unlike shade avoidance , where PIF4 acts redundantly with its homolog , PIF5 , to regulate elongation growth , PIF4 appears to perform a dominant role in driving high temperature-induced adaptive growth [1] , [14] , [16] , [32]–[34] . These studies suggest that , PIF4 , and possibly other PIF family members , have specialized and overlapping functions in regulating plant adaptive growth to different environmental stimuli . Our results support a scenario in which the auxin pathway acts downstream of the PIF4 transcriptional regulator in regulating high temperature-induced hypocotyl elongation . Supporting evidence for this hypothesis came from our genetic analysis showing that the axr1-12 mutation , which contains a mutation in a subunit of the heterodimeric RUB-E1 enzyme required for auxin signaling [35] , completely suppressed the long-hypocotyl phenotype of 35S-PIF4 seedlings ( Figure S8 ) . Based on our current knowledge of the auxin signaling pathway , auxin mediates the expression of auxin responsive genes through the inactivation of AUX/IAA transcriptional repressors that negatively control the activity of AUXIN REPONSE FACTOR ( ARF ) transcription factors [36] . In the context that many gain-of-function aux/iaa mutations are associated with reduced response to exogenous auxin , but developmental defects among these mutants are frequently more specific [36] , it is reasonable to speculate that specific Aux/IAA-ARF pair ( s ) may function in the PIF4-auxin pathway to mediate the specialized hypocotyl elongation process triggered by high temperature . In our genetic efforts to identify new components involved in the PIF4-auxin pathway in regulating high temperature-mediated hypocotyl elongation , we determined that SHY2/IAA3 , but not other IAA proteins tested , has a specialized function in mediating high temperature-induced hypocotyl elongation . It is of interest in future studies to identify the ARF transcription factor ( s ) interacting with SHY2/IAA3 in regulating high temperature-induced hypocotyl elongation .
Arabidopsis thaliana ecotypes Columbia ( Col-0 ) , Ler and WS were used as wild types . The pif4 mutant used in this study was the reported null allele pif4-2 [1] . Other plant materials used in this study were previously described: DR5:GUS [37] , 35S-PIF4 [19] , 35S:PIF4-HA [19] , yucca [7] , axr1-12 [38] , shy2-2 [2019] , slr-1 [21] , axr2-1 [22] , axr5-1 [23] and iaa28-1 [24] . yuc8 ( SALK_096110 ) was identified from the SIGnAL T-DNA collection [39] . All molecular manipulations were performed according to standard methods [40] . The PIF4 coding fragment was amplified by PCR and cloned into the AscI/PacI sites of the binary vector pMDC7 [17] , resulting in a chemical-inducible PIF4 expression construct . The construct was then transformed into Agrobacterium tumefaciens strain GV3101 ( pMP90 ) , which was used for transformation of Arabidopsis plants by vacuum infiltration [41] . Seeds were surface-sterilized for 15 min in 10% bleach , washed four times with sterile water , and plated on half-strength Murashige and Skoog ( MS ) medium . Plants were stratified at 4°C for 2 d in darkness and then transferred to a phytotrone set at 22°C with a 16-h light/8-h dark photoperiod or in continuous light for specific experiments . For high temperature treatment , plants were directly grown at 29°C in continuous light or young seedlings were transferred to 29°C in continuous light for different times . For qRT-PCR analysis , seedling were harvested and frozen in liquid nitrogen for RNA extraction . RNA extraction and qRT-PCR analysis were performed as previously described [37] . Primers used to quantify gene expression levels are listed in Table S1 . The GUS activity assays were performed as previously described [37] . One gram of 6-d-old seedlings of 35S:PIF4-HA transgenic plants [19] and the anti-HA antibody ( Abcam ) were used in ChIP experiments . Chromatin immunoprecipitation ( ChIP ) assays were performed as previously described [42] . The enrichment of DNA fragments was determined by semi-quantitative PCR analysis . Three independent biological repeats were performed . PIF4 and Luciferase ( Luc ) were synthesized by using the Rabbit Reticulocyte TNT system ( Promega ) [18] , [43] . The 60-bp YUC8 promoter probes containing G-box motifs were synthesized and labeled with biotin at the 3′ end ( Invitrogen ) . Cold competitor probes were generated from dimerized oligos of the YUC8 promoter region containing the wt-G-box ( CACGTG ) or mut-G-box ( CACGGG ) motifs , respectively . DNA gel-shift assays were performed as described [18] , [43] . Probe sequences are shown in Table S1 . The transient expression assays were performed in N . benthamiana leaves as previously described [44] . The YUC8 promoter was amplified with the primer pairs 5-CACCATCCGATATGATAACGAT-3 and 5-TGGAAGTTGTATTGGAAA-3 and cloned into pENTR using the pENTR Directional TOPO cloning kit ( Invitrogen ) . To generate YUC8 promoter with mutations , site-directed mutagenesis was used to delete the two G-boxes in the YUC8 promoter ( Figure 3 ) using the TaKaRa MutanBEST kit . Then , the two YUC8 promoter versions were fused with the luciferase reporter gene LUC through the Gateway reactions into the plant binary vector pGWB35 [45] to generate the reporter constructs pYUC8:LUC and pYUC8 ( mut ) :LUC . The PIF4 effector construct was the 35S:PIF4 . For this construct , the PIF4 coding fragment was amplified by PCR with the primer pairs 5-CACCATGGAACACCAAGGTTGGAG-3 and 5-GTGGTCCAAACGAGAACCGT-3 . Five independent determinations were assessed . Error bars represent SD . The experiments were repeated at least five times with similar results . For measurement of free IAA levels in wild-type and pif4 mutant hypocotyls in response to high temperature treatment , the hypocotyls of 6-d-old wild-type and pif4 mutant seedlings grown at 22°C and 29°C in continuous light , respectively , were harvested for free IAA measurement . For the wild-type seedlings grown at 29°C , the 2 mm length parts for each hypocotyl ( above the junction between hypocotyl and root ) were harvested for free IAA measurement . Eight-d-old seedlings of wild-type and 35S-PIF4 grown at 22°C in continuous light were harvested for free IAA measurement . Approximately 200 mg ( fresh weight ) of tissues were used for IAA extraction and measurement as previously described [46] . | Exposure of Arabidopsis to high temperature ( 29°C ) results in a dramatic hypocotyl elongation . The basic helix-loop-helix transcription factor PIF4 and the phytohormone auxin play essential roles in high temperature–mediated induction of Arabidopsis hypocotyl elongation . However , the possible molecular linkage between PIF4 and the auxin pathway in regulating high temperature–induced adaptative growth remains unknown . Here , we report that high temperature–induced elevation of YUCCA8 ( YUC8 ) transcripts and endogenous free IAA levels is dependent on the function of PIF4 . In particular , we provide evidence that PIF4 directly activates the expression of YUC8 to upregulate auxin biosynthesis , as a consequence , achieves high temperature–induced hypocotyl elongation . In addition , we found that SHY2/IAA3 is an important component of the PIF4–auxin pathway in regulating high temperature–induced hypocotyl elongation . Overall , our results establish a direct connection between the PIF4 transcription factor and the auxin pathway in regulating high temperature–induced adaptation growth . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biology"
] | 2012 | PIF4–Mediated Activation of YUCCA8 Expression Integrates Temperature into the Auxin Pathway in Regulating Arabidopsis Hypocotyl Growth |
Schistosomiasis is a tropical disease associated with high morbidity and mortality , currently affecting over 200 million people worldwide . Praziquantel is the only drug used to treat the disease , and with its increased use the probability of developing drug resistance has grown significantly . The Schistosoma parasites can survive for up to decades in the human host due in part to a unique set of antioxidant enzymes that continuously degrade the reactive oxygen species produced by the host's innate immune response . Two principal components of this defense system have been recently identified in S . mansoni as thioredoxin/glutathione reductase ( TGR ) and peroxiredoxin ( Prx ) and as such these enzymes present attractive new targets for anti-schistosomiasis drug development . Inhibition of TGR/Prx activity was screened in a dual-enzyme format with reducing equivalents being transferred from NADPH to glutathione via a TGR-catalyzed reaction and then to hydrogen peroxide via a Prx-catalyzed step . A fully automated quantitative high-throughput ( qHTS ) experiment was performed against a collection of 71 , 028 compounds tested as 7- to 15-point concentration series at 5 µL reaction volume in 1536-well plate format . In order to generate a robust data set and to minimize the effect of compound autofluorescence , apparent reaction rates derived from a kinetic read were utilized instead of end-point measurements . Actives identified from the screen , along with previously untested analogues , were subjected to confirmatory experiments using the screening assay and subsequently against the individual targets in secondary assays . Several novel active series were identified which inhibited TGR at a range of potencies , with IC50s ranging from micromolar to the assay response limit ( ∼25 nM ) . This is , to our knowledge , the first report of a large-scale HTS to identify lead compounds for a helminthic disease , and provides a paradigm that can be used to jump-start development of novel therapeutics for other neglected tropical diseases .
Schistosomiasis , also known as bilharzia , a debilitating disease resulting from the infection by the trematode parasite Schistosoma ssp . ( S . mansoni , S . mekongi , S . japonicum , S . haematobium , and S . intercalatum ) currently affects over 200 million people worldwide , mostly in developing countries [1] . A large percentage of those infected exhibit severe morbidity manifested as growth stunting , lassitude , and cognitive impairment [2] , and an estimated 280 , 000 people die annually from the disease in sub-Saharan Africa alone [3] . The primary route of infection is via unsafe river and lake water , which is widely used in sub-Saharan Africa and Southeast Asia , among other regions , for irrigation , drinking , cooking , and bathing . Larval parasite forms ( residing in and released by snails ) can penetrate the skin of people contacting the water . The lifecycle of Schistosoma is exceedingly complex , with the parasite going through a number of stages both outside and inside the human host . Once inside humans , it can survive for years , even decades [4] . The need to control schistosomiasis is acute and efforts have been ongoing for years on three main fronts: prevention ( via establishment and maintenance of sources of safe potable water ) , development of a vaccine , and use of drugs to treat the infection [1] . Although the number of schistosomiasis cases worldwide is indeed stunning , the number of drugs available to treat the disease is surprisingly small . Earlier in the 20th century , schistosomiasis was treated with highly toxic antimonial compounds , of which the most common was potassium antimonyl tartrate ( PAT , tartar emetic ) . During the past three decades the only drug used against the infection is praziquantel , which is administered orally , is stable , effective against all major schistosome species in a single dose , and relatively inexpensive [5] , [6] . However , because of high reinfection rates , praziquantel must be administered on an annual or semi-annual basis . While its exact mechanism of action is unclear , praziquantel is thought to affect the parasites by disrupting calcium homeostasis [7] , [8] . Preliminary reports of praziquantel-resistant cases , and the generation of praziquantel-resistant parasites in the laboratory [9]–[11] highlight the need for new drugs to treat the disease . Artemisinin has shown promise as a new drug for schistosomiasis [12] although its use for schistosomiasis may be restricted in areas of malaria transmission so that its use as an antimalarial is not put at risk . Simplified derivatives of artemisinin , the 1 , 2 , 4-trioxolanes , show promise and potential selectivity , but these , like the parent compound , are significantly less active against adult schistosome parasites [13] . Oxamniquine , used extensively in Brazil in the past , is effective only against S . mansoni and resistance has been reported further reducing its potential value in schistosomiasis control [10] . Studies of the schistosome life cycle have focused on the fact it can survive for decades in the blood stream of the human host without being severely affected by the immune system and the associated assault by various reactive oxygen species ( ROS ) . Since schistosomes do not have catalase to degrade hydrogen peroxide [14] , other mechanisms must exist within the parasite to degrade ROS . Two uniquely positioned S . mansoni enzymes have been recently described that seem to act in concert to provide an effective antioxidant “firewall” . Thioredoxin glutathione reductase ( TGR ) is a multifunctional selenocysteine-containing enzyme that catalyzes the interconversion between reduced and oxidized forms of both glutathione ( GSH ) and thioredoxin ( Trx ) , which are major contributors to the maintenance of redox balance in eukaryotes [15] . In contrast , humans possess two distinct enzymes , glutathione reductase ( GR ) and thioredoxin reductase ( TrxR ) , which specifically recognize GSH and Trx as substrates , respectively [16] . The apparent replacement of two human enzymes by one dual-specificity worm enzyme has created a metabolic and regulatory bottleneck in which the inactivation of a single target , TGR , might have an enhanced deleterious effect on both the maintenance of parasite's redox balance and on its “antioxidant firewall” . Indeed , recent small molecule inhibition and RNA interference experiments have shown that inactivation of TGR has profound effects on S . mansoni survival rates both in culture and in infected mice [17] . Another component of the S . mansoni “firewall” are the peroxiredoxins ( Prx ) , which are responsible for catalyzing the electron transfer to the main ROS agent hydrogen peroxide and , uniquely for schistosomes , from both GSH and Trx [18] . Thus , when TGR and Prx operate in concert , NADPH reducing equivalents are essentially transferred via TGR-catalyzed reaction to the oxidized forms of either Trx or GSH , while Trx or GSH in turn transfer reducing equivalents to hydrogen peroxide via Prx-catalyzed reactions ( Figure 1 ) . Such improved understanding of the organisms responsible for neglected tropical diseases ( NTDs ) presents opportunities for new drug development . However , private-sector biopharmaceutical interest in NTDs has traditionally been limited due to high risk and low expected return-on-investment of these projects , though this is beginning to change with the advent of increased philanthropic and public-private-government partnership funding [19] . A significant problem that remains , however , is the significant gap in technologies , expertise , and cultures between academic and biopharmaceutical organizations [20] . At the US National Institutes of Health ( NIH ) , the NIH Roadmap Molecular Libraries Initiative ( MLI ) was started in 2004 in part to address this problem . The MLI provides academic investigators with the pharma-scale infrastructure and technologies necessary to discover both chemical probes of physiology , and starting points for development of novel therapeutics for the rare and neglected diseases that are of less interest to the pharmaceutical sector [21] . The TGR/Prx work described here is the result of the first project officially accepted by the MLI in 2005 . Since inhibiting either TGR or Prx can potentially lead to schistosome death [22] , we chose to screen both enzymes in one assay as a reconstituted redox cascade . While TGR and Prx2 can be assayed individually , the separate assays are relatively less robust . TGR can be assayed in a relatively simple colorimetric assay by following the catalytic reduction of DTNB ( 5 , 5′ dithiobis ( 2-nitrobenzoic acid ) , Ellman's reagent ) by NADPH; Prx2 at present can only be assayed with thioredoxin as a substrate together with TGR or thioredoxin reductase , or in a coupled reaction involving yeast glutathione reductase , and HTS-compatible assays [23] have yet to be developed [17] , [18] , [24] . By performing the high-throughput screen against both enzymes ( present at equivalent levels in the assay ) , we were able to address both novel targets simultaneously while also combining target deconvolution and confirmation at the post-screen stage . In this report , we describe the miniaturization to 1536-well density of a cuvette-based assay for the TGR/Prx2 cascade which utilizes as a quantitative measure the decrease in fluorescence of the consumed NADPH substrate , the performance of a quantitative high-throughput screen ( qHTS ) [25] against 71 , 028 discrete compounds , and the initial characterization of several novel series of inhibitors . The application of qHTS , in which each library compound is assayed at a range of concentrations to generate a dose-response profile , facilitated triaging of actives for the purpose of structure-activity relationship ( SAR ) analysis and lead expansion .
Nicotinamide adenine dinucleotide phosphate ( NADPH ) , glutathione reduced form ( GSH ) , hydrogen peroxide , Tween-20 and potassium antimonyl tartrate ( PAT ) were procured from Sigma-Aldrich . DMSO Certified ACS Grade was from Fisher . The screening assay was performed in 100 mM phosphate buffer pH 7 . 4 containing 10 mM EDTA and 0 . 01% Tween-20 . Recombinant TGR with a fused bacterial-type SECIS element was expressed in the Escherichia coli strain BL21 ( DE3 ) ( Invitrogen ) in the presence of pSUABC in LB medium supplemented with 20 µM flavin adenine dinucleotide following conditions for optimal selenoprotein expression as described [17] . TGR was purified to homogeneity on an adenosine 2′ , 5′-diphosphate agarose ( Sigma ) column equilibrated with TE buffer as described [17] . TGR concentration was determined from the flavin adenine dinucleotide absorption ( ε463 = 11 . 3 mM−1cm−1 ) . The pure protein was dialyzed against PBS and stored at −80°C . Recombinant Prx2 in pRSETA was expressed in E . coli strain BLR ( DE3 ) pLysS ( Novagen ) as described [18] . Briefly , after a 3-hr induction in 1 mM IPTG , cells were sonicated in 5% monothioglycerol ( 3-mercapto-1 , 2-propanediol ) in 10 mM imidazole , 0 . 07 M Na2HPO4 , 0 . 01 M NaH2PO4 , and 0 . 15 M NaCl , pH 7 . 4 . The supernatant was filtered and Prx2 was purified to homogeneity on a His Trap column ( Amersham Biosciences ) . Protein purity was verified by SDS-PAGE . The purified protein was dialyzed against PBS and stored at −80°C until used . The 71 , 028 member library comprised two main subsets: 59 , 692 compounds from the NIH Molecular Libraries Small Molecule Repository ( www . mli . nih . gov ) , prepared as 10 mM stock solutions in 384-well plates and delivered by Biofocus DPI ( South San Francisco , CA , http://mlsmr . glpg . com/MLSMR_HomePage/ ) , and NCGC internal exploratory collection of approximately 11 , 336 compounds which consisted of several commercially available libraries of known bioactives ( 1280 compounds from Sigma-Aldrich ( LOPAC1280 library ) , 1120 compounds from Prestwick Chemical Inc . ( Washington , DC ) , 980 compounds from Tocris ( Ellisville , Missouri ) , 280 purified natural products from TimTec ( Newark , DE ) , 1980 compounds from the National Cancer Institute ( the NCI Diversity Set ) ) , 1408 National Institute of Environmental Health Sciences collection of known toxic compounds , as well as collections from other commercial and academic collaborators ( three 1000-member combinatorial libraries from Pharmacopeia ( Cranbury , NJ ) , 718 compounds from Boston University Center for Chemical Methodology and Library Development , 96-member peptide library from Prof . Sam Gelman's lab , University of Wisconsin , Madison , and 474 compounds from the University of Pittsburgh Center for Chemical Methodology and Library Development ) . The compound library ( 7 µL each in 1536-well Greiner polypropylene compound plate ) was prepared as DMSO solutions at initial concentrations ranging between 2 and 10 mM . Plate-to-plate ( vertical ) dilutions and 384-to-1536 compressions were performed on Evolution P3 dispense system equipped with 384-tip pipetting head and two RapidStak units ( Perkin-Elmer , Wellesley , MA ) . Additional details on the preparation of the compound library are provided in Inglese et al [25] . Titration of the known inhibitor PAT ( PubChem CID6328158 ) was delivered via pin transfer from a separate plate to the lower half of column 2 of each assay plate . The starting concentration of the control , dissolved in 1∶1 DMSO∶water , was 1 mM , followed by five-fold dilution points in duplicate , for a total of eight concentrations . Three µL of reagents ( 100 µM NADPH in columns 3 and 4 as negative control and 100 µM NADPH , 42 nM TGR , 700 µM GSH , 83 nM Prx2 mixture in columns 1 , 2 , 5–48 ) were dispensed into 1536-well Greiner black assay plates . Compounds and control ( 23 nL ) were transferred via Kalypsys PinTool equipped with 1536-pin array ( 10 nL slotted pins , V&P Scientific , Palo Alto , CA ) . The plate was incubated for 15 min at room temperature , and then a 1 µL aliquot of 400 µM NADPH/700 µM GSH was added , immediately followed by a 1 µL aliquot of 2 . 5 mM H2O2 to start the reaction . The plate was transferred to ViewLux high-throughput CCD imager ( Perkin-Elmer , Wellesley , MA ) where kinetic measurements ( 16 reads , one read every 30 sec ) of the NADPH fluorescence decrease were acquired using 365 nm excitation/450 nm emission filter set . During dispense , the reagent bottles were kept submerged into 4°C recirculating chiller bath to minimize degradation . All screening operations were performed on a fully integrated robotic system ( Kalypsys , San Diego , CA ) containing one RX-130 and two RX-90 anthropomorphic robotic arms ( Staubli , Duncan , SC ) . Library plates were screened starting from the lowest and proceeding to the highest concentration . Vehicle-only plates , with DMSO being pin-transferred to the entire column 5–48 compound area , were inserted uniformly at the rate of approximately one plate for every 50 library plates in order to monitor for and record any shifts in the background . Time course data were collected on per-assay plate basis and were processed using in-house developed software . For each sample and at each individual concentration , 16 time points were processed using ordinary least squares regression to determine slope and intercept of linear fit . Additionally , a difference ( delta ) of last and first time point was generated for each time course . For activity calculations , delta values were chosen while the calculated slope , intercept , and the raw time-course data were stored in the database . Screening data were corrected and normalized and concentration–effect relationships derived by using the GeneData Screener software package ( Basel , Switzerland ) . Percent activity was computed from the median values of the uninhibited , or neutral , control ( 48 wells located in column 1 and one-half of column 2 ) and the no-enzyme , or 100% inhibited , control ( 64 wells , entire columns 3 and 4 ) , respectively . For assignment of plate concentrations and sample identifiers , ActivityBase ( ID Business Solutions Ltd , Guildford , UK ) was used for compound and plate registrations . An in-house database was used to track sample concentrations across plates . Correction factors were generated from the DMSO plate data and applied to each assay plate to correct for systematic errors in assay signal potentially resulting from issues with reagent dispensers or decrease in enzyme specific activity . A four parameter Hill equation [26] was fitted to the concentration-response data by minimizing the residual error between the modeled and observed responses . Outliers could be identified and masked by modeling the Hill equation and asking if the differences exceeded those expected from the noise in the assay . The curve classification used is the same as described in Inglese et al . ( 2006 ) [25] . Briefly , concentration-response curves are placed into four classes: Class 1 contains complete concentration-response curves showing both upper and lower asymptotes and r2 values >0 . 9 . Class 2 contain incomplete concentration-response curves lacking the lower asymptote and show r2 values >0 . 9 . Class 3 curves are of the lowest confidence as they are defined by a single concentration point where the minimal acceptable activity is set at 3 SD of mean . Curves are classified as negative or positive depending on whether they exhibit signal decrease ( apparent inhibition ) or increase ( apparent activation ) . Finally , Class 4 contains compounds that do not show any concentration response curves and are therefore classified as inactive . A workflow was developed to facilitate a systematic approach in providing exhaustive analysis of structure activity relationships ( SAR ) . First , a set of criteria used to define rules of determining an active set of compounds for the assay . These include decisions on inhibitors and activators , selectivity and counter screen information , curve class ranges , background fluorescence , etc . For this assay , compounds that showed signal activation ( positive curve classes ) were regarded as active due to fluorescence and were thus filtered out . The criteria are implemented as filters that are applied to rapidly define a core active set of compounds . This process eliminates many positive series that appear to have SAR and show reasonable titration response curves , but are not of biological relevance to the targets [23] . Next , the range of curve classes was limited to −1 through −3 to select for compounds showing signal decrease . Once an active set of compounds was identified , hierarchical agglomerative clustering with a 0 . 7 Tanimoto cutoff was performed using Leadscope ( Leadscope Inc . , Columbus , OH ) fingerprints , which are ideally suited for two-dimensional scaffold-based based clustering [27] . For each cluster , maximal common substructures ( MCS ) were extracted , and a manual step of trimming the MCSs was performed to create a list of scaffolds . This clustering step typically has overlapping compounds and thus can lead to overlapping MCSs . This list of trimmed scaffolds is abridged to a canonical set . Each scaffold is then represented as a precise definition to indicate number of attachments , ring size variability , etc . All filters were then relaxed to include all negative assay data . In the initial clustering , a set of singletons was found . These compounds were reported upon separately with their individual activity profiles . SAR series and singletons were finally ranked by their activity profile . Screening actives and analogues sourced as powders from the respective original suppliers ( Sigma-Aldrich , NCI , Asinex , Chem Bridge , Tocris , Ambinter , and ChemDiv ) were dissolved in DMSO to produce 10 mM initial stock solutions . The samples were then serially diluted row-wise in 384-well plate in twofold steps for a total of 24 concentrations , from 10 mM to 1 . 2 nM . Upon completion of the 24-point dilution , solutions from two 384-well plates were transferred to duplicate wells of 1536-well compound plate . The last two rows of the 1536-well plate did not contain any test compound and were reserved for placement of positive and negative controls . The assay protocol for confirmation was essentially the same as that described in the qHTS protocol section . A Flying Reagent Dispenser ( FRD , Aurora Discovery , presently Beckman-Coulter ) [28] was used to dispense reagents into the assay plates . Assays were performed at 25°C in 0 . 1 M potassium phosphate , pH 7 . 4 , 10 mM EDTA using 100 µM NADPH . The Prx2 assay was based on the reduction of H2O2 by Prx2 in the presence of GSH measured by the reduction of the GSSG produced in a coupled assay with yeast glutathione reductase monitored by observing the decrease in A340 nm due to consumption of NADPH ( ε340 nm = 6 . 22 mM−1 cm−1 ) during the first three minutes [18] . The activity of TGR was determined with 3 mM 5 , 5′ dithiobis ( 2-nitrobenzoic acid ) ( DTNB , Ellman's reagent ) [29] following the increase in A410 nm due to the production of 2-nitro-5-thiobenzoic acid ( ε412 nm = 13 . 6 mM−1 cm−1 ) [17] , [18] .
The assay was initially developed and optimized using a spectrophotometer by following the decrease in absorbance at 340 nm associated with the consumption of NADPH substrate . During these studies , the main parameters of the assay , such as buffer conditions , concentration of each enzyme and substrate , DMSO and detergent tolerance , were tested and optimized ( data not shown ) . The optimized assay utilized TGR and Prx2 at final concentrations of 25 nM and 50 nM , respectively . The substrates' final concentrations were 200 µM NADPH , 700 µM GSH , and 500 µM H2O2 . The assay was miniaturized to 1536-well format by volume reduction and appropriate adjustment of stock concentrations of enzymes and substrates to reflect the volumes being combined . For example , the assay was started by the dispense of the two enzymes at 5/3 of their final concentration to account for the well volume increasing from 3 to 5 µL , while the hydrogen peroxide substrate was delivered as 5× solution to account for its dilution ( 1 µL to 5 µL final volume , see Materials and Methods , Table 1 ) . All three types of reagents ( enzymes , second aliquot of NADPH , and hydrogen peroxide ) were tested and were shown to be stable overnight at 4°C , a requirement for the execution of an uninterrupted fully automated screen on the Kalypsys robotic system ( Figure 2 ) . In addition , the signal being monitored was changed from absorbance to fluorescence in order to: 1 ) improve the signal strength , as UV-shifted absorbance assays are generally difficult to scale to a 1536-well density ( due to the combination of low extinction coefficient and short path length ) , and 2 ) minimize the quenching and inner-filter effects of a multitude of compounds which absorb light in this wavelength region [23] . In a typical uninhibited reaction in 1536-well plate , the well fluorescence changed from 370 relative fluorescence units ( RFU ) to 180 RFU within an eight-minute window . This resulting outcome , if registered as an end-point reading , and assuming zero change in the background , would yield a signal-to-background ( S∶B ) ratio of only approximately 2 . 1 . Therefore the assay was further improved by modification of the type of signal collected from single end-point to multipoint kinetic read . Thus , for each plate the reaction progress was recorded for 8 minutes at the rate of one fluorescence read every 30 seconds . Such kinetic mode data acquisition not only secured a more robust assay signal but minimized the interfering effects of dust and mildly fluorescent compounds ( see Discussion ) . In total , 453 assay plates were screened in one uninterrupted robotic run lasting approximately 75 hours . Dispenser malfunction resulted in deteriorated signal in two plates and since the issue was noted in real time , the two 7-point libraries containing the problematic plates , plus two DMSO-only plates , were scheduled for re-screening immediately after the end of the main run . In this manner , the re-screened series were tested using the same batch of reagents as last series of the main screen . The assay performed robustly , yielding an average Z′ value of 0 . 76 [30] . Overall , the Z′ factor remained flat with the screen progression , with minor shifts tracking the introduction of new batches of the two enzymes ( Figure 3A ) . The intraplate PAT control titration was stable throughout the screen progression , resulting in average IC50 of 14±8 nM and minimum significant ratio of 4 . 03 ( Figure 3B ) [31] . Each library compound was tested at a minimum of seven concentrations , ranging from 57 µM to 2 . 9 nM , and for each well , 16 time points were collected for a total of 9 , 562 , 432 data points . The screen and the preceding optimizations and validations consumed approximately 4 . 7 mg of TGR and 10 mg of Prx2 . The overall materials cost of the screen ( not including the cost of protein production ) was approximately $5 , 200 , or 0 . 85 cent per sample well , with approximately 80% of the costs associated with the assay microtiter plates and 15% attributed to NADPH . Unlike traditional HTS , qHTS provides concentration responses for all the compounds screened and allows determination of an AC50 value , defined as the half-maximal activity concentration , for each compound in the primary screen . In qHTS concentration response curves are classified as belonging to one of four groups based on efficacy ( response magnitude ) , presence of asymptotes , and goodness of fit of the curve to the data ( r2 ) . For the present screen , the activity associated with each well was computed from the change in fluorescence intensity over the time-course measurement period , normalized against control wells . In addition , the y-intercept of the reaction progress plot , typically equal to fluorescence at the first time point , was stored in the database and was used to further scrutinize purported actives . Compounds which showed activity but also had elevated y-intercept values were flagged as potential fluorescent artifacts . Analysis of the qHTS results revealed 39 actives characterized by full concentration-response curves and IC50 values of better than 10 µM . After exclusion of antimony-containing compounds , as well as various mercury- and other heavy metal-containing molecules , the following series and singletons were selected for further studies after SAR analysis ( Figure 4 ) : oxadiazole 2-oxides ( 5 actives out of 29 analogues in collection , IC50 potency range between 8 µM and inactive ) , phosphinic amides ( two compounds in the collection , one inactive and one active at 37 nM ) , phosphoramidite ( singleton active , IC50 of 560 nM ) , and isoxazolone ( singleton active , IC50 of 530 nM ) . In addition , a weaker series , quinolinyl sulfonamides ( 8 actives out of 47 total analogues in collection , IC50 potency range between 0 . 6 µM and inactive ) , was identified but noted to contain a number of both active and inactive members which were strongly fluorescent as judged by the extreme intercept values recorded during the screen . In contrast , neither the oxadiazole series nor the singleton actives exhibited any detectable autofluorescence ( Figure 5 ) . In addition to inhibitors , the screen yielded a number of apparent activators , that is , compounds for which the increase in concentration led to a fluorescence intensity change greater than that of the neutral control . Upon examination of the time-course plots associated with these activators it became evident that the signal enhancement originated from high starting fluorescence which decreased during the observation window and in many cases entirely obscured the assay-driven NADPH fluorescence change ( Figure 5C ) . While some of these compounds might be fluorescent substrates for either TGR or Prx2 , which get converted to non-fluorescent products , a large number might simply be reactive towards any one or more components of the assay milieu ( GSH , NADPH , and/or H2O2 ) . As such , their confirmation and mode of action is subject of separate study . In order to further confirm the qHTS actives and to expand the actives series , especially around the otherwise attractive singletons , powder samples were purchased from the original compound vendors and processed as described in the Methods . In addition to qHTS-identified compounds , untested analogues of the singletons were also procured in an attempt to support the singleton findings by the generation of small SAR series . The comparison of qHTS results , where applicable , and re-test results from independently acquired powder samples are shown in Figure 4 , first and second data columns , respectively . The overall confirmation rate was excellent with the exception of the sulfonamide series of actives , which showed wide shifts between qHTS and confirmatory assay ( results not shown ) . The apparent lack of confirmation for this series was consistent with the aberrant fluorescent values associated with many of its members . An analogue series built around a lower-potency benzoindolone singleton ( NCGC00038549 , IC50 of 3 . 9 µM ) failed to yield activity against the screening assay and against both TGR and Prx2 individual assays . The original activity of that singleton was therefore deemed an artifact . Gratifyingly , the previously-untested analogues of the phosphinic amides ( compounds 1–4 ) , phosphoramidite ( 13–16 ) , and isoxazolone ( 10–12 ) actives all showed activity with various degrees of potency , supporting and expanding the qHTS findings . Specifically , the “gap” in potency between the highly active 3 ( NCGC00042730 , qHTS IC50 of 37 nM , confirmed at 25 nM on re-test ) and the inactive distant analogue 4 ( NCGC00064648 ) was filled partially by the newly-acquired analogues 1 ( NCGC00093512 , IC50 of 247 nM ) and 2 ( NCGC00093512 , IC50 of 23 µM ) . Similarly , increased potency was achieved by the addition of analogues to the phosphoramidite ( from a singleton IC50 of 0 . 5 µM to a range of 0 . 2–2 µM ) and the isoxazolone ( from a singleton IC50 of 0 . 5 µM to a range of 0 . 1 µM to 9 µM ) . After establishing the activities of primary hits and new analogues against the screened dual-enzyme system , the compounds were further subjected to target deconvolution experiments . When tested against Prx2 in a GR-coupled hydrogen peroxide reduction assay none of the selected compounds showed activity up to the 50 µM top concentration tested ( and by extension , none were active against GR , an enzyme related to TGR ) . Prx2 was therefore ruled out as the target of any of the actives identified in the screen . Results from the TGR assay are shown in the last column of Figure 4 . The majority of active compounds demonstrated approximately the same , and in some instances improved ( most notably with 7 and 8 ) , potency against the isolated TGR as they did against the dual-enzyme system . These results not only confirm the initial findings from the screen , but also further support the hypothesis of TGR being the sole target of these actives . Additionally , all members of the sulfonamide series were inactive against both Prx2 and TGR , further strengthening the argument that their initial classification as actives was due to fluorescence interference originating from either the compound , impurities , or product ( s ) of its breakdown .
The stability , relatively low cost , and effectiveness of praziquantel has practically created a dependency on this single drug to treat schistosomiasis . Both the success of praziquantel and the general lack of incentives for large pharmaceutical companies to embark on research and development in the area of tropical diseases have led to a fairly dry pipeline for both drugs to treat schistosomiasis and basic research tools to study the lifecycle of this important parasite . To this end , we implemented a highly-miniaturized automated screen of the NCGC small molecule collection in an attempt to identify novel inhibitors of S . mansoni TGR or Prx2 , both of which have been recently validated as crucial S . mansoni enzymes and have been proposed as targets for drug development . Prior to HTS adoption , the assay employed monitoring NADPH absorbance . While such a format is very convenient , offering fast access to kinetic data via the use of standard spectrophotometers , measuring absorbance in the UV region in 1536-well density is rarely practical . A significant fraction of organic molecules , as well as dust and buffer components , absorb in the 350 nm range , thereby introducing unacceptably high levels of interference . Additionally , the relatively low extinction coefficient of NADPH coupled with the short optical path length of the plate well significantly reduces the signal available for detection . Because NADPH is naturally fluorescent , emitting at ∼450 nm , while its oxidized counterpart NADP is not , we switched the detection platform for the coupled reaction from absorbance to fluorescence , a step that parallels the application of profluorescent substrates in assays for phosphatases and proteases [23] , with the main difference being the fluorescence change trending from high to low in this reaction . Because of the anticipated fluorescence interference from compound library members in this blue-shifted detection region and because the output generated from NADPH is not very strong ( due to the combination of low extinction coefficient and quantum yield ) , we further modified the detection format of the assay to measure the reaction progress in kinetic mode as opposed to collecting a single end-point read . While kinetic , or time-course , measurements are routinely performed during assay development in low-throughput settings , their practical implementation during automated large-collection screens is not trivial . Unless the reaction under study is slow , only a fast-scanning reader or whole-plate imager ( such as the ViewLux ) can allow positionally-unbiased and rapid repeated measurements of 1536-well plates without significantly slowing down the overall plate processing speed . The collection of at least a two-point time course allows the effects of dust and fluorescent but otherwise inert library members to be subtracted out to reveal the true reaction course . Because the first time-point values ( when the enzymatic reaction has produced minimal amount of product ) associated with each compound well are stored in the database , a further analysis can be performed to flag interfering fluorescent library members [32] . An added benefit is that the signal-to-background computed from kinetic measurements significantly improves relative to end-point data and thus allows screening under conditions of low substrate conversion [33] . While in this screen we collected a total of 16 points per well , further optimization of the assay conditions could have resulted in shortened read time without the loss of sensitivity . The primary screen against the TGR/Prx2 cascade was performed in Quantitative High Throughput Screening ( qHTS ) format . In qHTS , every compound in the collection is tested over a range of concentrations , spanning from tens of micromolar to low nanomolar , to generate a complete concentration-response profile . As such , qHTS is best described as high throughput pharmacology , since as a result of its application , not only are potencies and efficacies assigned to each active compound but also false positives and negatives due to outliers associated with individual concentration responses are easily identified in the context of titration . Additionally , due to the built-in replicates in the testing of each compound , the need for laborious and infrastructure-intensive cherry-picking , original-result replication , and dose-response characterization are eliminated . In our present assay , the application of qHTS enabled us to not only skip the direct confirmation steps but also to combine the actives verification from independently-sourced powders with series expansion around limited SAR or singletons . In traditional single-concentration screening , singleton actives are necessarily treated with great caution given statistical uncertainties . In this study , the qHTS paradigm allowed us to confidently select the potent phosphinic amide , isoxazolone and phosphoramidite singletons 3 , 10 and 14 ( Figure 4 ) for further testing and that selection was later validated by the excellent confirmation of those actives and the successful expansion of the series . Separate , but equally important , is the aspect of reliability and robustness of screening data . qHTS , with the combination of dose-survey and replicate points , indeed offers uniquely rich and robust data sets for deposition in recently established public databases , such as PubChem . Additionally , in order to minimize the interfering effect of promiscuous inhibitors acting via colloidal aggregate formation [34] , [35] , we included detergent in the assay buffer . Throughout the entire screen , the assay performed in a robust manner , yielding an average Z′ value of 0 . 76 . Overall , the Z′ factor remained flat with the screen progression , with minor shifts tracking the introduction of fresh batches of enzyme . The availability of periodically computed Z′ , signal-to-background , and activity heatmaps throughout the screen progression , made possible by the development of fast data-processing tools in-house , significantly improved our response time when screen complications arose . For example , the dip in Z′ , also accompanied by noisy activity heatmap ( not shown ) , was noted almost in real-time and this allowed the appropriate concentration series to be scheduled for re-run within the same screening session . Figure 3B presents a cumulative plot of all intra-plate concentration-response curves of PAT throughout the entire screen . The narrow range of observed IC50 values serves as a further indication that the screen performed robustly from a standpoint of enzyme activity and responsiveness to inhibition . The titration curve displayed stability throughout the screen despite the fact that PAT is only partially soluble in DMSO and required formulation in high-percent water , leading to concerns about evaporation-related variability . Analyzing the trend in the intra-plate control as a function of screen progression allows one to ascertain the ‘health’ of the screening system as a whole , because the variations or dramatic shifts in potency of the control could be due to not only a deterioration in enzyme quality ( which could otherwise be detected from an shift in the S∶B value ) but also to problems with the pintool delivery of compounds . The absence of abrupt and significant shifts in the intra-plate control curve allows us therefore to conclude that the compound transfer remained uniform throughout the screen . The screen identified numerous arsenic , antimony , mercury , and other heavy-metal containing compounds ( data not shown; for complete set of actives , see PubChem , AID 448 ) . The antimony-containing compounds were largely similar to PAT and were therefore expected to be identified by this assay . Likewise , Hg-derivatives inhibited the enzymes strongly , as expected . While PAT and the gold-containing drug auranofin had been shown to inhibit thioredoxin reductase and TGR [17] , [36] , [37] , and while more recently arsenic trioxide has shown anticancer activity and its effect has been ascribed to Trx reductase-mediated apoptosis [38] , we restricted our analysis to novel nonmetal-containing compounds , primarily due to the fact that heavy metal-containing compounds frequently exhibit non-selective inhibition against a broad panel of enzymes and because nonmetal novel chemotypes against these previously-unscreened targets appeared to offer the greatest promise for optimizing potency and specificity . In this regard , the screen was successful , having resulted in identification of several distinct series of TGR inhibitors . It is noteworthy that the top actives from some series , such as the phosphinic amide 3 , oxadiazole 2-oxide 7 , and isoxazolone 11 , yielded IC50 values close to the final TGR assay concentration ( 25 nM in qHTS and confirmation and 15 nM in the TGR individual assay ) , thus approaching the limit of the detectable potency range . In terms of the lead actives there are several interesting points . For instance , the role of the benzothiazole heterocycle within the phosphinic amide series is apparently critical for inhibition as illustrated by the comparative values of analogues 3 , active and containing a benzothiazole moiety , and 4 , inactive and devoid of benzothiazole ( Figure 4 ) . The oxadiazole series contains several symmetric heterocyles ( a function of their synthetic ease ) and are known NO donors [39] , [40] . The presence of two phosphorus based small molecules may well relate to the presence of a selenocysteine in TGR and the relative electrophilic nature of this functionality . Studies to expand upon selected lead actives and further understand their mechanism of action are currently underway . Furthermore , in the Prx2 deconvolution assay , none of the top actives were found to inhibit GR , an enzyme closely related to TGR . This suggests that selective activity against the parasite ( which lacks GR ) and less toxicity to humans ( who have GR ) can be achieved . In summary , a kinetic-based qHTS against a pair of novel , validated targets from S . mansoni allowed fast and reliable identification of compounds active against this critical redox cascade . We have identified several novel structural series of TGR inhibitors , several of which are highly potent and should serve both as mechanistic tools for probing the redox balance in S . mansoni , and starting points for developing medicinal leads for much-needed new treatments for schistosomiasis . The work presented here effectively bridged the gap between academic target identification and the first steps of drug development for an important neglected disease [20] . Generalization of this paradigm to other neglected diseases could prove be a powerful approach to catalyzing new therapeutic development for NTDs . | Schistosomiasis , also known as bilharzia , is a tropical disease associated with high morbidity and mortality , currently affecting over 200 million people worldwide . Praziquantel is the only drug used to treat the disease , and with its increased use the probability of developing resistance has grown significantly . The Schistosoma parasites can survive for up to decades in the human host due in part to a unique set of antioxidant enzymes that continuously degrade the reactive oxygen species produced by the host's innate immune response . Two principal components of this defense system , thioredoxin/glutathione reductase ( TGR ) and peroxiredoxin ( Prx2 ) , have been recently identified and validated as targets for anti-schistosomiasis drug development . In search of inhibitors of this critical redox cascade , we optimized and performed a highly miniaturized automated screen of 71 , 028 compounds arrayed as 7- to 15-point dilution sets . We identified novel structural series of TGR inhibitors , several of which are highly potent and should serve both as mechanistic tools for probing redox pathways in S . mansoni and as starting points for developing much-needed new treatments for schistosomiasis . The paradigm presented here effectively bridges the gap between academic target identification and the first steps of drug development , and should be applicable to a variety of other important neglected diseases . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"chemical",
"biology/small",
"molecule",
"chemistry",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"infectious",
"diseases/helminth",
"infections",
"microbiology/parasitology",
"biochemistry/drug",
"discovery"
] | 2008 | Quantitative High-Throughput Screen Identifies Inhibitors of the Schistosoma mansoni Redox Cascade |
Epstein-Barr virus ( EBV ) is a human lymphocryptovirus that is associated with several malignancies . Elevated EBV DNA in the blood is observed in transplant recipients prior to , and at the time of post-transplant lymphoproliferative disease; thus , a vaccine that either prevents EBV infection or lowers the viral load might reduce certain EBV malignancies . Two major approaches have been suggested for an EBV vaccine- immunization with either EBV glycoprotein 350 ( gp350 ) or EBV latency proteins ( e . g . EBV nuclear antigens [EBNAs] ) . No comparative trials , however , have been performed . Rhesus lymphocryptovirus ( LCV ) encodes a homolog for each gene in EBV and infection of monkeys reproduces the clinical , immunologic , and virologic features of both acute and latent EBV infection . We vaccinated rhesus monkeys at 0 , 4 and 12 weeks with ( a ) soluble rhesus LCV gp350 , ( b ) virus-like replicon particles ( VRPs ) expressing rhesus LCV gp350 , ( c ) VRPs expressing rhesus LCV gp350 , EBNA-3A , and EBNA-3B , or ( d ) PBS . Animals vaccinated with soluble gp350 produced higher levels of antibody to the glycoprotein than those vaccinated with VRPs expressing gp350 . Animals vaccinated with VRPs expressing EBNA-3A and EBNA-3B developed LCV-specific CD4 and CD8 T cell immunity to these proteins , while VRPs expressing gp350 did not induce detectable T cell immunity to gp350 . After challenge with rhesus LCV , animals vaccinated with soluble rhesus LCV gp350 had the best level of protection against infection based on seroconversion , viral DNA , and viral RNA in the blood after challenge . Surprisingly , animals vaccinated with gp350 that became infected had the lowest LCV DNA loads in the blood at 23 months after challenge . These studies indicate that gp350 is critical for both protection against infection with rhesus LCV and for reducing the viral load in animals that become infected after challenge . Our results suggest that additional trials with soluble EBV gp350 alone , or in combination with other EBV proteins , should be considered to reduce EBV infection or virus-associated malignancies in humans .
Epstein-Barr virus ( EBV ) is a causative agent of infectious mononucleosis and is associated with a number of malignancies including lymphomas in immunocompromised persons , Hodgkin lymphoma , Burkitt lymphoma , and nasopharyngeal carcinoma . Currently no vaccine has been licensed to prevent EBV infection or disease . Most attempts to generate an EBV vaccine have focused on glycoprotein 350 ( gp350 ) as the immunogen . gp350 is the most abundant EBV glycoprotein in virions and on the surface of infected cells . gp350 binds to CD21 , the EBV receptor on B cells . EBV gp350 is spliced to form gp220 . gp350 is important for virus absorption to B cells and soluble gp350 can block EBV infection . Antibodies to gp350 neutralize virus in vitro [1] . EBV gp350 protects cottontop marmosets from B cell lymphomas when challenged with high titers of EBV [2] . Numerous studies have shown that gp350 purified from cells [3] , [4] , expressed as a recombinant protein [5] , [6] , or expressed from an adenovirus [7] or vaccinia vector [8] can protect marmosets from EBV lymphomas . Vaccinia virus expressing gp350 induced EBV neutralizing antibody in seronegative children and a showed a trend toward protection from EBV infection [9] . Vaccination of young adults with recombinant gp350 in alum/monophosphoryl lipid A induced EBV neutralizing antibodies and protected EBV seronegative volunteers from infectious mononucleosis , but not from EBV infection [10] , [11] . While gp350 is important for protection from infectious mononucleosis , EBV proteins expressed during latency are thought to be critical for controlling latent infection . The EBV nuclear antigen 3 ( EBNA-3 ) latency proteins are the primary targets of CD8 T cells in the blood of healthy EBV carriers [12] . The success of treating patients with EBV lymphoproliferative disease with infusions of EBV-specific T cells [13] , [14] , in which the EBNA-3 proteins represent the immunodominant epitopes , indicates the critical role of these viral proteins for protection from EBV disease . The importance of T cell responses to EBNA-3B was demonstrated in a patient who died from an EBV lymphoma after the tumor cells developed a large deletion in EBNA-3B which allowed the malignant cells to escape from EBV-specific cytotoxic T cells [15] . A peptide corresponding to EBNA-3A was used in a small vaccine trial in EBV-seronegative human volunteers [16] . Given the complexities and costs of EBV vaccine trials in humans , testing vaccines in animal models might allow more rapid comparison of candidate vaccines . Many animal studies using gp350 have been performed in cottontop tamarins , which have several limitations . These animals cannot be infected with EBV by the oral route , they do not develop a persistent infection similar to humans , and the animals do not express MHC class I A , B or C alleles [17] which have been associated with virus-specific cytotoxic T cells ( CTLs ) . In contrast , rhesus lymphocryptovirus ( LCV ) is naturally endemic in rhesus monkeys and reproduces most , if not all , of the features of EBV in these animals [18] . Infection of monkeys with rhesus LCV results in lymphadenopathy , splenomegaly , and atypical lymphocytes in some animals , and animals shed the virus from the oropharynx [19] . Unlike infection of cottontop tamarins with EBV , rhesus monkeys can be infected orally with rhesus LCV and the animals develop a persistent infection similar to that which occurs in humans . When animals are immunosuppressed some develop B cell lymphomas that contain rhesus LCV [20] . Rhesus LCV has an ortholog for each of the EBV genes; conversely each EBV gene has an ortholog in rhesus LCV [21] . The rhesus LCV genes can complement their human EBV orthologs in nearly all activities; thus , rhesus LCV should be an excellent model for studying EBV pathogenesis . While EBV gp350 has been shown to be protective against tumors in cottontop tamarins challenged with high titers of EBV and one study showed that gp350 reduced the incidence of infectious mononucleosis in humans , no vaccine studies have been performed using rhesus LCV in monkeys . Furthermore no studies have been reported involving a direct comparison of different EBV vaccines , including gp350 versus EBV latency proteins , in the same trial . We compared three rhesus LCV vaccines- ( a ) recombinant soluble rhesus LCV gp350 , ( b ) rhesus LCV gp350 expressed from replication-defective , single cycle , virus-like replicon particles ( VRPs ) derived from an attenuated strain of Venezuelan equine encephalitis ( VEE ) , and ( c ) a combination of rhesus LCV gp350 , EBNA-3A , and EBNA-3B each expressed in separate attenuated VRPs for their ability to protect rhesus monkeys against infection with rhesus LCV and to determine their long term effect on rhesus LCV DNA in the blood after challenge .
These experiments were approved by the Animal Care and Use Committees of the National Institute of Allergy and Infectious Diseases and the University of California , Davis . The studies were carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . Rhesus macaques were reared separately from rhesus LCV seropositive animals beginning at birth and serologic testing indicated that all animals were seronegative for rhesus LCV . Six to 18 month old animals were housed in pairs during the vaccination period , and housed separately after challenge . Animals were vaccinated by inoculation in the triceps muscle , and challenged with rhesus LCV by inoculation of the back of the throat with virus in 1 ml of cell culture media using a needleless syringe . Rhesus LCV was isolated from LCL8664 cells ( American Type Culture Collection , Manassas , VA ) . The cells were derived from a rhesus monkey with a malignant lymphoma [22] . LCL8664 cells were transfected with a plasmid expressing EBV BZLF1 using electroporation as described previously [23] , and after 5 days the cells were pelleted and virus was isolated as reported previously [24] . Rhesus LCV was titrated as previously described for human EBV [25] . Briefly , serial dilutions of virus were incubated with 1×105 rhesus peripheral blood mononuclear cells ( PBMCs ) and the cells were plated into wells of a 96 well plate with 0 . 5 ug/ml of cyclosporine A . After 6 weeks the titer of virus was determined by the method of Reed and Muench [26] . Modified vaccinia Ankara ( MVA ) expressing rhesus LCV gp350 and green fluorescent protein ( GFP ) was constructed by cloning rhesus LCV gp350 into plasmid pLW44 [27] . This plasmid contains the GFP gene linked to the vaccinia virus p11 promoter to facilitate screening of recombinant MVA . Due to a vaccinia transcription termination signal [28] in the rhesus LCV gp350 gene ( TTTTTGT , sequence position 1147 to 1153 ) , the 5′ half of the gene ( 1-1 , 291 ) was amplified by PCR using primers 5′-TCCCCCCGGGAACAATGGAAGCGGCTTTTCTG-3′ and 5′-ATACGCGTCGACTCTTCGGGTTGTCTGGTTGGAGC-3′ ( Xma I and Sal I sites are underlined ) , and the PCR product was digested with Xma I and Sal I and inserted into the corresponding restriction sites of plasmid pLW44 . The T at nucleotide 1147 was changed to C ( resulting in no change in the amino acid sequence of gp350 ) using the Quick Change Site-Directed Mutagenesis kit ( Stratagene ) and the resulting plasmid was referred to as pLWrhgp350-mA . After confirmation of the sequence , the 3′ half of the gene was amplified using primers 5′-TCCCCCCGGGGCAGCCACAAATGTCACCGCTGTT-3′ and 5′-ATACGCGTCGACCTAAACAGCGGTTTCAAATTC -3′ ( Xma I and Sal I sites underlined ) . The resulting PCR product was cut with XmaI and Sal I and inserted into the corresponding site of pLW44 to obtain plasmid pLWrhgp350-B . pLWrhgp350-mA was cut with Not I and Sex AI and the 5′ end of rhesus LCV gp350 was inserted into the Not I and Sex AI sites of pLWgp350-B to yield plasmid pLWrhgp350GFP . DF-1 ( a chicken embryo fibroblast cell line ) or primary chicken fibroblasts ( a gift from Linda Wyatt , NIH ) were infected with 0 . 05 plaque forming units ( pfu ) of MVA per cell and 2 hours later the cells were transfected with plasmid pLWrhgp350GFP . Plaques expressing GFP and rhesus LCV gp350 were isolated by successive rounds of plaque purification by freeze-thawing cells containing GFP-positive plaques and plating at limiting dilutions . The resulting virus , named MVA-gp350GFP , was propagated in DF-1 cells . To obtain MVA expressing rhesus LCV without GFP , plasmid pLWrhgp350GFP was digested with Kpn I which removes the GFP gene and religated to yield pLWrhgp350 . DF-1 cells were infected with MVA-gp350GFP and 2 hours later were transfected with pLWrhgp350 . Plaques that did not express GFP were isolated by plaque purification and the resulting virus was named MVA-gp350 . To produce rhesus LCV gp350-Fc protein , CV-1/EBNA-1 cells ( ATCC , Manassas , VA ) grown in DMEM/F-12 medium ( 1∶1 ) with 10% fetal bovine serum , were transfected with plasmid pDCrhgp350-Fc using DEAE-Dextran . After transfection , the media was changed to DMEM/F12 medium with 0 . 5% low immunoglobulin G fetal bovine sera ( HyClone , Logan , UT ) . One week after transfection , the media was collected , clarified by low speed centrifugation , and filtered through a 0 . 45 um filter . Recombinant rhesus LCV gp350-Fc was bound to protein A-Sepharose beads , eluted from the beads with 12 . 5 mM citric acid pH 2 . 2 , and collected in tubes containing 500 mM HEPES , pH 9 . 0 to neutralize the citric acid . To produce recombinant rhesus LCV EBNA-3A and EBNA-3B , Cos cells were transfected with plasmid pSGrhEBNA3A1 or pSGrhEBNA3B using Lipofectamine 2000 ( Invitrogen ) . Two days after transfection , lysates were prepared from the cells and proteins were separated by polyacrylamide gel electrophoresis . Proteins were stained with Coomassie blue , and bands containing EBNA-3A and EBNA-3B were excised from the gel . EBNA-3 proteins were eluted from the gel overnight in PBS and concentrated using a Centricon YM-100 filter ( Millipore ) . To produce antibody to rhesus LCV gp350 , 2 rabbits were immunized with 150 ug of rhesus LCV gp350-Fc fusion protein in complete Freund's adjuvant ( Animal Pharm Service Inc . , Sausalito , CA ) . Animals were boosted with 100 ug of gp350-Fc in incomplete Freund's adjuvant on days 28 , 42 , and 86 after the first vaccination; 2 weeks after the last boost the rabbits were bled and sera were obtained . To produce antibody to rhesus LCV EBNA-3A and EBNA-3B , mice were immunized three times , 3 weeks apart with 100 ug of pCIrhEBNA3A or pCIrhEBNA3B . Three weeks after the 3rd DNA immunization , the animals were boosted with 20 ug of EBNA-3A protein or 15 ug of EBNA-3B protein in complete Freund's adjuvant . Serum was collected from the mice 2 weeks later . For rhesus monkey vaccinations , rhesus LCV gp350-Fc protein was incubated with Alhydrogel 2% ( Brenntag Biosector , Accurate Chemical and Scientific Corp ) by mixing on a rotating wheel for 30 min at room temperature followed by addition of monophosphoryl lipid A ( Avanti Polar Lipids , Inc . , Alabaster , AL ) . Replication-defective attenuated Venezuelan equine encephalitis viruses ( VEE ) expressing rhesus LCV gp350 , EBNA-3A1 , or EBNA-3B were constructed by PCR amplification of the genes from plasmids pSGrhgp350 , pSGrhEBNA3A1-del , pSGrhEBNA3B-del and inserting the rhesus LCV genes into a VEE replicon vector . The replicon vector contains the VEE nsP1 , nsP2 , nsP3 , and nsP4 genes and an internal ribosome entry site ( IRES ) followed by a cloning site into which the rhesus LCV genes were inserted [32] . RNAs were produced from the replicon and VEE plasmid vectors using T7 polymerase . Vero cells were co-transfected with ( a ) helper RNA expressing VEE capsid , ( b ) helper RNA expressing VEE glycoprotein [33] , and ( c ) RNA obtained from replicon vectors expressing rhesus LCV gp350 , EBNA-3A , or EBNA-3B . The resulting transfections generated replication-defective , single-cycle , virus-like replicon particles ( VRPs ) [32] , [34] . Antibody to rhesus LCV viral capsid antigen ( VCA ) was determined by immunofluorescence ( VRL Laboratories , San Antonio , Texas ) . Antibody to rhesus LCV gp350 was measured using the luciferase immunoprecipitation system ( LIPS ) assay [25] . Cos cells were transfected with pREN3rhgp350 which encodes a fusion protein containing the rhesus LCV gp350 gene linked to the Renilla luciferase gene . Activity in transfected Cos cell lysates was determined by luminometry and expressed as luminometer units ( LU ) per ml as described previously [25] . To measure rhesus LCV gp350 antibody levels in animals , rhesus monkey plasma were diluted 1∶10 , and 1 ul was added to 1×107 light units ( LU ) of transfected Cos cell extract . Immunoprecipitations were performed by addition of protein A/G beads , and LU were determined by luminometry . A cut-off threshold limit was derived from the mean value plus 2 standard deviations of the background LU . All LU data shown represent the average of two independent experiments . DNA was isolated from 1−5×106 PBMCs using either a QIAamp DNA Blood Mini Kit ( Qiagen ) or an Easy-DNA Kit ( Invitrogen ) . Real time PCR for rhesus LCV DNA was performed with primers and probes that amplify rhesus LCV EBER1 [35] using the following conditions: 94°C for 15 sec , 60°C for 30 sec , and 72°C for 35 sec for a total of 40 cycles . Real time PCR was also performed to amplify the internal repeat 1 ( IR1 ) region of rhesus LCV ( which corresponds to the EBV Bam HI W fragments ) with primers 5′-AAATCTAAACTTTTGAGGCGATCTG-3′ and 5′-CCAACCATAGACCCGTTTCCT -3′ and probe 5′- ( 6-Fam ) -TCTCCGCGTGCGCATAATGGC- ( TAM RA ) -3′ using the following conditions: 50°C for 2 min and 94°C for 10 min for 1 cycle , followed by 94°C for 15 sec , 60°C for 1 min , for 45 cycles . Viral DNA was normalized using GAPDH [31] and results were expressed as DNA copies per 1×106 PBMCs . Real time reverse-transcriptase PCR was performed for rhesus LCV EBER1 . Total RNA was isolated from 5×106 PBMCs using Trizol ( Invitrogen , Calsbad , CA ) , and reverse transcription and PCR was performed using primers and probes as described previously [35] and PCR conditions described above . Rhesus monkey cell lines were used to present EBNA-3A , EBNA-3B , and gp350 to rhesus PBMCs . Lymphoblastoid cell lines ( LCLs ) , which express EBNA-3A and EBNA-3B , were constructed for each monkey by infecting PBMCs with rhesus LCV in the presence of cyclosporine A ( 500 ng/mL , Sigma-Aldrich ) and culturing the cells in RPMI 1640 with GlutaMax ( Invitrogen ) with 10% FBS and antibiotics . Cryopreserved PBMCs were thawed and cultured in RPMI 1640 with GlutaMax with 10% FBS , IL-2 ( 5 U/mL , from the National Cancer Institute ) and antibiotics in 12-well plates overnight . The following day , PBMCs were divided into 2 tubes ( 1−3×106 cells per tube ) and were cocultured with 2×106 autologous LCLs for 5 hours in the presence of 10 µg/mL of brefeldin A ( Sigma-Aldrich ) and 10 U/mL of IL-2 . The cells were then washed in PBS with 2% FBS and 2 mM EDTA , incubated with FITC-conjugated anti-CD8 monoclonal antibody ( clone RPA-T8 , BioLegend , San Diego , CA ) and APC-conjugated anti-CD4 monoclonal antibody ( clone OKT4 , BioLegend ) for 20 min , washed with PBS containing 2% FBS and 2 mM EDTA , incubated with Cytofix/Cytoperm buffer ( BD Bioscience , Franklin Lakes , NJ ) for 25 min , and washed with Perm wash buffer ( BD Bioscience ) . Cells were then incubated with PE-conjugated anti-IL-2 monoclonal antibody ( clone MQ1-17H12 , BioLegend ) and PE-Cy7-conjugated anti-IFN-γ monoclonal antibody ( clone B27 , BD Bioscience ) , washed with Permwash buffer , and resuspended in PBS with 2% FBS and 2 mM EDTA . As a negative control , PBMCs were cultured without LCLs , and mixed with LCLs after fixation of PBMCs with Cytofix/Cytoperm buffer . Data were acquired using a FACS Caliber ( BD Bioscience ) and analysis was performed using Flowjo software 8 . 8 . 4 ( Tree Star Inc . , Ashland , OR ) . The percent of rhesus LCV-specific cytokine T cell response was defined as the percent cytokine ( IL-2 or IFN-γ , or both ) positive CD4 or CD8 cells in LCL-stimulated PBMCs minus the percent cytokine positive CD4 or CD8 cells in unstimulated PBMCs . If unstimulated samples had a higher frequency of cytokine positive cells than stimulated samples , a value of 0% was assigned , instead of a negative value . To measure gp350-specific CD8 and CD4 T cell responses , LCLs were infected with either wild-type MVA , or MVA expressing rhesus LCV gp350 , at 3 TCID50 for 24 hours before coculture with PBMCs . PBMCs were thawed and cultured overnight as described above . The following day , PBMCs were divided into 3 tubes ( 0 . 8−2×106 cells per tube ) and cocultured with LCLs infected with either wild-type or gp350 expressing MVA for 5 hours in the presence of brefeldin A . As a negative control , PBMCs were cultured without LCLs and mixed with LCLs ( not infected with MVA ) after fixation of PBMCs with Cytofix/Cytoperm buffer . Staining and flow cytometry were done as described above . The percent of cytokine producing CD4 or CD8 cells in unstimulated PBMCs mixed with LCLs ( not infected with MVA ) after fixation ( negative control ) was subtracted from the percent of CD4 or CD8 cells in PBMCs stimulated with gp350 MVA-infected LCLs or wild-type MVA-infected LCLs . The percent of rhesus LCV gp350-specific CD4 or CD8 T cell response was defined as the percent of cytokine producing CD4 or CD8 cells in PBMCs stimulated with gp350 MVA-infected LCLs minus the percent of cytokine producing CD4 or CD8 cells in PBMCs stimulated with wild-type MVA-infected LCLs .
In order to determine if rhesus LCV encodes gp350 similar to its human EBV homolog , rabbits were immunized with purified rhesus LCV gp350-Fc fusion protein and serum was obtained . The rabbit serum detected proteins from 200−270 kDa in supernatant from cells transfected with plasmid expressing soluble gp350-Fc , but not with plasmid expressing GFP ( pGL3-GFP ) ( Fig . 1 , lanes 1 , 2 ) . To ensure that the rabbit antibody was specific for rhesus LCV gp350 , we determined that the antibody could detect full length gp350 in virus-infected cells . Full length rhesus LCV gp350 was inserted into modified vaccinia Ankara ( MVA ) . DF-1 cells were infected with MVA-gp350GFP or MVA alone and 16−24 hr later , lysates were prepared , and immunoblotted with the rabbit serum . Cells infected with MVA-gp350GFP , but not MVA alone produced a 250 kDa protein that reacted with the antibody ( Fig . 1 , lanes 3 , 4 ) . Similarly , Cos cells infected with virus-like replicon particles expressing rhesus LCV gp350 ( VRP-gp350 ) , but not cells expressing GFP ( VRP-GFP ) , expressed proteins of 220−250 kDa that reacted with the antibody ( Fig . 1 , lanes 5 , 6 ) . We were unable to detect rhesus LCV gp350 in LCL8664 cells treated with sodium butyrate or transfected with a plasmid expressing EBV BZLF1 ( data not shown ) , likely due to low levels of the glycoprotein . In order to express rhesus LCV EBNA-3A and EBNA-3B , we cloned the genes from LCL8664 cells into expression vectors and determined the sequence of the viral genes . While the sequence of rhesus LCV EBNA-3A was identical to the published sequence [21] , the sequence of rhesus LCV EBNA-3B was different . We found a T deleted at nucleotide 1744 and a C inserted at nucleotide 2206 of rhesus LCV EBNA-3B . The deletion at nucleotide 1744 results in a frameshift in the EBNA-3B sequence beginning at codon 582 , and the insertion at nucleotide 2206 restores the open reading frame to the published amino acid sequence at codon 735 so that the last 193 amino acids of the protein are unchanged ( Fig . 2 ) . This was verified for several PCR clones from LCL8664 cells and by direct sequencing of DNA from LCL8664 cells . Comparison of the amino acid sequence of rhesus LCV EBNA-3B reported here , in the region just prior and after the frameshift mutation ( acids 577−740 ) , with that of EBV AG876 EBNA-3B showed 31% identity , while comparison of the prior rhesus LCV EBNA-3B sequence [21] with EBV AG876 EBNA-2B showed only 16% identity . Taken together these findings suggest that the sequence reported here for rhesus LCV EBNA-3B is more likely to be the authentic sequence of the protein . Both EBV EBNA-3A [36] and EBNA-3B bind to RBP-Jκ and stimulate B cell proliferation . Since EBV EBNA-3A is critical for B cell growth transformation and survival [36] and EBNA-3-induced B cell proliferation might be problematic for a vaccine , we deleted the RBP-Jκ binding sites in rhesus LCV EBNA-3A and EBNA-3B . Mutation of the EBV EBNA-3A RBP-Jκ binding domain , TLGC ( amino acids 199−202 ) , to AAGA results in loss of function of the protein and reduces its ability to bind to RBP-Jκ . Rhesus LCV EBNA-3A and EBNA-3B also bind to RBP-Jκ [30] . Alignment of the amino acid sequence of rhesus LCV EBNA-3A with its EBV homolog predicts that the rhesus LCV EBNA-3A RBP-Jκ binding site TFAC ( amino acids 204 to 207 based on the sequence of Jiang et al . [30] , or amino acids 190−193 based on the sequence of Rivailler et al . [31] ) are positional homologs of the RBP-Jκ binding site TLGC ( amino acids 199−202 ) of EBV EBNA-3A . Similarly , alignment of rhesus LCV EBNA-3B with EBV EBNA-3B predicts that amino acids 208 to 211 ( TLGC ) of rhesus LCV EBNA-3B are positional homologs of EBV EBNA-3B amino acids 205 to 208 ( TLGC ) . Therefore , we deleted these four codons from rhesus LCV EBNA-3A and EBNA-3B in vectors expressing these genes . Based on the sequence of rhesus LCV EBNA3A ( rhEBNA3A ) , either of two methionines could be the first amino acid of the protein [30] . To determine which can be used for EBNA-3A , we made four EBNA-3A constructs- rhEBNA-3A1 ( which starts at the first methionine ) , rhEBNA-3A2 ( which starts at the second methionine ) , rhEBNA-3A1-del and rhEBNA3A2-del ( in which the four amino acid putative RBP-Jκ binding site in EBNA-3A was deleted ) . Transfection of Cos cells with pSGrhEBNA3A1 , pSGrhEBNA3A2 , pSGrhEBNA3A1-del , and rhEBNA3A2-del followed by Coomassie blue staining of cell lysates in PAGE gels showed bands of 147 kDa , 145 kDa , 147 kDa , and 144 kDa , respectively ( Fig . 3A , lanes 2−5 ) . These studies indicate that either the first or second methionine can be used for producing EBNA-3A . Transfection of Cos cells with pSGrhEBNA3B or pSGrhEBNA3B-del ( deleted for the four amino acid putative RBJ-κ binding site ) yielded a band of 150 kDa ( Fig . 3A , lanes 6 , 7 ) . Vero cells infected with VRP-EBNA-3A showed predominant bands of 102 and 88 kDa , while LCL8664 cells and rhesus LCV LCL-V showed a band of about 102 kDa ( Fig . 3B ) . Vero cells infected with VRP-EBNA-3B showed an upper band of 145 kDa and more intense bands from 105−120 kDa while LCL8664 cells and rhesus LCV LCL-V showed a band of 145 kDa ( Fig . 3C ) . Four rhesus LCV seronegative monkeys each received one of four inocula intramuscularly: ( a ) 50 ug of rhesus LCV soluble gp350-Fc protein ( soluble gp350 ) formulated in 800 ug alum and 50 ug monophosphoryl lipid A , ( b ) 1×108 infectious units ( IU ) of virus-like replication-defective VEE particles expressing rhesus LCV gp350 ( VRP-gp350 ) in 1 ml of DMEM with 10% FBS , ( c ) a combination of three separate replication-defective VEE particles expressing rhesus LCV gp350 ( VRP-gp350 ) , EBNA-3A ( VRP-EBNA-3A ) , and EBNA-3B ( VRP-EBNA-3B ) each at a titer of 1×108 IU in a total of 1 ml of DMEM with 10% FBS , or ( d ) PBS control . The rhesus LCV soluble gp350 used in our vaccine contains the extracellular domain of the glycoprotein fused to the Fc domain of human IgG , while the vaccine used in the large human trial [11] has the extracellular domain of EBV gp350 with a mutation in the gp220 splice site and no Fc protein fused to the glycoprotein . The alum/monophosphoryl lipid A adjuvant was chosen for rhesus LCV soluble gp350 , since this is the adjuvant that was used in the large human EBV gp350 study [11] . Animals were vaccinated at weeks 0 , 4 , and 12 . Serum antibody responses to gp350 in animals 5 weeks after the last vaccination showed that all animals vaccinated with soluble gp350 or VRP-gp350 ( alone or in combination with VRPs expressing EBNA-3A and EBNA-3B ) produced antibodies to the glycoprotein ( Fig . 4 ) . The geometric mean antibody level was significantly higher in animals vaccinated with recombinant soluble gp350 than in animals receiving VRP-gp350 ( p<0 . 05 ) or in animals that had been naturally infected with rhesus LCV ( p<0 . 05 ) . The geometric mean antibody titer in animals receiving VRP-gp350 ( alone or in combination with VRPs expressing EBNA-3A and EBNA-3B ) was not significantly different than in animals naturally infected with rhesus LCV . Rhesus LCV LCL-specific CD4 and CD8 T cell immune responses in monkeys were measured both pre- and post-vaccination . Rhesus LCV LCLs , which express EBNA-3A and EBNA-3B ( Fig . 3 ) , from each monkey served as antigen presenting cells . PBMCs from monkeys were incubated with autologous LCLs and cells were then stained for surface expression of CD4 and CD8 and for intracellular expression of IL-2 and IFN-γ . Before vaccination , the mean percentage of rhesus LCV-specific cytokine producing CD4 T cells was 0 . 0014±0 . 0013 , 0 . 0013±0 . 0014 , 0 . 0143±0 . 0135 and 0 . 0078±0 . 0045 ( mean ± SE ) for animals receiving PBS , soluble gp350 , VRP-gp350 , and combined VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B , respectively ( Fig . 5 ) . The mean percentage of rhesus LCV-specific CD4 T cells after vaccination was 0 . 0147±0 . 0165 , 0 . 0070±0 . 0053 , 0 . 0467±0 . 0500 and 0 . 0894±0 . 0516 for animals receiving PBS , soluble gp350 , VRP-gp350 , and combined VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B , respectively . The mean percentage of rhesus LCV-specific cytokine producing CD8 T cells pre-vaccine was 0 . 0111±0 . 0105 , 0 . 0140±0 . 0077 , 0 . 0334±0 . 0194 and 0 . 0223±0 . 0178 for monkeys receiving PBS , soluble gp350 , VRP-gp350 , and combined VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B , respectively . Post-vaccination , the mean percentage of rhesus LCV-specific CD8 T cells was 0 . 0315±0 . 0117 , 0 . 0260±0 . 0152 , 0 . 0390±0 . 0185 and 0 . 1834±0 . 1059 for PBS , soluble gp350 , VRP-gp350 , and combined VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B , respectively . While there was considerable variability among individual animals , only animals receiving combined VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B had a statistically significant increase in the percentage of rhesus LCV LCL-specific CD4 T cells ( p = 0 . 032 by T-test ) . Animals that received combined VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B had an increase in the percentage of rhesus LCV LCL-specific CD8 T cells after vaccination , but the difference did not reach statistical significance ( p = 0 . 065 by T-test ) . We did not observe CD4 or CD8 T cell responses to rhesus LCV gp350 in monkeys after vaccination using LCLs as antigen presenting cells , except for one animal that had an increase in rhesus LCV LCL-specific cytokine positive CD4 T cell response after vaccination with VRP-gp350 ( Fig . 5 ) . This is not surprising since we were unable to detect rhesus LCV gp350 in LCLs by immunoblot ( data not shown ) . Therefore , we looked for T cells responses to rhesus LCV gp350 by infecting LCLs with either MVA-gp350 ( which expresses rhesus LCV gp350 ) or wild-type MVA and used these LCLs as antigen presenting cells . We did not detect an increase in rhesus LCV gp350-specific PBMCs from animals vaccinated with soluble gp350 or VRP-gp350 ( data not shown ) , even though MVA-gp350 expresses the glycoprotein ( Fig . 1 ) . It is possible that a different method to present gp350 to PBMCs would have been more effective; nonetheless , these data suggest that soluble gp350 or VRP-gp350 did not induce a significant increase in cellular immune responses in monkeys . A challenge inoculum of rhesus LCV was titered in LCV seronegative rhesus monkeys . Five animals were initially given 14 TID50 ( infectious dose of virus needed to transform 50% of wells of cells in vitro ) of rhesus LCV by application of virus to the throat and all animals seroconverted . Based on these results , 10 weeks after the last vaccination , animals were challenged by the oral route with 50 TID50 of rhesus LCV . Antibody to rhesus LCV viral capsid antigen ( VCA ) was detected after challenge with rhesus LCV in all 4 animals that received PBS and all 4 that received VRP-gp350 . In contrast , 2 of 4 that received soluble gp350 and 3 of 4 animals that received a combination of VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B developed antibody to rhesus LCV VCA after challenge ( Fig . 6 ) . Interestingly , seroconversion was delayed to week 10 after challenge in the 2 animals that received soluble gp350 that became infected , while seroconversion occurred in weeks 3 to 8 in most of the other vaccine groups . Like EBV , rhesus LCV DNA is present at very low or undetectable copy numbers in PBMCs of healthy animals infected in the past , but is usually detected in the blood after initial infection [31] . Within 6 weeks after challenge , rhesus LCV DNA was detected in PBMCs in 2 of 4 animals that received soluble gp350 and in 3 of 4 animals that received a combination of VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B or PBS ( Fig . 7 ) . All 4 animals that received VRP-gp350 had detectable rhesus LCV DNA . To further verify that animals were protected from infection after challenge we tested PBMCs from animals for rhesus LCV EBER1 . EBER1 is present in thousands of copies in virus-infected B cells and is usually detected in the blood for life after infection of rhesus monkeys [35] . After challenge , rhesus LCV EBER1 was detected in PBMCs in 2 of 4 animals that received soluble gp350 and in 3 of 4 animals that received the combination of VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B ( Fig . 8 ) . One animal that received the VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B combination had a single positive level of EBER1 in the blood at 3 weeks after challenge and subsequently remained EBER1 negative . In contrast , all 4 animals that received VRP-gp350 or PBS had detectable EBER1 in the blood after challenge . Thus , all animals that seroconverted were positive for LCV EBER1 . In summary , animals receiving soluble gp350 had the best level of protection after challenge with the fewest numbers of animals with rhesus LCV DNA or rhesus LCV RNA in the blood and the lowest rate of seroconversion after challenge , while animals that received the combination of VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B had the next best level of protection . Analysis of fever , lymph node swelling , liver function tests , and CD4 to CD8 ratios after challenge did not show discernable differences in animals that received different vaccines or control PBS ( data not shown ) . This may not be surprising since there were small numbers of animals , all animals were all <3 years old ( and EBV infectious mononucleosis is rare in young children ) , and variability in animals after infection has been reported previously [19] . PBMCs were obtained from each of the vaccinated animals at 23 and 34 months after challenge . Using real time PCR with the rhesus LCV DNA EBER1 probe , we were only able to detect rhesus LCV DNA in PBMCs from 1 of the 16 animals 23 months after challenge ( data not shown ) . Therefore , we developed a more sensitive real time PCR assay using IR1 DNA ( corresponding to the Bam HI W repeats of EBV ) that are present at 5 . 7 copies in the rhesus LCV genome [21] . Using the more sensitive real time PCR assay we were able to detect rhesus LCV DNA in 5 of 5 monkeys that had been naturally infected ( data not shown ) . As expected we were unable to detect rhesus LCV DNA in animals that had been protected from challenge , therefore those animals were excluded from further analyses to avoid skewing the results . At 23 months after challenge , using the more sensitive real time PCR test , the mean rhesus LCV DNA copy number was 15 copies per 106 cells for animals vaccinated with soluble gp350 , 3 , 986 for animals that received the combination of VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B , 3 , 663 for animals receiving VRP-gp350 , and 1 , 504 for animals that received PBS ( Fig . 9A ) . At 34 months after challenge , the mean rhesus LCV DNA copy number was 120 copies per 106 cells for animals vaccinated with soluble gp350 , 0 for animals that received the combination of VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B , 3 , 605 for animals receiving VRP-gp350 , and 9 , 271 for animals that received PBS ( Fig . 9B ) . Thus , the rhesus LCV DNA copy number was lowest in animals vaccinated with soluble gp350 at 23 months after challenge , and was similar to the copy number in animals that received the combination of VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B at 34 months . Analysis of seropositive animals showed that rhesus LCV DNA was detected 23 or 34 months after challenge in 1 of 2 animals that received soluble gp350 , 2 of 3 animals that received the combination of VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B , 4 of 4 animals that received VRP-gp350 , and 3 of 4 animals that received PBS .
Two different types of vaccines have been developed to prevent disease and limit primary infection with EBV . Soluble EBV gp350 reduced the rate of infectious mononucleosis by 78% in young adults [11] . Alternatively , induction of cellular immunity to EBNA-3 has been proposed to limit the events occurring immediately after primary infection including virus replication in the throat and the expansion of virus-infected B cells [37] . Prior studies have shown that EBNA-3 epitopes are primary targets for EBV-specific CTLs in healthy persons , and therefore an EBV vaccine containing EBNA-3 epitopes has been proposed [38] , [39] . A peptide corresponding to EBNA-3A elicited peptide-specific T cell responses in EBV-seronegative human volunteers; 4 of 4 seronegative volunteers seroconverted to EBV asymptomatically , while 1 of 2 placebo recipients infected with EBV developed infectious mononucleosis [16] . Since rhesus LCV is considered one of the best animal models for EBV infection , we compared rhesus LCV soluble gp350 with VRPs expressing gp350 or VRPs expressing a combination of gp350 , EBNA-3A and EBNA-3B . Animals received three doses of the vaccines at 0 , 4 , and 12 weeks . We found that rhesus LCV soluble gp350 induced better protection against challenge virus than VRPs expressing a combination of gp350 , EBNA-3A and EBNA-3B . Animals vaccinated with soluble gp350 produced the highest levels of antibody to the glycoprotein and these levels were higher than those seen in monkeys naturally infected with rhesus LCV . Prior studies have shown that antibody to gp350 is likely the predominant component of neutralizing antibody to EBV [40] , [41] , [42] . In addition gp350 induces antibody-dependent cellular cytotoxicity which may also be important in controlling EBV infection [43] . Animals vaccinated with VRP expressing gp350 , or VRPs expressing gp350 , EBNA-3A , and EBNA-3B developed lower levels of antibody to gp350 and had less protection against acute infection than animals that received soluble gp350 . Thus , the high levels of antibody to gp350 are likely important for protection against acute infection with rhesus LCV . We compared soluble rhesus LCV gp350 with VRPs expressing gp350 with the expectation that expression of the viral glycoprotein in cells infected with VRPs might enhance the immunogenicity of gp350 beyond its ability to induce antibody . Animal studies have shown that neutralizing antibody to gp350 alone does not always correlate with protection from disease . When cottontop tamarins were vaccinated with replication-defective adenovirus expressing gp350 , non-neutralizing antibody to gp350 was induced , but the animals were protected against lymphoma [7] . In contrast , when cottontop tamarins were vaccinated with gp350 in liposomes , high titers of neutralizing antibodies were induced , but the animals were not always protected from lymphoma [44] . These studies showed protection from development of lymphoma , rather than protection from infection . Immunization of common marmosets with gp350 in alum resulted in neutralizing antibodies in some animals , but protection from infection ( defined by absence of seroconversion after challenge ) did not correlate with the presence of neutralizing antibodies [45] . Somewhat surprisingly we found that rhesus LCV soluble gp350 induced better protection against challenge virus than VRP expressing gp350 . Animals vaccinated with VRP expressing gp350 had antibody to the glycoprotein at levels comparable to animals naturally infected with rhesus LCV; however , the levels were significantly lower than in animals vaccinated with soluble gp350 . Animals vaccinated with alphavirus VRPs expressing EBNA-3A and EBNA-3B developed CD4 and CD8 cell responses to these proteins , while those vaccinated with VRPs expressing gp350 did not have detectable cellular responses to the glycoprotein . It is possible that the different methods used to present these antigens ( LCLs naturally expressing EBNA-3A and EBNA-3B versus cells infected with MVA expressing gp350 ) could be responsible for these differences . Alphavirus VRPs target dendritic cells , which are highly efficient antigen presenting cells , and are effective for inducing cellular immunity [46] . Prior studies in humans show that EBV EBNA-3A , EBNA-3B , and EBNA-3C are the main targets of CD8 T cells in humans , while EBV EBNA-1 is the principal target of CD4 T cells ( reviewed in [12] ) . While EBV gp350-specific CD8 T cells have been detected in patients during infectious mononucleosis [47] and gp350-specific CD4 T cells have been detected in healthy EBV carriers [48] , [49] , the level of these T cells has not been quantified relative to those against EBNA-3 . In general , the level of T responses to structural proteins is generally lower than that to latent proteins in healthy EBV carriers ( reviewed in [12] ) . After challenge of animals with rhesus LCV , animals vaccinated with soluble rhesus LCV gp350 had the best level of protection based on levels of rhesus LCV DNA or RNA in the blood and lower rates of seroconversion . While animals that received VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B had the next best level of protection from challenge and might have better protection from reactivation , than those receiving the other vaccine candidates , we could not test for protection against reactivation with the small number of animals in the current study . Although soluble gp350 induced the highest levels of antibody to gp350 and the best protection from acute infection , addition of potent EBV-specific T cell responses in combination with high levels of antibody might enhance the effectiveness of an EBV vaccine . Although an ideal vaccine would protect from infection with EBV , a vaccine that reduces the EBV DNA load might also be useful . The EBV DNA load is a predictor for development of certain EBV-associated malignancies [50] . EBV DNA is increased in the blood of transplant recipients prior to the development of EBV post-transplant lymphoproliferative disease [51] , and rituximab which lowers the viral load in the blood likely reduces the rate of post-transplant lymphoproliferative disease [52] . Patients with primary EBV infection after transplantation have high viral loads , and a 24-fold increased risk of post-transplant lymphoproliferative disease compared with seropositive transplant recipients [53] . Similarly , patients with HIV who progressed to B cell lymphoma had elevated levels of EBV in PBMCs and the level increased several months before developing lymphoma [54] . In order to determine if our vaccine reduced the level of rhesus LCV DNA in the blood , we developed a more sensitive assay for detection of viral DNA . With this assay we found that animals vaccinated with soluble gp350 that became infected with rhesus LCV after challenge had lower levels of rhesus LCV DNA in PBMCs at 23 and 34 months compared with animals that received vaccine control ( PBS ) . Taken together these finding suggest that an EBV vaccine that reduces the viral load after infection might also reduce the risk for development of certain EBV-associated malignancies . In summary , our findings indicate that a subunit vaccine that induces primarily humoral , rather than cellular immunity can result in a low virus load in animals that develop breakthrough infection after challenge with wild-type virus . At 23 months after challenge , animals vaccinated with soluble gp350 that became infected with rhesus LCV had ≥100-fold lower levels of rhesus LCV DNA in PBMCs than those vaccinated with VRP-gp350 , or the combination of VRP-gp350 , VRP-EBNA-3A , and VRP-EBNA-3B . Rhesus LCV DNA was still lower in PBMCs from animals vaccinated with soluble gp350 at 34 months after challenge compared with animals that received PBS . Thus , antibodies to a viral glycoprotein before challenge likely alter the primary infection in such a way as to result in a lower viral load years later . While the largest EBV subunit vaccine study performed to date showed that soluble gp350 protected against infectious mononucleosis , breakthrough infection still occurred; however , the authors did not report on the level of EBV DNA in the blood after breakthrough infection [11] . Based on our data , as well as observations of EBV DNA in PBMCs in certain malignancies , future EBV vaccine studies should test the ability of the vaccine to reduce viral loads in persons that become infected . | Epstein-Barr virus ( EBV ) is the primary cause of infectious mononucleosis and is associated with several cancers . Presently there is no licensed vaccine to prevent EBV diseases . Two types of candidate vaccines are under development; one involves immunization with the major glycoprotein ( gp350 ) on the outside of the virus , while the other involves vaccination with EBV proteins expressed during latency . We compared these two types of candidate vaccines in a rhesus monkey model of EBV and found that the gp350 vaccine induced better protection from infection . In addition , animals that received the rhesus EBV glycoprotein and became infected had a lower level of rhesus EBV DNA in the blood at 23 months after challenge than animals that received the rhesus EBV latency protein vaccine that subsequently were infected . Since levels of EBV DNA in the blood have been predictive for EBV lymphomas in transplant patients , the ability of rhesus EBV gp350 to reduce levels of rhesus EBV in the blood after infection suggests the EBV gp350 could have a role in reducing certain EBV-associated cancers . This is the first test of candidate vaccines in the rhesus monkey model of EBV and shows that this model should be useful in further evaluation of EBV vaccines . | [
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] | 2011 | Soluble Rhesus Lymphocryptovirus gp350 Protects against Infection and Reduces Viral Loads in Animals that Become Infected with Virus after Challenge |
Trichomonas vaginalis has an unusually large genome ( ∼160 Mb ) encoding ∼60 , 000 proteins . With the goal of beginning to understand why some Trichomonas genes are present in so many copies , we characterized here a family of ∼123 Trichomonas genes that encode transmembrane adenylyl cyclases ( TMACs ) . The large family of TMACs genes is the result of recent duplications of a small set of ancestral genes that appear to be unique to trichomonads . Duplicated TMAC genes are not closely associated with repetitive elements , and duplications of flanking sequences are rare . However , there is evidence for TMAC gene replacements by homologous recombination . A high percentage of TMAC genes ( ∼46% ) are pseudogenes , as they contain stop codons and/or frame shifts , or the genes are truncated . Numerous stop codons present in the genome project G3 strain are not present in orthologous genes of two other Trichomonas strains ( S1 and B7RC2 ) . Each TMAC is composed of a series of N-terminal transmembrane helices and a single C-terminal cyclase domain that has adenylyl cyclase activity . Multiple TMAC genes are transcribed by Trichomonas cloned by limiting dilution . We conclude that one reason for the unusually large genome of Trichomonas is the presence of unstable families of genes such as those encoding TMACs that are undergoing massive gene duplication and concomitant development of pseudogenes .
Trichomonas vaginalis , the most important sexually transmitted protist , causes vaginitis in women and urethritis in men [1]–[3] . In addition , Trichomonas increases the risk of HIV transmission , pelvic inflammatory disease , and spontaneous abortion [4] . Trichomonas lives under microaerophilic conditions in the lumen of the vagina by means of fermentation enzymes that are present in a modified mitochondrion called the hydrogenosome [5] . This organelle lacks enzymes of oxidative phosphorylation but makes hydrogen , and many of its fermentation enzymes were acquired from bacteria by horizontal gene transfer [6] . Trichomonas causes vaginitis when the protist adheres to the host epithelium and changes from a flagellated to an ameboid form [7] . Recent whole genome sequencing showed an ∼160-Mb Trichomonas genome encoding ∼60 , 000 proteins [8] . This genome is bigger than those of many other medically important protists but is characteristic of trichomonads . One reason for the large Trichomonas genome is the presence of hundreds of DNA transposons that include mariner elements and Mavericks [9] , [10] . Mavericks are of particular interest , because they are abundant , are ∼22-kb long , and so compose ∼30% of the genome . In addition , each Maverick contains 9 to 11 ORFs , such that Maverick proteins compose more than 50% of the predicted proteins of Trichomonas . Introns are rare and short , so the presence of large non-coding regions in Trichomonas genes cannot be an explanation for the large genome size [11] . We were interested in why some Trichomonas genes are present in so many copies and focused on one a large family of predicted transmembrane adenylyl cyclases ( TMACs ) . These TMACs are of particular note because ( 1 ) they have a predicted topology different from those of other metazoan and protist transmembrane cyclases , and they appear to have originated via gene duplication in Trichomonas and closely related species ( e . g . Tritrichomonas and Paratrichomonas; see below ) [12]–[15] , and ( 2 ) we discovered numerous in-frame stop codons and frame shifts in these genes , which made them a valuable dataset for exploring pseudogene evolution [16]–[20] . In addition to characterizing TMAC gene duplication and pseudogenes , we measured the mRNA levels of the TMAC genes and pseudogenes in trophozoites , and we determined whether recombinant cyclase domains from representative TMACs have adenylyl cyclase or guanylyl cyclase activity .
The genome of Trichomonas vaginalis strain G3 has been sequenced to ∼6× redundancy , so that it is likely that the majority of genes have been predicted [8] . The predicted proteins of Trichomonas present at the NCBI or at TrichDB [21] were searched using BLASTP and cyclase domains from TMACs of Dictyostelium discoideum , Homo sapiens , and Trypanosoma brucei , as well as those of the TMGCs of Homo sapiens [12]–[14] , [22] . We also used a full-length Trichomonas TMAC protein sequence ( TVAG_350120 ) and BLASTP to search the predicted proteins of Trichomonas or used this TMAC and TBLASTN to search Trichomonas scaffolds in the database at J . Craig Venter Institute ( JCVI ) or the WGS database at the NCBI . Intact TMAC genes , apparent TMAC pseudogenes ( see below ) , and partially sequenced TMAC genes due to assembly problems are listed in Data S1 . The full length TMAC protein sequence and TBLASN was also used to search EST sequences at the NCBI from Tritrichomonas foetus and Pentatrichomonas hominis . Transmembrane helices ( TMHs ) of TMACs were predicted using the Phobius combined transmembrane topology and signal peptide predictor [23] . Predicted proteins were examined for conserved domains using the CD search at the NCBI [24] . A representative set of 70 TMACs was aligned , and the conservation of sequences across the entire alignment was plotted using WebLogo [25] . Cyclase domains were aligned using MUSCLE ( Multiple Sequence Comparison by Log-Expectation ) [26] . The alignment was manually refined , and gaps were removed using BioEdit . The finished alignment was used to construct the phylogenetic tree using TREE-PUZZLE , a program to reconstruct phylogenetic trees from molecular sequence data by maximum likelihood method [27] . Additional trees were drawn using Parsimony ( Paup 4 . 0 ) or Bayesian methods [28] , [29] . As described above , phylogenetic trees were drawn using cyclase domains to determine the number of ancestors for the present set of TMAC genes . To determine whether duplication of segments of chromosomes contributed to the large number of copies of TMAC genes , we aligned whole scaffolds ( average size is ∼70 , 000 bp ) containing TMAC genes with each other [8] . In the rare instances where there was extensive overlap in flanking sequences , we discriminated sequences that contained open reading frames versus those that contained repetitive elements . We also looked among the flanking sequences ( as much as 40 kb on the two sides ) for repetitive families , mobile elements , and microsatellites , as defined in the NCBI annotation of the Trichomonas scaffolds [8] . We looked for examples of gene conversion using the set of 11 programs included in the Recombination Detection Program ( RDP ) [30] . We also used the program GeneConv to detect gene conversion [31] . Gene conversion events were called when the majority of the different programs identified the event . To identify TMAC pseudogenes , we took advantage of the absence of introns in any of the TMAC genes and the strict conservation of N-terminal TMHs and C-terminal cyclase domain in the predicted transmembrane cyclases [8] , [11] . Most of the TMAC pseudogenes were identified using the complete TMAC protein sequence ( TVAG_350120 ) and TBLASTN to search the scaffolds or contigs of Trichomonas at the JCVI or NCBI . Pseudogenes contained in-frame stop codons ( nonsense mutation ) and/or frame shifts that we could confirm by examining multiple independent primary sequence reads . In addition , we amplified the DNA around numerous of these stop codons by PCR to confirm their presence in the genome project G3 strain and to assess their occurrence in the B7RC2 and S1 strains . We also mapped the location of the various stop codons and frame shifts to determine whether any of them were present in more than one TMAC gene . This result would suggest that a pseudogene was duplicated . TMAC genes that were incomplete because they were at the edge of a contig were not considered pseudogenes . Additional pseudogenes were identified using the paralog and ortholog function at TrichDB [21] . Briefly , ∼175 predicted proteins of Trichomonas , many of which were given different names ( e . g . adenylate cyclase , guanylate cyclase , conserved hypothetical protein , etc . ) , were identified as paralogs or orthologs of the complete TMAC ( TVAG_350120 ) . TMAC pseudogenes were strongly suggested when these paralogs were present in an array of short proteins that spanned the length of a complete TMAC gene . In this case , the in-frame stop codons and/or frame shifts could be inferred by the prediction of multiple short proteins rather than a single full-length protein . Because stop codons and frame shifts in these pseudogenes identified using the paralog data base were not checked versus single reads , these pseudogenes are listed as putative in File S1 . While TMAC pseudogenes were identified by inspection , pseudogenes in cyclic nucleotide phosphodiesterases and other proteins in Table 1 were identified using a custom BLASTX and FASTX program that uses a protein template to look for in-frame stop codons or frame shifts in genomic DNA . In each case , we confirmed the stop codon or frame shift by examining multiple independent primary sequence reads in the GSS database at NCBI . The S1 strain of Trichomonas vaginalis , was received from Dr . B . N . Singh ( SUNY Health Science Center , Syracuse , New York ) , while the genome project G3 strain and B7RC2 strain were from Patricia Johnson ( UCLA ) . Trichomonas was grown at 37°C and sub-cultured every 24 hr in TYI-S-33 medium containing 10% adult bovine serum [32] . Trichomonas was diluted in medium to 102–3 cells/ml and cloned on plates containing 0 . 6% agarose [33] . Trichomonas was grown for seven days under anaerobic conditions . Individual clones were picked and sub-cultured in liquid medium in 48-well tissue culture plates , and RNA was isolated as described in the next section . Total Trichomonas RNA isolated using the RNAqueous-4PCR kit ( Ambion ) was treated with DNAse1 for 1 hr at 37°C . First strand cDNA synthesis was performed with RETROscript ( Ambion ) , using oligo dT primers for 1 hr at 42°C on ∼1 g RNA . PCR of Trichomonas cDNAs was performed using SYBR Green Master Mix with Rox from Roche Applied Science . Reverse transcriptase and template were separately omitted from negative controls , while primers to an actin gene ( TVAG_094140 ) were positive controls for RT-PCR . For primer sequences used in the RT-PCRs , please see Data S2 . Genomic DNA was isolated from one confluent flask ( ∼2×106 ) of Trichomonas , using the Wizard Genomic DNA purification kit ( Promega ) . PCR primers were designed to isolate representative DNAs encoding cyclase domains of two Trichomonas TMACs ( TVAG_013980 and TVAG_456550 ) . These PCR products were cloned into the pGEX-6p vector ( Amersham Biosciences ) [34] . Escherichia coli BL21 cells transformed with pGEX-6p were grown in LB medium and induced with 1 mM IPTG for 3 hrs at 30°C . Recombinant glutathione-S-transferase ( GST ) -cyclase fusion-proteins were purified with glutathione-agarose beads and released with 10 mM glutathione . Cyclase activities of GST-fusion enzymes were measured as described in [35] , and the colorimetric readout was measured according to manufacturer's instructions contained in adenosine 3′ , 5′-cyclic monophosphate ( cAMP ) and guanosine 3′ , 5′-cyclic monophosphate ( cGMP ) direct immunoassay kits ( Biovision Research products , CA ) . Each reaction contained 4 µg of GST-fusion protein and 2 mM ATP and 0 . 2 mM GTP when assaying for cAMP , or 2 mM GTP and 0 . 2 mM ATP when assaying for cGMP . A positive control was the manufacturer's enzyme , while a negative control was GST alone . Reactions were diluted and measured versus cAMP or cGMP standards according to manufacturer's instructions . Putative Trichomonas cyclic nucleotide phosphodiesterases were searched using Homo sapiens sequences [22] , [36] . Many of these putative phosphodiesterases were already predicted at TrichDB [21] . Cyclic nucleotide phosphodiesterase trees were made based on the amino acid sequences of conserved domain using the same methods as for the cyclase trees .
Using cyclase domains from TMACs of Dictyostelium discoideum , Homo sapiens , and Trypanosoma brucei , we identified ∼123 putative transmembrane cyclases in the predicted proteins of Trichomonas ( Data S1 ) [8] , [12]–[14] , [21] . The few Trichomonas cyclases that lack a set of TMHs appear to be truncated versions of the same gene family or to be present at the edge of a contig ( and so are incomplete because of assembly issues ) [8] . Each complete transmembrane cyclase is ∼1450 to ∼1700 amino acids long and contains a series of six or eight TMHs at the N-terminus ( Fig . S1 ) [23] . These TMHs are followed by an ∼300-aa domain that is relatively well conserved and predicted to be cytosolic . Four or six TMHs separate two extracellular domains . Finally , a microbial type 3 cyclase domain is present at the C-terminus in the cytosol [12] . Very similar cyclase domains are also present at the 3′ ends of ESTs of Tritrichomonas foetus and Paratrichomonas hominis ( data not shown ) . Because the 5′ ends of these ESTs were not sequenced , it is not possible to confirm that the entire TMAC genes are conserved in these other trichomonads . With the exception of the cyclase domain , there is no similarity between the predicted transmembrane cyclases of Trichomonas and the transmembrane cyclases of metazoans and protists unrelated to Trichomonas ( e . g . Trypanosoma or Plasmodium ) [12]–[15] . We conclude that all the duplications of the transmembrane cyclase genes likely occurred in trichomonads rather than in a common ancestor to all eukaryotes . We used phylogenetic methods to show that representative TMAC genes fall into two major groups of roughly equal size ( Fig . 1 ) . Trichomonas TMAC genes in A′ sub-group are more recently duplicated ( i . e . show shorter branch lengths ) than other members of group A and those of group B . While we used maximum likelihood methods to make the tree shown in Fig . 1 , similar trees were produced using parsimony and Bayesian treeing methods [28] , [29] . For numerous reasons , we think group A and group B TMACs are similar . The topology of groups A and B TMACs each matches that shown in Fig . 2A and Fig . S1 , and groups A and B TMACs have similar percentages of pseudogenes and similar patterns of expression by RT-PCR ( see below ) . In addition , recombinant cyclase domains from each group both have adenylyl cyclase activity ( see below ) . For comparison , we used the same phylogenetic methods to align 41 predicted cyclic nucleotide phosphodiesterases of Trichomonas , which are cytosolic enzymes that likely hydrolyze cAMP produced by TMACs ( Fig . S2 ) [8] , [21] , [36] . Many of the putative cyclic nucleotide phosphodiesterase genes of Trichomonas appear to be the result of recent duplication of a single ancestral gene ( group A in Fig . S2 ) . We wished to determine , if possible , the mechanism ( s ) for duplication of the TMAC genes . For the most part , there is only a single TMAC gene on a contig . Multiple TMAC genes are present on the same contig in just 12 of 90 instances , and the TMAC genes are tandemly repeated in just four cases . Other Trichomonas genes are not repeated in these contigs , so they do not resemble the subtelomeric regions of Plasmodium chromosomes , where more than one gene family is repeated [37] . There is strong evidence for a single gene conversion or a crossover event , in which both parent genes can be identified ( Fig . 3A ) [30] , [31] , [38] . In addition , there is indirect evidence for gene conversion , wherein the conserved cyclase domains of numerous TMAC pseudogenes have many fewer stop codons than non-conserved domains ( Fig . 2 and see next section ) . In about a dozen occasions , two TMAC genes each have the same flanking sequences that contain multiple open reading frames and short segments of repetitive DNA ( Fig . 3B ) . In the vast majority of cases , however , only the coding sequences of the TMAC genes are duplicated . There are no particular microsatellites , repetitive DNAs , or mobile elements closely associated with the duplicated TMAC genes ( Fig . S3 ) [8] . We identified a single occasion where a TMAC gene is interrupted by the insertion of a mobile element ( Fig . 3C ) . The duplication of Trichomonas cAMP phosphodiesterase genes also appears to be independent of flanking sequences or repetitive elements ( data not shown ) . A high percentage of Trichomonas TMAC genes ( ∼46% ) are pseudogenes , as they contain stop codons and/or frame shifts ( the vast majority ) or are truncated ( the minority ) ( Figs . 1 and 2 , Table 1 , and Data S1 ) . With one possible exception , these stop codons and frame shifts are unique , indicating that pseudogenes did not get duplicated . Conversely , the paucity of TMAC pseudogenes with many stop codons , frame shifts , and deletions suggests the possibility that older TMAC pseudogenes have been completely deleted from the Trichomonas genome . Similarly , the high percentage of synonymous versus non-synonymous mutations in the TMAC pseudogenes is consistent with the presence of recent purifying selection on these genes before they became pseudogenes [39] . The difference between the Poisson distribution and the actual distribution of the stop codons in TMAC genes suggests there is selection against the first in-frame stop , when protein-coding would be disturbed for the first time ( Fig . 2C ) . TMAC pseudogenes are frequent in both group A and group B . Stop codons in both groups A and B are less frequent in regions of the TMAC genes that encode the conserved domain of unknown function and cyclase domain ( Fig . 2D ) . A possible explanation is gene conversion , wherein a segment of a wild-type sequence replaces the corresponding segment of a homologous pseudogene sequence [30] , [31] . While the transmembrane cyclases have the highest percentage of pseudogenes ( 46% ) , 32% of ABC family transporters appear to be pseudogenes ( Table 1 ) . Other gene families have 16 to 18% pseudogenes ( cathepsin L-like cysteine peptidases , subtilisin-like serine proteases , and cyclic nucleotide phosphodiesterases ) , while numerous gene families have <8% pseudogenes ( Table 1 ) . We did not attempt to estimate the overall rate of pseudogenes in the 60 , 000 predicted protein-encoding genes of Trichomonas [8] , because many of these genes derive from Mavericks ( giant transposable elements ) [10] and we were unable to make protein models for many of the genes encoding hypothetical proteins . Many of the stop codons in the G3 TMAC genes ( 22 of 33 examined ) are present in orthologous genes of two other Trichomonas strains ( S1 and B7RC2 ) ( Fig . 4A ) . This result suggests that these TMAC pseudogenes were present in the common ancestor of all three Trichomonas strains . In contrast , five stop codons are only present in the G3 strain , suggesting these stop codons have arisen more recently ( Fig . 4B ) . Finally , there are six stop codons that are missing in either S1 or B7RC2 , so the order of their divergence from the common ancestor is not resolved ( Figs . 4C and 4D ) . Strict clonality , the presumed mode of reproduction in Trichomonas [40] , cannot explain this pattern of stop codons in the three lineages . Because there are so many different TMAC genes , we wondered whether multiple TMAC genes are expressed at the same time or whether a single TMAC gene is expressed at a time ( variant expression ) . Variant expression has been described for surface antigens of Giardia , Plasmodium , and Trypanosoma [37] , [41] , [42] . In Giardia and Plasmodium variant expression occurs in part because there are different adherence functions to the surface proteins . Similarly , Trichomonas TMACs may have different functions in signal transduction . To begin to answer this question , we prepared mRNAs from two clones of Trichomonas that were isolated on soft agar [33] . RT-PCRs showed that 4 of 5 TMAC genes tested are expressed by each Trichomonas clone ( Fig . 5A and Data S2 ) . We used qRT-PCR to show that the abundance of TMAC mRNAs isolated from an uncloned population of Trichomonas varies widely ( Fig . 5B ) . We found that there are greater differences between the expressions of mRNAs within a group ( A or B ) of TMACs than between groups A and B of TMACs . The expressions of 12 TMAC pseudogenes do not differ statistically from those of 53 intact TMAC genes . This result is consistent with the idea that nonsense mutations and frame shifts happened recently , so the promoters are still intact . Two cyclase domains from Trichomonas transmembrane cyclases , one arbitrarily chosen from group A ( TVAG_456550 ) and one from group B ( TVAG_013980 ) , were expressed as glutathione-S-transferase ( GST ) -fusion enzymes in bacteria and incubated with ATP or GTP [34] , [35] . Each recombinant Trichomonas cyclase showed adenylyl cyclase activity but no measurable guanylyl cyclase activity . For the group A cyclase , the Km for ATP is 520±10 µM , and the specific activity is 6 . 1×10−12 mol/min/µg . For the group B cyclase , the Km for ATP is 710±10 µM , and the specific activity is 8 . 5×10−12 mol/min/µg . We conclude that the Trichomonas transmembrane cyclases are adenylyl cyclases and have similar kinetics .
The very large genome of Trichomonas [8] may be partially explained by the presence of large , unstable families of genes such as those encoding TMACs that are undergoing massive gene duplication and concomitant development of pseudogenes ( Figs . 1 and 2 and Data S1 ) . Gene duplication and pseudogene formation both appear to be recent , as many TMAC genes are very similar to each other; numerous stop codons present in the genome project strain are not present in TMAC genes of other laboratory strains ( Fig . 4 ) ; and mRNAs for many pseudogenes are still abundant ( Fig . 5 ) [16]–[20] . Because we were unable to make good models for many of the unique Trichomonas proteins , we could not determine an overall rate of pseudogenes in Trichomonas . Based on the data in Table 1 , though , it appears that the rate of Trichomonas pseudogenes is at least 5% . In GenBank there are 1354 Trichomonas genes annotated as pseudogenes ( ∼2% of the total 60 , 000 genes predicted ) [8] . Trichomonas pseudogenes include 97 BspA genes , 42 kinases , 227 ankyrin repeat proteins , and 696 hypotheticals . However , only 5 of the 56 TMAC pseudogenes identified here are annotated as such in GenBank , suggesting the number of Trichomonas pseudogenes has been grossly underestimated . Regardless , the percentage of pseudogenes in Trichomonas is much greater than the percentages of pseudogenes ( <0 . 1% in each ) of protists with a similar microaerophilic life-style ( Giardia and Entamoeba ) [43] . Very high rates of pseudogenes , however , have been noted in proteins of Trypanosoma cruzi and Trypanosoma brucei that show variant expression [44] , [45] . Stop-codons of TMAC pseudogenes are surprisingly polymorphic ( Figs . 2 and 4 ) might be a useful target for studying the population biology of Trichomonas . The TMAC pseudogene sequences provide more precise information than methods that use restriction fragment length polymorphisms or pulse-field gel electrophoresis [46]–[48] . The TMAC pseudogene PCRs also demonstrate reassortment of polymorphic loci that cannot be explained by a strictly clonal reproduction of Trichomonas strains , as has been suggested [40] . While sexual reproduction ( consistent with reassortment of genetic markers ) has not been demonstrated in Trichomonas , the protist appears to have some of the conserved machinery for meiosis [8] , [49] . Recent studies of Giardia , another microaerophilic protist , suggest there is reassortment of markers consistent with sex [50] . The Trichomonas cAMP-mediated signal transduction system predicted here differs in two fundamental ways from those of metazoans and Dictyostelium [12] , [13] , [51] , [52] . First , the sequences the Trichomonas TMACs and cyclic nucleotide phosphodiesterases are unique . Second , Trichomonas TMACs and cyclic nucleotide phosphodiesterases are present in more copies than in metazoans , while predicted Trichomonas G protein-coupled receptors ( GPCRs ) are fewer than in metazoans ( data not shown ) [21] , [53] . While the large number of TMACs in Trichomonas may be explained by their rapid duplication and concomitant conversion to pseudogenes , we cannot easily explain the relative paucity of GPCRs in Trichomonas . One possible explanation for the low rate of GPCRs is that the heterotrimeric G-proteins are activated independent of GPCRs , as has been noted in Caenorhabditis elegans [54] . Finally , there is genetic and biochemical evidence for heterotrimeric G-proteins that likely interact with Trichomonas TMACs [55] , [56] . The absence of synteny around most TMAC genes ( Fig . 3 ) suggests gene duplication is not secondary to duplication of chromosomes or portions of chromosomes . The absence of repetitive elements around TMAC genes ( Fig . S3 ) suggests these elements are not involved or are so unstable that they have been lost . Because only coding sequences of most TMAC genes are duplicated , it is possible that retrotransposition is involved . However , the absence of introns in duplicated TMAC genes cannot be used as an argument for retrotransposition , because the vast majority of Trichomonas genes lack introns [8] , [11] . As many of the TMAC genes were recently duplicated , it was disappointing that we were unable to find a “smoking gun” that would provide the mechanism of duplication . In contrast , some of the 911 Trichomonas BspA genes are arranged in clusters with as many as 17 genes , consistent with several tandem duplication events [57] . The present studies cannot determine whether the TMAC pseudogenes are “junk” or have some function [16] . For example , by gene conversion ( for which there is both direct and indirect evidence in Trichomonas ) ( Figs . 2 and 3 ) , TMAC pseudogenes may be a source of alternative cyclase sequences for intact TMAC genes . Alternatively , TMAC pseudogene mRNAs ( Fig . 5 ) may be involved in regulating expression of intact TMAC genes . Most Trichomonas gene families do not have nearly the percentage of pseudogenes ( 46% ) observed in TMAC genes ( Table 1 ) . Indeed some rather large gene families ( e . g . Rab GTPases and small GTP-binding proteins ) have very few pseudogenes . While these large families of Trichomonas genes certainly contribute to the enormous size of the genome , we do not know why there are so many copies of these genes . The results of the RT-PCR ( Fig . 5 ) suggest that multiple TMAC genes are expressed at the same time . We cannot rule out the possibility that some organisms under some conditions differentially express TMAC mRNAs , as these assays were performed with mRNA from single colonies containing a few thousand Trichomonas rather than mRNA of a single Trichomonas . We also tested the majority of TMAC mRNAs on uncloned protists , and trichomonads were all growing under similar culture conditions . However , variant expression , where each Trichomonas parasite expresses a single TMAC gene at a given time , seems unlikely . Because there are so many TMAC genes , we assume that they play a role in pathogenesis [3] , [7] , [8] , [58] . However , we do not know what signals are being transduced by TMACs . The whole genome sequence of Trichomonas also predicts a set of histidine kinases like those of bacteria and fungi [8] , [59] but does not predict receptor-kinases that phosphorylate Ser , Thr , or Tyr ( like those of metazoans and Entamoeba ) [60] , [61] . In summary , while the bioinformatic and experimental methods here have generated numerous novel findings concerning gene duplication and pseudogene development in Trichomonas , we are a long way from relating these findings to pathogenesis . | Trichomonas vaginalis is the only medically important protist ( single-cell eukaryote ) that is sexually transmitted . The ∼160-Mb Trichomonas genome contains more predicted protein-encoding genes ( ∼60 , 000 ) than the human genome . To begin to understand why there are so many copies of some genes , we chose here to study a large family of genes encoding unique transmembrane cyclases . Our most important results include the following . More than 100 transmembrane cyclase genes do not result from chromosomal duplications , because for the most part only the coding regions of the genes , rather than flanking sequences , are duplicated . Almost half of the transmembrane cyclase genes are pseudogenes , and these pseudogenes are polymorphic among laboratory strains of Trichomonas . Messenger RNAs for numerous transmembrane cyclases are expressed simultaneously , and representative cyclase domains have adenylyl cyclase activity . In summary , the large family of Trichomonas genes encoding transmembrane adenylyl cyclases results from massive gene duplication and concomitant development of pseudogenes . | [
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] | 2010 | Trichomonas Transmembrane Cyclases Result from Massive Gene Duplication and Concomitant Development of Pseudogenes |
Formation of the 30S initiation complex ( 30S IC ) is an important checkpoint in regulation of gene expression . The selection of mRNA , correct start codon , and the initiator fMet-tRNAfMet requires the presence of three initiation factors ( IF1 , IF2 , IF3 ) of which IF3 and IF1 control the fidelity of the process , while IF2 recruits fMet-tRNAfMet . Here we present a cryo-EM reconstruction of the complete 30S IC , containing mRNA , fMet-tRNAfMet , IF1 , IF2 , and IF3 . In the 30S IC , IF2 contacts IF1 , the 30S subunit shoulder , and the CCA end of fMet-tRNAfMet , which occupies a novel P/I position ( P/I1 ) . The N-terminal domain of IF3 contacts the tRNA , whereas the C-terminal domain is bound to the platform of the 30S subunit . Binding of initiation factors and fMet-tRNAfMet induces a rotation of the head relative to the body of the 30S subunit , which is likely to prevail through 50S subunit joining until GTP hydrolysis and dissociation of IF2 take place . The structure provides insights into the mechanism of mRNA selection during translation initiation .
Initiation is the most regulated step of translation , at which the ribosome selects mRNAs according to their translation initiation regions ( TIR ) and establishes a correct reading frame . In bacteria , translation initiation is promoted by three initiation factors ( IF ) , IF1 , IF2 , and IF3 [1] , [2] . The small ribosomal subunit , 30S , is recruited to single-stranded mRNA regions at the TIR [3] . The Shine-Dalgarno sequence ( SD ) of the mRNA binds to the anti-Shine-Dalgarno sequence ( ASD ) of 16S rRNA in a cleft between the head and the platform at the back of the 30S subunit , whereas mRNA wraps in a groove that encircles the neck of the 30S subunit [4]– . IFs bind to the 30S subunit and work synergistically to accelerate initiation and ensure the choice of the correct start codon and initiator tRNA . IF1 is a one-domain compact protein which binds at the A site of the 30S subunit [7] . IF2 is a multi-domain GTPase that plays a major role in the recruitment of fMet-tRNAfMet to the 30S IC . The highly conserved C-terminal half of the protein ( CTD ) contains the GTP- and fMet-tRNAfMet-binding domains . The role of the less conserved N-terminus ( NTD ) is not clear , except that it may provide an additional anchor for IF2 on the 30S subunit [8] , [9] . Docking of fMet-tRNAfMet to the complex of the 30S subunit with mRNA and IFs completes the formation of the 30S IC [10] . The initiator tRNA is held in a characteristic position by two interactions: one involving the tRNA decoding stem which is buried in the P site of the 30S subunit , and the other between IF2 and the tRNA acceptor end [11] . The orientation of fMet-tRNAfMet in the complex differs from the canonical P-site position and was designated as P/I state [12] . IF2 and fMet-tRNAfMet provide a large interaction surface for binding the 50S subunit [11]–[13] . Formation of the 30S IC constitutes an important step for the selection of a favorable TIR . The ability of the ribosome to screen the TIR and to check for the fidelity of codon-anticodon interaction strongly depends on the presence of IF3 [14] . However , a structure of a 30S IC with IF3 is not available . IF3 consists of two domains , IF3C and IF3N , connected by a linker . The structures of the separate domains are known , but not of the full-length protein . The binding site for IF3 on the ribosome has been examined by a variety of biochemical techniques and by cryo-EM [15]–[18] . Overall , IF3 appears to bind to the platform of the 30S subunit , but the domain orientation of IF3 and exact binding contacts for each domain remain controversial . IF3 has several functions during translation initiation . It interferes with ribosomal subunit association [19] , affects the rates of tRNA association to and dissociation from the P site [20] , [21] , and ensures the fidelity of translation initiation [22]–[24] . Most importantly , IF3 is crucial for sensing the TIR of mRNA . In the absence of IF3 , the ribosome is largely unable to discriminate against unfavorable TIRs or incorrect initiation codons [14] , [25] . During the last years , structures of the 30S IC and 70S IC in the absence of IF3 [11] , [13] and of the 70S IC with density attributable to IF3 [12] have been reported . However , the structure of the complete 30S IC has remained elusive , probably due to intrinsic structural dynamics of the 30S subunits . Comparison of the 70S IC structures in the GTP- and GDP-bound states of IF2 revealed that in the pre-hydrolysis state the 30S subunit is found in a rotated ( ratcheted ) state relative to the 50S subunit . Consistent with this notion , single molecule FRET data have shown that docking of the 50S subunit to the 30S IC initially forms the 70S IC that is in the rotated conformation , and that GTP hydrolysis by IF2 promotes the rearrangement from the rotated to non-rotated state , thus enabling the ribosome to progress into the elongation phase [26] . Here , we present the cryo-EM reconstruction of the complete 30S IC containing all three IFs , mRNA , and fMet-tRNAfMet . We analyze the conformational changes of the 30S subunit induced by binding of IFs and tRNA and identify the position and orientation of IF3 and of the NTD of IF2 . Unlike the 30S IC obtained in the absence of IF3 [11] , the complete 30S IC studied in the present work reflects a translation initiation intermediate that is fully competent in the selection of the TIR and the correct start codon . The data show a rotation of the head of the 30S subunit upon formation of the 30S IC and suggest how the conformational changes of the 30S subunit could regulate 50S subunit joining and mRNA selection .
The 30S IC was prepared from E . coli 30S subunits , IF1 , IF2 , IF3 , fMet-tRNAfMet , mRNA , and a GTP analog , GDPNP ( Materials and Methods ) . The mRNA used for complex formation was m002 , which contains a 9-nt SD and a 5-nt spacer between the SD and the AUG start codon [27] . The extended SD provides strong interactions with the ASD in the 16S rRNA and drastically reduces the dissociation rate of IF3 [14]; m002 is similar to mRNAs with “enhanced” SD used in previous structural and biochemical studies [11]–[14] , [26] , [28] . An initial set of 48 , 000 particles resulted in a cryo-EM map that showed density which could be attributed to tRNA and IF2 on the surface of the 30S subunit ( Figure 1A ) at positions expected from a previous cryo-EM reconstruction of the 30S subunit in complex with initiator tRNA and IF2 [11] . However , the densities in our initial reconstruction did not reveal structural details of the tRNA or IF2 , suggesting heterogeneity of the sample due to differences in the occupancy of the 30S subunit with ligands and/or distinct conformational states . Separation into two classes by non-supervised maximum likelihood–based classification ( ML3D ) [29] , a recently developed tool that has been successfully applied to other ribosomal samples [30] , and independent image processing yielded the 3D maps shown in Figure 1B and 1C . The EM map for class 1 particles ( 23% of all particles ) showed no density for IFs or tRNA ( Figure 1B ) . Class 2 accounted for the majority ( 77% ) of the particles , and the corresponding 3D map revealed the presence of fMet-tRNAfMet and IFs ( Figure 1C ) . In the latter reconstruction , the putative densities for IF2 and tRNA could be seen at a higher density threshold compared with the map obtained from the total set of images . Structural details of the initiator tRNA from the X-ray structure [31] and of IF2 known from previous cryo-EM reconstructions [11]–[13] were clearly recognizable . Importantly , high-density threshold rendering revealed density for the SD-ASD duplex for both classes ( Figure S1 ) , indicating that class 1 represented 30S·mRNA complexes , rather than vacant 30S subunits . The resolutions estimated at 0 . 5/0 . 14 cut-off criteria in the Fourier shell correlation ( FSC ) were 16 . 8/14 Å ( total set of images ) , 21/17 Å ( class 1 , representing the 30S-mRNA complex ) , and 18 . 3/15 Å ( class 2 , representing the 30S IC ) . Classification into more classes or re-classification of class 2 did not result in improvement of resolution or identification of further conformational states . The limited resolution of the reconstructions may reflect inherent flexibility within the 30S IC induced by IF3-IF1 binding to the 30S subunit , as suggested by kinetic experiments [14] . After removing the density corresponding to the 30S subunit from the class 2 map ( Figure 1C ) , positions for tRNAs and IFs could be assigned , which were consistent with previous structural and biochemical studies ( Figure 1D and 1E ) . The main part of the resulting density could be attributed to the IF2·fMet-tRNAfMet complex placed along the shoulder and the cleft between the head and the body of the 30S subunit . The density for IF1 was not obvious within the 30S IC map , because its binding site was covered by the IF2 structure . However , fitting the crystal structure of IF1 bound to the 30S subunit [7] suggested that , in our map , IF1 can be embedded in the boundary between the 30S subunit and IF2 ( Figure 1D ) in the cleft formed by the 530 loop , helix 44 of 16S RNA , and ribosomal protein S12 [7] . The moderate resolution of the current 3D map and the lack of a complete atomic model for IF2 ( see below ) precluded an unambiguous definition of the boundary between IF1 and IF2 . Additional densities observed close to the initiator tRNA and the platform of the 30S subunit were assigned to IF3 ( Figure 1E ) . Thus , the cryoEM map of the 30S IC had densities attributable to all the elements of the complete complex ( see below for further details of fitting ) . To investigate whether the binding of initiation factors and fMet-tRNAfMet changed the conformation of the 30S subunit , we compared the structures of 30S subunits from classes 1 and 2 ( Figure 2 ) . When the maps were superimposed in such a way as to produce a maximal correlation of the 30S bodies , a clockwise rotation of the head toward the E site of the subunit in the 30S IC was found , compared to the 30S·mRNA complex ( Figure 2A ) . Measured by the position of the globular domain of ribosomal protein S13 as a reference point , the displacement was about 10 Å , corresponding to a rotation of the 30S head of approximately 4–5° . The direction and extent of the rotation in the 30S IC map are very similar to those found in the 70S IC ( Figure 2B ) [12] , as opposed to the conformation of the 30S subunit in the map attributed to the 30S·mRNA complex ( Figure 2C ) . The crystal structure of full-length E . coli IF2 is not available so far . To interpret the density attributed to IF2 and to identify the interactions of IF2 within the 30S IC , a domain homology model of IF2 was constructed ( Figure 3 ) . The C-terminal half of the protein ( CTD; G2 , G3 , C1 , C2 domains ) is highly conserved , whereas the N-terminus ( NTD; N1 , N2 , G1 domains ) varies in both amino acid composition and length and is absent in archaea [32] . The NTD is lacking in IF2/eIF5B from Methanobacterium thermoautotrophicum , the crystal structure of which is available [33] and which has been used for homology modeling of IF2 from T . thermophilus and E . coli [11] , [12] . To model the full-length E . coli IF2 , including the NTD which was not visualized previously , we combined several tertiary structures based on sequence homology ( Figure S2 ) . Rigid-body fitting was carried out independently for each of the modeled homologous domains . Subsequently , all of them were combined into a single structural model , which was fitted into the density of the 30S IC using MD-based flexible fitting ( Materials and Methods ) . In the MD simulations , secondary structure elements were conserved , and most of the large changes are related to long loops , especially in the NTD region . During the flexible fitting the cross-correlation between atomic coordinates and the EM map improved significantly ( from 0 . 87 to 0 . 95 estimated at the current resolution ) . The evolution of the fitting was also monitored by comparison with the initial IF2 model , and in the last cycles the RMSD values between the initial and flexible-fitted models reached a plateau of 8 Å . The overall arrangement of domains in the CTD of the present model of IF2 ( Figure 3 ) is in agreement with the previous models based on cryo-EM [11]–[13] , except for some differences that are discussed below . The expected volume of the modeled NTD domains ( irrespectively of the uncertainties of their modeled positions ) nicely accounted for the observed cryo-EM density indicating the location of the NTD at the 30S subunit surface . In the 30S IC , IF2 makes extensive contacts with 16S rRNA ( helices h5 and h14 ) , IF1 , and S12 ( Figure 4 ) . The model suggests that the contacts to IF1 and ribosomal protein S12 are made by the NTD of IF2 ( Figure 4A ) , in line with the ability of the isolated NTD of E . coli IF2 to bind to the 30S subunit [9] , [34] . Domain G3 of IF2 interacts with helix h5 of 16S rRNA and domain C1 of IF2 with helix h14 ( Figure 4B ) . This set of connections between IF2 and the 30S subunit is similar to the one described in the 70S IC [12] , whereas in the 30S IC lacking IF3 the contacts between IF2 and the 30S subunit were seen as small connections involving G3 domain from IF2 and helices h5 and h14 from the 16S rRNA [11] . Interactions with IF1 or S12 were not observed , probably due to the shortened NTD in IF2 from T . termophilus . The position of IF2 on the present 30S IC does not interfere with intersubunit bridges formed upon 50S subunit joining ( Figure 4C ) . The only bridge in the vicinity of IF2 is bridge B8 , which is established via helix h14 [35] . However , the residues involved in the connection with the 50S subunit ( highlighted orange in Figure 4B ) are on the face of the helix opposite to the IF2 contact , suggesting that this interaction of IF2 with the 30S IC does not interfere with the binding of the 50S subunit . Comparison of the fMet-tRNAfMet position in the present 30S IC reconstruction with crystal structures of ribosomes with tRNAs in A , P , and E sites ( Figure 5 ) reveals that the anticodon loop of fMet-tRNAfMet is positioned essentially in the P site of the 30S subunit , while the tRNA elbow in the 30S IC is tilted approximately 10° towards the E site ( Figure 5A ) . The tilt is similar to that in the P/I intermediate position for the initiator tRNA previously described in cryo-EM studies of the 30S IC and 70S IC [11] , [12] . However , unlike the P/I state visualized before , where the acceptor arm of the tRNA was shifted towards the E site [11] , [12] , the CCA end of fMet-tRNAfMet in our reconstruction is oriented towards the A site by its interaction with the C2 domain of IF2 ( Figure 5B ) . The differences in the orientation are clearly seen at the junction between fMet-tRNAfMet and the C2 domain of IF2 in the respective complexes ( Figures 5C , D and S3 ) . Notably , the present 30S IC and the 70S IC reported by [12] have been prepared in the presence of all three IFs and are from the same organism and yet the observed P/I positions are clearly distinct ( Figure 5C , D ) . To distinguish between the two states of the initiator tRNA , we name the state observed in our structure P/I1 . Compared to the P-site tRNA , the P/I1 position requires a rotation of the acceptor stem of the tRNA of around 15° ( Figure 5 ) . Apart from the density for the fMet-tRNAfMet·IF2 complex , a bilobed density was observed connecting the 30S platform and the elbow region of the fMet-tRNAfMet ( Figure 1E ) . Subtracting the volume occupied by the 30S·fMet-tRNAfMet·IF2 complex from the 30S IC reveals a density comprising two domains connected by a linker . According to its size and position , the density was attributable to IF3 ( Figure 6 ) . Rigid-body fitting of the atomic coordinates of the N- ( IF3N ) and C-terminal ( IF3C ) globular domains of IF3 from Geobacillus stearothermophilus [36] linked by an α-helix yielded a good fit of the EM map . For detailed fitting , IF3C from E . coli was used [37] . The density for IF3 in our cryo-EM map had to be visualized at a lower threshold compared to that of tRNA·IF2 ( Figure S4 ) , presumably due to incomplete occupancy of the 30S IC with IF3 . Further sorting of the class 2 particles did not separate distinct conformational/occupancy states . The two domains of IF3 were placed in such a way that IF3N contacted the elbow region of fMet-tRNAfMet , whereas IF3C was engaged in interactions with the 30S subunit . Cross-correlation measurements support the assignment of IF3 domains , since the alternative arrangement after domain swapping reduces the coefficient from 0 . 79 to 0 . 65 . IF3C was bound to the 790 loop of h24 of 16S rRNA in the vicinity of fMet-tRNAfMet; however , no direct contact between IF3C and tRNA was found ( Figure 6B ) . To examine whether the conformation of the 30S IC was suitable for 50S subunit joining , we aligned our 30S IC map with that of the 70S IC [12] . The position for IF3C in the 30S IC would impair the formation of bridge B2b at the interface between the ribosomal subunits ( Figure 7A ) , suggesting that in this arrangement IF3 in the complex physically impairs subunit joining . The fMet-tRNAfMet·IF2 complex provided an extensive surface area for the interaction with the 50S subunit ( Figure 7B ) . Binding of the 50S subunit would position the junction between IF2 and the CCA end of fMet-tRNAfMet close to the peptidyl transferase center . The region containing the sarcin-ricin loop ( SRL ) of 23S rRNA docked accurately in a cleft formed by domains G2 and C1 of IF2 . The GTPase domain , G2 , is oriented toward the SRL in the same way as domain I of EF-Tu [38]; the contact with the SRL is expected to be important for the GTPase activation of IF2 [39] .
In this study we present the cryo-EM reconstructions of the complete E . coli 30S IC containing initiator tRNA , mRNA , and all three initiator factors , as well as of the 30S subunit in the complex with the mRNA alone . The comparison of the 30S IC and the 30S·mRNA complex revealed that the 30S subunits were present in different configurations in the two complexes . In the 30S IC , the head of the 30S subunit was rotated with respect to the body , which can be attributed to the presence of IFs and fMet-tRNAfMet . Early site-directed crosslinking studies suggested that the formation of the SD-ASD complex in the absence of IFs places the mRNA in a “standby” position , from which it is shifted backward , closer to the P site upon binding of initiation factors , in particular IF3 , before the interaction with the large subunit takes place [40] . Similarly , crystal structures of the 70S complexes indicated that mRNA moves in the 3′→5′ direction with simultaneous clockwise rotation and lengthening of the SD duplex , bringing it into contact with ribosomal protein S2 [41] . The conformational change in the 30S IC induced by factor binding moves the head of the subunit in the right direction along the pathway of the mRNA and could promote the reported back-tracking of the messenger . At the current resolution , the conformation of the 30S subunit in the 30S IC is the same as in the 70S IC before GTP hydrolysis by IF2 , where the 30S subunit is found in a rotated ( ratcheted ) orientation with respect to the 50S subunit [12] when compared with a 70S post-initiation complex [42]; it should be noted that the origin of initiation components ( E . coli ) and the mRNAs ( extended SD sequence ) are very similar in the present study and in [12] , allowing for such detailed comparisons . The change in the 30S configuration is similar to that described within other 70S complexes along several steps of translation ( see Figure S5 ) [30] , [43]–[52] , but in the current study , the 30S conformation does not require the presence of the 50S subunit . Conformational changes of the 30S subunit were not described in the 30S initiation complex lacking IF3 [11] . This suggests that binding of IFs , in particular IF3 , could induce or stabilize the altered 30S subunit conformation . This conformational state of the 30S subunit appears to be retained upon 50S subunit joining until GTP hydrolysis and dissociation of IF2 [26] . The positions of fMet-tRNAfMet and IF2 in the complete 30S IC are similar to , but not identical with , those found in the previously reported reconstructions . As in all available structures , the anticodon stem of the tRNA is buried in the P site of the 30S subunit . However , the position of the tRNA CCA end differs in the reported P/I states . Comparing the 30S IC with the 70S IC from E . coli ( both with GDPNP ) suggests that , upon binding of the 50S subunit , the orientation of the CCA end of the tRNA changes from the P/I1 state , pointing towards the A site of the peptidyl transferase center ( 30S IC; this article ) , towards the P/I state , which resides between the P and E sites ( 70S IC; [12] ) . The position of the C2 domain of IF2 to which the fMet moiety of fMet-tRNAfMet is bound changes accordingly . A somewhat different P/I state was reported for the 30S IC from T . thermophilus formed with GTP in the absence of IF3 [11] , which may reflect the known effect of IF3 on the stability of the tRNA binding to the 30S IC . Alternatively , different P/I states may reflect the flexibility of the CCA end of the tRNA . It is possible that all described P/I positions can be sampled during the transition of the initiator tRNA toward the final P site and are important for discrimination of mRNAs with unfavorable TIR ( see below ) . The density in the cryo-EM map accounted for the entire IF2 and allowed us to map the domain contacts of the factor . As expected , the C2 domain of IF2 binds fMet-tRNAfMet at the single-stranded acceptor end and the fMet moiety of fMet-tRNAfMet [53] . Kinetic studies suggested that IF2 binds to the 30S subunit independent of the tRNA and recruits fMet-tRNAfMet to the 30S IC [10] . The NTD of IF2 contacts IF1 and S12 , the latter interaction in line with the biochemical evidence suggesting that the isolated NTD of E . coli IF2 can bind to the 30S subunit [9] , [34] . The ribosome-bound IF2 provides a large surface area for the joining of the 50S subunit , which would dock in a correct position for the activation of GTP hydrolysis in IF2 . IF1 binds at the A site of the 30S subunit [7] . On the ribosome , IF1 from E . coli interacts with IF2 , stabilizes IF2 binding [9] , and accelerates IF2-dependent fMet-tRNAfMet recruitment [1] , [2] . The present results suggest that the stimulatory effect of IF1 may be mediated by a direct contact with the NTD of IF2 . In contrast , thermophilic IF1 does not interact with the NTD of IF2 and does not augment IF2 functions [54]; consistently , no direct contact between IF1 and IF2 was found in the T . thermophilus 30S IC reconstruction [11] . The NTD region is significantly shorter in IF2 from T . thermophilus compared to E . coli , suggesting that the IF1–IF2 interaction is not universally conserved [54] . IF3 binds simultaneously to the fMet-tRNAfMet via IF3N and to the 30S subunit at the 790 loop of 16S rRNA via IF3C . The position of IF3C is consistent with hydroxyl radical probing data [17] which located the IF3C binding site close to helices h23 , h24 , and h45 at the 30S platform in the vicinity of the P site ( Figure 5B ) . Mutation of nucleotide 791 of h24 resulted in a 10-fold decrease of the affinity for IF3 [55] . The 790 loop of h24 , which by hydroxyl radical footprinting was located in the vicinity of IF3C [17] , was found in contact with IF3C in our reconstruction . However , IF3N in the present complex assumes an orientation that differs from previous models and contacts the elbow region of fMet-tRNAfMet . Notably , most of the footprinting probes from IF3N failed to cleave 16S rRNA , making the previous placement of the N domain uncertain [16] , [17] . In the cryo-EM reconstruction of the 70S IC [12] , an extra density at the platform of the 30S subunit was attributed to IF3 . Although no detailed modeling was carried out in that work , the position of IF3N , contacting the elbow region of the initiator tRNA , appears similar to our 30S IC . The position of IF3C in the 70S IC , filling the space between helix H69 of the 50S subunit and initiator tRNA and contacting the anticodon arm of fMet-tRNAfMet , seems shifted compared to that in the present 30S IC reconstruction , consistent with the necessity to remove IF3C from the binding site of bridge B2b . The different IF3 positions in the 30S IC and 70S IC may reflect the rearrangement of IF3C upon binding of the 50S subunit; further structural work on the 70S IC complexes will be necessary to substantiate this notion . In E . coli , mRNAs typically contain a SD sequence of 5 nt or less and a 5–9 nt spacer between the SD sequence and the initiation codon [56] . Variations within the SD region or in the distance between the SD sequence and the start codon strongly influence the efficiency of translation [57] , [58] . Kinetic evidence suggested that the regulation occurs at the step of the conversion of the 30S IC into the translating 70S IC , i . e . 50S subunit joining and dissociation of IF3 and IF1 [14] , and that the ribosomes discriminate against an mRNA with a strong SD sequence and a short spacer to the start codon , such as the one used in this study . The present structure suggests several potential mechanisms by which the rate of 50S subunit joining may be regulated . One very likely reason is the positioning of IF3C , which hinders the formation of the intersubunit bridge B2b to the 50S subunit . In this case , the rate-limiting step for 50S subunit association and IF3 release observed in kinetic experiments [14] may reflect an IF3 rearrangement—for example , the movement of IF3C away from the 790 loop of 16S rRNA , which would allow the bridge to form . Another possible mechanism for tuning 50S subunit joining is the orientation of the 30S subunit head relative to the body , which is rotated in the complex with IF3 , but not in the 30S complex without IF3 [11] . It is conceivable that the relative movement of the head of the 30S subunit alters the formation of bridges during 50S subunit joining , even though the body of the 30S subunit , IF2 , and initiator tRNA provide multiple docking interactions . Consistent with this notion , kinetic experiments suggest that the omission of IF3 restores the rapid 50S subunit joining even for 30S IC with an extended SD sequence [14] . Yet another reason for slow 50S subunit joining may be the particular orientation of IF2 and fMet-tRNAfMet observed in the present 30S IC compared to the complex without IF3 [11] on an mRNA with extended SD sequence; both orientations would be compatible with 50S subunit joining , but one of them might be more favorable . In this case , IF3 and IF1 may affect the positions of fMet-tRNAfMet and IF2 through their respective direct contacts . Apparently , a strong SD-ASD interaction stabilizes the 30S IC in the given conformation , which is maintained through 50S subunit joining [12] and the dissociation of IF1 and IF3 [13] , and relaxes only after GTP hydrolysis and dissociation of IF2 [26] . While most of the structural and functional data published so far pertain to mRNAs with a very strong SD [11]–[13] , [26] , [59] , it would be important in the future to obtain structures of initiation complexes with other , more physiological mRNAs .
30S subunits from E . coli , IFs , and fMet-tRNAfMet were prepared as described [14] . 30S subunits ( 0 . 1 µM ) , IF1 ( 0 . 3 µM ) , IF2α ( 0 . 2 µM ) , IF3 ( 0 . 3 µM ) , 002 mRNA ( 0 . 6 µM ) , fMet-tRNAfMet ( 0 . 6 µM ) , and GDPNP ( 0 . 5 mM ) were incubated at 37°C for 15 min in buffer A ( 50 mM Tris-HCl , pH 7 . 5 , 70 mM NH4Cl , 30 mM KCl , and 7 mM MgCl2 ) . Immediately before grid preparation and vitrification , the mixture was diluted to 30 nM 30S IC with buffer A containing 0 . 5 mM GDPNP . Thin carbon was floated onto Quantifoil grids ( Quantifoil Micro Tools GMBH , Jena , Germany ) . A 3 . 5-µl aliquot of the sample was placed on each grid . Grids were blotted , plunge-frozen in liquid ethane , and stored in liquid nitrogen until data collection . Low-dose images were taken on Kodak SO-163 films in a JEM-2200FS electron microscope ( JEOL ) operated at 200 kV at a magnification of 50 , 000 . Micrographs were scanned on a Z/I Photoscan scanner ( Zeiss ) with a step size of 14 µm , resulting in a final pixel size of 2 . 82 Å . A collection of 238 micrographs was assigned to one of 28 defocus groups ranging from 0 . 6 to 4 µm underfocus . The 3D reconstruction for the total set of images followed reference-based projection matching in Spire-Spider package [60] . The final resolution was determined by using the Fourier-shell correlation curve with a 0 . 5 cut-off . Non-supervised maximum-likelihood classification ( ML3D ) of the images was used from the Xmipp package [29] , [61] . Based on the maxima of the probability functions upon convergence of the likelihood optimization , the dataset was separated into two groups . The data accounting for each group were further refined separately , following the same procedure as described for the total number of particles . Rigid-body fitting of atomic coordinates was performed semi-automatically in Chimera [62] . Homology modeling was carried out using the Swiss-Model server [63] . Since for the entire E . coli IF2 no homolog of known structure was found , proteins and protein domains with the highest sequence similarity corresponding to domains and sub-domains of IF2 were searched by BLAST [64] . Parts of IF2 were modeled based on the respective homolog structure . Residues 1–50 were modeled using the N-terminal subdomain of IF2 from E . coli ( pdb code 1ND9; [65] ) ; residues 51–185 with the dynamin-like protein BDLP from Nostoc punctiforme ( pdb code 2J68; [66] ) ; residues 186–390 with the homologous region of aIF2 from S . solfataricus ( pdb code 3CW2; [67] ) ; residues 391–559 ( corresponding to the G2 domain ) and 560–672 ( corresponding to the G3 domain ) using the IF2/eIF5B from M . thermautotrophicus ( pdb code 1G7S; [33] ) ; residues 673–779 using the C1-subdomain of IF2 from B . stearothermophilus ( pdb code 1Z9B; [68] ) ; and residues 780–890 ( C2 domain ) based on the fMet-tRNAfMet-binding domain of IF2 from B . stearothermophilus ( pdb code 1D1N; [69] ) . Independent rigid-body fitting of each modeled region into the IF2 density map was performed using Chimera [62] . The initial relative positions of domains was taken as described in the crystal structure for aIF5B [33] , taking into account the interaction between domain C2 of IF2 and the initiator tRNA . Connecting residues were adjusted using the Swiss-PDB Viewer software [70] . Molecular dynamics-based flexible fitting of the assembled model was carried out by Flex-EM software [71] . The cryoEM maps for the 30S·mRNA complex ( class 1 ) and for the 30S IC ( class 2 ) have been deposited in the Electron Microscopy Data Bank , http://www . ebi . ac . uk/pdbe/emdb/ ( accession numbers 1770 and 1771 , respectively ) . | Translation is the process by which a ribosome converts the sequence of a messenger RNA ( mRNA ) —produced from a gene—into the sequence of amino acids that comprise a protein . Bacterial ribosomes each have one large and one small subunit: the 50S and 30S subunits . Initiation of translation entails selection of an mRNA , identification of the correct starting point from which to read its code , and engagement of the initial amino acid carrier ( tRNA ) . These events take place in the 30S subunit and require the presence of three initiation factors ( IF1 , IF2 , IF3 ) . Formation of this 30S initiation complex precedes joining with the 50S subunit to assemble the functional ribosome . By using a cryo-electron microscopy approach to visualize the structures without fixation or staining , we have determined the structure of a complete 30S initiation complex and identified the positions and orientations of the tRNA and all three initiation factors . We found that the presence of the initiation factors and tRNA induces rotation of the head relative to the body of the 30S subunit , which may be essential for rapid binding to the 50S subunit and for regulating selection of the mRNA . IF3 had not been seen previously in the context of the 30S structure and its visualization gives insight into a potential role in preventing association of the two ribosomal subunits . These findings are important for understanding how the interplay of elements during the early stages of translation selects the mRNA and regulates formation of functional ribosomes . | [
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] | 2011 | The Cryo-EM Structure of a Complete 30S Translation Initiation Complex from Escherichia coli |
Serological antibody levels are a sensitive marker of pathogen exposure , and advances in multiplex assays have created enormous potential for large-scale , integrated infectious disease surveillance . Most methods to analyze antibody measurements reduce quantitative antibody levels to seropositive and seronegative groups , but this can be difficult for many pathogens and may provide lower resolution information than quantitative levels . Analysis methods have predominantly maintained a single disease focus , yet integrated surveillance platforms would benefit from methodologies that work across diverse pathogens included in multiplex assays . We developed an approach to measure changes in transmission from quantitative antibody levels that can be applied to diverse pathogens of global importance . We compared age-dependent immunoglobulin G curves in repeated cross-sectional surveys between populations with differences in transmission for multiple pathogens , including: lymphatic filariasis ( Wuchereria bancrofti ) measured before and after mass drug administration on Mauke , Cook Islands , malaria ( Plasmodium falciparum ) before and after a combined insecticide and mass drug administration intervention in the Garki project , Nigeria , and enteric protozoans ( Cryptosporidium parvum , Giardia intestinalis , Entamoeba histolytica ) , bacteria ( enterotoxigenic Escherichia coli , Salmonella spp . ) , and viruses ( norovirus groups I and II ) in children living in Haiti and the USA . Age-dependent antibody curves fit with ensemble machine learning followed a characteristic shape across pathogens that aligned with predictions from basic mechanisms of humoral immunity . Differences in pathogen transmission led to shifts in fitted antibody curves that were remarkably consistent across pathogens , assays , and populations . Mean antibody levels correlated strongly with traditional measures of transmission intensity , such as the entomological inoculation rate for P . falciparum ( Spearman’s rho = 0 . 75 ) . In both high- and low transmission settings , mean antibody curves revealed changes in population mean antibody levels that were masked by seroprevalence measures because changes took place above or below the seropositivity cutoff . Age-dependent antibody curves and summary means provided a robust and sensitive measure of changes in transmission , with greatest sensitivity among young children . The method generalizes to pathogens that can be measured in high-throughput , multiplex serological assays , and scales to surveillance activities that require high spatiotemporal resolution . Our results suggest quantitative antibody levels will be particularly useful to measure differences in exposure for pathogens that elicit a transient antibody response or for monitoring populations with very high- or very low transmission , when seroprevalence is less informative . The approach represents a new opportunity to conduct integrated serological surveillance for neglected tropical diseases , malaria , and other infectious diseases with well-defined antigen targets .
There is large overlap in the distribution of global disease burdens attributable to neglected tropical diseases ( NTDs ) , malaria , enteric infections and under-vaccination . Despite nearly a decade of advocacy for integrated monitoring and control [1] , prevailing surveillance efforts maintain a single-disease focus , and the high cost of fielding surveys to collect specimens means that programs conduct surveillance infrequently or not at all . High throughput , multiplex antibody assays enable the simultaneous measurement of quantitative antibody responses to dozens of pathogens from a single blood spot [2] . When coupled with existing surveillance platforms , multiplex antibody assays could enable the global community to more quickly identify public health gaps , including: recrudescence of NTD or malaria transmission in elimination settings , stubborn areas of high transmission , emerging infectious diseases , and under-vaccination . Of particular interest are methods to analyze measurements collected in cross-sectional surveys because most large-scale global surveillance efforts use this design ( e . g . , immunization coverage surveys , malaria indicator surveys , transmission assessment surveys for NTD elimination programs , demographic and health surveys ) . A unique attribute of antibody measurements is that they provide an immunological record of an individual’s exposure or vaccination history , and thus integrate information over time [3] . Yet , the information contained in circulating antibodies varies greatly by pathogen and antibody measured , and it is this complexity that presents challenges to the use of antibody measurements for integrated surveillance . Most previous studies have reduced quantitative antibody measurements to seropositive and seronegative groups by choosing a cut point , and then have used models to estimate seroconversion rates from age-dependent seroprevalence as a measure of pathogen transmission [3 , 4] . The choice of seropositivity cut point can be ambiguous for many pathogens , as examples in this article will illustrate , and can vary widely in lower transmission settings depending on the reference population or statistical method used [5] . A second challenge in lower transmission settings is that seropositive individuals are extremely rare , and so accurate estimates of seroprevalence require large samples [6] . Conversely , in high transmission settings , seroprevalence can fail to capture the immune response from repeated infections where antibody levels increase following each exposure and wane over time [7 , 8] . Thus , analytical methods that use the quantitative response directly avoid the difficulty of defining cut points , accommodate complex , dynamic changes in antibody levels that can present difficulties to seroconversion models [4] , and may provide higher resolution information in very low- or very high transmission settings . To our knowledge there has not been a broad-based assessment for whether quantitative antibody measurements present an opportunity for integrated surveillance across diverse pathogens . Two recent contributions in the malaria literature proposed mathematical models to measure changes in transmission from quantitative antibody responses [8 , 9] . Both models require strong parametric assumptions such as constant rates of antibody acquisition and loss over different ages , or constant transmission over time , which may be difficult to justify for many pathogens of interest in an integrated surveillance platform . Our objective was to develop a general and parsimonious method to measure changes in infectious disease transmission from quantitative antibodies . We approached the problem from a different perspective than mathematical modeling , and instead focused on recent advances in machine learning and statistical estimation theory to measure differences in transmission within or between populations . We also aimed to assess whether the method could generalize across diverse pathogens that can be measured in multiplex assays , such as neglected tropical diseases , malaria , and enteric pathogens . A widely observed phenomenon across infectious diseases is that changes in pathogen transmission result in a “peak shift” of infection intensity by age: as transmission intensity declines in a population , the age-specific prevalence and intensity of infection tends to rise more slowly at younger ages and peak at lower overall levels [10] . We sought to extend this observation to measure changes in transmission using quantitative antibody levels rather than measures of patent infection-an approach suggested by mathematical models of parasite immunity [10 , 11] with empirical support in a comparison of populations with varying helminth transmission intensity [12] . We focused on a general mechanism of acquired immunity elicited by most infectious pathogens . Children are born with maternal immunoglobulin G ( IgG ) antibodies that wane over the first 3–6 months of life , and from ages 4–6 weeks begin to produce their own IgG antibodies in response to antigen exposure [13] . The aggregation of individual IgG responses generates a curve of population average IgG levels that rises in the first years of life until it plateaus at adult levels [14] . Transferred maternal immunity-a function of maternal immunologic memory-likely influences the magnitude of the population-average IgG curve’s intercept near birth [13] . Antigen exposure is needed to maintain antibodies in blood , either by stimulating the proliferation of memory B-cells to replenish short-lived plasma cells or by stimulating the production of non-germinal center short-lived plasma cells [14] . Antigen exposure induces rapid proliferation and differentiation of short-lived B-cells , with somatic hypermutation leading to increased affinity following each exposure . As transmission declines , population-average serum IgG levels should rise more slowly as the age of first infection increases and repeated exposures become infrequent . For pathogens that elicit antibody responses that wane over time , the number of long-lived antibody secreting cells should decline without recent antigen exposure [14] , which in turn should be reflected in a lower plateau of the age-dependent antibody curve . We therefore hypothesized that reduced pathogen transmission would cause pathogen-specific IgG antibody curves to increase more slowly with age and plateau at lower levels , and that quantifying changes in the curves would provide a robust and sensitive measure of changes in transmission within or between populations .
To test this hypothesis , we examined age-dependent antibody responses ( “age-antibody curves” ) to diverse pathogens in populations with likely differences in transmission intensity . We fit age-antibody curves with a data adaptive , ensemble machine learning algorithm that can include additional covariates to control for potential confounding [15] . The curves represent a predicted mean antibody level by age ( a ) for each exposure group ( x ) , which we denote E ( Ya , x ) in the statistical methods . We used the age-adjusted mean antibody response within each group ( x ) as a summary measure of transmission , denoted E ( Yx ) , and estimated differences between group means . For example , below we describe an analysis of age-antibody curves using antibody response to the Wuchereria bancrofti Wb123 antigen in a population before ( X = 0 ) and after ( X = 1 ) mass drug administration ( MDA ) . We estimated a separate curve in the population before E ( Ya , 0 ) and after E ( Ya , 1 ) MDA , and tested for differences between the curves by comparing summary mean Wb123 response between the two measurements , E ( Y1 ) —E ( Y0 ) , averaged over age and potentially other confounding covariates ( statistical methods include details ) . The age-adjusted mean antibody response equals the area under the age-antibody curve ( S1 Text ) . The approach thus integrates the steepness of the curve’s initial rise at young ages as well as its sustained magnitude at older ages , with lower transmission measured by reductions in group means . Comparing group means intuitively represents an average difference between groups across all points in the curves . If particular age ranges are of interest , such as young children , then the mean can be estimated over restricted regions of the age-antibody curve . Mauke , Cook Islands was endemic for W . bancrofti in decades past , and in 1987 there was an island-wide MDA of all individuals ≥5 years old with diethylcarbamazine . The present analysis included serum samples from two cross-sectional measurements of the permanent resident population; the first in 1975 ( N = 362 , approximately 58% of the population ) and the second in 1992 , 5 years after the island-wide MDA ( N = 553 , approximately 88% percent of the population ) [16] . Both studies preserved serum samples by freezing them in liquid nitrogen within hours of collection and storing them at -80°C . Serum samples were tested for IgG antibody levels to the Wb123 antigen using a Luciferase Immunoprecipitation System ( LIPS ) assay , as previously described in detail [17] . Data presented are in luminometer units from averaged duplicate samples . We re-analyzed data from the original assessment of the effect of the MDA campaign on Wb123 antibody levels [16] using the statistical methods described below . We estimated separate age-antibody curves in 1975 and 1992 . To make statistical comparisons between the curves , we estimated means for each survey year and differences between surveys , stratified by 5 year age group for ages ≤20 years old . For a subsample of 114 individuals who were measured in both 1975 and 1992 , we compared Wb123 antibody levels in subgroups defined by whether they had circulating antigen to adult W . bancrofti—an indication of active infection-at one or both time points . We plotted individual changes in Wb123 antibody levels to visualize antibody acquisition and loss in different subgroups . The Garki Project , led by the World Health Organization and the Government of Nigeria , included a comprehensive malaria intervention study that took place in 22 villages in the rural Garki District , Nigeria ( 1970–1976 ) [18] . We obtained publicly available study datasets for this analysis ( http://garkiproject . nd . edu ) . The intervention included a combination of insecticide spraying and mass drug administration of surfanene-pyrimethamine in 1972–1973 , along with targeted distribution of chloroquine to children <10 and self-reporting fever cases in the 1974–75 post-intervention period . The study documented large reductions in the proportion of individuals testing positive for Plasmodium falciparum infection by microscopy as a result of the intervention . In a subset of two control villages and six intervention villages , the study collected multiple serological measures that have been described in detail [18] . Briefly , the study collected serum from all members present in a village in eight rounds that alternated between wet and dry seasons . We limited the analysis to 4 , 774 specimens collected from individuals <20 years old because that age range captured nearly all of the change in the age-antibody curve ( median serum samples per round in each village: 74 , range: 19–158 ) . Serological survey rounds 1–2 took place in the wet and dry season before the intervention started , rounds 3–5 took place during the active intervention period at 20 , 50 , and 70 weeks after intervention initiation , and rounds 6–8 took place at 20 , 40 , and 90 weeks after the conclusion of intervention activities . The sixth measurement was collected in the intervention villages only . From each participant , finger prick blood samples were collected in two 0 . 4-ml heparinized Caraway tubes for immunological testing . Individuals contributed between 1 and 8 samples over the course of the study ( median = 3 ) . We focused on P . falciparum antibody response measured with the IgG indirect fluorescent antibody ( IFA ) test . We converted IFA titers to the log10 scale and then estimated mean IFA titre by age separately for intervention and control villages in each measurement round . We compared curves using the difference between age-adjusted means . We repeated the analysis at the village level to make separate comparisons of each individual intervention village against control to examine curves and measures of transmission at smaller spatial scale . The study collected extensive wet season entomological measurements in three of the villages with serological monitoring . The co-located entomological and serological measurements enabled us to compare village-level mean antibody levels and seroprevalence with the wet season entomological inoculation rate ( EIR ) as the transmission intensity changed in the intervention villages . EIR estimates from Table 4 of the original study [18] were used in the analysis . The EIR represents the number of sporozoite positive bites per person over each wet season , and was estimated by multiplying the man-biting rate by the sporozoite positive rate in night-bite collections . Night-bite collections were conducted every 2 weeks using 2 indoor and 2 outdoor stations per village , with 2 human bait collectors in each station throughout the night . We estimated village level mean IFA antibody titers restricted to serum samples collected during the same periods of EIR monitoring , and we measured the association between village level mean antibody titers and the EIR with the Spearman rank correlation coefficient . After completing the primary analysis that estimated age-antibody curves by survey for control and intervention villages , we noticed a reduction in age-adjusted geometric mean antibody titers between wet and dry survey rounds 1–2 . We followed-up this observation with a secondary analysis , restricted to the control villages , that estimated age-antibody curves separately by survey round , which corresponded to wet and dry seasons: 1971 wet ( survey 1 ) , 1972 dry ( survey 2 ) , 1972 wet ( survey 3 ) , 1973 dry ( survey 4 ) , and 1973 wet ( survey 5 ) [18] . Control villages were not measured in survey 6 , and surveys 7–8 took place in the 1974 and 1975 wet seasons; we excluded surveys 7–8 from the secondary analysis because we were interested in comparing transmission in adjacent wet and dry seasons . Our analysis of enteric pathogen antibody measurements relied on two existing data sources . Haiti samples were collected from a longitudinal cohort of 142 children , enrolled between the ages of 1 month and 6 years on a rolling basis from 1991–1999 to monitor lymphatic filariasis , and the selection of samples from the Haiti cohort has been described in detail [19] . Children were followed up to 9 years ( median 5 years ) and each child was measured approximately once per year . At each measurement , the study collected finger prick blood samples . The multiplex bead assay techniques and antibody results for the Cryptosporidium parvum recombinant 17-kDa and 27-kDa antigens [20] , the VSP-5 fragment of Giardia intestinalis variant-specific surface protein 42e [21] , and the Entamoeba histolytica lectin adhesion molecule ( LecA ) [22] have been described [19 , 23] . Enterotoxigenic Escherichia coli ( ETEC ) heat labile toxin β subunit [24] and lipopolysaccharide ( LPS ) from Salmonella enterica serotype Typhimurium ( Group B ) [25] were purchased from Sigma-Aldrich ( St . Louis , MO ) . Purified recombinant norovirus GI . 4 and GII . 4 New Orleans [26] virus-like particles from a baculovirus expression system [27] were kindly provided by J . Vinje and V . Costantini ( CDC , Atlanta , GA ) . Proteins and LPS were coupled to SeroMap beads ( Luminex Corp . Austin , TX ) at 120 μg per 12 . 5 x 106 beads in phosphate-buffered saline at pH 7 . 2 and were included in the multiplex bead assays previously described [19] . As part of a serologic study in the United States ( USA ) [28] , our lab ( JWP , PJL ) had banked 86 anonymous blood lead samples collected in 1999 from children ages 0–6 years . The USA samples were tested contemporaneously with the Haiti longitudinal cohort using the same techniques and bead preparations [19] . We used these anonymous samples from the USA to compare antibody curves with the Haitian children . For each enteric antibody , we estimated separate age-antibody curves in the USA and Haiti using all measurements collected at ages <5 . 5 years ( ages of overlap between the sample sets ) . We then estimated geometric means for each population and differences between means as described in the statistical methods . A cross-sectional survey measures an individual’s quantitative antibody level ( Y ) , age ( A ) , and other characteristics ( W ) . Many surveillance efforts are also interested in differences in antibody levels by one or more exposures ( X ) , which could be confounded by A and W . We assumed the observed data O = ( Y , A , W , X ) ~ P0 arose from a simple causal model ( S2 Text includes additional details ) : W = fW ( UW ) ; A = fA ( UA ) ; X = fX ( A , W , UX ) ; Y = fY ( X , A , W , UY ) . Study protocols for Mauke were approved by the government of the Cook Islands and the NIAID Institutional Review Board , and all adult subjects provided written informed consent . Consent for children was obtained by verbal assent as well as written consent from legal guardians . The Haiti study protocol was reviewed and approved by the Centers for Disease Control and Prevention’s Institutional Review Board and the Ethical Committee of St . Croix Hospital ( Leogane , Haiti ) and all subjects provided verbal consent . Human subjects review boards approved a verbal consent process because the study communities had low literacy rates . Mothers provided consent for young children , and children 7 years or older provided assent .
There was a distinct shift in the W . bancrofti Wb123 age-antibody curve before and five years after a single diethylcarbamazine MDA ( Fig 1a ) , and differences between curves show more gradual antibody acquisition with age in the post-MDA measurement ( Fig 1b ) . As previously noted [16] , mean Wb123 antibody levels declined in individuals who tested positive for circulating filarial antigen before MDA ( a sign of active infection ) but had no detectable circulating antigen post-MDA , as well as among those who tested negative for circulating antigen at both time points ( Fig 1c ) . Together , these results show that slower antibody acquisition combined with antibody loss , presumably a reflection of lowered transmission potential post-MDA , underlie the curve shift . Seroprevalence estimates for Wb123 followed a similar pattern as the quantitative antibody response ( S1 Fig ) . A caveat of the Wb123 seroprevalence analysis was that the seropositivity cutoff , chosen to have near perfect sensitivity and specificity with respect to controls [17] , fell in the center of the Wb123 distribution in the post-MDA measurement ( lower transmission ) ( S1 Fig ) . This observation makes it more difficult to argue that there were two distinct seropositive and seronegative populations-an assumption avoided when relying directly on quantitative antibody levels . Compared to control villages , there was a consistent shift in P . falciparum age-antibody curves with increased length of the insecticide spraying and MDA intervention in the Garki project ( Fig 2a ) . During the active intervention period , children in intervention villages exhibited a sharp drop in antibody levels from birth and a more gradual increase in antibody levels compared with children in control villages . Mean IFA titers demonstrated group comparability before intervention , reduced transmission during intervention , and a transmission resurgence after the intervention period ( Fig 2a ) —a pattern that corresponded closely with rates of patent parasitemia measured in the original study [18] . Age-dependent seroprevalence curves followed a similar pattern to the quantitative antibody results , but changes due to intervention were less pronounced because reductions in seroprevalence were only detectable among children < 5 years old ( Fig 2b ) . When estimated at the finer resolution , village level rather than in aggregate , mean antibody titers more clearly distinguished intervention and control villages compared with seroprevalence ( S2 Fig ) . Village level mean antibody titers correlated strongly with wet season EIR ( Spearman’s ρ = 0 . 75 ) and with seroprevalence ( Spearman’s ρ = 0 . 93 , Fig 3 ) . Malaria transmission was highly seasonal during the study , with more intense vector transmission and incident infections in the wet seasons [18] . In a secondary analysis , we restricted the population to control villages and fit age-antibody curves separately by survey rounds 1–5 , which corresponded to sequential wet and dry seasons . We observed a distinct shift in the age-antibody curve , consistent with lower transmission in the dry season , but only among children <5 years old; older children exhibited far less seasonal variation in mean IFA antibody titers compared with children <5 years ( Fig 4 ) . Age-antibody curves for IgG antibody responses to protozoan , bacterial , and viral enteric pathogens were consistent with lower levels of enteric pathogen transmission in the USA ( Fig 5 ) . The Haiti and USA populations likely illustrate enteric antibody curves near the bounds of high and low transmission environments , and show that as transmission declines the curves flatten . The results illustrate both the consistency of the general pattern across diverse taxa as well as the facility with which the analysis method generalizes to multiplex applications where numerous antibodies can be measured from a single blood spot . In most cases , enteric pathogen antibody distributions did not show obvious seropositive and seronegative subpopulations , and seropositivity cutoff values varied when estimated using different sample sets ( S3 Fig ) . In most cases , seropositivity cutoffs using the Haiti specimens alone fell outside the observed range of the antibody distributions ( S3 Fig ) .
We have shown that diverse , pathogen-specific serum IgG levels follow a characteristic shape with increasing age , and that changes in transmission are reflected in a shift of the age-antibody curve that can be summarized by changes in mean antibody levels . Consistent with our hypothesis , reduced transmission produced age-antibody curves that rose more slowly and plateaued at lower levels . The generality and consistency of the age-antibody relationship across diverse infectious diseases , populations , and diagnostic platforms suggest that this simple , robust methodology constitutes a useful way to measure changes in transmission for pathogens with serum IgG antigen targets . Our results support the use of quantitative antibody levels to measure changes in pathogen transmission as a complement or alternative to seroprevalence and other metrics based on a binary response . For infections that generate lifelong immunity , a characteristic of many vaccine preventable diseases , seroprevalence provides information about population-level immunoprotection and information beyond the first exposure is lost . However , for infections that are partially or transiently immunizing , examples from this study illustrate that mapping a quantitative antibody measurement to seroprevalence can lose substantial information . For example , the Garki project analysis illustrated that in a high transmission setting , the intensive insecticide spraying and MDA intervention reduced P . falciparum antibody titer across ages 0–20 years , but reduced seroprevalence only among children <5 years ( Fig 2 ) . The reduction of antibody levels across a broader age range in the quantitative analysis was presumably caused by less immune system boosting in older individuals living in intervention villages-an effect missed when using seroprevalence . Conversely , in lower transmission settings where seropositive individuals are rare , quantitative antibody levels can still provide information about reduced exposure . Waning W . bancrofti Wb123 antibody levels among individuals in Mauke without circulating antigen ( Fig 1c ) provided another example for how quantitative responses could provide more information about gradations in exposure that are lost with binary , positive/negative assays . These findings are broadly consistent with recent comparisons of quantitative antibody and seroprevalence estimates in the malaria context [9] . Indeed , quantitative antibody levels could provide complementary , high resolution information alongside more traditional metrics of infection to identify heterogeneous transmission in populations-a recent example illustrated the value of using malarial antibody levels directly to identify transmission hotspots in Cambodia [35] , and similar applications could be possible for NTDs and other infectious diseases . Many pathogens whose infections elicit partial or waning immunity have complex immunology that results in a unimodal distribution of antibody levels in a population , which makes it difficult or impossible to identify distinct seropositive and seronegative groups . The W . bancrofti and enteric pathogen analyses provided many examples where seropositivity cutoffs either could not be estimated or fell in the center of unimodal ( rather than bimodal ) distributions ( S1 and S3 Figs ) . In those cases , a comparison based on mean antibody levels obviated the need to choose a cutoff . Mean antibody levels should require fewer observations to estimate precisely than seroprevalence since reducing a quantitative measure to a binary measure results in a theoretical loss of >36% of Fisher’s information [36] . A sample of 20 individuals is unlikely to provide accurate information about seroprevalence or seroconversion rates [6] , but could provide a reliable estimate of mean antibody levels-the village-level analyses in the Garki project showed that use of P . falciparum quantitative antibodies led to larger and more precise estimates of differences between control and intervention groups than seroprevalence when estimated in small samples ( S2 Fig ) . This could be a particular advantage for serological surveillance in population-based surveys where sampling clusters often include fewer than 30 people [37] , and our labs are currently working on more formal guidance for sampling designs based on quantitative antibody levels . The use of data-adaptive , ensemble machine learning to fit antibody curves and compare means has several strengths in the context of developing a generalized methodology for integrated surveillance . The approach is: implemented in open-source software , extremely flexible , easy to adjust for potential confounding covariates , minimally biased , and highly efficient [15 , 30 , 38] . Ensemble approaches have been successful in cases where no single model is likely to be correct across diverse applications-for example , cause of death classification in the Global Burden of Disease studies [39] , mortality prediction in intensive care units [40] , or predicting malaria incidence from diverse antibody panels [41] . An ensemble library can include a range of models or algorithms , and if simpler models perform better they will be upweighted in the estimation [15] . Previous statistical methods have used quantitative antibody levels to measure differences in pathogen transmission by estimating parameters such as infection rates [41–43] , seroconversion rates [3 , 4 , 44] , or antibody acquisition rates [8 , 9 , 44] . Incidence and seroconversion rates are epidemiologically useful , but to estimate them from quantitative antibody levels requires strong modeling assumptions , or well-characterized longitudinal cohorts that directly measure the parameter of interest to train models , or both . Measuring differences in transmission directly from antibody levels with age-antibody curves requires neither modeling assumptions nor well-characterized cohorts to train models or fit parameters . This could be an advantage for integrated surveillance platforms where pathogens vary greatly in their specific immunology and most lack detailed longitudinal cohorts to characterize their antibody infection profiles . The ensemble fits revealed consistent shifts in the age-antibody curve with lower transmission , but individual curves followed age-dependent patterns that varied by pathogen and setting . Data-adaptive , nonparametric algorithms tended to perform better than simpler models in terms of cross-validated R2 , but there was no member of the ensemble that performed best across all pathogens and transmission settings ( S4 Fig ) . We included in the ensemble library an antibody acquisition model developed for malaria [9] , but that particular model underperformed in comparison with more flexible algorithms such as smoothing splines ( S4 Fig ) . This result suggests it may be difficult to develop a single model that describes the full diversity of age-dependent antibody response across very different infectious diseases , and underscores the value of considering an ensemble approach for broad analyses envisioned through integrated surveillance . The specific antibody kinetics and the age range in which the curves are estimated will influence the sensitivity of this approach to detect changes in transmission . Curves fit using antibodies with shorter half-lives should theoretically exhibit shifts more quickly with changes in transmission . Microarray screening efforts to identify malarial antibodies with a range of half-lives [41] open the possibility for discovering antibodies with high sensitivity to measure changes in transmission over short periods . With antibodies measured in multiplex , future work could develop methods to combine multiple antigens expressed by the same pathogen into a single quantitative response-a composite measure could prove more robust to differential immunogenicity arising from differences in host genetics . Our results show that serological surveillance among children captures the period of greatest change in the age-antibody curve , and analyses using children would be less susceptible to longer-term “cohort effects” that could influence the age-antibody relationship for antibodies with long half-lives [45] . Children are likely the most sensitive population to measure reductions in transmission: age-specific immunological profiles of malaria and vaccine response to diverse pathogens show that young children lose antibodies more quickly than adults because short-lived B cells predominate in young children , and antigen presentation and helper T-cell function increase with age [7 , 46 , 47] . Seasonal reductions in P . falciparum antibody titers among children <5 during the dry season when transmission was less intense were consistent with this observation ( Fig 4 ) . Surveillance activities that measure a very narrow age range , such as transmission assessment surveys to monitor lymphatic filariasis elimination programs ( which only measure children ages 6–7 years ) , cannot estimate a full age-antibody curve but the summary mean would still provide a robust measure of adjusted mean antibody levels to compare populations ( Fig 1b ) . Quantitative IgG antibody response integrates information about an individual’s pathogen exposure over time [3] - a characteristic of particular import for community-based surveillance of pathogens with low annual incidence and pathogens that cause many asymptomatic infections . Low incidence and asymptomatic presentation make community-based surveillance of changes in transmission difficult because either scenario requires very large numbers of specimens to be tested to identify incident infections . For example , Cryptosporidium parvum is implicated as a major pathogen of concern due to its contribution to hospitalized cases and prolonged episodes of diarrhea [48] , but community-based studies of Cryptosporidium sp . require the collection of thousands of stool specimens . Large studies are needed because , even in hyper-endemic settings , rates of incident infections fall below a single episode per person-year [49] , and because intermittent shedding of small numbers of oocysts in the stools of some infected individuals can make detection difficult [50] . We have illustrated that full age-antibody curves can be estimated with as few as 100–300 observations spread over different ages , which suggests they could be useful in the surveillance of pathogens with low annual incidence , or asymptomatic infections that clinical surveillance activities typically miss . There are two main limitations of the approach . First , mean antibody levels do not estimate a direct epidemiologic transmission parameter , such as the incidence or force of infection . Thus , while mean antibody levels provide a flexible , sensitive method to measure differences in transmission within- or between populations , they provide only indirect information about the relative importance or health burden of different pathogens . Using the same underlying statistical method with binary outcomes to estimate seroprevalence ( Fig 2 , S1 Fig ) partly addresses this limitation at the cost of losing some information , and our labs are actively working to extend these general methods to estimate a pathogen’s force of infection . A second limitation is that if a quantitative antibody assay has no global reference standard to translate arbitrary units into antibody titers , it will be difficult to make direct comparisons of mean antibody levels across different assays and studies . Until such reference standards exist , direct comparisons based on quantitative age-antibody curves and their summary means are only possible when comparing two or more surveys-or separate groups within a survey-for the same antibody response measured using the same assay . Assay standardization is a common challenge of any serological surveillance , so this limitation is shared by all methods that measure changes in transmission from antibody assays . The development of global reference standards for antibody assays used in infectious disease surveillance [51] , as currently exist for many vaccine-preventable diseases , would facilitate between-study comparisons . This study focused on IgG responses to lymphatic filariasis , malaria , and enteric pathogens measured in blood , but the method should apply to other immunoglobulin isotypes , other specimen types , and other infectious diseases . For example , similar shifts in IgE curves have been documented in populations with different soil transmitted helminth transmission [12] , salivary IgG and IgA norovirus assays have been developed [52] , and NTDs such as trachoma [53] , dengue [54] , and chikungunya [55] all have well-defined antigens that would be amenable to this methodology . Mean antibody response in defined geographic areas over time could translate directly to mapping activities used to target intervention programs and monitor transmission or immunization coverage . The ability to combine dozens of recombinant antigens into multiplex bead assays opens the possibility for high-throughput , integrated infectious disease surveillance that includes pathogens targeted for elimination such as NTDs and malaria alongside newly emerging pathogens , and vaccine preventable diseases [51] . The methods developed here provide a very general tool for integrated surveillance of antibody response from such data . | Global elimination strategies for infectious diseases like neglected tropical diseases and malaria rely on accurate estimates of pathogen transmission to target and evaluate control programs . Circulating antibody levels can be a sensitive measure of recent pathogen exposure , but no broadly applicable method exists to measure changes in transmission directly from quantitative antibody levels . We developed a novel method that applies recent advances in machine learning and data science to flexibly fit age-dependent antibody curves . Shifts in age-dependent antibody curves provided remarkably consistent , sensitive measures of transmission changes when evaluated across many globally important pathogens ( filarial worms , malaria , enteric infections ) . The method’s generality and performance in diverse applications demonstrate its broad potential for integrated serological surveillance of infectious diseases . | [
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"res... | 2017 | Measuring changes in transmission of neglected tropical diseases, malaria, and enteric pathogens from quantitative antibody levels |
We have taken an engineering approach to extending the lifespan of Caenorhabditis elegans . Aging stands out as a complex trait , because events that occur in old animals are not under strong natural selection . As a result , lifespan can be lengthened rationally using bioengineering to modulate gene expression or to add exogenous components . Here , we engineered longer lifespan by expressing genes from zebrafish encoding molecular functions not normally present in worms . Additionally , we extended lifespan by increasing the activity of four endogenous worm aging pathways . Next , we used a modular approach to extend lifespan by combining components . Finally , we used cell- and worm-based assays to analyze changes in cell physiology and as a rapid means to evaluate whether multi-component transgenic lines were likely to have extended longevity . Using engineering to add novel functions and to tune endogenous functions provides a new framework for lifespan extension that goes beyond the constraints of the worm genome .
Recent advances in genome technology and systems biology have made it possible to use engineering approaches to create new biological systems . Examples include the construction of a synthetic genetic oscillator in bacteria [1] , engineering quorum sensing ( the ability to respond to population density ) in yeast by integrating signaling components from the plant A . thaliana [2] , and creating an artificial bacterial cell using a genome consisting only of chemically-synthesized DNA [3] . Here , we expand bioengineering to a complex phenotype , longevity , in a multicellular animal , C . elegans . Despite being extremely complex , aging has at least three features that make it an attractive trait to improve by engineering . First , many pathways are involved in aging , such as stress response , repair of oxidative damage , protein quality control , developmental drift and innate immune response to pathogens [4]–[6] . The diverse nature of these aging pathways allows multiple avenues to engineer changes that may extend lifespan . Second , there is a great diversity in lifespan between different species , from two weeks for C . elegans , to 80 years for humans , to over 200 years for whales or clams [6]–[9] . This observation shows the remarkable dynamic range of over a thousand fold in lifespan encoded by different genomes . Third , most animals in the wild ( including C . elegans ) die from predation and disease rather than old age [10]–[11] . Thus , aging is not under the force of natural selection and represents the system-wide degeneration of processes due to evolutionary neglect . As a result , an engineering approach to slow aging seems more feasible than engineering improvements in other biological processes ( e . g . development ) because it may be easier to repair damaged processes in old animals rather than to improve highly-functional pathways in young animals . We chose to use C . elegans because it has a short lifespan of two weeks and a strong genetic toolkit making it a good platform for engineering longer lifespan . We first used a variety of approaches to identify genes with well-characterized roles in critical aging pathways that can be used as components to extend lifespan in transgenic worms . In particular , we were able to extend lifespan by expressing genes from zebrafish with cellular functions that are not normally found in worms . Having created a list of components that each extends lifespan singly , we then used a modular approach to increase lifespan by increments . We generated transgenic worms that contain an increasing number of aging components , and showed that there was a corresponding increase in lifespan . The framework and goal of our engineering approach to aging are fundamentally different from those in a study of the biology of aging . The main goal of our approach is to add components in order to extend the worm lifespan without a direct need to understand the mechanisms underlying this lifespan extension . For example , our modular approach aims to combine lifespan-extending components without aiming to determine whether these components act in the same or in different pathways . Additionally , in our engineering approach , we are not constrained to genes or pathways derived only from the worm genome . Rather , we can use novel molecular functions derived from long-lived organisms in order to extend worm lifespan .
Our goal is to use an engineering approach to generate C . elegans strains that are long-lived but that develop normally , are fertile , and are generally healthy . We began by accumulating a set of genes that individually extend lifespan . The first and easiest way to obtain an aging component is to select genes that have already been shown to extend lifespan when overexpressed; we generated expression vectors for four such genes ( hsf-1 , activated aakg-2 , sod-1 , daf-16 ) [12]–[15] . Transgenic worms were generated by microinjection of the gene of interest , a co-transformation marker ( unc-119 ( + ) ) and an aging biomarker ( sod-3::mCherry ) . We compared the lifespan for each of the transgenic strains to the lifespan from a control transgenic strain containing unc-119 ( + ) and sod-3::mCherry alone ( see Table S1 for complete list of components ) . In most cases , we generated two separate transgenic strains and measured their lifespan to verify reproducibility . Three of the genes ( hsf-1 , activated aakg-2 , sod-1 ) showed extended lifespan . hsf-1 encodes heat shock transcription factor that induces expression of many stress-resistance genes that can extend lifespan [16] . aakg-2 encodes the gamma subunit of AMP-activated protein kinase , a regulatory signaling molecule that responds to low ATP/AMP ratios and plays a key role in the stress response [17] . sod-1 encodes cytosolic superoxide dismutase that catalyzes the dismutation of superoxide radicals ( O2− ) into hydrogen peroxide [18] , which could reduce damage accumulation and extend lifespan . Consistent with previous results [12]–[14] , we found that overexpression of hsf-1 , activated aakg-2 and sod-1 extended lifespan by ∼30% , ∼45% , and 25% , respectively ( Figure 1a–1c , Table 1 , Table S3 ) . The second way to obtain an aging component is a candidate gene approach using C . elegans genes that act in known aging pathways . One such aging pathway is proteostasis , which counteracts damage accumulation to proteins by removing old , damaged proteins [19]–[20] . Increasing the rate of protein turnover should lower accumulation of damaged proteins and may extend lifespan [21] . We overexpressed a gene involved in chaperone-mediated autophagy ( lmp-2 ) [22] and a gene involved in proteostasis ( uba-1 ) . We found that that lmp-2 but not uba-1 resulted in extended lifespan compared to a control strain ( Figure 1d , Table 1 , Table S3 ) . lmp-2 is the ortholog of mammalian lamp2A , which encodes the lysosome-associated membrane protein type 2A receptor involved in chaperone-mediated autophagy that is responsible for the degradation of approximately 30% of cytosolic proteins in conditions of stress [23] . Overexpression of lamp2A in old mice results in lower intracellular accumulation of damaged proteins and improved organ function [22] . The third approach was expressing orthologous genes from either the zebrafish or the human genome that act in known aging pathways . We selected genes from zebrafish and humans as they have much longer lifespans than worms ( 4 years or 80 years vs 2 weeks , respectively ) [6] , [8] , [24] . We expected that vertebrate genes from aging pathways may be more efficient at delaying aging than orthologous genes from worms . Furthermore , zebrafish live at a similar range of temperatures as C . elegans and therefore zebrafish proteins should be capable of functioning at the ambient temperature used to grow worms ( 20°C ) . We selected four zebrafish and one human gene that are orthologous to C . elegans genes that act in known aging pathways: D . rerio sod1 , D . rerio msra , D . rerio foxo3A , D . rerio psmb1 and human aldh2 . We used upstream regions from C . elegans genes that were homologous to the zebrafish gene to drive expression of zebrafish cDNAs . For each construct , we generated transgenic worms and measured their lifespan under normal growth conditions . Of the five genes tested , only expression of D . rerio sod1 in transgenic worms resulted in longer lifespan than a control strain ( Figure 1e , Table 1 , Table S3 ) . D . rerio sod1 encodes superoxide dismutase and is the ortholog of C . elegans sod-1 . D . rerio sod1 and C . elegans sod-1 extended lifespan to a similar extent . The fourth approach to find an aging component was to select zebrafish genes with functions that are absent from the worm genome , and test whether adding them to worms can have a beneficial effect . One such function is mitochondrial uncoupling , which allows protons to leak into mitochondria without producing ATP [25] . According to the uncoupling to survive hypothesis , mitochondrial proton leakage may be beneficial because reduction of the proton motive force should reduce production of reactive oxygen species and thereby reduce damage accumulation during aging [25] . We chose to introduce mitochondrial uncoupling activity into worms using the zebrafish ucp2 gene , which encodes one of the mitochondrial uncoupling proteins . A similar experiment to add human ucp2 to Drosophila has been done previously , although it is not clear whether Drosophila has endogenous uncoupling activity and thus it is unclear if this previous experiment involves adding a new function to Drosophila [26] . The evolutionary tree for ucp genes shows that ucp-4 is the most ancient , contained in the genomes of all animals ( Figure S1a ) . Worms contain only a single related gene ( ucp-4 ) that encodes a protein that does not have mitochondrial uncoupling activity but rather is a transporter for succinate [27]–[28] . We generated transgenic worms that express zebrafish ucp2 from the worm ucp-4 promoter . As a control , we also generated worms that overexpress worm ucp-4 . We found that expression of zebrafish ucp2 extended the median lifespan of worms by about 40% ( Figure 1f , Table 1 , Table S3 ) . In contrast , overexpression of worm ucp-4 did not extend lifespan in two independent transgenic strains ( Figure 1f , Table S2 ) . Another example of new functionality added to the worm is the addition of vertebrate lysozyme activity . Worm lifespan is limited by mild pathogenic effects from E . coli , which is used as a standard food source [29]–[30] . All lysozymes have bacterial cell wall hydrolase activity that degrades peptidoglycans and thus are key players of the innate immune defense system providing protection against bacterial pathogens [31] . There are ten lysozyme genes in C . elegans , all belonging to a clade shared with microbes such as D . discoideum and E . histolytica ( Figure S1b ) . Vertebrates contain a large number of lysozyme genes , including a second clade that is derived solely from metazoans . Lysozyme genes from this clade contain a distinct anti-bacterial activity besides cell wall hydolase activity , which involves direct interaction of lysozyme with the bacterial cell membrane resulting in membrane leakage [31] . We generated transgenic worms expressing a zebrafish lysozyme gene from the second clade ( lyz ) . We found that zebrafish lyz extended the median lifespan of worms by about 30% ( Figure 1g , Table 1 , Table S3 ) . In contrast to D . rerio lyz , overexpression of worm lys-1 did not extend lifespan in two independent transgenic strains ( Figure 1g , Table S2 ) , consistent with previously published results [32] . We used various worm- and cell-based assays to validate that the aging components were expressed and to determine what changes in cell physiology and stress were induced . One reason for this is to provide evidence that the aging components extended lifespan by the expected mechanism . Since we tested each of the seven aging components with each of the assays , another reason is to also determine whether the aging component induced unexpected changes in cell physiology , which might indicate indirect activation of secondary aging pathways . A third reason is that the cellular assays could be used as a rapid and practical means to identify transgenic worms that are likely to have extended lifespan . To examine expression of the transgenes , we performed RT-PCR experiments using RNA extracted from fourth larval stage hermaphrodites . We found that the three vertebrate genes were expressed and that the four worm aging genes were over-expressed 5–18 fold in the transgenic strains compared to the endogenous gene in the control strain ( Table S4 ) . ATP production by the mitochondria is directly related to the production of reactive oxygen species and damage accumulation . Furthermore , ATP levels is thought to be associated with dietary restriction and the subsequent induction of protective pathways [33]–[34] . We measured ATP levels in extracts from fourth larval stage worms for each of the transgenic worms and found that worms expressing ucp2 or aakg-2 ( sta2 ) had lower overall levels of ATP compared to controls ( Figure 2a ) . Uncoupling protein would be expected to lower ATP levels by lowering the proton gradient in mitochondria , and thus lowering ATP production . Our results are consistent with previous experiments showing that vertebrate ucp2 genes have mitochondrial uncoupling activity when expressed in yeast and in flies [26] , [35]–[36] . Activation of AMPK is thought to increase catabolic pathways that generate ATP while decreasing ATP-consuming processes [17] . Thus , aakg-2 ( sta2 ) transgenic worms were expected to have increased ATP levels , opposite to the observed result . Lysozymes are anti-bacterial enzymes that could extend lifespan by combating bacterial pathogenicity . If lysozyme acts by combating mild pathogenicity stemming from E . coli , then it should not be able to extend lifespan when worms are grown on non-pathogenic B . subtilis . We determined the lifespan of control and lyz transgenic worms when grown on B . subtilis , and found no difference ( Figure 2b , Table S5 ) . This result strongly indicates that the mechanism of lifespan extension by zebrafish lysozyme involves combating mild pathogenicity from E . coli . Mild stress can extend lifespan by inducing protective pathways , a phenomenon referred to as hormesis . We tested for induction of the stress-responsive genes hsp-16 . 2 and hsp-16 . 11 using RT-PCR . We found that hsf-1 and aakg-2 ( sta2 ) transgenic worms showed higher expression of hsp-16 . 2 and hsp-16 . 11 than controls ( Figure 2c ) . hsf-1 but not aakg-2 ( sta2 ) was expected to induce expression of stress response genes . Next , we examined resistance to oxidative damage , which accumulates with age . One way to measure susceptibility to oxidative damage is to measure resistance to oxidative stress from paraquat , a chemical that generates superoxide ions . We found that C . elegans sod-1 , zebrafish sod1 , aakg-2 ( sta1 ) and hsf-1 conferred resistance to paraquat ( Table 2 ) . C . elegans sod-1 and zebrafish sod1 encode superoxide dismutase , an enzyme that reduces levels of oxygen free radicals that could directly counteract the effects of paraquat . aakg-2 ( sta2 ) and hsf-1 were not expected to affect oxidative damage directly . Another way to examine oxidative damage in worms is to directly detect oxidized residues in proteins from a whole worm extract in a Western blotting assay . In old worms , ucp2 and hsf-1 worms showed lower levels of oxidative damage compared to controls ( Figure 2d ) . ucp2 could decrease levels of oxidative damage by decreasing the proton motive force in mitochondria and reducing production of reactive oxygen species . Reduced levels of oxidative damage in hsf-1 transgenic worms was not anticipated . C . elegans sod-1 and D . rerio sod1 might be expected to reduce oxidative damage by reducing levels of reactive oxygen species , but neither showed an effect in this assay . Tallying the results from both assays for oxidative damage , we found expected evidence for reduced oxidative damage in three strains ( ucp2 and C . elegans sod-1 , D . rerio sod1 ) as well as unanticipated results for two strains ( hsf-1 and aakg-2 ( sta2 ) ) . We next examined activation of the FOXO transcription factor DAF-16 , which is a key regulator of aging [6] . Activation of DAF-16 can be measured by expression of a sod-3 reporter gene , which is one of its downstream targets [37] . We compared the level of expression of a sod-3::mCherry reporter in each of the seven long-lived worms to control worms in middle-aged hermaphrodites . We observed that aakg-2 ( sta2 ) and hsf-1 transgenic worms showed increased expression of sod-3::mCherry whereas zebrafish lyz showed decreased expression ( Figure 2e ) . aakg-2 encodes a kinase that phosphorylates DAF-16 , and would be expected to induce sod-3 expression [13] , [38] . For zebrafish lyz , one possibility is that lysozyme could reduce pathogenicity from E . coli used as food . Mild pathogenicity is known to activate DAF-16 and induce expression of the downstream target sod-3 . Lastly , lower ATP levels in ucp2 transgenic worms might extend lifespan using mechanisms shared by dietary restriction . If so , then worms that receive both dietary restriction and ucp2 might not live longer than worms receiving either condition alone . We found that dietary restriction alone extended median lifespan 18% , ucp2 alone extended lifespan 40% and that dietary restriction of ucp2 worms extended lifespan 40% compared to controls ( Figure 3a , Table S6 ) . Thus , dietary restriction did not further extend the lifespan of ucp2 worms that were fed normally . Table 3 provides a summary of the results from the seven cell- and worm-based assays using the seven transgenic lines expressing aging components . Except for lmp-2 , we obtained either direct or indirect evidence that each of the components was expressed and acting as expected . Furthermore , we also obtained evidence that some aging components produced changes in cell pathways that were indirect , providing evidence for cross-talk between different aging pathways in C . elegans . For example , the aakg-2 ( sta2 ) strain also shows induction of the two hsp protein chaperones that function in a protective stress pathway . According to the disposable soma theory , evolution of organisms in the wild requires a balance between allocation of metabolic resources for somatic maintenance or reproduction [10] . We tested whether there was a reduction in brood size in our engineered strains . Five transgenic strains with long lifespan had similar brood size and two showed a decrease in fertility compared to the control strains ( Figure S2 ) . These results show that the aging components can extend lifespan without reducing fertility . The seven aging components are individually capable of extending lifespan 25–50% . Because aging is a complex phenomenon affected by many pathways , our strategy to extend lifespan further was to use a modular approach by combining different aging components in a single transgenic strain to progressively extend lifespan . Additionally , we needed to develop a scheme to rapidly test whether or not combining genes in a new transgenic strain has a beneficial effect . This is because lifespan analysis requires four weeks for normal worms , and becomes even more tedious as lifespan increases . Our approach was to first use the cell- and worm-based assays described above to rapidly test whether worms expressing multiple aging components show a beneficial effect . Results showing that a multi-component strain shows protective changes in several pathways or stronger effects in a single aging pathway compared to single-components lines would be encouraging that it will live a long time . We started by generating two transgenic worm strains that each contain two components; one combination ( dual-1 ) contains aakg-2 ( sta2 ) and zebrafish ucp2 and the other combination ( dual-2 ) contains hsf-1 and zebrafish lyz ( Table 4 ) . These four components include two zebrafish genes that add new functionality to the worm ( ucp2 and lyz ) and two C . elegans genes that showed the largest increase in lifespan ( aakg-2 ( sta2 ) and hsf-1 ) . We used qRT-PCR to show that the components in the dual-module worms were expressed at levels equivalent to those from the single-module worms ( Table S4 ) . One reason for choosing the two components in dual-1 was that six assays were affected by either aakg-2 ( sta2 ) or ucp2 single-expressing worms , suggesting that this combination might be able to affect a large number of aging pathways . We analyzed dual-1 worms with all six assays to examine changes in cell physiology resulting from expression of the two aging components . For four assays ( ATP level , paraquat resistance , oxidative damage and sod-3 expression ) , the change relative to controls seen for the dual-1 strain was greater than the changes seen with either the aakg-2 ( sta2 ) or ucp2 single strains ( Figure 4a , 4b , 4d; Table 2 ) . For the dietary restriction assay , the results with dual-1 were similar to aakg-2 ( sta2 ) and ucp2 single-module worms; specifically , the lifespan of dual-1 was not further extended by dietary restriction ( Figure 3a–3c ) . For induction of the hsp genes , the dual-1 strain showed a smaller change than the aakg-2 ( sta2 ) strain by itself ( Figure 4c ) . Thus , the dual-1 strain showed changes in a greater number of assays than in either of the single-expressing lines , and oftentimes the changes were larger in magnitude . The dual-2 strain contains components ( hsf-1 and D . rerio lyz ) that showed changes in a total of five cell assays when expressed singly ( Table 3 ) . We tested dual-2 in four of the cell assays ( paraquat resistance , hsp induction , oxidative damage and sod-3 expression ) , and saw changes in the first three but not sod-3 expression with respect to control worms ( Figure 4a–4d , Table 2 ) . Paraquat resistance was greater in dual-2 than in each of the two single lines , but changes in hsp induction and oxidative damage relative to control worms was less or equivalent to the changes found in the hsf-1 and D . rerio lyz single-module lines ( Figure 4c , 4d; Table 2 ) . For sod-3 , hsf-1 increases but D . rerio lyz decreases its expression . In dual-2 , sod-3 expression is not different than in control worms suggesting that the opposite effects from these two genes cancel out ( Figure 4b ) . Finally , we measured the lifespan of the dual-expressing lines , and compared them to the lifespan of the single-expressing lines and controls . Two independent dual-1 lines had an increase in median lifespan of ∼80% , compared to an increase of 35–48% from either of the single components ( Figure 5a , Table 4 , Table S7 ) . Two independent dual-2 lines had an increase in median lifespan of ∼60% , compared to an increase of 28–37% from either of the single components alone ( Figure 5b , Table 4 , Table S7 ) . These results show that expression of two components can have an additive effect on lifespan . We extended the method by generating transgenic lines that each express three components . Specifically , triple-1 was generated based on dual-1 with the addition of D . rerio lyz , and thus contains aakg-2 ( sta2 ) , D . rerio ucp2 and D . rerio lyz . Triple-2 was based on dual-2 with the addition of aakg-2 ( sta2 ) , and thus contains hsf-1 , D . rerio lyz and aakg-2 ( sta2 ) . We examined expression of the components in the triple-expressing lines using qRT-PCR , and found that each of the genes in the triple-expressing lines was expressed at levels comparable to those from the corresponding single-expressing line ( Table S4 ) . For triple-1 and triple-2 , the three constituent aging components can affect each of the seven assays when expressed individually . We examined triple-1 and triple-2 using five of the cell assays ( ATP level , hsp induction , paraquat resistance , oxidative damage and sod-3 expression ) . Triple-1 and triple-2 showed changes in all five assays with respect to control worms ( Figure 4a–4d , Table 2 ) . We then measured the lifespan of the two triple-expressing lines and found that two independent triple-1 lines showed 97–105% increased lifespan and two independent triple-2 lines showed 84–92% increased lifespan ( Figure 5a , 5b; Table S7 ) . Finally , we generated a quadruple-expressing line containing four different components: hsf-1 , D . rerio lyz , aakg-2 ( sta2 ) and D . rerio ucp2 . We measured expression of these genes in the quadruple line to determine if their expression was as high in the quadruple-expressing as in the single-expressing lines . We found that hsf-1 was expressed at a comparable level but that aakg-2 ( sta2 ) , Dr ucp2 and Dr lyz , were expressed at about 50% of the previous levels ( Table S4 ) . The quadruple-expressing line showed changes in all five cell assays with respect to control worms , and was the most resistant to paraquat among all strains ( Table 2 and Figure 4a–4d ) . The lifespan of this quadruple-expressing line was increased 130% compared to control , and this result was verified in a second quadruple-expressing line ( 125% increased ) ( Figure 5c , Table S7 ) . Taken together , our results show a monotonic increase in lifespan: single-expressing lines ( 28–47% ) , double-expressing lines ( 57% to 84% ) , triple-expressing lines ( 84–105% ) and quadruple-expressing lines ( 125–130% ) . Besides living for the longest time of any of the engineered strains , the quadruple also has a long health span . Quadruple worms reach the L4 larval stage in approximately 72 hours , similar to control worms . Young adult quadruple worms appear and move normally when viewed using a dissecting microscope . For control worms , the median lifespan is about 18 days at which point most of the animals still alive show limited mobility . For quadruple worms , the median lifespan is 40 days but most of the surviving animals move well , similar to 14 day-old control worms ( Videos S1 , S2 , S3 ) . These observations indicate that we have extended the time that quadruple worms are mobile and healthy . This is important for lifespan engineering , as one would optimally want to extend the healthy portion over the morbid time at the end of life .
This paper uses an engineering approach to build healthy and long-lived worms . Our approach was to choose genes from well-studied aging pathways that can be used as components to extend lifespan , and then validate that they are active using a variety of molecular and cellular assays . In this initial study , we used four approaches to find seven aging components . In the first approach , three components ( hsf-1 , aakg-2 ( sta2 ) and sod-1 ) were obvious choices because they were previously known to extend lifespan when overexpressed in worms [12]–[14] . Future genetic studies of aging are likely to reveal many more genes that extend lifespan when overexpressed , each time providing a new aging component . Secondly , one component was chosen based on prediction from theory; specifically , lmp-2 was selected because it is involved in chaperone-mediated autophagy [23] . Overexpression of this component is expected to increase protein degradation , reduce steady-state levels of protein damage , and thereby extend lifespan . In this case , not only did we generate an aging component that can be used in our study , but we were also able to validate a prediction made from theory and thus provide support for the role of protein turnover in aging . In the third approach , we used orthologous genes from zebrafish rather than genes from C . elegans . We found D . rerio sod1 and C . elegans sod-1 extended lifespan to a similar extent . It will be interesting to continue to compare orthologous genes from D . rerio and C . elegans in order to determine whether there may be a systematic advantage to selecting genes from a longer-lived species . Lastly , we showed that we can extend lifespan by expressing new functions in C . elegans . The first function is mitochondrial uncoupling activity , which is absent from C . elegans [28] . Previous work has shown that human ucp2 extends the lifespan of D . melanogaster , although it is not clear whether this involves adding a new activity to flies because it is not known whether flies have an endogenous mitochondrial uncoupling activity [26] . The second novel function is vertebrate lysozyme , which has an additional anti-bacterial function not found in C . elegans lysozymes [31] . We found that worms expressing either uncoupling protein or lysozyme from D . rerio have a longer lifespan than control worms . Each of the aging components can extend lifespan about 30–50% by themselves . To extend lifespan beyond this amount , we combined four different aging components in the same line and extended lifespan to 130% . As the number of components increases beyond four , lifespan assays will become more time-consuming and less practical . For practical purposes , we showed that we can rapidly use cell- and worm-based assays to assess whether a multi-component strain is a good candidate for extended longevity , before having to perform the lifespan assay itself . There was generally good agreement between the results from the cell assays and lifespan; e . g , high levels of sod-3 expression correlated well with extended lifespan in the multi-component strains . It will be interesting to determine how much further one can extend lifespan by adding additional components . Previous studies have already shown that daf-2 mutant worms that lack a germline have a five-fold increase in longevity [39] . Future engineering efforts may also be able to achieve extreme longevity . Although data from our cell assays indicate that a certain aging pathway may be active , it is difficult to formally conclude that the observed activity is the cause for longer lifespan . This is because any of the aging components may have an unknown second activity . For instance , our results show that expression of superoxide dismutase results in paraquat resistance , consistent with a reduction in oxidative damage . However , recent evidence suggests that this enzyme extends lifespan not through an oxidative damage pathway but by another undefined mechanism [40] . Whether or not the precise mechanism is reduction of oxidative damage , superoxide dismutase does indeed extend lifespan and serves our purposes as an aging component to engineer longer-lived worms . This work provides a proof of principle that one can engineer longer lifespan in C . elegans by adding new components . New technologies in DNA construction , increased knowledge of aging pathways , and improved methods to fine-tuning gene expression will add powerful tools to engineering lifespan . For instance , it will soon be possible to synthesize large stretches of DNA containing many genes from any organism , worm or otherwise , in order to express multiple genes from a genetic pathway . For example , the innate immune system of vertebrates is a source of new anti-bacterial proteins that could significantly improve resistance to pathogenicity in C . elegans [41] . Additionally , expression of vertebrate-specific chaperones and mitochondrial proteins in worms may improve their proteostasis and energy balance pathways , respectively [42]–[43] . Adding exogenous components from vertebrates is a powerful strategy that goes beyond the natural constraints of the C . elegans genome to engineer worms with increased lifespan and healthspan .
All C . elegans strains were maintained and handled as previously described [44] . 5-fluoro-2′-deoxyuridine ( FUDR , Sigma ) plates were made by supplementing NGM agar media with 30 µM of FUDR . Genes from C . elegans used in this study were amplified by PCR from N2 worm genomic DNA . Generation of constructs containing zebrafish or human cDNA used worm upstream regulatory sequence as defined by the promoterome [45] . If the required promoter was not part of promoterome , all intergenic sequence upstream of the gene of interest was used . cDNA of the gene of interest was obtained from Open Biosystems . The 3′ UTR was from the intron-containing unc-54 gene . Transgenic strains were made by microinjecting unc-119 worms with the gene of interest at 10 ng/µl and PD4H1 ( unc119 ( + ) ; sod3::mCherry ) [46] at 80 ng/µl . To generate transgenic worms containing two or three genes , each of the genes of interest was injected at 10 ng/µl and PD4H1 at 70 ng/µl . sod-3::mCherry is a reporter for daf-16 activity . Life span analyses were conducted on FUDR plates at 20°C as previously described [47] . At least 80 worms were used for each experiment . Age refers to days following adulthood , and p-values were calculated using the log-rank ( Mantel-Cox ) method . Individuals were excluded from the analysis if their gonad was extruded or if they desiccated by crawling onto the edge of the plate . Fourth larval stage worms were washed with M9 buffer and pelleted in a centrifuge . RNA was extracted by addition of 500 µl of Trizol ( Invitrogen ) to 50 µl of worm pellet , followed by six freeze-thaw cycles in liquid nitrogen . RNA extraction was performed according to the Trizol protocol from the manufacturer . Gene expression was determined by reverse transcription of 0 . 5 µg total RNA with the Superscript III kit ( Invitrogen ) followed by quantitative PCR analysis on a Step One Plus real time PCR machine ( Applied Biosystems ) with iQ SYBR green ( Bio-Rad ) using act-1 RNA as a control . The experiments were conducted in triplicate . Expression level of a gene of interest relative to act-1 was determined by calculating the difference in the number of cycles between the gene of interest and act-1 . The level of expression in a transgenic strain compared to a control strain is the difference in the normalized number of cycles , with one cycle being equivalent to two-fold difference in expression . Assays were performed in triplicate as previously described [48] . Briefly , four day old adult hermaphrodites were immersed in S-basal media containing 50 mM or 200 mM of paraquat . The number of dead worms was scored every hour by touch-provoked movement until all worms were dead . For each strain , median survival was determined by plotting Kaplan Meier survival curves containing 150 worms . About 200 L4 hermaphrodites were collected , washed four times with S-basal buffer in an eppendorf tube , boiled for 20 minutes and quickly frozen in −80°C . All samples were processed on the same day . A Roche ATP Bioluminscent HSII kit was used to measure ATP concentrations , which measures bioluminescence emitted by the ATP-dependant oxidation of D-luciferin catalyzed by luciferase . ATP concentrations were determined using a standard curve derived from bioluminescence of known ATP concentrations ( HSII kit ) . A Wallac 1420 multilabel counter luminometer ( Victor2 , Perkin Elmer ) was used to measure levels of bioluminescence . A BioRad protein assay kit was used to measure protein concentrations using a Beckman Coulter DU 640 spectrophotometer . ATP concentrations were normalized to absolute protein concentrations . Each assay was repeated in triplicate , and the average ATP concentration and SD were calculated . The sDR method was performed as described in [49] with slight modifications . Overnight cultures of E . coli OP50 were grown at 37°C and collected by centrifugugation at 3 , 000 rpm for 30 minutes ( Sorvall Legend RT ) to collect bacterial cells . DR plates were prepared by adding 0 . 75×108 cfu of OP50 and ad libitum ( AL ) plates were prepared by adding 0 . 75×1011 cfu of OP50 to NGM-FUDR plates . Worms were grown on NGM plates and synchronized hermaphrodites were transferred overnight to fresh NGM plates with OP50 and 30 µM of FUdR in order to prevent growth of progeny . Day 1 adult animals were then transferred to DR or AL plates . To maintain bacterial concentration , worms were transferred to fresh DR or AL plates every other day . Oxidative damage was assessed using an Oxyblot assay kit ( Millipore ) to detect carbonylated proteins as previously described [50] . About 100 worms synchronized at day 1 or day 14 of adulthood were collected , washed twice with M9 buffer and boiled for 20 min in lysis buffer [50] . Carbonyl groups were derivatized to 2 , 4-dinitrophenylhydrazone ( DNP-hydrazone ) , and were then detected by Western blotting with a DNP-specific antibody . Total protein levels in the extract were measured by nanodrop and 9 mg of protein lysate was loaded in each lane . Quantification of carbonylated proteins was achieved by taking the ratio of DNP staining to tubulin staining . Levels of carbonylated protein were compared in three independent samples of one day old and 14 day old adult worms . Fluorescence images of sod-3::mCherry were taken as described [30] . Briefly , 10 age-synchronized worms at day 9 of adulthood were transferred to 1 mM aldicarb-NGM plates for 2–3 hours to induce paralysis [51] . Worms were then photographed using a 20× lens on a Zeiss AxioPlan Fluorescent Microscope . Levels of mCherry expression ( in the head and the first two pairs of intestinal cells ) were analyzed using ImageJ [30] . For any given comparison , all pictures were taken on the same day with the same microscope settings . Results from three independent sets of 10 worms were used to calculate the average expression level and SD . | We used bioengineering to extend the lifespan of C . elegans by expressing genes acting in critical aging pathways . We overexpressed five genes that act in endogenous worm aging pathways , as well as two genes from zebrafish encoding molecular functions not normally present in worms . For example , we used zebrafish genes to alter mitochondrial function and innate immunity in ways not normally available to C . elegans and extended worm lifespan by ∼40% . Next , we used a modular approach to extend lifespan by 130% by combining up to four components in the same strain . These results provide a platform to build worms having progressively longer lifespans . This project is conceptually similar to using engineering to increase the useful lifespan of a primitive machine ( 1931 Model T ) using both parts from the model T as well as parts from a more advanced machine ( 2012 Toyota Corolla ) . Our results open the door to use engineering to go beyond the constraints of the C . elegans genome to extend its lifespan by adding non-native components . | [
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] | 2012 | An Engineering Approach to Extending Lifespan in C. elegans |
Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies . Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences . Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously . In particular , new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies , which cannot be revealed by pairwise analyses alone . In this paper , we develop a method for estimating time-varying spike interactions by means of a state-space analysis . Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework . We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters . This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons , thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis . Furthermore , the method can estimate dynamic higher-order spike interactions . To validate the inclusion of the higher-order terms in the model , we construct an approximation method to assess the goodness-of-fit to spike data . In addition , we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data , e . g . , data from awake behaving animals . The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics . Finally , we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand .
Precise spike coordination within the spiking activities of multiple single neurons is discussed as an indication of coordinated network activity in the form of cell assemblies [1] comprising neuronal information processing . Possible theoretical mechanisms and conditions for generating and maintaining such precise spike coordination have been proposed on the basis of neuronal network models [2]–[4] . The effect of synchronous spiking activities on downstream neurons has been theoretically investigated and it was demonstrated that these are more effective in generating output spikes [5] . Assembly activity was hypothesized to organize dynamically as a result of sensory input and/or in relation to behavioral context [6]–[10] . Supportive experimental evidence was provided by findings of the presence of excess spike synchrony occurring dynamically in relation to stimuli [11]–[14] , behavior [14]–[19] , or internal states such as memory retention , expectation , and attention [8] , [20]–[23] . Over the years , various statistical tools have been developed to analyze the dependency between neurons , with continuous improvement in their applicability to neuronal experimental data ( see [24]–[26] for recent reviews ) . The cross-correlogram [27] was the first analysis method for detecting the correlation between pairs of neurons and focused on the detection of stationary correlation . The joint-peri stimulus time histogram ( JPSTH ) introduced by [11] , [28] is an extension of the cross-correlogram that allows a time resolved analysis of the correlation dynamics between a pair of neurons . This method relates the joint spiking activity of two neurons to a trigger event , as was done in the peri-stimulus time histogram ( PSTH ) [29]–[31] for estimating the time dependent firing rate of a single neuron . The Unitary Event analysis method [25] , [32] , [33] further extended the correlation analysis to enable it to test the statistical dependencies between multiple , nonstationary spike sequences against a null hypothesis of full independence among neurons . Staude et al . developed a test method ( CuBIC ) that enables the detection of higher-order spike correlation by computing the cumulants of the bin-wise population spike counts [34] , [35] . In the last decade , other model-based methods have been developed that make it possible to capture the dependency among spike sequences by direct statistical modeling of the parallel spike sequences . Two related approaches based on a generalized linear framework are being extensively investigated . One models the spiking activities of single neurons as a continuous-time point process or as a discrete-time Bernoulli process . The point process intensities ( instantaneous spike rates ) or Bernoulli success probabilities of individual neurons are modeled in a generalized linear manner using a log link function or a logit link function , respectively [36]–[38] . The dependency among neurons is modeled by introducing coupling terms that incorporate the spike history of other observed neurons into the instantaneous spike rate [38]–[40] . Recent development in causality analysis for point process data [41] makes it possible to perform formal statistical significance tests of the causal interactions in these models . Typically , the models additionally include the covariate stimulus signals in order to investigate receptive field properties of neurons , i . e . , the relations between neural spiking activities and the known covariate signals . However , they are not suitable for capturing instantaneous , synchronous spiking activities , which are likely to be induced by an unobserved external stimulus or a common input from an unobserved set of neurons . Recently , a model was proposed to dissociate instantaneous synchrony from the spike-history dependencies; it additionally includes a common , non-spike driven latent signal [42] , [43] . These models provide a concise description of the multiple neural spike train data by assuming independent spiking activities across neurons conditional on these explanatory variables . As a result , however , they do not aim to directly model the joint distribution of instantaneous spiking activities of multiple neurons . In contrast , an alternative approach , which we will follow and extend in this paper , directly models the instantaneous , joint spiking activities by treating the neuronal system as an ensemble binary pattern generator . In this approach , parallel spike sequences are represented as binary events occurring in discretized time bins , and are modeled as a multivariate Bernoulli distribution using a multinomial logit link function . The dependencies among the binary units are modeled in the generalized linear framework by introducing undirected pairwise and higher-order interaction terms for instantaneous , synchronous spike events . This statistical model is referred to as the ‘log-linear model’ [44] , [45] , or Ising/spin-glass model if the model contains only lower-order interactions . The latter is also referred to as the maximum entropy model when these parameters are estimated under the maximum likelihood principle . In contrast to the former , biologically-inspired network-style modeling , the latter approach using a log-linear model was motivated by the computational theory of artificial neural networks originating from the stationary distribution of a Boltzmann machine [46] , [47] , which is in turn the stochastic analogue of the Hopfield network model [48] , [49] for associative memory . The merit of the log-linear model is its ability to provide a well-defined measure of spike correlation . While the cross-correlogram and JPSTH provide a measure of the marginal correlation of two neurons , these methods cannot distinguish direct pairwise correlations from correlations that are indirectly induced through other neurons . In contrast , a simultaneous pairwise analysis based on the log-linear model ( an analogue of the Ising/spin-glass analysis in statistical mechanics ) can sort out all of the pair-dependencies of the observed neurons . A further merit of the log-linear model is that it can provide a measure of the ‘pure’ higher-order spike correlation , i . e . , a state that can not be explained by lower-order interactions . Using the viewpoint of an information geometry framework [50] , Amari et al . [44] , [45] , [51] demonstrated that the higher-order spike correlations can be extracted from the higher-order parameters of the log-linear model ( a . k . a . the natural or canonical parameters ) . The strengths of these parameters are interpreted in relation to the lower-order parameters of the dual orthogonal coordinates ( a . k . a . the expectation parameters ) . The information contained in the higher-order spike interactions of a particular log-linear model can be extracted by measuring the distance ( e . g . , the Kullback-Leibler divergence ) between the higher-order model and its projection to a lower-order model space , i . e . , a manifold spanned by the natural parameters whose higher-order interaction terms are fixed at zero [44] , [52]–[54] . Recently , a log-linear model that considered only up to pairwise interactions ( i . e . , an Ising/spin-glass model ) was proposed as a model for parallel spike sequences . Its adequateness was shown by the fact that the firing rates and pairwise interactions explained more than % of the data [55]–[57] . However , Roudi et al . [54] demonstrated that the small contribution of higher-order correlations found from their measure based on the Kullback-Leibler divergence could be an artifact caused by the small number of neurons analyzed . Other studies have reported that higher-order correlations are required to account for the dependencies between parallel spike sequences [58] , [59] , or for stimulus encoding [53] , [60] . In [60] , they reported the existence of triple-wise spike correlations in the spiking activity of the neurons in the visual cortex and showed their stimulus dependent changes . It should be noted , though , that these analyses assumed stationarity , both of the firing rates of individual neurons and of their spike correlations . This was possible because the authors restricted themselves to data recorded either from in vitro slices or from anesthetized animals . However , in order to assess the behavioral relevance of pairwise and higher-order spike correlations in awake behaving animals , it is necessary to appropriately correct for time-varying firing rates within an experimental trial and provide an algorithm that reliably estimates the time-varying spike correlations within multiple neurons . We consider the presence of excess spike synchrony , in particular the excess synchrony explained by higher-order correlation , as an indicator of an active cell assembly . If some of the observed neurons are a subset of the neurons that comprise an assembly , they are likely to exhibit nearly completely synchronous spikes every time the assembly is activated . It may be that such spike patterns are not explained by mere pairwise correlations , but require higher-order correlations for explanation of their occurrence . One of the potential physiological mechanisms for higher-order correlated activity is a common input from a set of unobserved neurons to the assembly that includes the neurons under observation [25] , [61]–[63] . Such higher-order activity is transient in nature and expresses a momentary snapshot of the neuronal dynamics . Thus , methods that are capable of evaluating time-varying , higher-order spike correlations are crucial to test the hypothesis that biological neuronal networks organize in dynamic cell assemblies for information processing . However , many of the current approaches based on the log-linear model [44] , [45] , [53] , [55] , [56] , [61] , [62] , [64] , [65] are not designed to capture their dynamics . Very recently two approaches were proposed for testing the presence of non-zero pairwise [66] and higher-order [67] correlations using a time-dependent formulation of a log-linear model . In contrast to these methods , the present paper aims to directly provide optimized estimates of the individual time-varying interactions with confidence intervals . This enables to identify short lasting , time-varying higher-order correlation and thus to relate them to behaviorally relevant time periods . In this paper , we propose an approach to estimate the dynamic assembly activities from multiple neural spike train data using a ‘state-space log-linear’ framework . A state-space model offers a general framework for modeling time-dependent systems by representing its parameters ( states ) as a Markov process . Brown et al . [37] developed a recursive filtering algorithm for a point process observation model that is applicable to neural spike train data . Further , Smith and Brown [68] developed a paradigm for joint state-space and parameter estimation for point process observations using an expectation-maximization ( EM ) algorithm . Since then , the algorithm has been continuously improved and was successfully applied to experimental neuronal spike data from various systems [38] , [69]–[71] ( see [72] for a review ) . Here , we extend this framework , and construct a multivariate state-space model of multiple neural spike sequences by using the log-linear model to follow the dynamics of the higher-order spike interactions . Note that we assume for this analysis typical electrophysiological experiments in which multiple neural spike train data are repeatedly collected under identical experimental conditions ( ‘trials’ ) . Thus , with the proposed method , we deal with the within-trial nonstationarity of the spike data that is expected in the recordings from awake behaving animals . We assume , however , that dynamics of the spiking statistics within trials , such as time-varying spike rates and higher-order interactions , are identical across the multiple experimental trials ( across-trial stationarity ) . To validate the necessity of including higher-order interactions in the model , we provide a method for evaluating the goodness-of-fit of the state-space model to the observed parallel spike sequences using the Akaike information criterion [73] . We then formulate a hypothesis test for the presence of the latent , time-varying spike interaction parameters by combining the Bayesian model comparison method [74]–[76] with a surrogate method . The latter test method provides us with a tool to detect assemblies that are momentarily activated , e . g . , in association with behavioral events . We test the utility of these methods by applying them to simulated parallel spike sequences with known dependencies . Finally , we apply the methods to spike data of three neurons simultaneously recorded from motor cortex of an awake monkey and demonstrate that a triple-wise spike correlation dynamically organizes in relation to a behavioral demand . The preliminary results were presented in the proceedings of the IEEE ICASSP meeting in 2009 [77] , as well as in conference abstracts ( Shimazaki et al . , Neuro08 , SAND4 , NIPS08WS , Cosyne09 , and CNS09 ) .
For a given number of neurons , , we can construct state-space log-linear models that contain up to the th-order interactions ( ) . While the inclusion of increasingly higher-order interaction terms in the model improves its accuracy when describing the probabilities of spike patterns , the estimation of the higher-order log-linear parameters of the model may suffer from large statistical fluctuations caused by the paucity of synchronous spikes in the data , leading to an erroneous estimation of such parameters . This problem is known as ‘over-fitting’ the model to the data . An over-fitted model explains the observed data , but loses its predictive ability for unseen data ( e . g . , spike sequences in a new trial under the same experimental conditions ) . In this case , the exclusion of higher-order parameters from the model may better explain the unseen data even if an underlying spike generation process contains higher-order interactions . The model that has this predictive ability by optimally resolving the balance between goodness-of-fit to the observed data and the model simplicity is obtained by maximizing the cross-validated likelihood or minimizing the so-called information criterion . In this section , we select a state-space model that minimizes the Akaike information criterion ( AIC ) [73] , which is given as ( 11 ) The first term is the log marginal likelihood , as in Eq . 10 . The second term that includes is a penalization term . The AIC uses the number of free parameters in the marginal model ( i . e . , the number of free parameters in ) for . Please see in the Methods subsection ‘Selection of state-space model by information criteria’ for an approximation method to compute the marginal likelihood . Selecting a model that minimizes the AIC is expected to be equivalent to selecting a model that minimizes the expected ( or average ) distance between the estimated model and unknown underlying distribution that generated the data , where the ‘distance’ measure used is the Kullback-Leibler ( KL ) divergence . The expectation of the KL divergence is called the KL risk function . One of the goals of a time resolved analysis of spike correlation is to discover dynamical changes in the correlated activities of neurons that reflect the behavior of an animal . This implies the necessity of dealing with the within-trial nonstationarity that is typically present in the data from awake behaving animals . However , we know from other correlation analysis approaches that , if not well corrected for , nonstationary spike data bear the potential danger of generating false outcomes [25] , [83] . Here we deal with the within-trial nonstationarity of the data by using the state-space log-linear model while assuming identical dynamic spiking statistics across trials ( across-trial stationarity ) . In order to correctly detect the time-varying correlation structure within trials , we apply to the state-space log-linear model a Bayesian model comparison method based on the Bayes factor ( BF ) [74]–[76] , and combine it with a surrogate approach . The BF is a likelihood ratio for two different hypothetical models of latent signals , e . g . , in our application , different underlying spike correlation structures . Using the BF , we determine which of the two spike correlation models the spike data supports . By computing the BF for a particular task period in a behavioral experiment , we can test whether the assumed correlation structure appears in association with the timing of the animal's behavior . In the following , we denote a specific task period of interest by the time period . In this study , the BF , , is defined as the ratio of the marginal likelihoods of the observed spike patterns , , in the time period under different models , or , assumed for the hidden state parameters , ( 12 ) By successively conditioning the past , the BF is computed by the multiplication of the bin-by-bin one-step BF given at time as . Here , the bin-by-bin BF at time , , can be calculated as ( see the Methods subsection , ‘Bayesian model comparison method for detecting spike correlation’ ) , ( 13 ) where is the space of the interaction parameters , , for the model , ( ) . In Eq . 13 , is the filter density and is called the one-step prediction density , both of which are obtained in the Bayesian recursive filtering algorithm developed in the Methods section ( cf . Eqs . 25 , 26 and Eqs . 31 , 32 ) . Therefore , the bin-by-bin BF at time , , is the ratio of the odds ( of opposing models ) found by observing the spike train data up to time ( filter odds , the numerator in Eq . 13 ) to the odds predicted from without observing the data at time ( prediction odds , the denominator in Eq . 13 ) . Thus , an unexpected synchronous spike pattern that significantly updates the filter odds for the interaction parameters from their predicted odds gives rise to a large absolute value for the BF . Because the posterior densities are approximated as a multivariate normal distribution in our filtering algorithm , the BF at time can be easily computed by using normal distribution functions . Please see the subsection , ‘Bayesian model comparison method for detecting spike correlation’ , in the Methods section for the derivation of Eq . 13 and detailed analysis of the BF . The BF becomes larger than 1 if the data , , support model as opposed to as an underlying spike correlation structure and becomes smaller than 1 if the data support model as opposed to . Alternatively , it is possible to use the logarithm of the BF , known as the ‘weight of evidence’ [75] which becomes positive if the data support model as opposed to model and negative in the opposite situation . Below , we display the results for the BF in bit units ( logarithm of the BF to base 2 ) , i . e . , the weight of evidence . By sequentially computing the bin-by-bin BF , we can obtain the weight of evidence in a period as the summation of the local weight of evidence: . An intuitive interpretation of the BF values is provided in the literature [74] , [76] . For example , in [76] , a BF ( weight of evidence ) from 1 . 6 to 4 . 3 bit was interpreted as ‘positive’ evidence in favor of against . Similarly , a BF from 4 . 3 to 7 . 2 bit was interpreted as ‘strong’ evidence , and a BF larger than 7 . 2 bit was found to be ‘very strong’ evidence in favor of against . While the classical guidelines are useful in practical situations , they are defined subjectively . Thus , in this study , in order to objectively analyze the observed value of the BF , we combine the Bayesian model comparison method with a surrogate approach . In this surrogate method , we test the significance of the observed BF for the tested spike interactions by comparing it with the surrogate BFs computed from the null-data generated by destroying only the target spike interactions while the other structures such as the time-varying spike-rates and lower-order spike interactions are kept intact . The BF in a behaviorally relevant sub-interval can be computed from the optimized state-space log-linear model fitted to the entire spike train data in . Here , for the purpose of testing spike correlation in the sub-interval , we recommend to use in the state model because the autoregressive parameters are optimized for entire spike train data , which are not necessarily optimal for the sub-interval . Similarly , a typical trial-based experiment is characterized by discrete behavioral or behaviorally relevant events , e . g . movement onset after a go signal or a cue signal for trial start , etc . Thus , on top of the expected smooth time-varying change in the spike-rate and spike-correlation , sudden transitions may be expected in their temporal trajectories . Because we use time-independent smoothing parameters ( i . e . , hyper parameter in Eq . 8 ) that were optimized to entire data ( see the EM algorithm in the Methods section ) , such abrupt changes may not be captured very well . This may cause a false detection or failure in the detection of the spike correlation at the edge of a task period . For such data , we suggest applying the Bayesian model comparison method to state-space models which are independently fitted to each of the task periods ( or relatively smooth sub-intervals within each task period ) .
In this study , we introduced a novel method for estimating dynamic spike interactions in multiple parallel spike sequences by means of a state-space analysis ( see Methods for details ) . By applying this method to nonstationary spike train data using the pairwise log-linear model , we can extend the stationary analysis of the spike train data by the Ising/spin-glass model to within-trial nonstationary analysis ( Figure 3 ) . In addition , our approach is not limited to a pairwise analysis , but can perform analyses of time-varying higher-order spike interactions ( Figure 4 ) . It has been discussed whether higher-order spike correlations are important to characterize neuronal population spiking activities , assuming stationarity in the spike data [54]–[59] . Based on the state-space model optimized by our algorithm , we developed two methods to validate and test its latent spike interaction parameters , in particular the higher-order interaction parameters , which may dynamically change within an experimental trial . In the first method , we selected the proper order for the spike interactions incorporated in the model under the model selection framework using the approximate formula of the AIC for this state-space model ( In Methods , ‘Selection of state-space model by information criteria’ ) . This method selects the model that best fits the data overall across the entire observation period . The selected model can then be used to visualize the dynamic spike interactions or for a performance comparison with other statistical models of neuronal spike data . However , more importantly , the detailed structure of the transient higher-order spike interactions needs to be tested locally in time , particularly in conjunction with the behavioral paradigm . To meet this goal , we combined the Bayesian model comparison method ( the Bayes factor ) with a surrogate method ( In Methods , ‘Bayesian model comparison method for detecting spike correlation’ ) . The method allows us to test for the presence of higher-order spike correlations and examine its relations to experimentally relevant events . We demonstrated the utility of the method using neural spike train data simultaneously recorded from primary motor cortex of an awake monkey . The result is consistent with , and further extended the findings in the previous report [8]: We detected an increase in triple-wise spike interaction among three neurons in the motor cortex during the preparatory period in a delayed motor task , which was also tightly locked to the expected signals . Although the analysis was done for a limited number of neurons , smaller than the expected size of an assembly , it demonstrates that the nonstationary analysis of the higher-order activities is useful to reveal cooperative activities of the neurons that are organized in relation to behavioral demand . Of course , further analysis is required to strengthen the findings made above including a meta-analysis of many different sets of multiple neurons recorded under the same conditions . In this study , we adopted the log-linear model to describe the higher-order correlations among the spiking activities of neurons . There are , however , other definitions for ‘higher-order spike correlation’ . An important alternative concept is the definition based on cumulants . Using the cumulants of an observed count distribution from a spike train pooled across neurons , Staude et al . developed an iterative test method that can detect the existence of a high amplitude in the jump size distribution of the assumed compound Poisson point process ( CPP ) model for the pooled spike train [34] , [35] . This method can detect an assembly from a few occurrences of synchronous spike events to which many neurons belong to , typically by using the lower-order cumulants of the observed spike counts . In contrast , the information geometry measure for the higher-order spike correlation used in this study aims to represent the correlated state that cannot be explained by lower-order interactions . Consequently , the information geometry measure extracts the relative strength of the higher-order dependency to the lower-order correlated state . Therefore , the presence of positive higher-order spike correlations does not necessarily indicate that many neurons spike synchronously whenever they spike because such activities can be induced by positive pairwise spike correlations alone [65] , [85] , [86] ( see also Figure 8A and B in the Methods section ) . In contrast , the cumulant-based correlation method by Staude et al . [34] infers the presence of ‘higher-order correlation’ for such data by determining the presence of high amplitudes in the jump size distribution of the assumed CPP model . Yet another important tool for analyzing higher-order dependency among multiple neurons is the copula function , a standardized cumulative distribution function used to model the dependence structure of multiple random variables ( see [87]–[89] for an analysis of neurophysiological data using the copula , including an analysis for higher-order dependency [89] ) . In summary , it should be remembered that the analysis method used for the higher-order dependency among neuronal spikes inherits its goal from the assumed model for spike generation as well as a parametric measure defined for the ‘higher-order’ spike correlation [34] , [62] . Although we face a high-dimensional optimization problem in our settings , we are able to successfully obtain MAP estimates of the underlying parameters because of the simplicity of the formulation of the state-space model: The use of the log-concave exponential family distributions [50] , [90] in both the state and observation models guarantees that the MAP estimates can be obtained using a convex optimization program . At each bin , the method numerically solves a nonlinear filter equation to obtain the mode of the posterior state density ( the MAP estimates , see Eqs . 28 , 29 , and 30 in Methods ) . With only a few ( 3–8 ) Newton-Raphson iterations , the solution reaches a plateau ( the increments of all the elements of the updated state space vector are smaller than ) . The entire optimization procedure can be performed in a reasonable amount of time: On a contemporary standard laptop computer it takes no longer than 30 seconds to obtain smooth estimates of a full log-linear model for neurons ( bins , Figure 4 ) , which includes 100 EM iterations . The method is even faster when approximating the posterior mode using the update formula Eq . 30 without any iterations , using the one-step prediction mean as an initial value . This fast approximation method suggested in [69] could even be utilized in a real-time , on-line application of our filter ( the filtering method applied to a single trial , , using predetermined hyper-parameters ) at the cost of estimation accuracy . The pairwise analysis can be applied up to about neurons simultaneously to derive time-dependent pair interactions . However , the current version of the algorithm does not scale to a larger number of neurons because the number of spike patterns that need to be considered suffers from a combinatorial explosion . The major difficulty arises from the coordinate transformation from the -coordinates to the -coordinates that appear in the non-linear filter equation ( Eq . 31 in Methods ) . The coordinate transformation is required in this equation to calculate the innovation signal , i . e . , the difference between the observed synchrony rates , , and the expected synchrony rates ( -coordinates ) based on the model . We numerically derived the exact -coordinates by marginalizing the dimensional joint probability mass function computed from the -coordinates . Thereby , a full knowledge of the probability mass function is required even if the model considers only the lower-order interactions . Because this is a common problem in the learning of artificial neural networks [46] , [47] , [91] , sampling algorithms such as the Markov chain Monte Carlo method have been developed to approximate the expectation parameters , , without having to compute the partition function [92] . The inclusion of such methods allows us to analyze the time-varying low order spike interactions from a larger number of parallel spike sequences . Recent progress [93] , e . g . , in the mean field approach and/or the minimum probability flow learning algorithm for an Ising model , may allow us to further increase the number of neurons that can be treated in this nonstationary pairwise analysis . Nonetheless , the method presented here , which aims at a detailed analysis of the dynamics in higher-order spike interactions , may not easily scale to massively parallel spike sequences that can be analyzed by other methods such as those based on the statistics pooled across neurons . Thus , we consider it to be important to combine the detailed analysis method proposed in this study with other state-of-the-art analysis techniques in practical applications . For example , test methods based on population measures such as the Unitary Event method and cumulant-based inference method [34] , [35] allow us to detect the existence of statistically dependent neurons in massively parallel spike sequences . If the null-hypothesis of independence among those neurons is not rejected in these methods , we can exclude those neurons from any further detailed analysis of their dynamics using the methods proposed in this study . Several critical assumptions made in the current framework need to be addressed . First , it was assumed in constructing the likelihood ( Eq . 7 ) that no spike history effect exists in the generation of a population spike pattern . Second , we assumed the use of identically and independently distributed samples across trials when constructing the likelihood ( Eq . 7 ) . The first assumption may appear to be strong constraint given the fact that individual neurons exhibit non-Poisson spiking activities [94] . However , as in the case of the estimation of the firing rate of a single neuron , the pooled spike train across the ( independent ) trials is assumed to obey a Poisson point process because of the general limit theorem for point processes [30] , [31] , [95] , [96] . This is because most of the spikes in the pooled data come from independent different trials . They are thus nearly statistically independent from each other , even if the individual processes are non-Poisson . Similarly , in our analysis , we used statistics from a pooled binary spike train , assuming independence across trials: The occurrences of joint spikes in the binary data pooled across trials are mostly independent of each other across bins . Because these joint spike occurrences are sparse ( i . e . , they rarely happen closely to each other in the same trial ) , it is even more feasible to assume their statistical independence across bins . Third , however , while pooling independent and identical trials ( the second assumption ) may validate the first assumption of the independence of the samples across bins , that assumption of independently and identically distributed samples across trials has itself been challenged [97] , [98] and is known to be violated in some cases , e . g . , by drifting attention , ongoing brain activity , adaptation , etc . It is possible that the trial-by-trial jitter/variation in the spike data causes spurious higher-order spike correlation . Thus , as discussed in the section on the application of our methods to real neuronal data , it is important to examine the stationarity of the spike train data across trials . Note that , not only the firing rates , but also the spike synchrony can be shaped on a longer time scale by repeatedly practicing a task [19] . In fact , the current analysis method can be used to examine the long-term evolution of pairwise and higher-order spike interactions across trial sessions by replacing the role of a bin with a trial , assuming within-trial stationarity . It will be a challenge to construct a state-space log-linear model that additionally applies a smoothing method across trials ( see [98] for such a method for a point process model ) . The present method is left with one free parameter , namely the bin-width . The bin-width determines the permissible temporal precision of synchronous spike events . Very large bin-widths result in binary data that are highly synchronized across sequences , while very small bin-widths result in asynchronous multiple spike sequences . In the latter case , we might overlook the existing dependency between multiple neural spike sequences due to disjunct binning [99] ( but see [57] , [100] that aim to overcome such a problem by modeling the spike interactions across different consecutive time bins ) . Within our proposed modeling framework , which focuses on instantaneous higher-order spike correlations , it is important to catch the innate temporal precision of the neuronal population under investigation using the appropriate bin-width . Thus , the choice may be guided by the biophysical properties of the neurons . However , it may be of advantage to derive the bin-width in a data-driven manner . For example , in the context of an encoding problem , the proper bin-width can be chosen based on the goodness-of-fit test for single neuron spiking activities [101] , conditional on the spiking activities of the other neurons [40] . For questions about the relation of coincident spiking to stimulus/behavior , the bin-width may be selected based , for example , on the predictive ability of an external signal . For this goal , it is important to search the optimal bin-width using elaborate methods such as those developed in the context of the Unitary Event analysis method [99] ( see [25] for a review of related methods ) . A substantial number of studies have demonstrated that stimulus and behavioral signals can be decoded simply based on the firing rates of individual neurons . At the same time , it has been discussed whether spike correlations , particularly higher-order spike correlations , are necessary to characterize neuronal population spiking activities [54]–[58] or to encode or decode information related to stimuli [53] , [60] , [102] . At this point in time , a smaller number of dedicated experiments have supported the conceptual framework of information processing using neuronal assemblies formed by neurons momentarily engaged in coordinated activities , as expressed by temporally precise spike correlations ( see [6] , [7] , [9] , [10] for reviews of these experiments ) . Nevertheless , it is possible that the current perspective on this subject has been partly formed by a lack of proper analysis approaches for simultaneously tracing time-varying individual pairwise spike interactions , and/or their higher-order interactions . Indeed , we demonstrated by the time-resolved higher-order analysis that three cortical neurons coordinated their spiking activities in accordance with behaviorally relevant points in time . Thus our suggested analysis methods are expected to be useful to reveal the dynamics of assembly activities and their neuronal composition , as well as for testing their behavioral relevance . We hope that these methods help shed more light on the cooperative mechanisms of neurons underlying information processing .
In this subsection , we review the known mathematical properties of a log-linear model for binary random variables . These properties are used in constructing recursive filtering/smoothing formulas in the next section . Using the multi-index , ( see the Results subsection ‘Log-linear model of multiple neural spike sequences’ ) , the probability mass function ( Eq . 1 ) , , where and ( ) , and the expectation parameters ( Eq . 2 ) are compactly written as ( 14 ) and ( 15 ) where is a feature function , here representing an interaction among the neurons indicated by the multi-index , ( Eq . 3 ) . The - and -coordinates are dually flat coordinates in the exponential family probability space [44] , [50] , and the coordinate transformation from one to the other is given by the Legendre transformation [40] , [50] . From Eq . 14 , the log normalization function , , is written as ( 16 ) The first derivative of the log normalization function , , with respect to ( ) , provides the expectation parameter , : ( 17 ) Let be the negative entropy of the distribution: ( 18 ) Eqs . 17 and 18 complete the Legendre transformation from -coordinates to -coordinates [44] , [50] . The Legendre transformation transfers the functional relationship of and to the equivalent relation in the dual coordinates , and . The inverse transformation is given by Eq . 18 and . Using the log normalization function , we can obtain the multivariate cumulants of with respect to the random variables , . The cumulant generating function of the exponential family distribution is given as . Let us compactly write the partial derivative with respect to ( i . e . , ) as . Then , the first order cumulant is given as , as shown in Eq . 17 . In general , the cumulants of the exponential family distribution are given by the derivatives of the log normalization function . Thus , the second derivative of yields the second-order cumulant , ( by the cup , , we mean the multi-index representation of an union of the elements of the two multi-indices , e . g . , if and , then ) : ( 19 ) for . is known as the Fisher metric with respect to the natural parameters . Eqs . 17 and 19 are important relations used in this study to construct a non-linear filtering equation for a dynamic estimate of the natural parameters because we approximate the log-linear model ( Eq . 14 ) with a precision of up to a ( log ) quadratic function ( cf . Eqs . 28 and 29 ) . Similarly , the higher-order derivatives yield higher-order multivariate cumulants . For example , the third-order derivative yields the third order cumulant , , where . The pseudo distance between two different distributions , and is defined using the Kullback-Leibler ( KL ) divergence ( 20 ) We represent distribution by using -coordinates as , and by using -coordinates as . Here , we used for the -parameters of ( and for -parameters of ) in order to differentiate it from the representation of distribution in the -coordinates ( and the representation of in the -coordinates ) . Then , the KL-divergence between the two distributions , and , is computed as [44] , [50] ( 21 ) We develop a non-linear recursive Bayesian filtering/smoothing algorithm and its optimization method in order to trace dynamically changing spike interactions from parallel spike sequences . To reach this goal , we use the expectation-maximization ( EM ) algorithm [68] , [73] , [103] , [104] , which is known to efficiently combine the construction of the posterior density of a state ( the natural parameters ) and the optimization of the hyper-parameters . This method maximizes the lower bound of the log marginal likelihood , Eq . 10 . Using Jensen's inequality and nominal hyper-parameters , , the lower bound of the log marginal likelihood with hyper-parameters is given by ( 22 ) Here , represents a negative entropy . The maximization of the lower bound with respect to is equivalent to maximizing the expected complete data log-likelihood in Eq . 22 , known as the -function: ( 23 ) The expectation in the above equation is read as . We maximize the -function by alternating the expectation ( E ) and maximization ( M ) steps . In the E-step , we obtain the expected values with respect to in Eq . 23 using a fixed . In the M-step , we obtain the hyper-parameter , , that maximizes Eq . 23 . The resulting is then used in the next E-step . The details of each step are now given as follows . The method developed in the previous subsection is applicable to a full log-linear model , as well as a model that considers an arbitrary order of interactions . In order to select the most predictive model among the hierarchical models in accordance with the observed spike data , we select the state-space model that minimizes the Akaike information criterion ( AIC ) for a model with latent variables [73] , [105]: ( 40 ) Here , is the log marginal likelihood ( Eq . 10 ) and is the number of free hyper-parameters in the prior distribution . For the th order model , the number of natural parameters is given by . The number of free parameters in the prior distribution is computed as , where each term corresponds to the number of free parameters in , , and . Note that the AIC applied to the state-space model is sometimes referred to as the Akaike Bayesian information criterion ( ABIC ) [73] . In the following , we derive the approximation method to evaluate the AIC for the state-space log-linear model . The log marginal likelihood , Eq . 10 , can be written as ( 41 ) We make a log quadratic approximation to evaluate the integral . To accomplish this , we denote ( 42 ) with ( 43 ) The Laplace approximation of the integral in Eq . 42 is given as [80] ( 44 ) By applying Eqs . 42 , 43 and 44 to Eq . 41 , the log marginal likelihood is approximated as ( 45 ) We confirmed that the log-quadratic approximation provided a better estimate of the marginal likelihood than the first order approximation used in [77] by comparing them with a Monte Carlo approximation of the integral in Eq . 42 . We select the state-space log-linear model that minimizes the AIC ( Eq . 40 ) , where the log marginal likelihood is approximated using Eq . 45 . For comparison with the AIC , we compute two other information criteria that employ different forms of the penalization term . The Bayesian information criterion [79] , [80] ( also known as Schwartz's criterion ) are obtained by replacing the penalization term of Eq . 40 , , with : ( 46 ) Shimodaira's predictive divergence for indirect observation models ( PDIO ) [81] is given as ( 47 ) Here , we redefine as a one-dimensional vector of free hyper-parameters , while denotes the one-step operator of EM iteration . To obtain the Jacobian matrix for the EM operator , we follow the algorithm described in Meng and Rubin [111] . In this method , the Jacobian matrix was approximated using a numerical differentiation of the EM operator . By perturbing one hyper-parameter and then computing a one-step EM iteration , numerical differentiations of the hyper-parameters with respect to the perturbed hyper-parameter were obtained . An entire Jacobian matrix was approximated by repeating the process while changing the hyper-parameter to be perturbed . In this subsection , we formulate a method for detecting the hidden structure of spike interaction by means of a Bayesian model comparison based on the Bayes factor ( BF ) [74]–[76] . The BF is a ratio of likelihoods for the observed data , based on two different assumptions about the hidden states ( model and ) . Here we reiterate the definition of the BF for model as opposed to model used in this paper ( cf . Eq . 12 ) : ( 48 ) The BF becomes larger than 1 if the data , in a time period , supports model as opposed to model , and becomes smaller than 1 if the data supports model as opposed to model . The BF can be computed from the one-step prediction and filter density obtained in the method developed in the preceding subsection . From Eq . 48 , the BF can be rewritten as ( 49 ) Let us define the bin-by-bin BF for the spike data at time as ( 50 ) Using Bayes' theorem , we obtain [74]–[76] ( 51 ) for . Using Eq . 51 , we can rewrite the bin-by-bin BF as ( 52 ) where and denote spaces that the natural parameters occupy , supported by models and , respectively . Here , and are the filter density and one-step prediction density , respectively . By sequentially computing Eq . 52 , we can obtain the BF with respect to the sub-interval as . A test with the following models in the sub-interval allows us to detect a momentarily active cell assembly of more than two neurons by the presence of simultaneously positive th-order spike interactions . In the th-order log-linear model of neurons , the natural parameters , ( ) , represent the th-order spike interactions among neurons denoted in index . We examine whether a subset of neurons among the total neurons simultaneously exhibit positive th-order interactions . Let be the subset of neurons from neurons , e . g . , from if and . Let be an -subset from , e . g . if . Then , the model where the subset neurons simultaneously exhibit positive th-order interactions ( ) and its complementary hypothesis ( ) are represented as ( 53 ) ( 54 ) for . The remaining parameters are real: ( and . excluding ) . These parameters are integrated out in Eq . 52 . The above definition of the assembly is a clique [12] , a subset in which each neuron is connected to every other neuron through the positive th-order interactions . Depending on the assembly structure one wishes to uncover , other models can be tested such as one in which neurons bounded in non-exclusive manner . | Nearly half a century ago , the Canadian psychologist D . O . Hebb postulated the formation of assemblies of tightly connected cells in cortical recurrent networks because of changes in synaptic weight ( Hebb's learning rule ) by repetitive sensory stimulation of the network . Consequently , the activation of such an assembly for processing sensory or behavioral information is likely to be expressed by precisely coordinated spiking activities of the participating neurons . However , the available analysis techniques for multiple parallel neural spike data do not allow us to reveal the detailed structure of transiently active assemblies as indicated by their dynamical pairwise and higher-order spike correlations . Here , we construct a state-space model of dynamic spike interactions , and present a recursive Bayesian method that makes it possible to trace multiple neurons exhibiting such precisely coordinated spiking activities in a time-varying manner . We also formulate a hypothesis test of the underlying dynamic spike correlation , which enables us to detect the assemblies activated in association with behavioral events . Therefore , the proposed method can serve as a useful tool to test Hebb's cell assembly hypothesis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"physics",
"mathematics",
"statistical",
"mechanics",
"computational",
"neuroscience",
"statistics",
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] | 2012 | State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data |
The growth and survival of intracellular parasites depends on the availability of extracellular nutrients . Deprivation of nutrients viz glucose or amino acid alters redox balance in mammalian cells as well as some lower organisms . To further understand the relationship , the mechanistic role of L-arginine in regulation of redox mediated survival of Leishmania donovani promastigotes was investigated . L-arginine deprivation from the culture medium was found to inhibit cell growth , reduce proliferation and increase L-arginine uptake . Relative expression of enzymes , involved in L-arginine metabolism , which leads to polyamine and trypanothione biosynthesis , were downregulated causing decreased production of polyamines in L-arginine deprived parasites and cell death . The resultant increase in reactive oxygen species ( ROS ) , due to L-arginine deprivation , correlated with increased NADP+/NADPH ratio , decreased superoxide dismutase ( SOD ) level , increased lipid peroxidation and reduced thiol content . A deficiency of L-arginine triggered phosphatidyl serine externalization , a change in mitochondrial membrane potential , release of intracellular calcium and cytochrome-c . This finally led to DNA damage in Leishmania promastigotes . In summary , the growth and survival of Leishmania depends on the availability of extracellular L-arginine . In its absence the parasite undergoes ROS mediated , caspase-independent apoptosis-like cell death . Therefore , L-arginine metabolism pathway could be a probable target for controlling the growth of Leishmania parasites and disease pathogenesis .
Leishmaniasis , one of the most neglected tropical diseases , is considered as a major global threat spread over 98 countries throughout 5 continents . Among different forms of leishmaniasis , Visceral Leishmaniasis ( VL ) , the most severe one , has a disease burden of 0 . 2 to 0 . 4 million cases with a mortality rate of 20 , 000 to 40 , 000 reported per year [1] . Leishmania donovani , the etiological agent of Indian VL , is an obligatory parasite that harbors inside the sand fly mid-gut and the macrophages of their mammalian host [2 , 3] . Maintenance of their niches is crucial for survival and replication of these parasites . Cell death in form of apoptosis carefully controls the population of these parasites which in turn helps in disease progression [4 , 5] . Apoptosis or Programmed cell death ( PCD ) plays a critical role in development and defense response of multicellular organisms [6] . In unicellular trypanosomatids including Leishmania sp . , apoptosis is a triggered response to different stimuli ranging from heat shock [7–9] , reactive oxygen species ( ROS ) [10–13] , antiparasitic drugs [14 , 15] , starvation [16–18] to antimicrobial peptides [19–20] . Sequentials events encompassing accumulation of reactive oxygen species ( ROS ) , lipid peroxidation , release of intracellular calcium ( Ca2+ ) into cytosol , mitochondrial membrane depolarization , externalization of phosphatidyl serine on the outer leaflet , leakage of cytochrome-c into cytosol and induction of caspases resulting in DNA cleavage characterize the onset of apoptosis [21 , 22] . Though pathogenic protozoa lacks genes encoding caspases , the involvement of caspase like protease activity has been reported in the regulation of death process of some unicellular organisms [23] . Studies of Lee and co-workers showed that Leishmania promastigote undergoes an apoptosis like cell death independent of caspase activities after exposure with antimony [24] . When a cell fails to maintain cellular homeostasis utilizing the total available antioxidant capacity oxidative stress is generated that expedites the process of apoptosis [25] . To protect cells from ROS mediated apoptosis , the parasite must carefully control the level of ROS by upregulating antioxidant defense . Polyamines are one of the crucial molecules that have been shown to exert antioxidant activity [26 , 27] . Amino acids in eukaryotes , serve as building blocks in protein biosynthesis and regulates osmotic balance by functioning as osmolytes . In some eukaryotes L-arginine , the precursor for the production of polyamines is not synthesized denovo and is imported to support cellular growth and to protect the cells under diseased conditions [28] . Apoptotic stimuli affect both cellular processes cell proliferation and apoptosis [29] . The role of L-arginine in the regulation of cell survival and apoptosis of some higher eukaryotes have been reported [30 , 31] . Piacenza et al . showed the role of L-arginine in modulation of apoptotic death of T . cruzi epimastigotes [32] . Despite these advances , the precise role of L-arginine in the regulation of redox balance and ROS mediated apoptosis is still unclear in protozoan parasites particularly in Leishmania sp . In the present study , we have shown that L-arginine starvation hinders cell growth and proliferation of Leishmania parasite . As Leishmania parasite lacks the biosynthetic pathway of L-arginine , it upregulates the transport of L-arginine in starved conditions . While investigating the possible reason behind this reduced cell viability , we found that L-arginine deprivation downregulates the production of polyamines that are necessary for the Leishmania parasite and alters redox balance characterized by increased ROS level . This results in increased lipid peroxidation and NADP+/NADPH ratio followed by decreased Superoxide dismutase ( SOD ) activity and thiol levels . Simultaneously , it was observed that arginine starvation induces phosphatidyl serine externalization , mitochondrial membrane depolarization , release of intracellular calcium and cytochrome-c that ultimately damages DNA and promotes apoptosis . Collectively our study reveals , for the first time , the role of L-arginine in redox homeostasis and apoptosis-like cell death in Leishmania parasites .
Leishmania donovani strain AG83 ( MHOM/IN/1983/AG83 ) originally obtained from an Indian Kala-azar patient was maintained routinely in golden hamsters , as described earlier [33] . In this study , Leishmania parasites were grown for 2 generations at 22°C separately in amino acids free and L-arginine free RPMI media ( Invitrogen ) and supplemented with 10% heat-inactivated dialyzed fetal bovine serum ( FBS ) ( Gibco-BRL ) , 25 mM HEPES , pH-7 . 4 , 4 mM NaHCO3 , 100 U/ml of penicillin G-sodium ( Sigma-Aldrich ) and 100 mg/ml of streptomycin ( Sigma-Aldrich ) and these parasites were used for our experimental purpose . As per our experimental set up , the exponential phase promastigotes were further grown either in single amino acid ( For eg . L-arginine , lysine , glutamine , proline ) containing or single amino acid depleted RPMI medium ( Invitrogen ) . The effect of L-arginine on the viability of L . donovani promastigotes were analyzed by 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenylterazolium bromide ( MTT ) assay as described previously [34 , 35] . Leishmania promastigotes of the exponential phase were grown in complete RPMI media ( Invitrogen ) , RPMI media with different concentrations ( 0–200 mg/L ) of L-arginine ( Sigma-Aldrich ) , single amino acid free or supplemented RPMI media as well as complete amino acid free RPMI media for 0–120 hrs . The OD was recorded on an ELISA reader ( Multiskan EX; Thermo Fisher Scientific , Waltham , MA ) at 570 nm and percent cell viability was determined . The growth of the parasite in absence of L-arginine but presence of L-lysine , glutamine , proline ( all from Sigma-Aldrich ) or L-ornithine , putrescine , spermidine , spermine ( all from Merck-Millipore ) as well as in presence of L-arginine was assessed by trypan blue dye exclusion method . Viable cells were quantified by counting the number of non-stained cells . Results were expressed as mean±SD for three independent experiments performed in triplicate . Rate of proliferation of the parasite was analyzed using Cell Proliferation ELISA BrdU colorimetric kit ( Roche ) as per manufacturer’s instruction as described previously [36] . Briefly ~1×105 parasites/100 μl media were cultured in a 96 well microplate . At different time points , the cells were incubated with 1 μl of 5-Bromo-2-DeoxyUridine ( BrdU ) labelling agent ( final conc . 10 μM ) for 5 hr and its uptake was measured at 450 nm on an ELISA reader ( Multiskan EX; Thermo Fisher Scientific , Waltham , MA ) . Results were expressed as mean±SD for three independent experiments performed in triplicate . THP-1cells were treated with 20 nM phorbol 12-myristate 7-acetate ( PMA ) for 12 hr to become adherent , matured macrophage-like phenotype . Non-adherent cells were removed by washing with RPMI without FBS . The cells were infected with Leishmania parasite for 6 hrs at parasite/macrophage multiplicities of 10:1 . The unbound parasites were removed by washing with RPMI without FBS followed by additional incubation of upto 24 and 48 hr . The cells were then fixed and stained with May-Gruenwald giemsa and observed under bright field microscope at 100X with oil immersion . Percent infected macrophage was calculated by counting the number of infected cells and parasite load was determined by counting the number of amastigotes per 100 macrophages . Each measurement was performed in triplicate and the data was expressed as mean±SD of three independent experiments . Uptake of L-arginine , lysine , glutamine and proline in Leishmania parasites was measured using a protocol as described elsewhere [37] with minor modification . Briefly , after individual incubations , promastigotes ( 1×107 parasites/ml ) were pelleted down , washed twice with cold Earle’s Based Salt Solution ( EBSS ) containing ( in mM ) 117 NaCl , 26 NaHCO3 , 5 KCl , 1 . 8 CaCl2 , 1 NaH2PO4 , 0 . 8 MgSO4 , 5 . 5 glucose and resuspended in 100 μl of [3H]-L-arginine ( 0 . 5–5 μCi/ml ) using stock from 1 mCi/ml ( specific activity-51 . 5 Ci/mmole ) or [3H]-lysine , [3H]-glutamine and [3H]-proline ( 1 μCi/ml ) ( Perkin-Elmer , Singapore ) at 25°C . Uptake was stopped at different times by adding 200 μl of 50 mM ice-cold L-arginine ( non-radioactive ) . Parasites were then washed twice with EBSS ( 200 μl ) , lysed in 200 μl lysing solution ( 0 . 1% SDS , 100 mM NaOH ) and radioactivity was measured by liquid scintillation counter ( Tri-Carb 2810 TR , Perkin Elmer , USA ) . Uptake was expressed as nmol/107 cells/min . Each measurement was performed in triplicate and data was expressed as mean±SD of three independent experiments . AD-Ld and AS-Ld parasites were harvested after 96 hr of incubation . RNA was extracted and c-DNA was prepared from 20μg of total RNA using c-DNA synthesis kit ( Invitrogen ) . The c-DNAs were then amplified by PCR for polyamine biosynthetic and thiol metabolic pathway genes , such as ornithine decarboxylase ( ODC ) , spermidine synthase ( SPS ) , gamma-glutamyl cysteine synthetase ( γ-GCS ) , trypanothione synthetase ( TryS ) , trypanothione reductase ( TR ) , cytoplasmic tryparedoxin ( cTXN ) and cytoplasmic tryparedoxin peroxidase ( CTP ) using gene specific primers ( Supplementary S1 Table ) . PCR was performed and analysis was done according to the conditions as described previously [35] . Each measurement was performed in triplicate and data was expressed as mean±SD of three independent experiments . Arginase activity in L-arginine depleted Leishmania donovani ( AD-Ld ) and L-arginine supplemented Leishmania donovani ( AS-Ld ) parasite lysates was measured using a micromethod as described previously [38 , 39] . Briefly , after individual incubations , cells were lysed in 0 . 1% Triton X-100 and added with 25 mM Tris-HCl . The cell lysate was then mixed with 10 mM MnCl2 and heated for 10 min at 56°C for enzyme activation . Following addition of 0 . 5 M L-arginine ( pH 9 . 7 ) to the cell lysate and incubation at 37°C for 15–20 min , the reaction was stopped with H2SO4 ( 96% ) /H3PO4 ( 85% ) /H2O ( 1/3/7 , v/v/v ) mixture . α-isonitrosopropiophenone was added to the mixture and heated at 95°C for 30 min . The urea concentration was then measured spectrophotometrically at 540 nm and total protein concentration was estimated through Bradford method . Each measurement was performed in triplicate and data was expressed as mean±SD of three independent experiments . The level of intracellular polyamines such as putrescine and spermidine in the leishmania parasites grown in arginine depleted and arginine supplemented media ( AD-Ld and AS-Ld ) for 24–120 hr were identified and quantified by high performance liquid chromatography ( HPLC ) using a prederivatization method as described previously [39 , 40] . Briefly , after individual incubations , the parasites were harvested , washed with PBS ( pH 7 . 4 ) and disintegrated with 5% ( w/v ) trichloroacetic acid for overnight . After centrifugation the supernatants were collected and neutralized with saturated NaHCO3 solution . Dansylation of the mixture was done using dansyl chloride ( 20 mg/ml ) ( Merck ) in acetone ( Merck , HPLC grade ) at 50°C for overnight . The polyamines were extracted with toluene ( Merck , HPLC grade ) , and evaporated under nitrogen stream . The residue was dissolved in acetonitrile ( Merck , HPLC grade ) and analyzed by HPLC on a C18 reverse-phase column ( Shimadzu , Japan ) . Dansylated polyamines were detected and quantified by spectrofluorometric measurement ( excitation wavelength 330 nm , emission wavelength 510 nm ) . The peak areas , retention times were recorded and calculated by a PC Integration Pack Programme . Polyamines were expressed as nmol per 107 promastigotes . There were three replicates in each test and the data were the mean±SD of three independent observations . Leishmania parasites were grown in different invitro culture conditions ( For eg . arginine depletion , AD-Ld; arginine depletion but L-ornithine supplementation , AD-Ld/Orn+; arginine depletion but putrescine supplementation , AD-Ld/Put+; arginine depletion but NAC treatment , AD-Ld/NAC+ and arginine supplementation , AS-Ld ) for 0–120 hr . After individual incubations were over , the cells were harvested and the level of intracellular ROS was monitored by staining the cells with the oxidative fluorescent dye 2' , 7'-dichlorodihydrofluorescein diacetate ( H2DCFDA ) ( 0 . 4 μM ) ( Sigma ) for 15 min at 37°C and then analyzed using LS-55 spectrofluorometer ( Perkin Elmer , USA ) as described previously [41] . Absorbance and emission wavelength were 504 and 529 nm respectively . There were three replicates in each test , and the data were the mean±SD of three independent observations . Leishmania parasites were grown in different invitro culture media for 0–120 hr as mentioned earlier . After individual incubations , AD-Ld , AD-Ld/Orn+ , AD-Ld/Put+ , AD-Ld/NAC+ and AS-Ld parasites were harvested , washed with 1X PBS and dissolved in 15% SDS-PBS solution . The total fluorescent lipid peroxidation products were estimated spectrofluorometrically with excitation at 360 nm and emission at 430 nm . The results were expressed as relative fluorescence units with respect to quinine sulphate ( 1 mg/ml in 0 . 5 M H2SO4 ) [42 , 43] . The data were mean±SD for three independent experiments performed in triplicate . The total intracellular reduced thiol level was measured in deproteinized cell extracts from Leishmania parasites grown for 72–120 hr in different invitro culture conditions ( AD-Ld , AD-Ld/Orn+ , AD-Ld/Put+ , AD-Ld/NAC+ and AS-Ld ) by the method as described elsewhere [44] . Briefly , the cells were harvested , washed with a buffer [0 . 14 M Na3PO4 , 0 . 14 M K3PO4 , 0 . 14 M NaCl , and 3 mM KCl , pH-7 . 4] and suspended in 25% trichloroacetic acid . The denatured protein and cell debris were removed by centrifugation . Thiol content in the supernatant was determined with 0 . 6 mM 5 , 5’-dithio-bis ( 2-nitrobenzoic acid ) ( DTNB , Ellman’s reagent ) in 0 . 2 M Na3PO4 buffer , pH 8 . 0 . DTNB derivatives of thiols were estimated spectrophotometrically at 412 nm . There were three replicates in each test , and the data were expressed as mean±SD of three independent observations . SOD activity was measured by using SOD assay kit ( SOD assay kit , Calbiochem ) as per manufacturer’s instruction . Briefly , after individual incubations , the parasites , as mentioned earlier , grown in different invitro culture conditions ( AD-Ld , AD-Ld/Orn+ , AD-Ld/Put+ , AD-Ld/NAC+ and AS-Ld ) for 72–120 hr , were pelletted down , homogenized in cold 20 mM HEPES buffer ( pH-7 . 2 , containing 1 mM EGTA , 210 mM mannitol and 70 mM sucrose ) and the supernatants were collected for assay . To 10 μl of supernatant , 200 μl of diluted radical detector was added followed by addition of 20 μl of diluted xanthine oxidase and incubation for 20 minutes . The absorbance was measured at 450 nm . One unit of SOD was defined as the amount of enzyme required to exhibit 50% dismutation of the superoxide radical . Data were expressed as mean±SD for three independent experiments performed in triplicate . NADP+/NADPH ratio for the AD-Ld , AD-Ld/Orn+ , AD-Ld/Put+ , AD-Ld/NAC+ and AS-Ld parasites were assayed spectrophotometrically using NADP/NADPH Quantification Kit ( Sigma ) as per manufacturer’s instruction as described [45] . The reduced coenzyme was measured spectrophotometrically at 450 nm . Results were mean±SD for three independent experiments performed in triplicate . Annexin-V-FITC staining was performed by the method as described previously with Annexin-V-FLUOS staining kit ( Roche ) as per manufacturer’s instructions [46] . Briefly , after individual incubations , the Leishmania parasites grown in different invitro culture conditions ( AD-Ld , AD-Ld/Orn+ , AD-Ld/Put+ , AD-Ld/NAC+ and AS-Ld ) for 72–120 hr were harvested , washed twice in PBS ( pH 7 . 2 ) , resuspended in HEPES buffer followed by addition of Annexin V-FITC and PI . The cells were then incubated for 15 min in the dark at 25°C and acquired on BD FACS Aria II flow cytometer followed by analysis with BD FACS Diva software . Results were expressed as mean±SD for three independent experiments performed in triplicate . Change in mitochondrial membrane potential ( Δψm ) in Leishmania promastigotes was estimated using JC-1 dye as described previously [46] . Briefly , after 72–120 hr of incubation , Leishmania parasites grown in different invitro conditions ( For eg . arginine depletion , AD-Ld; arginine depletion but L-ornithine supplementation , AD-Ld/Orn+; arginine depletion but putrescine supplementation , AD-Ld/Put+; arginine depletion but NAC treatment , AD-Ld/NAC+; L-arginine supplementation , AS-Ld and L-arginine supplementation but FCCP treatment , AS-Ld/FCCP+ ) were harvested and incubated with 10 μM JC-1 for 10 min at 37°C . The cells were washed , resuspended in media followed by fluorescence measurement using LS-55 spectrofluorometer ( Perkin Elmer , USA ) . Relative Δψm value was expressed as ratio of the reading at 590 nm to the reading at 530 nm . Results were expressed as mean±SD from three independent experiments performed in triplicate . Intracellular Calcium concentration in AD-Ld , AD-Ld/Orn+ , AD-Ld/Put+ , AD-Ld/NAC+ , AD-Ld/EGTA+ and AS-Ld parasites was measured following the protocol and using the fluorescent probe Fura-2-AM as described previously [43] . Each measurement was performed in triplicate and the data were expressed as mean±SD for three independent experiments . The occurrence of DNA nicking generated during apoptosis was detected by the Terminal deoxynucleotidyl transferase dUTP nick end labelling ( TUNEL ) assay as described previously using In situ Cell Death Detection kit , Fluorescein ( Roche ) as per manufacturer’s instruction [47] . Briefly , AD-Ld , AD-Ld/Orn+ , AD-Ld/Put+ , AD-Ld/NAC+ and AS-Ld parasites were harvested as mentioned earlier and dissolved in 100 μl of 1X PBS followed by addition of 50 μl of Fixation and Permeabilizing solution and incubation at 4°C for 15 min . The cells were pelleted down , washed with 1X PBS , resuspended in 50 μl of TUNEL mixture and incubated at 22°C BOD incubator for 1 hr . Finally , the cells were washed and resuspended in PBS and analyzed by flow cytometry ( BD FACS Aria II ) . Each measurement was performed in triplicate and the data were expressed as mean±SD for three independent experiments . DNA fragmentation was assessed using Cell Death Detection ELISA plus kit ( Roche ) as per manufacturer’s instruction . Briefly , after individual incubations , AD-Ld , AD-Ld/Orn+ , AD-Ld/Put+ , AD-Ld/NAC+ and AS-Ld parasites were harvested , lysed and reacted with biotin-coupled mouse monoclonal anti-histone antibody . The complex was detected by peroxidase-conjugated mouse monoclonal anti-DNA antibody and ABTS [2 , 2’-azino-bis ( 3-ethylbenzthiazoline-6-sulphonic acid ) ] as a developing reagent to quantify the cytoplasmic nucleosomes generated due to DNA fragmentation . Absorbance was measured in a Thermo Multiskan EX plate reader at 405 nm and the results were expressed as relative percentages . Each measurement was performed in triplicate and the data were expressed as mean±SD for three independent experiments . Leishmania parasites grown in different invitro conditions ( For eg . arginine depletion , AD-Ld; arginine depletion but L-ornithine supplementation , AD-Ld/Orn+; arginine depletion but putrescine supplementation , AD-Ld/Put+; arginine depletion but NAC treatment , AD-Ld/NAC+; L-arginine supplementation , AS-Ld and L-arginine supplementation but Camptothecin-B treatment , AS-Ld/CPT+ ) were harvested after 96 hrs of incubation and washed twice with 1X PBS , suspended in cell fractionation buffer ( ApoAlert cell fractionation kit , BD ) , homogenized and cytosolic & mitochondrial fractions were separated by following the manufacturer’s instructions ( Clontech , Palo Alto , CA , USA ) [47] . For confirmation of the semi-quantitative RT-PCR data , 50μg cytosolic fractions collected from AD-Ld and AS-Ld parasites were run on 12% SDS-polyacrylamide gel , immunoblotted and probed with rabbit polyclonal anti-TryS ( 1:5000 ) , anti-TR ( 1:2000 ) and anti-CTP ( 1:5000 ) antibodies . For cytochrome-c detection , the cytosolic fraction from all the treated parasites were immunoblotted and probed with the rabbit polyclonal anti-cytochrome-c antibody ( 1:1000 ) . Horseradish peroxidase-conjugated secondary antibody ( 1:10000 ) was used for all the cases and alpha-tubulin ( α-Tub ) was used as an endogenous control . The protein band was visualized by enhanced chemiluminescence reaction and expressed as fold change . To determine the caspase activity ( caspase—1 , 3 , 5 , 8 and 9 ) of Leishmania parasites , a fluorogenic homogeneous caspase assay was performed using the Caspase Family Fluorometric Substrate Set plus kit ( Bio-Vision ) according to manufacturer’s instructions . The fluorometric measurement was recorded with a LS-55 spectrofluorometer ( Perkin Elmer , USA ) with excitation at 400 nm and emission at 505 nm . Each measurement was performed in triplicate and data was expressed as mean±SD of three independent experiments . Statistical analysis was performed using GraphPad Prism Program ( Version 6 . 0 , GraphPad Software , USA ) . All results were shown as mean±SD . Statistically significant differences were determined using students t-test . P values equal or below 0 . 05 were considered significant .
In this study , we used RPMI-1640 without L-arginine media and supplemented with upto 200 mg/L ( 1 . 149 mM ) of L-arginine to characterize the role of L-arginine in the growth and survival of L . donovani promastigotes [48 , 49] . Results showed that the percent cell viability decreased with increasing time interval . A significant difference ( ~1 . 65 fold , ~2 . 0 fold and ~2 . 8 fold ) in reduction in cell viability was observed at 72 hr , 96 hr and 120 hr respectively for L-arginine deprived parasites compared to control parasites ( Fig 1A ) . The viability of the Leishmania parasite in single amino acid free ( for eg . L-arginine , lysine , glutamine and proline ) as well as complete amino acid free ( AA- ) RPMI media were also analyzed . It was found that the viability of the Leishmania parasite is significantly reduced in amino acid deprived media and L-arginine deprived media compared to normal RPMI media ( ctrl ) . However , the reduction in cell viability observed in lysine , glutamine or proline free media was insignificant ( S1A Fig ) . Simultaneously , cell viability in amino acid free but single amino acid supplemented ( 200 mg/L ) media was also investigated . The result indicates that single amino acid L-arginine supplementation restored the cell viability upto ~80% whereas other amino acids failed to restore the growth ( S1B Fig ) . BrdU cell proliferation assay was conducted and found in all cases , that the rate of cell proliferation increased with increasing time intervals up to 48 hr . However , the rate of cell proliferation decreased from the 48 hr time point onwards . Despite L-arginine supplemented parasites ( L-Arg 100–200 mg/L ) showing considerably increased proliferation up to 120 hr , the rate of cell proliferation decreased gradually in L-arginine deprived parasites ( L-Arg 50 mg/L and L-Arg 0 mg/L ) up to 120 hr ( Fig 1B ) . The minimum Leishmania parasites proliferation rate was observed when grown in L-arginine depleted media . The decrease in rate of cell proliferation was found to be ~2 . 0 fold , ~3 . 5 fold and ~4 . 8 fold at 72 hr , 96 hr and 120 hr respectively for L-arginine deprived parasites compared to parasites grown in normal RPMI media . Experiments , to further understand the inhibition of growth , induced by L-arginine deprivation , were conducted to investigate whether supplementing equal amount of other amino acids such as lysine , glutamine and proline ( 200 mg/L ) to the culture medium could reverse the effect . The addition of these amino acids did not produce any noticeable significant changes in the growth patterns of arginine depleted Leishmania donovani ( AD-Ld ) ( Fig 1C ) . To confirm the importance of L-arginine in the growth of the Leishmania parasite inside macrophages , we infected THP-1 , a human monocytic macrophage like cell line with Leishmania parasite in the presence or absence of S- ( 2-Boronoethyl ) -L-cysteine ( BEC ) , an inhibitor of L-arginine-utilizing enzyme , arginase for 24 hr and 48 hrs . The sole objective behind the use of BEC was to prevent the macrophages from utilizing the available L-arginine in the extracellular milieu . This was to create an L-arginine deprived condition for the macrophages and to examine the resulting effect on parasite survival by determining the parasitic load and percent infectivity . It was found that the parasite load ( Number of amastigotes per 100 macrophages ) decreased by ~1 . 8 fold and ~2 . 5 fold at 24 hr and 48 hr respectively in presence of BEC ( Fig 1D and 1E ) when compared to control . Simultaneously , percent infectivity also decreased by ~2 . 1 fold and ~2 . 3 fold respectively at 24 hr and 48 hr in presence of BEC ( Fig 1F ) . Therefore , our results indicate that inhibition of arginase or limited L-arginine availability led to reduced level of infection . The importance of L-arginine was further characterized by measuring the radioactive L-arginine transport in Leishmania parasite . Firstly , experiments were performed to determine a concentration kinetics of radioactive L-arginine ( 0–5 μCi/ml ) using stock from 1 mCi/ml ( specific activity-51 . 5 Ci/mmole ) and measure its transport in Leishmania parasites grown in L-arginine deprived or supplemented media . Results indicated that the uptake of L-arginine increases with increasing extracellular radioactive L-arginine concentration ( S2 Fig ) . The rate of increase in transport was found to be comparatively higher in arginine depleted Leishmania donovani ( AD-Ld ) compared to arginine supplemented Leishmania donovani ( AS-Ld ) . In both cases , measurable amounts of L-arginine uptake was observed when we used 1 μCi/ml of extracellular 3H L-arginine ( S2 Fig ) . To understand how quickly L-arginine is taken up by the Leishmania parasite , we performed a time kinetics of L-arginine transport according to previously described methods [37] . We treated AD-Ld and AS-Ld parasites with 3H L-arginine for 0–32 minutes and found that within 4 minutes of incubation the uptake increases at a very high rate and afterwards reaches a plateau in case of AD-Ld parasites . In comparison , AS-Ld parasites uptake still occurred but with very slow rate ( Fig 2A ) . This result indicated that the rate of L-arginine uptake depended on the time of incubation and most of the L-arginine was uptaken by the Leishmania parasites within 8 minutes . Therefore , based on the results obtained we selected 1 μCi/ml dose of L-arginine for 8 minutes incubation in all the subsequent experiments . To further understand the effect of L-arginine starvation on the transport of L-arginine in a broader time interval , we performed transport measurement experiments in Leishmania parasites grown in L-arginine depleted media supplemented with different conc . of L-arginine ( 0–200 mg/L ) for different time intervals ( 0–120 hr ) . It was found that L-arginine transport increased gradually with increasing time of incubation . AD-Ld parasites were found to uptake L-arginine at a very high rate in comparison to AS-Ld parasites . Significant difference in L-arginine uptake was observed 72 hours onwards . ~2 . 7 fold , ~3 . 8 fold and ~7 . 1 fold increase in L-arginine uptake was observed for AD-Ld parasites compared with AS-Ld parasites at 72 hr , 96 hr and 120 hr respectively ( Fig 2B ) . The transport of L-arginine in the presence of other amino acids such as lysine , glutamine and proline was also measured ( Fig 2C ) . However , no difference in uptake was observed between AD-Ld and AD-Ld/Lys+ or AD-Ld/Gln+ or AD-Ld/Pro+ parasites . On the other hand , when we measured the transport of lysine ( Fig 2D ) , glutamine ( Fig 2E ) and proline ( Fig 2F ) in AD-Ld and AS-Ld parasites , no difference in uptake was observed . Taken together , these results indicate that L-arginine deprivation in Leishmania parasite specifically up-regulates its transport in time and concentration dependent manner . The polyamine biosynthetic and thiol metabolic pathway enzymes such as ODC , SPS , γ-GCS , TryS , TR , c-TXN and CTP are involved in the synthesis polyamines , trypanothione and tryparedoxin utilizing L-arginine as a primary substrate . Polyamines play a crucial role in the growth and survival of trypanosomatids including Leishmania parasites . The effect of L-arginine starvation on the transcripts of these enzymes were also determined . A significant decrease in transcript abundance of ODC ( ~4 . 7 fold ) , SPS ( ~4 . 0 fold ) , γ-GCS ( ~4 . 0 fold ) , TryS ( ~5 . 5 fold ) , TR ( ~3 . 6 fold ) , cTXN ( ~2 . 7 fold ) and CTP ( ~3 . 3 fold ) was observed for AD-Ld parasites as assessed by semi-quantitative RT PCR ( Fig 3A ) . The expression of some of the enzymes of the polyamine and thiol metabolic pathway ( For eg . TryS , TR and CTP ) was also evaluated at the translational level by immunoblot analysis . A significant decrease in protein level of Trys ( ~2 . 8 fold ) , TR ( ~2 . 3 fold ) and CTP ( ~2 . 2 fold ) was observed for AD-Ld parasites compared to AS-Ld parasites ( Fig 3B ) . To study the effect of L-arginine availability in regulation of arginase , the first enzyme of arginine metabolism , the activity of the enzyme was also measured . Fig 4A shows the changes in arginase activity in arginine supplemented Leishmania donovani ( AS-Ld ) and arginine depleted Leishmania donovani ( AD-Ld ) parasites for 0–120 hr . Differences in arginase activity were apparent from 72 hours post incubation ( Fig 4A ) . ~2 . 1 fold , ~3 . 1 fold and ~4 . 0 fold decrease in arginase activity was observed for AD-Ld parasites when compared with AS-Ld parasites at 72 hr , 96 hr and 120 hr respectively . Subsequently , the level of intracellular polyamines ( putrescine and spermidine ) were quantified by HPLC . Interestingly , we found that at all the time intervals the level of polyamines were lower in arginine depleted Leishmania donovani ( AD-Ld ) parasites compared to arginine supplemented Leishmania donovani ( AS-Ld ) parasites . At 96 hr and 120 hour a marked difference in putrescine level ( ~3 . 7 and ~2 . 9 fold ) was observed ( Fig 4B ) , whereas the difference in spermidine content was found to be very less in AD-Ld and AS-Ld parasites ( ~1 . 7 and ~1 . 3 fold ) ( Fig 4C ) . However , the concentration of both polyamines decreased with increasing time in all cases . We also determined the growth of the parasite in L-arginine depleted media and L-arginine depleted but supplemented with 200 mg/L of L-ornithine , putrescine , spermidine and spermine , in order to confirm that the inhibition of growth of Leishmania parasite in L-arginine deprived media is due to polyamine depletion . We have chosen this concentration of L-ornithine and polyamines because 200 mg/L of L-arginine was the optimum concentration required for Leishmania growth . L-ornithine and putrescine supplementation was found to almost completely restored the growth of the parasite ( Fig 4D and 4E ) . In comparison , spermidine restored growth of the parasite to lesser extent ( Fig 4F ) and spermine had very negligible effects on restoration of growth ( Fig 4G ) . The involvement of different forms of ROS including superoxide and hydrogen peroxide ( H2O2 ) has been reported to play a crucial role in regulation of cellular growth and cell death [50] . In this study , we targeted to explore whether L-arginine deprivation triggered intracellular ROS accumulation and induced apoptosis like cell death in L . donovani parasites . A gradual increase in ROS level was observed for arginine depleted Leishmania donovani ( AD-Ld ) promastigotes ( Fig 5A ) whereas no significant increase in ROS level was observed for arginine supplemented Leishmania donovani ( AS-Ld ) parasites . However , when AD-Ld parasites were pre-incubated with NAC , a ROS scavenger , the elevated ROS level fell to the control level . When , we compared the difference in ROS production between AD-Ld and AS-Ld parasites , it was found that ~2 . 4 fold , ~3 . 3 fold and ~3 . 5 fold induction in ROS level was observed after 72 hr , 96 hr and 120 hr of incubation respectively . Hence , this result indicated that induction in intracellular ROS production might be the possible reason for reduced cell viability for AD-Ld parasites . Further , total fluorescent lipid peroxidised products were spectrophotometrically measured in arginine depleted Leishmania donovani ( AD-Ld ) and arginine supplemented Leishmania donovani ( AS-Ld ) parasites at 0–120 hr . A gradual significant increase in fluorescence intensity was observed for AD-Ld parasites with increasing time of incubation whereas in case of AS-Ld parasites , the increase was not significant ( Fig 5B ) . Non protein thiols are important antioxidants which maintain cellular redox homeostasis through nullifying oxidative perturbations [51] . A gradual decrease in thiol levels was observed for arginine depleted Leishmania donovani ( AD-Ld ) parasites with increasing time interval , whereas in case of arginine supplemented Leishmania donovani ( AS-Ld ) parasites a slow increase in thiol level was observed . Significant difference ( for eg . ~1 . 6 fold , ~2 . 0 fold and ~2 . 3 fold decrease ) in thiol content between AD-Ld and AS-Ld parasites was observed at 72 hr , 96 hr and 120 hr respectively ( Fig 5C ) . The results were consistent with the increased ROS production due to L-arginine deprivation in Leishmania parasites . To determine whether this oxidative stress generated in L . donovani in case of L-arginine deprivation is associated with reduced antioxidant level , we measured the activity of an anti-oxidant enzyme , SOD , a metal containing enzyme that is present in parasite L . donovani [52] . ~2 . 2 fold , ~3 . 1 fold and ~3 . 3 fold decrease in SOD activity was observed for arginine depleted Leishmania donovani ( AD-Ld ) parasites compared to arginine supplemented Leishmania donovani ( AS-Ld ) parasites at 72 hr , 96 hr and 120 hr of incubation respectively ( Fig 5D ) . Therefore , this decrease in SOD activity in AD-Ld parasites might be one of the reason for increased ROS production in Leishmania parasites and hence decreasing the cell viability . NADP , an enzymatic cofactor shuttles between the reduced ( NADPH ) and oxidized ( NADP ) forms , profoundly supports the major antioxidants and redox regulatory enzymes , by providing reducing potential in the form of NADPH [53] . A gradual increase in relative NADP+/NADPH ratio was observed for arginine depleted Leishmania donovani ( AD-Ld ) parasites with increasing time of incubation , whereas no such change was observed for arginine supplemented Leishmania donovani ( AS-Ld ) parasites ( Fig 5E ) . A significant difference in relative NADP+/NADPH ratio was observed for AD-Ld parasites for 72 hr onwards compared to AS-Ld parasites . For eg . ~1 . 7 fold and ~1 . 9 fold increase in NADP+/NADPH ratio was observed for AD-Ld parasites at 96 hr and 120 hr of incubation . This is correlated with increased ROS production in AD-Ld parasites compared to AS-Ld parasites . To elucidate the mechanism of cell death induced by L-arginine deprivation , both arginine depleted Leishmania donovani ( AD-Ld ) and arginine supplemented Leishmania donovani ( AS-Ld ) promastigotes were stained with Annexin-V-FITC and PI followed by analysis in flow cytometer . Although in both cases , gradual increase in apoptotic cells was observed , the percent of apoptotic cells was higher in case of L-arginine deprivation ( Fig 6 ) . Significant differences in percent apoptotic cells were observed at 72 hr , 96 hr and 120 hr ( 3 . 3 fold , 4 . 0 fold and 3 . 6 fold respectively ) . However , when AD-Ld parasites were supplemented with ornithine ( 200 mg/L ) or putrescine ( 200 mg/L ) or pre-incubated with NAC , a ROS scavenger , the percent apoptotic cells decreased to the control level . Apoptosis in protozoan parasites such as Leishmania is characterized by significant fall in mitochondrial membrane potential ( Δψm ) [10] . JC-1 , a cationic lipophilic mito-sensor dye accumulates in the mitochondria of healthy cells . Our assessment of Δψm using JC-1 showed a gradual decrease in fluorescence intensity for arginine depleted Leishmania donovani ( AD-Ld ) parasites ( Fig 7A ) . Simultaneously ~2 . 1 fold , ~3 . 5 fold and ~3 . 1 fold decrease in fluorescence intensity was observed for arginine depleted Leishmania donovani ( AD-Ld ) parasites when compared to arginine supplemented Leishmania donovani ( AS-Ld ) parasites at 72 hr , 96 hr and 120 hr incubation respectively . However , when AD-Ld parasites were supplemented with ornithine ( 200 mg/L ) or putrescine ( 200 mg/L ) or pre-incubated with NAC , the fluorescence intensity increased to the control level . Simultaneously , when AS-Ld parasites were treated with FCCP , a decrease in fluorescence intensity was observed . This result clearly demonstrated that overproduced ROS in L . donovani promastigotes led to alterations in mitochondrial membrane potential which validates previous reports [54] . Cytosolic increase in Ca2+ level has been found to be associated with mitochondrial membrane depolarization under oxidative stress in L . donovani [12] . A gradual increase in intracellular calcium concentration was observed for arginine depleted Leishmania donovani ( AD-Ld ) parasites whereas in case of arginine supplemented Leishmania donovani ( AS-Ld ) parasites no such increase was observed for 0–120 hr . Here , we found ~2 . 5 fold , ~3 . 1 fold , ~3 . 5 fold and ~3 . 2 fold increase in intracellular calcium level for AD-Ld parasites when compared to AS-Ld parasites at 48 hr , 72 hr , 96 hr and 120 hr respectively ( Fig 7B ) . However , when AD-Ld parasites were supplemented with ornithine ( 200 mg/L ) or putrescine ( 200 mg/L ) or pre-incubated with NAC , the fluorescence intensity decreased to the control level . Furthermore , incubation with extracellular Ca2+ chelator , EGTA decreased the intracellular Ca2+ to normal level as depicted in the case of AS-Ld parasite . Therefore , the above findings mechanistically elucidate that ROS generation due to L-arginine deprivation causes irrepairable damage to calcium channels resulting in enhancement of intracellular calcium reserve followed by loss of Δψm leading to an apoptosis-like cell death . Cytochrome-c , localized in the inner mitochondrial membrane space , comprises the electron transport chain and is released into the cytosol during the initial steps of apoptosis . We performed western blot hybridization ( Fig 7C ) to determine the localization of cytochrome-c in arginine depleted Leishmania donovani ( AD-Ld ) and arginine supplemented Leishmania donovani ( AS-Ld ) parasites . A small amount of cytochrome-c was found in the cytoplasmic fraction of the AS-Ld promastigotes whereas cytochrome-c was abundant in the cytoplasmic fraction of AD-Ld promastigotes . ~3 . 1 fold increase in cytochrome-c band intensity was observed for AD-Ld parasites compared to AS-Ld parasite at 96 hr . However , when AD-Ld parasites were supplemented with ornithine ( 200 mg/L ) or putrescine ( 200 mg/L ) or pre-incubated with NAC , significant decrease in release of cytochrome-c compared to AD-Ld parasites were observed . Significant increase in release of cytochrome-c was also observed for AS-Ld parasites treated with camptothecin ( CPT ) . Caspase-like protease activity responsive to different apoptotic stimuli has been reported to control the cellular death of unicellular kinetoplastid parasites [10 , 24] . Therefore , in the present study , we investigated whether there is any change in caspase-like protease activity due to L-arginine deprivation in L . donovani parasites . However , we did not find any significant increase in caspase activity ( Caspase-1 , 3 , 5 , 8 , 9 ) in arginine depleted Leishmania donovani ( AD-Ld ) parasites as compared to arginine supplemented Leishmania donovani ( AS-Ld ) parasites ( S3A–S3E Fig ) . The occurrence of DNA nicking and internucleosomal DNA digestion is a hallmark of apoptosis-like cell death [55] . TUNEL assay based analysis detected FITC labeled dUTP attached to the nick ends through TdT and revealed the presence of nicking of DNA in AD-Ld parasites . ~ 2 . 0 fold , ~2 . 5 fold and ~2 . 5 fold increase in percent TUNEL positive cells were observed for arginine depleted Leishmania donovani ( AD-Ld ) parasites compared to arginine supplemented Leishmania donovani ( AS-Ld ) parasites at 72 hr , 96 hr and 120 hr of incubation respectively ( Fig 7D ) . However , when AD-Ld parasites were supplemented with ornithine ( 200 mg/L ) or putrescine ( 200 mg/L ) or pre-incubated with NAC , significant decrease in TUNEL positive cells were observed when compared to AD-Ld parasites . To investigate whether L-arginine deprivation of L . donovani cells also resulted in occurrence of DNA fragmentation , we carried out genomic DNA fragmentation assay by determining cytoplasmic histone associated mono and oligonucleosomes as described in ‘‘Materials and methods” . ~ 2 . 7 fold , ~3 . 3 fold and ~2 . 9 fold increase in percent DNA fragmentation was observed for arginine depleted Leishmania donovani ( AD-Ld ) parasites compared to arginine supplemented Leishmania donovani ( AS-Ld ) parasites at 72 hr , 96 hr and 120 hr of incubation respectively ( Fig 7E ) . Furthermore , when AD-Ld parasites were supplemented with ornithine ( 200 mg/L ) or putrescine ( 200 mg/L ) or pre-incubated with NAC , significant decrease in percent DNA fragmentation compared to AD-Ld parasites were observed .
The importance of L-arginine for the growth of Leishmania parasite was demonstrated in 1970s by Krassner et al . [56] indicating that these parasites import this amino acid for the growth in invitro culture media [56 , 57] . However , how L-arginine is utilized by the Leishmania parasite and promotes growth has not been clearly elucidated . Our study revealed that the growth of the Leishmania parasite is hindered in absence of L-arginine . Simultaneously the rate of proliferation of Leishmania parasite also gets reduced in absence of L-arginine , whereas L-arginine supplementation again increases the rate of proliferation , indicating that the growth and proliferation of the Leishmania promastigote depends on the availability of L-arginine in the extracellular milieu . Therefore , the decrease in Leishmania growth and viability is associated with reduced proliferation . Interestingly supplementation of L-arginine deprived parasite with other amino acids such as lysine , glutamine or proline alone could not restore the growth of the parasite . The survival of the Leishmania parasite inside macrophage also gets reduced in presence of inhibitor of arginase . Therefore , it indicates that L-arginine is essential for the growth of the parasite and other amino acids could not act as replacement . This establishes the importance and uniqueness of this amino acid in the survival of the Leishmania parasite . Findings from our experiment is in agreement with the previous report [58 , 59] which states that as there is no evidence for denovo synthesis of L-arginine in the Leishmania parasite , its metabolism depends on extracellular supplies , making L-arginine , an essential amino acid for the growth and survival . Similar type of result was also observed for T . gondii parasite , an obligate intracellular parasite [60] . L-arginine deprivation also resulted in its increased uptake . However , no difference in uptake of other amino acids such as lysine , glutamine or proline was observed between AD-Ld and AS-Ld parasites . Our observation is in agreement with the studies of Darlyuk I et al . [61] and indicates that upon sensing low concentration of L-arginine , L . donovani promastigotes specifically increases the L-arginine uptake probably by upregulating the expression of its transporter . Polyamines ( PAs ) like putrescine and spermidine provide critical biosynthetic input in the synthetic machinery of trypanothione , thereby promoting growth and survival of trypanosomatid protozoa [62] . The metabolic process which generates L-ornithine following enzymatic hydrolysis of L-arginine involves arginase on one hand and ODC on the other , to generate putrescine via decarboxylation of L-ornithine . Our previous study suggested that the ROS generated due to autooxidation of amphotericin B was efficiently nullified by polyamine biosynthetic and thiol metabolic pathway enzymes in resistant parasite [35] . Therefore , it is evident that L-ornithine , putrescine along with spermidine feeds the cellular antioxidant machinery to cope up with different forms of oxidative stress . In this study , we found a very low m-RNA expression of polyamine biosynthetic and thiol metabolic pathway genes such as ODC , γ-GCS , SPS and in particular TryS , TR , c-TXN and CTP for L-arginine deprived parasites whereas in case of L-arginine supplemented parasites all these genes were upregulated . The activity of arginase , the first enzyme of arginine metabolism was also found to be downregulated for L-arginine deprived parasites . The activity of the enzyme depends on the availability of the substrate . Here , as the substrate , L-arginine is limited; the activity of the downstream enzyme gets decreased both at transcript and translational level as observed through semi-quantitative RT-PCR and immunoblot analysis respectively . It can therefore be concluded that due to non-availability of arginine , the expression of polyamine biosynthetic and thiol metabolic pathway enzymes get downregulated and that results in the decreased production of polyamines such as putrescine and spermidine . To understand the mechanism of reduced growth rate and cell viability of L . donovani parasite in L-arginine depleted media , we assayed intracellular ROS generation . Interestingly , significant increase in intracellular ROS generation was observed for AD-Ld parasites compared to AS-Ld parasites . It was also observed that L-arginine supplementation decreases the ROS level . Induction of ROS due to nutrient deprivation ( such as glucose and amino acid ) has been reported previously [63] . To the best of our knowledge this is the first report describing ROS generation due to L-arginine deprivation in L . donovani . Therefore , it can be concluded that ROS generation might be one of the causes for reduced cell viability and growth of Leishmania parasite in L-arginine depleted media . Studies of Das et al . [64] showed that curcumin treatment to Leishmania parasite leads to an increase in lipid peroxides levels . Increase in lipid peroxide with increasing time was also observed for L . donovani after spiningerin treatment by Sardar et al . [43] . In this study , we found a significant increase in lipid peroxide level for Leishmania promastigotes grown in L-arginine depleted media with increasing time interval , whereas L-arginine supplementation restores this increase in lipid peroxidation . Hence , it can be concluded that ROS generation due to L-arginine deprivation might be one of the causes for increasing lipid peroxidation that ultimately leads to reduced cell viability and growth of Leishmania parasite . The free radicals elicited by the phagocytic macrophages is successfully evaded by the parasitic protozoan Leishmania via utilization of elaborate network of non-protein thiols like glutathione and trypanothione [51] . In this study , a significant difference in thiol level between AD-Ld and AS-Ld parasite was observed with increasing time of incubation . The decreased intracellular thiol level in AD-Ld parasites is correlated with increased intracellular ROS production leading to decreased parasite survival . The cellular concentration of ROS is controlled by anti-oxidant enzymes . The balance between ROS generation and ROS elimination by antioxidant enzymes helps to maintain cellular function . Therefore , a decrease in anti-oxidant enzyme levels leads to an overall increase in intracellular ROS levels and causes cell death [65] . The involvement of SOD in detoxification of reactive superoxide radicals produced by activated macrophages and survival had been described in Leishmania promastigotes [66] . In this study , we tried to investigate the probable reason of reduced cell viability of Leishmania donovani in L-arginine deprived condition . As increased ROS level is associated with reduced cell survival and apoptosis and SOD is an anti-oxidant enzyme that helps to neutralize ROS , we measured the activity of SOD . In this study , a gradual decrease in SOD activity was observed for Leishmania parasites grown in L-arginine deprived media , whereas in case of parasites grown in L-arginine enriched media , SOD activity gradually increased . Therefore , it is obvious that this reduced SOD activity led to increased ROS generation in L-arginine deprived condition . Finally , we concluded that the reduction in cell viability of AD-Ld parasite was due to increased ROS generation and decreased thiol levels . NADP+/NADPH ratio critically regulates the oxidative homeostasis inside cell . In this study an increased NADP+/NADPH ratio was observed for L-arginine deprived parasites with increasing time interval which further supports our hypothesis of ROS production due to L-arginine deprivation resulting in reduced viability of Leishmania parasite . Apoptosis-like phenomenon was observed in L . donovani promastigotes upon treatment with hydrogen peroxide [10 , 12] and nutrient deprivation [24] whereas staurosporine treatment [67] and serum withdrawal [18] demonstrated similar changes in case of L . major promastigotes . To the best of our knowledge , this is the first report where we showed significant increase in early apoptotic cells in L-arginine deprivation condition with increasing time of incubation . When we measured the alteration in mitochondrial membrane potential ( Δψm ) , we found that as expected with increasing time interval , a significant decrease in Δψm was observed for AD-Ld parasites compared to AS-Ld parasites . High ROS levels bring about cationic influx in cytosol which leads to mitochondrial dysfunction and depolarization [68] . With already established evidences of calcium influx modulating the activation of different proteases and regulating the exhibition of phosphatidyl serine on the outer leaflet of plasma membrane during apoptosis [69] , we considered to measure the Ca2+ concentration in L-arginine deprived and L-arginine supplemented Leishmania parasites . Interestingly , a gradual increase in intracellular cytosolic Ca2+ level was observed for AD-Ld parasites whereas no such change was observed for AS-Ld parasites . Simultaneously , significant increase in cytosolic cytochrome-c was also observed for AD-Ld parasite compared to AS-Ld parasite . Therefore , the increase in cytosolic Ca2+ and cytochrome-c is correlated with increased ROS level resulting in decrased cell viability in L-arginine deprivation condition . Collectively , our results clearly suggest that the biochemical basis behind reduced L-arginine availability and increased ROS generation is due to downregulation of SOD activity , increased NADP+/NADPH ratio , reduced thiol level , release of intracellular calcium and decreased mitochondrial membrane potential ( Fig 8 ) . As the apoptotic pathways are stringently regulated by caspases , the presence of caspase-like activity in the parasites under L-arginine deprivation condition was examined . However , recent apoptosis work challenges the accepted roles of caspase proteases [23] . Dolai et al . [70] also reported ROS dependent but caspase independent apoptosis-like cell death in L . major promastigotes . Interestingly in our study , no significant increase in caspase activity was observed for AD-Ld parasites compared to AS-Ld parasites with increasing time intervals . To further confirm apoptosis-like cell death of Leishmania parasite , we analyzed DNA nicking by TUNEL assay using flow cytometer and DNA fragmentation by Cell Death Detection ELISA . As expected , a significant increase in TUNEL positive cells and DNA fragmentation was observed for AD-Ld parasites compared to AS-Ld parasites with increasing time intervals . From this study , it can be concluded that L-arginine is a crucial molecule for survival and growth of Leishmania parasite , in absence of which the parasite undergoes an apoptosis-like cell death . Our study is the first report describing the mechanism of L-arginine dependent regulation of cell survival and apoptosis in L . donovani promastigotes ( Fig 8 ) . The molecular mechanism for L-arginine regulated caspase independent apoptosis-like cell death involves ROS mediated DNA degradation through intracellular calcium influx and subsequent depolarization of mitochondrial membrane potential along with cytosolic cytochrome-c release . Therefore , this important amino acid might be targeted for the efficient control of Leishmania growth and thereby occurrence of VL . | Leishmania donovani , the causative agent of Indian Visceral Leishmaniasis , resides in the gut of the insect vector and the macrophages of their mammalian host and avail nutrients for survival . Nutrient deprivation such as glucose or amino acid alters redox balance in mammalian cells as well as some lower organisms . However , the role of L-arginine , in regulation of redox balance and L . donovani survival yet not properly elucidated . In the present study , we found that L-arginine deprivation from the culture medium hinders growth and proliferation of Leishmania promastigotes . Starvation of L-arginine downregulates the expression of polyamine biosynthetic and thiol metabolic pathway enzymes leading to decreased production of polyamines in Leishmania parasites . Moreover , deprivation of L-arginine alters redox balance in Leishmania promastigotes characterized by the concomitant increase in ROS and decreased antioxidant level . Furthermore , L-arginine deprivation triggered phosphatidyl serine externalization , alteration in mitochondrial membrane potential , release of intraellular calcium and cytochrome-c followed by DNA damage . In summary , the growth and survival of Leishmania depends on the availability of extracellular L-arginine , in absence of which the parasite undergoes ROS mediated , caspase-independent apoptosis-like cell death . Therefore , targeting L-arginine metabolism pathway could be an alternative approach for controlling Leishmania growth and hence disease outcome . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2016 | Deprivation of L-Arginine Induces Oxidative Stress Mediated Apoptosis in Leishmania donovani Promastigotes: Contribution of the Polyamine Pathway |
The spread of antibiotic resistance is always a consequence of evolutionary processes . The consideration of evolution is thus key to the development of sustainable therapy . Two main factors were recently proposed to enhance long-term effectiveness of drug combinations: evolved collateral sensitivities between the drugs in a pair and antagonistic drug interactions . We systematically assessed these factors by performing over 1 , 600 evolution experiments with the opportunistic nosocomial pathogen Pseudomonas aeruginosa in single- and multidrug environments . Based on the growth dynamics during these experiments , we reconstructed antibiotic combination efficacy ( ACE ) networks as a new tool for characterizing the ability of the tested drug combinations to constrain bacterial survival as well as drug resistance evolution across time . Subsequent statistical analysis of the influence of the factors on ACE network characteristics revealed that ( i ) synergistic drug interactions increased the likelihood of bacterial population extinction—irrespective of whether combinations were compared at the same level of inhibition or not—while ( ii ) the potential for evolved collateral sensitivities between 2 drugs accounted for a reduction in bacterial adaptation rates . In sum , our systematic experimental analysis allowed us to pinpoint 2 complementary determinants of combination efficacy and to identify specific drug pairs with high ACE scores . Our findings can guide attempts to further improve the sustainability of antibiotic therapy by simultaneously reducing pathogen load and resistance evolution .
The rise of antibiotic resistance is reducing the arsenal of available drugs to treat bacterial infections [1–3] . Some infections are already nearly untreatable because the infecting pathogens are resistant to virtually all available drugs [4 , 5] . The identification and establishment of new antibiotics has become a major focus of national and international health programs , and substantial investments have been directed towards drug discovery , for example , by the United States and the European Union [6–10] . Yet even if these attempts succeeded and dozens of novel compounds became available tomorrow , the antibiotic crisis would not subside . The evolution of resistance is inevitable , and new drugs will be incapacitated within short time periods [2 , 3] . So how can we hamper this evolutionary march towards resistance ? To some extent , we cannot escape the open-ended arms race between compound discovery and resistance evolution . Nevertheless , we may still use evolutionary thinking to enhance treatment efficacy and sustainability [11] . Combination therapy , the simultaneous deployment of 2 or more drugs , is commonly proposed [12] . Indeed , WHO has endorsed it as the first-line strategy to treat diseases such as tuberculosis , malaria , or HIV [13–15] . However , the nature of the drug combination is crucial for treatment success because initially effective combinations may maximize selection for antibiotic resistance [16 , 17] . The approach of experimental evolution has proven highly informative on exploring the dynamics that shape the emergence and spread of drug resistance [11 , 18] . Using this approach , drug pairs were previously suggested to be most effective at limiting bacterial adaptation if ( i ) antimicrobials display collateral sensitivity , such that bacteria that evolve resistance to one of the compounds immediately suffer exacerbated suppression by the other [19–22] , or ( ii ) antibiotics interact antagonistically , such that they inhibit each other’s effect [16 , 23 , 24] . A mathematical model indicated that the latter empirical findings may not be generally applicable but depend on the exact conditions during evolution [25] . In particular , synergistic drug pairs generally favor bacterial clearance but only sometimes low adaptation rates . The strong reduction in population size by synergistic drugs decreases the likelihood of resistance mutations emerging and increases the chances of population extinction . However , these effects only correlate with low adaptation rates when resource competition is weak . When resource competition is high , resistance mutations have a strong selective advantage and may spread rapidly through the population due to competitive release . Under these conditions , antagonistic rather than synergistic drugs are most efficient in reducing adaptation rates [25] . To date , few experimental data are available to explore these particular model predictions—and , moreover , test the role of evolutionary trade-offs , such as the evolved collateral sensitivities—on bacterial adaptation in multidrug environments . In the current study , we performed a systematic analysis using an experimental evolution approach and the gram-negative opportunistic human pathogen Pseudomonas aeruginosa as a model . We evaluated 38 drug pairs for their ability to effectively constrain bacterial adaptation in multidrug environments and calculated 2 antibiotic combination efficacy ( ACE ) networks based on either the rate of adaptation or bacterial clearance ( i . e . , frequency of population extinction ) . These measures provide complementary information on treatment efficacy . First , population extinction represents the ultimate aim of any antibiotic intervention; its frequency is a highly informative indicator of treatment efficacy under our specific experimental conditions , in which antibiotics are always applied at sublethal doses . Second , for the surviving populations , we further evaluated increases in growth rates as a measure of the bacteria’s adaptive potential in antibiotic environments [16] . We subsequently employed complementary statistical approaches , including an integrative Bayesian network ( BN ) analysis , to disentangle the relative impacts of drug interaction type and evolved collateral effects between individual drugs on the characteristics of the inferred ACE networks . For selected drug pairs , we additionally explored to what extent adaptation to the combinations is driven by the single-component drugs or by initial drug inhibitory levels .
Antibiotic interactions are defined as synergistic , additive , or antagonistic when the drug pair has a stronger , equivalent , or weaker inhibitory effect on bacterial growth than the corresponding single drugs ( i . e . , monotherapies ) , respectively . Here , we determined this interaction quantitatively using an estimator denoted α [17] . This estimator is obtained from a quadratic regression applied to growth measurements as a function of different drug proportions of 2 drugs . The concentration of each of the single drugs is chosen to fall onto the line of equal dose , in our case defined to inhibit 75% of growth ( i . e . , inhibitory concentration [IC] 75; Fig 1A , S1 Fig and Table 1 ) . The estimator α describes the shape of the resulting response in growth whereby positive values indicate synergism and negative values antagonism ( Fig 1B ) . This approach has two advantages: first , it provides a statistical framework for testing the significance of positive or negative α; and second , its inference is less laborious than alternative procedures , thus facilitating characterization of a larger number of drug interactions . Even though the approach was carefully evaluated previously [17] , we specifically validated its suitability for our model system . We compared the inferred α values for 8 selected combinations ( S2A Fig and S3 Fig ) to the corresponding results obtained with one of the commonly used alternative methods , based on Bliss independence and the checkerboard approach ( S1 Data for a key to all datasets and S2 Data ) , as previously described for Escherichia coli [16 , 26] . This comparison demonstrated that α correlates significantly with the degree of synergy ( S ) , irrespective of whether S is calculated from the average of all viable concentrations across a grid defined by the 2 drugs ( ABij = Ai + Bj; S2B Fig ) or from combinations for which the 2 individual drugs had the same level of inhibition ( ABij for which IC50[Ai] = IC50[Bj] , S2C Fig ) . We thus conclude that the α estimator provides an informative , quantitative indicator of a 2-drug interaction . We subsequently evaluated the interactions among 12 different antibiotics representing 5 classes ( Table 1 ) . We chose these drugs as representatives of the main classes of antibiotics , which are commonly used in combination to treat P . aeruginosa and to which most clinical P . aeruginosa strains are still susceptible [27–29] . Even though this choice could have introduced a bias in the overall pattern of inferred interaction types , these should nevertheless be representative of the clinically applied drug combinations . We characterized drug interactions for almost all of the possible combinations , resulting in a total of 52 measures that we summarized in an interaction network ( Fig 1C , S3 Fig , S1 Table , S3 Data ) . Overall , synergistic combinations were more common than other interaction types ( synergistic = 24/52; additive = 14/52; and antagonistic = 14/52 ) . Combinations between cell wall inhibitors ( β-lactams ) and aminoglycosides most often produced synergisms , whereas those including ciprofloxacin ( CIP ) had exclusively antagonistic effects ( Fig 1C ) . We used evolution experiments to assess ACE , which is the ability of drug combinations to constrain bacterial adaptation either through population extinction or , in the case of surviving populations , reduced adaptation rates . Based on the inferred drug interactions and the previously obtained frequencies of collateral sensitivity between 8 of the considered antibiotics ( Fig 2 ) [30] , we selected 38 drug pairs covering all different types of drug interactions and collateral effects . Based on this choice of drugs , we evolved a total of 1 , 672 populations through serial transfers into fresh media containing the respective antibiotics using a transfer period of 12 h and a total of 10 transfers ( total duration of 120 h; Fig 3A and S4 Data ) . We assessed bacterial adaptive potential by integrating quantitative growth measurements taken in 15-min intervals from each evolving population ( a total of 783 , 464 measurements for all treatments and populations; for a validation of our optical density ( OD ) measures as a proxy for bacterial growth , see Materials and methods and S4 Fig ) . For each population in a growth season , we then calculated the growth rate r during the exponential phase ( Fig 3B ) . Following previous work [16] , we defined the rate of adaptation as the change in growth rate over time for each evolving population ( Fig 3C; for a validation of using growth characteristics as a proxy of evolutionary adaptation , see Materials and methods and S5 Fig ) . For subsequent analysis , we focused on the results of the 50:50 drug proportion ( S6 Fig ) and the single-drug treatments ( S7 Fig ) . We reconstructed the 2 ACE networks based either on adaptation rates of the surviving populations ( Fig 4A ) or on population extinctions ( Fig 4B ) . Below , we first describe the patterns seen in the ACE networks , while their statistical analysis is explained in the next section . In all cases but one ( for carbenicillin [CAR] plus gentamicin [GEN] , all populations went extinct ) , adaptation to the combination treatment was possible . However , the rates of adaptation varied substantially across the different drug combinations , with lower rates of adaptation ( below the 50th quantile ) predominantly , but not exclusively , seen among antagonistic combinations that included CIP ( Fig 4A; S8 Fig and S9 Fig show separate ACE networks for each drug interaction type and the 2 types of evolved collateral effects , respectively ) . Several synergistic drug pairs , combining an aminoglycoside with either a penicillin or carbapenem , led to similarly low rates of adaptation ( below the 50th quantile , S8 Fig ) . Moreover , almost all cases of collateral sensitivity included in this study were associated with reduced adaptation rates ( S9 Fig ) . This was not the case for combinations with cross-resistance . Furthermore , when estimating clearance efficacy , we found that extinctions almost exclusively occurred with the synergistic combinations ( Fig 4B , S8 Fig ) . The synergistic combinations that did select for lower rates of adaptation did not necessarily have higher rates of extinction and vice versa ( populations surviving synergistic combinations were not necessarily adapting more slowly; see azlocillin [AZL] plus streptomycin [STR] , cefsulodin [CEF] plus CAR , or ticarcillin [TIC] plus GEN; S8 Fig ) . We next performed 2 types of statistical analyses to assess to what extent the overall characteristics of the 2 ACE networks are determined by the 2 considered predictors of combination efficacy: interaction type inferred from α ( Fig 1C ) and collateral sensitivity profiles previously obtained from experimentally evolved resistant populations of P . aeruginosa ( Fig 2 , [30] ) . We first used a BN approach to assess the relationships among the considered variables ( i . e . , adaptation rate , extinction frequency , drug interaction , and frequency of collateral resistances [FCR] ) . The BN approach is based on a constraint-based interleaved incremental association algorithm [31–33] to dissect the relationships between our variables ( see Materials and methods for details ) . The results are summarized in the BN ( Fig 5A ) , in which nodes represent the different variables and arrows indicate the inferred dependencies . The BN analysis revealed that the type of antibiotic interaction strongly influenced the proportion of extinction , but not the rate of adaptation . Instead , the rate of adaptation was found to depend solely on the frequency of collateral sensitivities . No other dependency was inferred by the analysis . Based on the BN structure , we calculated the conditional probabilities for the inferred dependencies between the frequencies of collateral sensitivity and the rates of adaptation as well as for the proportion of extinction and drug interaction type . In particular , we used the different types of evolved collateral effects ( i . e . , partial collateral sensitivity , partial cross-resistance , and cross-resistance; none of the combinations evaluated during evolution had complete collateral sensitivity between their components , as shown in Fig 2 ) and calculated the conditional probability of obtaining the distribution of observed adaptation rates across 5 equal quantile bins ( Fig 5B , top panel ) . Similarly , given the different drug interaction types ( synergism , additivity , and antagonism ) , we calculated the conditional probabilities of different extinction frequencies across 5 equal quantile bins ( Fig 5B , bottom panel ) . These 2 additional analyses describe more clearly the inferred dependencies within the BN . Antibiotic combinations for which at least half of the populations had collateral sensitivity against one or both of the individual drug components ( i . e . , partial collateral sensitivity; purple bars in Fig 5B , top panel ) have a higher probability of selecting for low but not high rates of adaptation . Conversely , combinations with partial or complete cross-resistance ( green bars in Fig 5B , top panel ) have a higher probability of producing the top scores of inferred adaptation rates . In addition , high probabilities of extinction are associated with synergistic and additive combinations , whereas the reverse is found for antagonistic drug pairs ( Fig 5B , bottom panel ) . We further validated the inferred dependencies between variables using partial correlation analysis , following the approach previously established for a similar analysis of combination efficacy in E . coli [16] . This approach allowed us to control for drug pair membership using the average rate of adaptation towards the corresponding single drugs of a particular combination as a covariate ( Materials and methods ) . Statistical significance was subsequently inferred using a permutation test [16] . This analysis revealed a significant correlation between the FCR and the rate of adaptation ( ρs = 0 . 52 , P = 0 . 038 ) and between the proportion of extinction and the drug interaction type α ( ρs = 0 . 51 , P = 0 . 043 ) , but not between the FCR and the proportion of extinction ( ρs = 0 . 39 , P = 0 . 146 ) or the drug interaction α and the rate of adaptation ( ρs = 0 . 3 , P = 0 . 262 ) . This analysis , based on a distinct statistical approach , thereby corroborated the findings of the BN analysis . We conclude that synergistic drug interactions enhance bacterial clearance , whereas collateral sensitivity limits the adaptive potential of the bacteria . We next assessed whether the ability of bacteria to adapt to the combination is mainly driven by adaptation to only one of the drugs rather than dependent on a unique property of the antibiotic pair . For our dataset , we related the inferred rates of adaptation in the combination treatments to those inferred for the corresponding single-drug environments ( S10 Fig ) . We first compared the 2 corresponding monotherapies of a given drug pair and defined the drug leading to lower rates of adaptation as the stronger component ( i . e . , higher ability to minimize resistance evolution ) and the other as the weaker component ( i . e . , lower ability to minimize resistance evolution ) . Thereafter , we calculated the relative rate of adaptation of the combination by standardizing it against either the stronger or the weaker component of the pair . The resulting ACE networks are shown in Fig 6A and 6B , respectively . Interestingly , the original ACE network for adaptation rates ( Fig 4A ) is more similar to that standardized by the weaker but not the stronger component drug ( Fig 6; S2 Table ) . This suggests that the characteristics of the original ACE network ( Fig 4A ) , and thus the efficacy of drug combinations to reduce adaptation rates , is primarily driven by adaptation to the stronger component , which—if accounted for by the standardizing scheme—removes important properties of the network ( see as prominent examples the disappearance of the strong reduction in adaptation rate for doripenem [DOR] plus TIC , or DOR plus PIT [piperacillin + tazobactam]; Fig 4A and Fig 6A ) . We further evaluated influence of the component drugs by repetition of the BN analysis . We found that the dependency observed between the FCR and the rates of adaptation of the combinations disappeared when the latter is weighted by the stronger but not the weaker component drug ( Fig 6C ) . At the same time , the dependency between drug interaction and extinction frequency remained , while no additional relationship was revealed . Similar results were obtained when we repeated the correlation analysis with standardized adaptation rates . The originally identified correlation between the FCR and the rate of adaptation was no longer significant when the latter was standardized by adaptation to the stronger component drug ( ρs = 0 . 33 , P = 0 . 21 ) , yet it still showed a statistical trend when we standardized by the weaker component drug ( ρs = 0 . 45 , P = 0 . 078 ) . In these 2 analyses , drug interaction did not correlate significantly with the weighted adaptation rates ( ρs < 0 . 47 , P > 0 . 09 ) . These results consistently indicate that adaptation to the stronger component drug influences adaptation to the combination and that this is dependent on the evolved collateral effects . We next performed a separate evolution experiment with 4 selected combinations to assess to what extent the inherently different starting levels of inhibition—imposed by each type of interaction during the first season of growth ( Fig 1B and S3 Fig ) —influenced both the number of extinctions and adaptation rates . We performed this evolution experiment with 4 selected combinations with different interaction profiles: 2 interacting synergistically ( GEN plus CAR and STR plus PIT ) and 2 antagonistically ( GEN plus CIP and Tobramycin [TOB] plus CIP ) . For these combinations , we varied the initial inhibition level of the combination across 8 steps , ranging from IC50 to >IC90 . Populations were serially transferred into fresh media as explained before ( S5 Data; and for the obtained changes in growth rate r , see S11 Fig ) . This separate evolution experiment revealed that initial inhibitory levels of the tested combinations are significantly related to the rates of adaptation , irrespective of combination identity or drug interaction type ( GLM , F1 , 336 = 37 . 735 , P < 0 . 001; Fig 7A and S3 Table ) . In particular , increasing levels of inhibition are generally associated with higher rates of adaptation , suggesting that strong inhibition increases selection for an adaptive response [34 , 35] . At higher levels of inhibition , the synergistic and antagonistic combinations produce clearly distinct responses , especially regarding population extinction . Here , the 2 synergistic pairs are associated with a significant increase in the number of extinct populations ( logistic regression , F12 , 336 = 21 . 15 , P < 0 . 001; Fig 7B and S4 Table ) , while antagonistic combinations produced almost no extinction at all . Moreover , at the very high initial inhibitory levels , antagonistic pairs showed a sudden drop in adaptation rates ( Fig 7A ) , as expected from previous work [16 , 24] . A similarly strong reduction is not observed for the synergistic combinations , possibly owing to the fact that only few populations survived and could be used to infer adaptation rates . Taken together , the results from this separate evolution experiment suggest that the generally higher inhibition levels of the synergistic pairs in our main evolution experiment could potentially have contributed to higher adaptation rates for this type of combination ( even though these were not found to be significantly increased compared to those for other interaction types; see above ) . This seems less likely the case for extinction events , which are generally more frequent in treatments with synergistic rather than antagonistic combinations , irrespective of the initial inhibition level .
Our study provides a systematic experimental analysis of the efficacy of antibiotic combination therapy in the opportunistic human pathogen P . aeruginosa . Based on evolution experiments with 38 distinct combinations , ACE networks were reconstructed for 2 complementary measures of treatment efficacy: the frequency of population extinctions and the reduction in adaptation rates . Subsequent statistical analyses identified the likely ACE determinants: Synergistic drug interactions enhanced the frequency of extinction , even at the same inhibitory level as antagonistic interactions , while reduced adaptation rates depended on the evolved collateral sensitivities among the drugs . The latter effect is likely driven by adaptation to the stronger component drug in a pair . Consequently , our findings suggest that treatment efficacy against P . aeruginosa can be optimized by drug combinations , which interact synergistically to increase bacterial clearance and which can evolve collateral sensitivity to each other to slow down the rate of adaptation . The use of BN analysis enhanced dissection of the determinants of ACE . The BN approach has been widely applied across different fields of biology in recent years but not yet in studies on antibiotic resistance evolution [33 , 36–39] . Its accessible graphical output and the underlying probabilistic theory facilitate the inference of causal relationships between different variables [31 , 32] . It further offers estimation of conditional probabilities that reflect the strength of the inferred dependencies; a strategy well suited for the stochastic nature of biological systems and their measurements [40] . The latter is important for the analysis of antibiotic resistance evolution , for which we are mainly interested in anticipating bacterial adaptation based on distinct drug properties or deployment strategies [11 , 12 , 41–43] . The suitability of the BN approach for analysis of drug resistance evolution was corroborated with a previously established statistical approach , based on partial correlation analysis [16] , which identified a significant relationship for the same pairs of variables . Our analyses consistently revealed that synergistic drug interactions are an important ACE determinant , especially in terms of bacterial clearance ( Fig 4A ) . The particular importance of bacterial elimination as a component of treatment efficacy was previously considered in a mathematical model [25] but has not yet been evaluated empirically . The previous model assessed the effect of antibiotic interactions on treatment efficacy [25] by modifying a previous infection model based on data from mice infected with P . aeruginosa [44] . The model is related to the design of our main evolution experiment in that the concentration of a particular drug in a combination is standardized by its inhibitory effect in monotherapy . The model predicted contrasting treatment outcomes for synergistic combinations: On the one hand , synergism enhances extinction , most likely because it strongly reduces population size , thereby decreasing the likelihood of new resistance mutations arising . On the other hand , if resistance emerges , synergism increases the selective advantage of the resistant mutants through competitive release , enhancing bacterial adaptation [25] . Our experimental results are consistent with both alternatives . Although synergism mainly favored bacterial extinction ( Figs 5–7 ) , it was in several cases associated with low adaptation rates ( Fig 4A ) . However , in our study , the effect of drug interaction on adaptation rate was always insignificant , irrespective of the analytical approach . Interestingly , we found higher population extinction for synergistic rather than antagonistic combinations also at low initial inhibitory concentrations ( Fig 7 ) . This finding cannot have resulted from the stronger reduction in population size ( i . e . , inhibitory levels were the same for the 2 interaction types ) but must have depended on other properties of the synergistic drug pairs . A likely explanation may be found in the mechanism underlying synergism , which can rely on increased membrane permeability induced by one of the drugs , subsequently enhancing cellular uptake of the second drug [45] . Such mechanisms may have a cumulative effect across time [45] and/or may generally be difficult to counter . This , in turn , limits the number of suitable resistance mutations and ultimately increases the likelihood of extinction . A detailed exploration of this effect clearly warrants further research . Our experiments further identified the potential to evolve collateral sensitivity as a key determinant of low adaptation rates . This result is generally consistent with previous work on E . coli and Staphylococcus aureus [46 , 47] , although this is the first time it has been shown for P . aeruginosa . Adaptation rates are thus significantly influenced by evolutionary trade-offs , whereby adaptation to one of the drugs of a pair constrains adaptation to the other . Our findings and those of colleagues [46 , 47] thereby highlight that such trade-offs may not only improve treatment when drugs are applied sequentially , as originally proposed for evolved collateral sensitivities in E . coli ( i . e . , collateral sensitivity cycling; [20–22] ) . Instead , they can also optimize combination therapy . Our analysis further revealed that the involved dynamics are likely driven by adaptation to the stronger component drug of a pair ( Fig 6 ) . This suggests that , if adaptation to the stronger component comes with a higher likelihood of collateral sensitivity to the second drug , adaptation to the combination is systematically slowed down , as , for example , for CIP plus STR or CIP plus CAR ( Fig 2 , Fig 4A ) . In contrast , when adaptation to the stronger drug is more likely to cause cross-resistance , then this can enhance adaptation to the combination , as seen for GEN plus STR or CAR plus CEF ( Fig 2 , Fig 4A ) . The further exploration of these trade-offs represents a promising avenue to improve treatment efficacy . Our finding of the high clearance efficacy of synergistic combinations shows some consistency with clinical practice . For P . aeruginosa , we predominantly observed drug synergism between β-lactams and aminoglycosides ( Fig 1C ) . These 2 antibiotic classes are also most commonly used in combination therapy against this pathogen [29 , 48 , 49] . Our results empirically confirm the potency of the β-lactam–aminoglycoside combinations , especially penicillin–aminoglycoside pairs , in causing higher numbers of extinct replicate populations ( Fig 4B and S8 Fig ) . In some cases , the populations surviving these specific combinations also adapted more slowly ( e . g . , STR plus PIT or TIC plus TOB in Fig 4A and 4B , and S8 Fig ) . Furthermore , the effectiveness of these combinations may not only be caused by drug synergism but additionally by reciprocal collateral sensitivity that can evolve among these pairs [30] . Our systematic analysis performed under controlled laboratory conditions thus provides empirical support for the often experience-driven choice in clinical treatment . In the future , the clinical applicability of our results should be further explored . For example , we identified high clearance efficacy of certain combinations of penicillins and cephalosporins ( Fig 4B ) or low adaptation rates if fluoroquinolones ( e . g . , CIP ) were combined with aminoglycosides or penicillins ( Fig 4A ) . It would be of particular interest to corroborate these patterns for clinical isolates in laboratory experiments or under clinical conditions . In summary , our systematic analysis of antibiotic combinations identified the role of drug interactions and evolved collateral effects in determining 2 complementary properties of treatment efficacy . The comprehensive dataset collected in our study may serve as a useful reference for further exploration of effective therapy , including more detailed statistical analyses such as those that use the potency of pairwise interactions to estimate higher-order drug effects [50 , 51] . Our approach and the specific results obtained may , moreover , help to improve the design of medical treatment with the 2-fold aim of minimizing pathogen burden and reducing resistance evolution . A similar combined assessment of the efficacy of drug interaction and evolved collateral effects may not only be applicable to other pathogens and infectious diseases . It could similarly help to improve cancer therapy , as previously evaluated for selected cancer types and drug interactions [52–55] .
All experiments were conducted with P . aeruginosa PA14 . Cells were grown at 37 °C in sterile M9 minimal medium supplemented with 0 . 2% glucose and 0 . 1% casamino acids . All antibiotics were prepared according to the manufacturer’s instructions and filter sterilized before each experiment ( Table 1 ) . All experiments were carried out in randomized 96-well plates shaken and incubated at 37 °C in BioTek Eon plate readers , which were also used for regular measurement of ODs in 15-min intervals . Randomization schemes of plates for each experiment were different from each other . All analyses were performed using the R platform ( version 3 . 3 . 2 ) unless specified otherwise [56] . We tested 14 different concentrations of each drug in order to establish dose-response relationships after 12 h of incubation . For all concentrations , a 1- to 2-ml 10× stock was prepared and then diluted in a randomized 96-well plate with 6 replicates per concentration , resulting in 90 replicates per antibiotic and 1 , 080 for all treatments . Ten microliters of an isogenic bacterial population of PA14 were added to a final volume of 100 μl , equivalent to 104 to 105 CFU/ml initial population size . In addition , 2 types of controls were included: one without antibiotic and a second one without both antibiotic and bacteria , each also replicated 6 times . We used a logistic regression to analyze the dose-response relationship of each drug using the package “drc” in R [57] . The obtained models ( S1 Fig ) allowed accurate calculation of different levels of inhibitory concentrations for each drug , including the minimum inhibitory concentration ( MIC; here defined as the concentration inhibiting >90% of growth ) . To measure the type of interaction using the checkerboard approach , we considered 9 concentrations of each antibiotic in a pair , including a no-drug control , and distributed them randomly across a 96-well plate . Each pair was evaluated twice . Plates were incubated at 37 °C for 12 h with constant shaking and regular OD measurements taken every 15 min . We then calculated the growth rate r for each individual well and combination by fitting a linear regression of growth over time during the exponential phase . Exponential phase was generally observed during 195 to 360 min of each season . We subsequently determined the degree of synergy of any drug pair AB using the Bliss independence method described previously [16]: S= ( rA0/r00 ) ( r0B/r00 ) − ( rAB/r00 ) , such that rA0 represents the growth rate at a given concentration of drug A in the absence of B , and vice versa for r0B . r00 is the growth rate of the no-drug control , and rAB is the growth rate at any concentration in which drugs A and B are found together . The degree of synergy S was only calculated for drug combinations that had growth rates larger than 0 . Positive values indicate synergism , whereas negative ones denote antagonism . To classify the interaction between 2 drugs , we considered an environment in which each drug separately inhibits 75% ± 10% of bacterial growth ( IC75 ) . For each combination , we evaluated 11 treatments: 9 different proportions of a given pair of antibiotics , a control of uninhibited growth , and a control with only M9 medium . Nine replicates for all treatments were considered , except for the M9 control that consisted of only 6 wells . This resulted in 81 replicates per drug combination and 4 , 212 for all 52 antibiotic pairs . OD measurements were taken every 15 min for 12 h , resulting in a total of 48 data points per individual replicate and 202 , 176 for all combinations and replicates . To determine whether interactions were antagonistic , synergistic , or additive , we used a t test on the second-order term ( α ) of a quadratic regression of our data , as established previously [17] . The α parameter expresses convexity or concavity of observed bacterial-density data in the model q ( θ ) = αθ2 + βθ + γ , such that θ represents any drug proportion between any drugs A and B ( Fig 1B ) . Positive values of α indicate synergy and negative values antagonism . We considered our previously published data on the evolved collateral effects of highly resistant populations of P . aeruginosa PA14 [30] and used the frequency of cross-resistance in all possible pairwise combinations of 8 of the drugs considered in this study . Briefly , the FCR counts the number of populations resistant to drug A that show collateral resistance to drug B , and vice versa , relative to the total number of populations resistant to A and B . Values close to 0 indicate reciprocal collateral sensitivity , and those close to 1 denote cross-resistance . We categorized the obtained values into 4 different groups and built a collateral sensitivity network ( Fig 2 ) : complete collateral sensitivity ( FCR ≤ 0 . 25 ) , partial collateral sensitivity ( 0 . 25 < FCR ≤ 0 . 5 ) , partial cross-resistance ( 0 . 5 < FCR < 0 . 75 ) , and complete cross-resistance ( FCR ≥ 0 . 75 ) . Based on the interaction profile and the collateral sensitivity and/or resistance [30] scores , we selected a total of 38 different combinations for a series of evolution experiments ( Fig 3A ) . For all combinations , we included 5 different proportions of the combined antibiotics , an uninhibited control , and an M9 control , resulting in 44 populations per combination , randomly distributed in a 96-well plate ( 2 combinations were included in a single plate ) , for a total of 1 , 672 populations . The concentration was set for each individual drug to inhibit bacterial growth by 75% ( IC75 ) . We considered 10 transfers ( hereafter referred to as seasons ) of 1% volume into fresh plates every 12 h ( approximately 120 generations ) . For each season , OD600 measurements were taken every 15 min , resulting in 48 measurements per replicate and season and a total of 781 , 440 measurements across all replicate populations . All plates were frozen at −80 °C with 1:4 ( v/v ) of 86% glycerol . To validate our OD measurements as a proxy for bacterial growth during evolution , we replicated the conditions of the first season for 4 selected combinations ( only the 1:1 proportion ) , 6 corresponding single-drug treatments , and a no-drug control . We focused on those combinations and the corresponding monotherapies for which we also evaluated the influence of initial drug inhibitory level ( Fig 7 ) and the evolution of resistance ( S5 Fig ) . Each treatment was replicated 8 times . After 12 h of evolution , we performed a dilution series and standard plating techniques to count viable colony-forming units ( CFUs ) for all replicates and treatments . The obtained CFUs were then correlated with the endpoint OD measurements ( S4 Fig ) . We found a significant correlation between our OD measurements and the CFU counts at the end of season 1 ( Spearman rank correlation test , ρs = 0 . 782 , P < 0 . 001 ) . To further validate the OD measurements , we performed a similar correlation analysis for the same combinations and corresponding monotherapies , using evolved bacteria from the final transfer of the separate , focused evolution experiment , in which the influence of initial drug inhibitory levels was assessed . The evolved material was thawed from the frozen stock cultures , then exposed to 1 full season of experimental evolution under the exact treatment conditions already experienced by populations during the evolution experiment . Thereafter , CFUs were counted using a dilution series on Agar plates , as outlined above , and then compared to the OD measures obtained during the above repetition of a full season . As before , CFUs were significantly correlated with the corresponding OD measurements ( Spearman rank correlation test , ρs = 0 . 339 , P = 0 . 002 ) . We further validated the suitability of changes in growth characteristics as a proxy for evolutionary adaptation and therefore genetically fixed alterations by re-assessing cryo-preserved material from the last transfer of experimental evolution . This analysis was performed with material from the separate evolution experiment , which tested the influence of initial inhibitory levels , and further details are outlined below in the description of this experiment . We first calculated the growth rate r as described above for each evolving population , treatment , and season . Subsequently , we considered the rate of adaptation for each evolving line as defined previously [16]: Radapt=Δr2×tadapt , such that Δr represents the change in growth rate over 10 seasons of growth , and the time of adaptation , tadapt , corresponds to the interpolated time at which a population reached half of its maximum growth rate . This measurement reflects how quickly resistance spreads in a population in a serial transfer experiment . To determine to what extent adaptation to the drug combinations was determined by adaptation to each of the individual drugs , we measured which of the individual components in a drug pair led to lower and higher rates of adaptation . The single antibiotic in a pair that alone led to lower rates of adaptation was considered as the stronger of the components and the other as the weaker one . The adaptation rate of each combination was then standardized by the adaptation rate of either its weaker or stronger component drug . The 2 types of standardized adaptation rates were visualized in ACE networks and statistically evaluated ( see below ) . We used BN analysis to assess the directional relationship between 4 variables , including the inferred drug interaction type , the frequency of collateral sensitivities , the adaptation rates , and the frequency of population extinctions . The entire BN analysis was repeated with the different types of inferred adaptation rates , including those obtained for the combinations in the main experiment and then those that we standardized by either the stronger or the weaker component drug . The BN analysis generally followed 2 steps . In the first step , the approach identifies variables that are related to each other and visualizes these as nodes in a network between variables . In this step , it further infers the direction of each relationship and represents these as arrows in the network , thereby implying a causality between the connected variables [31] . To achieve this first step , the model first infers the graphical structure of the network by analyzing the probabilistic relations between all nodes and thereafter constructs the network by setting directions for the identified connections while satisfying an acyclicity constraint [58] . We implemented BN analysis employing a constraint-based interleaved incremental association–optimized algorithm [59] to reduce the likelihood of obtaining false positives and to obtain possible probabilistic dependencies between our variables: drug interaction type ( categorical: synergism , additivity , or antagonism ) , FCR ( categorical: complete collateral sensitivity , partial collateral sensitivity , partial cross-resistance , and complete cross-resistance ) , proportion of extinction ( numerical ) , and rates of adaptation ( numerical ) . We only included combinations with complete sets of data and then followed the algorithm’s default parameters . From the obtained dependencies , we estimated the conditional probabilities associated with the linked variables over an array of different values . All tests were performed in R using the “bnlearn” package [60] . To validate the inferred dependencies from the BN analysis , we additionally performed correlation analysis combined with permutation tests , following the approach previously established for a similar analysis of ACE in E . coli [16] . For each round of permutation , we calculated correlation coefficients , ρs , between any two given variables x and y by permuting the values of x while keeping y constant , as in [16] . For each test , we considered 10 , 000 permutations and estimated the P value as the proportion of the obtained distribution of correlation coefficients that had an absolute value larger than the absolute value obtained for the observed ρs [16] . This approach was used to correlate the measures of collateral effects and drug interaction to proportion of extinction and , later on , to the standardized adaptation rates . Furthermore , to account for the effect of adaptation to the single drugs ( z ) in the main analysis with nonstandardized adaptation rates , we performed a partial correlation analysis with z as a covariate , generally following the previously established approach [16] . For this , we first obtained the residuals from the linear regression of x on z and those of y on z , such that y corresponds to the adaptation rates of the combination . Then , to estimate the correlation coefficient between x and y , with z as a covariate , we employed the permutation test as explained above using the residuals of the corresponding regressions [16] . To evaluate the effect of the starting inhibition level of the combinations , we considered a second round of evolution experiments as described above . This time , the level of inhibition of the combination was fixed instead of that of the individual drug treatments . Briefly , concentrations of each drug were mixed 1:1 so that each would inhibit between 50% and 75% of growth . These were then diluted to obtain a range of different inhibition levels and to evaluate their effect on growth in P . aeruginosa after 12 h of incubation at 37 °C . Evolution experiments were then initiated for 4 different combinations that included 11 different treatments: a no-drug control , the individual monotherapies , and 8 different inhibition levels ranging from approximately IC50 to >IC90 of each combination . Each treatment was replicated 8 times and distributed randomly in 96-well plates . We used the focused set-up of the above separate evolution experiment to validate the suitability of growth measurements as a proxy for evolutionary adaptation . Evolutionary adaptation assumes that changes are genetically fixed rather than due to phenotypic ( i . e . , physiological ) responses . To assess this , we studied cryo-preserved material from the last drug-free season of the evolution experiment and regrew them under defined antibiotic conditions . Purely phenotypic adaptations to antibiotics are unlikely to have persisted for this material , which was grown under antibiotic-free conditions for 12 to 16 h ( equivalent to a minimum of 6 generations ) and additionally subjected to a cryo-preservation step . Therefore , any persistent changes in growth characteristics under antibiotic exposure are likely based on genetic changes and thus indicate evolutionary adaptation . For this analysis , we considered material evolved in the presence of 2 synergistic ( i . e . , GEN plus CAR and STR plus PIT ) or 2 antagonistic combinations ( i . e , CIP plus GEN and CIP plus TOB ) , in all cases set to either IC50 or >IC90 , and also included material from the corresponding monotherapies . A total of 4 replicate populations was studied for each of the various evolution treatments and compared to the ancestral PA14 . Changes in growth characteristics were inferred from dose-response curves in a 2-fold dilution series of each of the antibiotics included in the pair . The evolved relative changes in resistance were calculated as the area under the curve ( AUC ) of the dose-response curve for each of the populations and then divided by that of the ancestral PA14 . The results are shown in S5 Fig . They highlight a general increase in growth characteristics and thus resistance across the various treatment groups even if not significant in all cases ( based on a 1-sample Wilcoxon test with μ = 1 ) . We conclude that , overall , the observed changes in growth characteristics have a genetic basis and are not exclusively due to phenotypic responses . Therefore , we consider the recorded changes in growth characteristics to provide a meaningful proxy for evolutionary adaptation . | Bacterial infections are commonly treated with a combination of antibiotic drugs . However , not all combinations are equally effective , and success is variable . One reason for this variation is that we usually do not know to what extent bacteria are able to adapt to different types of drug combinations . If they can and do adapt , then antibiotic resistance can spread , potentially aggravating the current antibiotic crisis . In the current study , we therefore asked whether combination therapy can be improved by considering the evolutionary potential of the bacteria . To address this question , we systematically assessed the efficacy of antibiotic combinations using controlled laboratory evolution experiments with the opportunistic human pathogen Pseudomonas aeruginosa as a model . We found that 2 factors consistently increase treatment efficacy . First , synergism between the combined drugs ( i . e . , the 2 drugs enhance each other’s effects ) increases the rate of bacterial population extinction and thus clearance rate . Second , evolved trade-offs such as collateral sensitivity ( i . e . , evolution of resistance to one drug increases susceptibility to the other drug ) limit the ability of bacteria to adapt to the antibiotic pair . Our findings may help to optimize combination therapy by focusing on drug pairs that interact synergistically and also lead to evolved collateral sensitivities . | [
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"ev... | 2018 | Antibiotic combination efficacy (ACE) networks for a Pseudomonas aeruginosa model |
Recent advances in microRNA target identification have greatly increased the number of putative targets of viral microRNAs . However , it is still unclear whether all targets identified are biologically relevant . Here , we use a combined approach of RISC immunoprecipitation and focused siRNA screening to identify targets of HCMV encoded human cytomegalovirus that play an important role in the biology of the virus . Using both a laboratory and clinical strain of human cytomegalovirus , we identify over 200 putative targets of human cytomegalovirus microRNAs following infection of fibroblast cells . By comparing RISC-IP profiles of miRNA knockout viruses , we have resolved specific interactions between human cytomegalovirus miRNAs and the top candidate target transcripts and validated regulation by western blot analysis and luciferase assay . Crucially we demonstrate that miRNA target genes play important roles in the biology of human cytomegalovirus as siRNA knockdown results in marked effects on virus replication . The most striking phenotype followed knockdown of the top target ATP6V0C , which is required for endosomal acidification . siRNA knockdown of ATP6V0C resulted in almost complete loss of infectious virus production , suggesting that an HCMV microRNA targets a crucial cellular factor required for virus replication . This study greatly increases the number of identified targets of human cytomegalovirus microRNAs and demonstrates the effective use of combined miRNA target identification and focused siRNA screening for identifying novel host virus interactions .
Human cytomegalovirus ( HCMV ) is a highly prevalent infectious disease , infecting greater than 30% of the population . Although normally asymptomatic in healthy individuals , HCMV infection is a significant cause of morbidity and mortality in immunocompromised populations , individuals with heart disease and recipients of solid organ and bone marrow transplants [1]–[8] . HCMV is also the leading cause of infectious congenital birth defects resulting from spread of the virus to the unborn fetus . Reactivation of virus from a latent infection , rather than primary infection , is often responsible for HCMV associated pathologies [9]–[13] . The capacity of HCMV to strictly regulate the expression of its own genes and to manipulate host gene expression is crucial to the virus's ability to replicate and its success in maintaining a persistent infection [14] . Studies in our lab and others have demonstrated that herpesviruses have evolved to encode microRNA ( miRNA ) genes , enabling regulation of the virus's gene expression profile as well as altering the host environment by targeting cellular transcripts . Recent reports have demonstrated roles for viral miRNAs in suppressing apoptosis , immune evasion and regulation of viral replication through targeting of both cellular and viral gene expression [15] . HCMV encodes at least 14 pre-miRNAs corresponding to a total of 27 mature miRNA species [16]–[20] . Clear functions have not been shown for the majority of HCMV miRNAs . However , these regulatory RNAs have been shown to target genes involved in viral latency , immune evasion , and cell cycle control [21] . We previously demonstrated that the HCMV miRNA , UL112-1 , restricted viral acute replication through targeting of the major immediate early gene IE72 , suggesting this miRNA may play a role in establishing and maintaining viral latency [22] . Others have since shown that targeting of immediate early genes by viral miRNAs may be a fundamental mechanism involved in herpesvirus latency regulation [23]–[26] . UL112-1 has also been shown to target the major histocompatibility complex class I-related chain B ( MICB ) resulting in reduced killing by NK cells [27] . Despite these advances , identification of miRNA targets remains challenging . Until we have a greater understanding of the rules governing miRNA target interaction , bioinformatic strategies alone continue to produce unreliable results , especially for viral miRNAs , which in most cases do not display significant evolutionary conservation . Biochemical approaches have provided an alternative means for the identification of miRNA targets . One such approach , RISC immunoprecipitation ( RISC-IP ) has proved effective in identifying both cellular and viral targets [28] . Recently , we used a RISC-IP approach to identify multiple cellular targets of US25-1 , an HCMV miRNA expressed at high levels during acute infection [29] . Here we use a combined approach of RISC-IP profiling in infected cells combined with focused siRNA screening to identify host targets of HCMV miRNAs that have significant effects on virus replication . Our results , using a laboratory strain as well as a clinical strain of virus greatly increases the number of identified and validated HCMV miRNA targets . Furthermore , the results show that the V-ATPase complex , involved in acidification of endosomal compartments , is essential for HCMV virus replication and is targeted by the HCMV miRNA US25-1 .
RISC-IP techniques have recently been used to identify targets of viral miRNAs [29] , [30] . The approach relies on the stable interaction of the miRNA associated RISC protein complex with the targeted transcript . Following lysis of cells the RISC complexes are immunoprecipitated using direct antibodies that recognize Argonaute 2 . RNA is then isolated , labeled and analysed by microarray to identify transcripts which are significantly enriched due to miRNA targeting . In a previous study we used this technique to identify targets of a single miRNA , US25-1 , in the context of HEK293 cells [29] . Here we used the same basic approach to identify targets in primary human fibroblast cells infected with either the laboratory adapted AD169 strain of HCMV or the clinical strain TR . In both cases cells were infected at a high multiplicity of infection ( MOI ) of three and cells harvested three days post infection . Following lysis and immunoprecipitation , RNA was isolated by trizol extraction and analysed by microarray using the Illumina HumanRef-8 platform which contains probes for approximately 24 , 000 well annotated genes ( Figure 1A ) . In addition to uninfected cells , RNA was analysed from infected cells immunoprecipitated with pre-immune serum instead of anti-Argonaute 2 antibody . To determine the level of enrichment , lysate was sampled before immunoprecipitation to establish total levels of transcript expression . Enrichment was then calculated as the transcript level of the IP sample divided by the total RNA sample . To determine transcripts specifically targeted by HCMV miRNAs , the level of enrichment from infected samples was divided by the level of enrichment in uninfected samples . However , infection with HCMV results in significant perturbation of total levels of many cellular transcripts through mechanisms unrelated to miRNA expression . False positive enrichment attributed to viral miRNA targeting can therefore occur due to down regulation of total RNA levels in the infected sample , where IP background levels remain relatively unchanged . To overcome this , a correction calculation was introduced using the results from the control IP using the pre-immune serum pull down . As enrichment values in this sample would only occur through changes in total levels due to AD169 infection , rather than any effective enrichment through specific immunoprecipitation , false enrichment could be effectively subtracted from the data sets generated with anti-Argonaute 2 pull downs . Example calculations are shown in supplemental figure S1 . The results indicate that greater than 96% of transcripts showed little or no enrichment in infected cells compared to uninfected cells , as would be expected if virus miRNAs are targeting a specific subset of transcripts ( Figure 1B ) . In cells infected with AD169 , 686 transcripts were enriched two fold or more with enrichment levels as high as 28 . 9 fold for the top target ATP6V0C . Enrichment levels were slightly lower for TR infected cells with 442 genes enriched 2 fold or higher with the highest level of enrichment for COMMD10 at 19 . 8 fold ( Figure 1C ) . The lower enrichment in TR infected cells was expected as the clinical strain replicates less efficiently than AD169 in primary fibroblast cells , resulting in lower levels of miRNA expression ( data not shown ) . Given that HCMV miRNAs are completely conserved between TR and AD169 , with the exception of miR-148D-1 , which is deleted from AD169 due to genome rearrangements , a similar suite of enriched genes would be expected from each pull down experiment . Indeed , 222 of the 442 genes enriched by two fold or more in cells infected with TR , were also enriched in the AD169 sample . This is a highly significant level of correlation ( P = 3 . 2×10−233 as determined by hypergeometric distribution analysis ) ( Figure 1D ) that validates the biological reproducibility of the system . Of the top 30 most highly enriched transcripts from AD169 infected cells , 26 were also enriched at least two fold in pull downs from TR infected cells , giving a high level of confidence that these genes are specifically enriched due to HCMV miRNA targeting . Table 1 lists the top 30 most highly enriched genes from cells infected with AD169 with corresponding enrichment values for TR . The complete data sets are shown in supplemental tables S1 and S2 . As pull down experiments were performed in the context of viral infection , any one , or a combination of , HCMV encoded miRNAs could target the identified transcripts . It is also possible that transcripts could be enriched through targeting by an induced cellular miRNA . Target interaction between miRNAs and transcripts rely heavily on binding between the 5′ end of miRNAs , specifically nucleotides 1 through 8 , known as the seed sequence . To define which HCMV miRNA has the potential to target the identified transcripts we predicted seed match sites using the online algorithm RNAHybrid for the 14 most abundant HCMV miRNAs . Stringent parameters of full Watson Crick base pairing with bases 1–7 or 2–8 were employed with the top 30 putative targets analysed . All but three of the transcripts contained at least one seed match to the major HCMV encoded miRNAs , with most transcripts containing targets sites for multiple HCMV miRNAs ( Figure 2 ) . The majority of target sites reside within the open reading frame of the transcripts with only 14 of the 30 transcripts containing predicted seed matches for HCMV miRNAs within the 3′UTR . Although there is evidence that cellular miRNA targeting is heavily constrained to the 3′UTR region of transcripts [31]–[33] , a number of studies , including our own , suggest that these constraints may not always be applied to viral miRNA targeting . In fact targeting by US25-1 was shown to predominantly target sites within the 5′UTR [29] . Full analysis of transcripts is shown in Supplemental Table S3 . To delineate the miRNA target interactions , we compared the RISC-IP data from infected cells with the previous published study generated from cells transfected with US25-1 [29] . Figure 3A represents heat map analysis comparing enrichment profiles from the top 30 enriched transcripts of cells infected with AD169 or TR , with cells transfected with either a plasmid expressing US25-1 or immunoprecipitations using a synthetic US25-1 mimic containing a biotin moiety . The majority of transcripts show clear enrichment with AD169 and TR as would be expected . In addition , highly enriched genes from infected cells were also significantly enriched in cells only expressing US25-1 , demonstrating that these transcripts are targeted by US25-1 in the context of viral infection . Six genes , including the top target from the previous study , CCNE2 , and the top target from this study , ATP6V0C , were in the top 30 enriched genes from cells infected with AD169 or cells transfected with US25-1 ( Figure 3B and C ) . Although the combined data sets provide strong evidence that these six genes are targeted by US25-1 in the context of virus infection , it is possible that other viral miRNAs may also target these genes , potentially complicating further validation . To determine whether this was the case , additional RISC-IP analysis was carried out comparing wild type AD169 virus to two recombinant AD169 viruses in which either US25-1 had been deleted , or the entire US25 region , encoding both US25-1 and 2 , was deleted . Enrichment levels for each of the six genes identified from the previous analysis was determined by direct RT-PCR using specific primer probe sets ( Figure 4 ) . To allow direct comparison , enrichment values for wild type pull downs were set at 100% ( actual enrichment values are displayed above each bar for reference ) . All six targets showed significant enrichment in infected cells compared to uninfected cells , validating the results from the original microarray experiments . Four of the six genes , ATP6V0C , CCNE2 , BCKDHA and LGALS3 , also showed a near complete loss of enrichment from cells infected with either US25 knock out viruses , indicating that US25-1 is required for enrichment of these transcripts . The results were less clear-cut for NUCB2 and SGSH . Although the levels of enrichment were reduced , the reduction was not statistically significant , suggesting that other viral miRNAs may be involved in targeting these genes . Additional validation of genes from the top 30 most enriched transcripts also showed significant enrichment in infected samples compared to uninfected samples , again validating the original microarray data ( Supplemental Figure S2 ) . However , no other genes showed a complete loss of enrichment in the knock out viruses . Transcripts that were not predicted to be targeted by US25-1 , such as LIN28B , showed enrichment in both wild type and knock out virus , confirming that successful enrichment from the knock out virus infected cells had occurred . In addition no enrichment was detected in control transcripts such as beta actin ( data not shown ) . No significant enrichment was observed following transfection with mimics corresponding to US25-2-3p , US25-2-5p or a US25-1 mimic containing a mutated seed region , demonstrating that neighboring miRNAs do not play a role in the enrichment of the six identified targets and that the seed region of US25-1 is necessary for effective enrichment ( Supplemental Figure S3A ) . Although these results confirmed that US25-1 RISC complexes bind to the identified transcripts it remained necessary to determine whether these interactions were functional and resulted in effects on gene expression . In our previous study we demonstrated that targeting of CCNE2 by US25-1 resulted in reduced cyclin E2 expression and conversely deletion of US25-1 from the virus resulted in increased expression of cyclin E2 in the context of virus infection . Using the same approach the effect of US25-1 on the expression levels of all six identified genes was investigated . Primary human fibroblast cells were infected at high MOI with either wild type AD169 or US25-1 KO virus and protein levels for the six genes compared by western blot analysis ( Figure 5A ) . Uninfected cell lysates were also included as well as cells transfected with a siRNA specific for the target gene , to confirm the specificity of the antibody . As has been shown before , CCNE2 levels were higher in the knock out virus infected cells compared to the wild type infected cells . In addition , ATP6V0C , BCKDHA , LGALS3 all showed increased levels of expression in cells infected with the US25-1 knock out virus , whereas NUCB2 and SGSH did not show significant difference between wild type infected cells and knockout infected cells . Representative western blots are shown in Figure 5A with direct quantitation from three biological repeats shown in Figure 5B . These results correspond well with the RISC-IP data , indicating that ATP6V0C , CCNE2 , BCKDHA and LGALS3 are targeted by US25-1 and deletion of this miRNA results in near complete abrogation of the inhibitory effects , whereas NUCB2 and SGSH may be targeted by additional viral miRNAs , or other virally induced mechanisms . Further supporting the role of US25-1 in targeting of these cellular genes , transfection of fibroblast cells with US25-1 mimic RNA results in significant knockdown at RNA levels of the putative US25-1 targets . The exception to this is SGSH , where transfection of US25-1 reproducibly resulted in a two-fold increase in total RNA levels . Whether this is due to a direct effect of US25-1 binding to the SGSH transcript or secondary effects from regulation of other US25-1 targets is unclear and will require further study . In addition , transfection of US25-2-3p and US25-2-5p did not result in significant knockdown of ATP6V0C , again supporting the role of US25-1 alone in targeting these genes ( Supplemental Figure S3B ) . Our previous study showed that US25-1 predominantly targets the 5′UTR of transcripts and five of the six genes ( including the previously identified CCNE2 ) have potential target sites within the 5′UTR for US25-1 ( Supplemental Figure S5 ) . However , ATP6V0C contains a 7mer target site for US25-1 downstream of the 5′UTR within the open reading frame . To determine whether the US25-1 target within the open reading frame is responsible for the observed knockdown in ATP6V0C protein expression we carried out luciferase assays using a construct containing the target site from ATPV0C cloned into the 3′UTR of the reporter construct psiCheck2 ( Figure 6A ) . The construct was co-transfected into HEK293 cells with either US25-1 mimic , or a non-targeting control siRNA . A CCNE2 luciferase construct was included as a positive control . US25-1 mimic induced a significant reduction in luciferase expression , compared to the negative control siRNA for both constructs ( Figure 6C ) . Furthermore , mutation of the ATP6V0C target seed region to a BamHI restriction site resulted in restoration of the luciferase activity , indicating that the target site identified in ATP6V0C is both sufficient and necessary for US25-1 specific inhibition of gene expression . Transfection of a US25-1 mimic with a mutated seed sequence that corresponds to the mutated ATP6V0C luciferase construct did not reduce expression of the wild type ATP6V0C luciferase construct , but did inhibit expression of the mutated ATP6V0C construct ( Figure 6B and C ) . Although it is clear that US25-1 targets and regulates the identified genes in the context of viral infection it is less clear whether these targets are functionally relevant . It is possible that viral miRNAs target many genes , but only a few are important to the virus while other targets represent fortuitous or irrelevant targets in the context of infection . Previous studies have established that cell cycle control and expression of cyclin E proteins are intimately involved in HCMV biology . However , the potential role of the remaining 5 targets is unclear , as they have not been reported as host factors involved in HCMV replication . To investigate their potential role , replication of HCMV was analysed following siRNA knockdown of each of the individual US25-1 targets in primary human fibroblast cells . Knock down of each gene was confirmed by RT-PCR ( Supplemental figure S4A ) . Cells were infected , post siRNA transfection , at an MOI of 1 with the clinical strain TB40E , which expresses GFP fluorescence protein under control of the SV40 promoter . This allows continuous monitoring of virus levels through GFP fluorescence . As can be seen from Figure 7A , knockdown of SGSH resulted in a modest increase in virus replication . In contrast knockdown of ATP6V0C resulted in significant reduction in virus replication at all time points . To rule out the possibility that the effects on virus replication were caused by artifactual or non-targeting effects , the assay was repeated using three additional independent siRNAs targeting different regions of the ATP6V0C transcript ( Figure 7B ) . All three siRNAs resulted in the same reduction of GFP fluorescence . To determine the effect on production of infectious virus , plaque assays were conducted following transfection of fibroblast cells with siRNA pools targeting ATP6V0C , SGSH or a negative control siRNA . The results support and confirm the GFP screen with a modest but statistically significant increase in replication in cells transfected with SGSH siRNA ( Mann-Whitney U Test: p = 0 . 0039 ) and a more dramatic reduction in virus production in cells transfected with ATP6V0C siRNA ( Figure 7C ) . In fact , knock down of ATP6V0C resulted in almost complete block in virus production , indicating that expression of ATP6V0C is essential for HCMV virus production and suggests that acidification of endosomal compartments is required for HCMV acute replication . Cell viability assays demonstrate that the reduction in virus replication was not due to cellular toxicity caused by ATP6V0C knockdown and transfection of the small RNAs did not induce an interferon response ( Supplemental Figure S4B and C ) . However , previous reports have indicated that ATP6V0C may have functions independent of endosomal acidification [34] . To determine whether the observed inhibition of virus replication is due to a defect in endosomal acidification , fibroblast cells were transfected with siRNAs targeting ATP6V1A and ATP6V1H , components of the same vacuolar ATPase complex . Disruption of any of the essential components has been shown to be sufficient to destabilize the complex . Knockdown of either ATP6V1A or ATP6V1H resulted in a similar reduction in HCMV replication compared to cells in which ATP6V0C had been knocked down ( Figure 7D ) . These results support the conclusion that acidification of the endosomal compartments by V- ATPase is essential for efficient HCMV replication and this gene is targeted by the HCMV miRNA US25-1 .
Despite recent advances in our understanding of miRNA transcript interaction , identification of valid targets remains challenging . The nature of miRNA targeting , where functional effects may rely on multiple miRNAs targeting a single transcript or multiple genes within single pathways being targeted , requires a system wide approach to elucidate the functions of miRNAs . Recent studies have used such approaches to identify targets of gamma-herpesvirus miRNAs [30] , [35]–[38] . However , no systematic screening approach has been presented for HCMV in the context of viral infection . Here we use a RISC-IP approach to identify putative targets of HCMV miRNAs in the context of viral infection , an important step towards generating a global understanding of the role these small regulatory RNAs play in the biology of HCMV and herpes viruses in general . Using a laboratory strain of HCMV and clinical strain we identified a total of 906 transcripts that were enriched by at least two fold over immunoprecipitations from uninfected cells , 222 of which were enriched by both viruses . Relatively few cellular targets of HCMV miRNAs have been previously published [27] , [39] , [40] . Of those , BclAF1 and RANTES did not show significant enrichment in infected cells . In the case of RANTES and BclAF1 it is possible that the effects are cell type specific or the complex formed between the transcript and RISC is not stable and therefore does not result in enrichment . MICB was significantly enriched in both uninfected and infected cells correlating well with previous studies indicating that both cellular and viral miRNAs target this gene . Many of the targets identified in this study have not previously been linked to HCMV , and in many cases , have not been linked to virus infections in general . Only a fraction of host genes have been investigated for potential roles in viral infections . Systematic analysis of viral miRNA targets can effectively exploit target identification for the discovery of novel host factors that play important roles in the biology of HCMV . Here we verify the effectiveness of this approach with the identification of at least two genes that have significant effects on HCMV replication . Knockdown of ATP6V0C resulted in attenuation of viral replication , while knockdown of SGSH resulted in an increase in viral replication . The most highly enriched target identified in this study , ATP6V0C , is a component of the Vacuolar ATPase , which is responsible for acidification of endosomal compartments [41] . Knockdown of this gene resulted in striking inhibition of virus replication with almost no infectious virus detected during growth curve analysis . Acidification of endosomes has previously been shown to be required for HCMV entry into endothelial and epithelial cells through receptor mediated endocytosis . However , infection of fibroblast cells occurs through direct fusion with the plasma membrane and has been demonstrated to be pH independent [42] . The attenuation of HCMV replication through siRNA targeting of ATP6V0C is therefore unlikely to be due to a defect in viral entry . In support of this , although GFP levels were reduced in siRNA knockdown experiments , all cells were clearly GFP positive 24 hours post infection ( Supplemental Figure S6 ) . An alternative explanation could involve the marked reorganization of intracellular membranous organelles during the formation of HCMV assembly compartment [43] . A block in endosomal acidification may interfere with this process resulting in attenuation of virus replication and virion assembly . Interestingly , a previous report indicated that US25-1 expression has a negative effect on acute replication of HCMV [44] . This effect was not specific , as adenovirus replication was also inhibited , suggesting targeting of a cellular factor was responsible for the phenotypic effects . Our findings suggest this cellular factor may be ATP6V0C and acidification of endosomal compartments may be a necessary process for efficient replication of DNA viruses in general . SGSH is involved in heparin sulphate degradation in the lysosomal compartment . Initial attachment of HCMV virions to target cells has been shown to occur through binding of viral glycoprotein B with heparin sulphate moieties on the cell surface [41] . However , it is unlikely that disruption of this pathway would result in higher levels of heparin sulphate on the cell surface . Western blot analysis in this study shows that infection with HCMV results in significant reduction in SGSH levels , and although this reduction appears to occur independently of US25-1 , the result suggests targeting of this gene plays an important role in the replication of the virus . The question remains as to why the virus would target a cellular gene , such as ATP6V0C , required for efficient replication . We previously demonstrated that UL112-1 attenuates HCMV replication through direct targeting of the immediate early gene IE72 and suggested that this represents a mechanism of establishing or maintaining viral latency [22] . Targeting of ATP6V0C may represent a similar mechanism , possibly blocking assembly and release of virions during latent infection . Alternatively , targeting by US25-1 may be unrelated to viral replication , but rather serve a different function such as immune evasion . Acidification has been shown to be required for efficient signaling by endosomal resident toll like receptors and for efficient MHC class II presentation [45] , [46] . Blocking acidification of endosomes through targeting of ATP6V0C may be an effective way for the virus to interfere with both innate and adaptive immune response . In conclusion , this study greatly increases the number of putative and validated targets of HCMV miRNAs . The use of systematic miRNA target analysis with focused siRNA screening is an effective strategy for the identification of novel host virus interactions . Finally the V-ATPase complex is an essential host factor in HCMV replication and is targeted by the HCMV miRNA US25-1 .
Normal human dermal fibroblast ( NHDF ) cells ( Clonetics ) were cultured in Dulbecco's modified Eagle's medium supplemented with 10% fetal calf serum and penicillin-streptomycin-L-glutamine . HCMV strain AD169 was obtained from the American Type Culture Collection ( Rockville , Md . ) . TR HCMV was obtained from Dr Jay Nelson . TB40E GFP was obtained from Dr Goodrum [47] . All HCMV strains were grown on primary fibroblast cells following infection at low MOI . Virus preps were purified over 10% sorbitol gradients . RISC-IP analysis was carried as out previously described [28] , [48] . In brief for systematic analysis of HCMV miRNA targets , primary human fibroblast cells were infected at a MOI of three with either AD169 or TR . Three days post infection cells were lysed , samples taken for total RNA and miRNP complexes immunoprecipitated using anti Ago2 antibody followed by streptavidin bead pull down . RNA was isolated using Trizol and analyzed for quality using an Agilent Bioanalyzer and transcript levels determined on the Illumina HumanRef-8 platform . Microarray data was analyzed using Gene sifter software . Enrichment of specific transcripts , through association with miRNP complexes was determined by dividing the immunoprecipitated levels of transcripts by the total levels . Analysis of specific genes by RT-PCR was conducted using the same protocol and parameters , except specific primer probe sets were used instead of microarray analysis . Primer probe sets were purchased from Lifetechnologies . For mimic RISC-IPs the same procedure was followed except 293T cells were transfected with 40 nM of mimic RNA and cells were harvested 48 hours post transfection . Argonaute specific antibody was generated by immunization of rabbits with a peptide corresponding to the N terminal region of Argonaute 2 ( 5-MYSGAGPALAPPAPPPPIQGYAFKPPPRPD3′ ) . Transcript sequences were down loaded from NCBI using RefSeq ID's . Predicted binding between HCMV miRNAs and putative target transcripts were determined using the online algorithm RNAhybrid ( http://bibiserv . techfak . uni-bielefeld . de/rnahybrid/ ) [49] . Parameters were selected to include Watson-Crick base pairing between either nucleotides 1 to 7 or 2 to 8 . Full transcript data was searched for seed sequence matches using a Java based script program . miR-US25-1 pre-miRNA coding region was deleted from AD169 BAC clone using BAC technology as previously described [50] . Briefly a PCR amplified cassette containing FRT flanked Kanamycin was recombined into AD169 BAC genome replacing the miR-US25-1 coding region using primers listed in Supplemental table S4 . Sequence in italics indicates regions homologous to FRT flanked Kanamycin cassette with remaining sequence homologous to recombination site in HCMV genome . The Kanamycin cassette was then removed by recombining the FRT sites through inducible FLIP recombinase . The resulting BAC was isolated and electroporated into human primary fibroblast cells to produce infectious virus . Schematic representations of recombination strategies are shown in Supplemental Figure S7 . Cells were transfected with small RNAs using RNAiMAX lipofectamine reagent ( Life technologies ) according to manufacturer's guidelines with the following modifications . Fibroblast cells were double transfected with 20 pmol ( 40 nM ) of small RNA per 24 well 8 hours apart . Cells were either infected or harvested 24 hours post transfection for siRNAs or 48 hours post transfection for mimics . Control cells were transfected with a non-targeting negative control siRNA ( Qiagen – cat 1027310 ) . The sequence and siRNA IDs are listed in Supplemental Table S5 . Total RNA was harvested using Trizol with concentrations and RNA quality determined by nano-drop spectrophotometer analysis . 100 ng of total RNA was DNAse treated ( Promega ) then reverse transcribed using high capacity cDNA reverse transcription kit ( ABI ) . Real time PCR was carried out using gene specific primer probe sets from ABI on a Rotor gene 3000 ( Corbet Research ) . Relative expression levels were determined by delta delta Ct calculation with levels corrected to GAPDH levels . Human primary fibroblast cells were grown in either 10% serum supplemented DMEM before infection at a multiplicity of 3 with either wild type AD169 , miR-US25-1 , or miR-US25-1/2 knock out virus . 72 hours post infection , cells were harvested using SDS sample loading buffer . 30 ul of protein sample were loaded and proteins were probed using primary antibodies to ATP6V0C ( Aviva ) , BCKDHA ( Cambridge Biosciences ) , CCNE2 ( Abcam ) , LGALS3 ( Cambridge Biosciences ) , NUCB2 ( Sigma ) , and SGSH ( Genetex ) according to manufacturer's specifications . Protein loading was normalised to GAPDH ( Sigma ) . IR800 or IR680 dye conjugated anti-rabbit IgG and anti-mouse IgG secondary antibodies were purchased from LiCor . Blots were imaged using infrared fluorescence of appropriately tagged secondary antibodies and quantified using a LiCOR Odyssey scanner and software . ATP6V0C luciferase constructs were created using custom oligonucleotides corresponding to the genomic region between nucleotides 146 and 226 downstream of the transcriptional start site , flanking the bioinformatically predicted miR US25-1 target site ( GAGCGGT starting at nucleotide 186 ) . For the ATP6V0C mutant construct , the miR US25-1 target site was replaced with a BAMHI restriction site . These inserts were cloned downstream of the renilla luciferase reporter gene of the pSicheck 2 dual luciferase construct ( Promega ) . Cloning oligonucleotides are shown in Supplemental table S4 Luciferase constructs were co-transfected with miR US25-1 mimic or control mimic ( IDT ) into HEK293 cells using Lipofectamine 2000 reagent according to the manufacturer's instructions . Cells were harvested 48 hours post transfection and luciferase levels measured using Promega's dual luciferase reporter kit . CCNE2 luciferase constructs were created and assays were performed as described previously [29] . For virus growth curve analysis by GFP fluorescence 96 well plates seeded with primary human fibroblast cells were transfected with siRNAs at a final concentration of 2 nM using RNAiMAX transfection reagent ( Life Technologies ) . Specific siRNAs for ATP6V0C ( S80 ) , ATP6V1A , ATP6V1H BCKDHA , CCNE2 , LGALS3 , NUCB2 , and SGSH were obtained from Life Technologies . 24 hours post transfection , cells were infected at a MOI of 1 . The MOI was empirically determined to provide robust signal without inducing extensive cell death through CPE . Twenty-four hours post infection cells were washed three times and overlayed with fresh complete DMEM media without phenol red pH indicator ( Lonza ) and GFP levels monitored using Biotech Synergy HT plate reader . For plaque assays 24 well plates seeded with HCMV were transfected with pooled siRNAs for ATP6V0C ( Life Technologies ) and SGSH ( Thermo Scientific ) . 24 hours post transfection , cells were infected at an MOI of 1 . 24 hours post infection cells were washed three times and at indicated time points the cell monolayer was scraped into the media and the media and cells collected and frozen . Standard plaque assays were carried out on human primary fibroblast cells overlayed with carboxy methyl cellulose . | Human cytomegalovirus is a prevalent pathogen . Like other herpesviruses , human cytomegalovirus expresses small regulatory RNAs called microRNAs . The focus of this study was to understand the role of these RNAs in the context of viral infection and to use this information to identify novel host factors involved in human cytomegalovirus biology . We used a biochemical approach that allowed us to systematically identify cellular genes targeted by virus microRNAs . Because the virus targets these genes , it is reasonable to propose that these genes play an important role during infection . We confirmed this hypothesis using a second screen in which we knocked down expression of a number of the identified targets of the virus microRNAs . Knock down of one of the targets , a cellular factor called ATP6V0C , resulted in an almost complete block in production of infectious virus . These data suggest that endosomal acidification is crucial to HCMV replication , and the virus targets this process by microRNA regulation . | [
"Abstract",
"Introduction",
"Results",
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"and",
"Methods"
] | [] | 2013 | Systematic MicroRNA Analysis Identifies ATP6V0C as an Essential Host Factor for Human Cytomegalovirus Replication |
To evaluate the effect of insecticide spraying for vector control and elimination of infected dogs on the incidence of human infection with L . infantum , a randomized community intervention trial was carried out in the city of Teresina , Brazil . Within each of ten localities in the city , four blocks were selected and randomized to 4 interventions: 1 ) spraying houses and animal pens with insecticide; 2 ) eliminating infected dogs; 3 ) combination of spraying and eliminating dogs , and 4 ) nothing . The main outcome is the incidence of infection assessed by the conversion of the Montenegro skin test ( MST ) after 18 months of follow-up in residents aged ≥1 year with no previous history of visceral leishmaniasis ( VL ) . Reactions were measured at 48–72 h , induration of ≥5 mm considered positive . Interventions were executed after the baseline interview and repeated 6 and 12 months later . The effects of each type of intervention scheme on the incidence of infection were assessed by calculating relative risks and 95% confidence intervals using Poisson population-averaged regression models with robust variance . Among the 1105 participants , 408 ( 37% ) were MST positive at baseline . Of the 697 negatives , only 423 ( 61% ) were reexamined at the end of the follow-up; 151 ( 36% ) of them converted to a positive MST . Only dog culling had some statistically significant effect on reducing the incidence of infection , with estimates of effectiveness varying between 27% and 52% , depending on the type of analysis performed . In light of the continuous spread of VL in Brazil despite the large scale deployment of insecticide spraying and dog culling , the relatively low to moderate effectiveness of dog culling and the non-significant effect of insecticide spraying on the incidence of human infection , we conclude that there is an urgent need for revision of the Brazilian VL control program .
Zoonotic visceral leishmaniasis ( VL ) is a severe neglected tropical disease leading to 4 . 5 to 6 . 8 thousand new cases each year in the Americas , mainly those living in poverty [1] , [2] . In this region , the disease is caused by the protozoan parasite Leishmania infantum ( syn = Leishmania chagasi ) , which is transmitted by the bite of female sandflies from the genus Lutzomyia , and dogs are considered the main source of infection in urban settings [3] , [4] . Those who are infected usually exhibit no symptoms , but some 5–10% will develop clinical signs of the disease during the course of infection [5] , [6] . Clinical VL is commonly characterized by fever , weight loss , hepatosplenomegaly , and pancytopenia , and is usually fatal if untreated [7] , [8] . Malnutrition and genetic factors may play a role in the risk of developing clinical VL after infection [5] , [9] , [10] . Brazil accounts for some 90% of the disease burden in the Americas , with an estimate of 4 . 2 to 6 . 3 thousand cases per year and fatality rates around 7% [1] . A gradual process of VL urbanization started in the early 1980s in Brazil , initially causing epidemics in the cities of Teresina , Natal and São Luis , all located in the Northeast of the country , and later spreading to other major urban centers [11] . Between 2008 and 2010 , 11 , 581 autochthonous VL cases were reported in 1 , 392 Brazilian municipalities , with 70% of cases occurring in only 165 municipalities , which had a total population of 40 million persons and included 12 state capitals and 52 cities with >100 , 000 inhabitants . Currently , the VL control program of the Brazilian Ministry of Health recommends two strategies for reducing the risk of transmission: ( i ) vector population control by means of residual insecticide spraying and environmental management , and ( ii ) culling of seropositive dogs in areas with moderate to high levels of transmission [12] . However , both strategies have proven unsuccessful in interrupting transmission [4] , [13] , [14] . Indeed , a systematic review of studies conducted in Latin America concluded that there is a lack of scientific evidence to support the effectiveness of such interventions [15] . Ten community intervention trials evaluated the effectiveness of dog-culling and residual insecticide spraying strategies , alone or in combination , and findings were contradictory . The authors of the review identified frequent methodological problems , such as small number of clusters for comparison , lack of comparability between groups in terms of exposure to infection , use of inaccurate diagnostic methods for detecting infection in human and dogs , small sample sizes , and high rates of loss to follow-up [15] . Because there are few alternatives for controlling zoonotic VL we attempted to address the methodological problems of previous community intervention studies and designed a cluster randomized trial to assess the effectiveness of dog culling and residual insecticide spraying in the reduction of incidence of human VL infection . The trial was conducted during the years of 2004–06 in the city of Teresina , Brazil , one of the largest endemic areas for VL in Brazil . We present our findings and conclusions following the recommendations of the updated version of the CONSORT statement [16] .
Teresina is the capital of the State of Piauí , located in the Northeast region of Brazil at 05°05′ latitude South and 42°48′ longitude West and 339 km inland at 72 m above sea level . Its population of 814 , 230 inhabitants ( 2010 ) occupies an area of 1 , 392 km2 with a population density of 584 . 94 inhabitants/km2 . The climate is tropical , with mean annual temperature 27°C and annual rainfall 1 , 300 mm . The highest temperatures occur between August and December , and the rainy period occurs from January until April . The periphery of the city has areas of pasture and tropical forest , including babassu and carnauba palm groves , with the predominant vegetation cover consisting of medium-sized bushes . Until 1980 , only sporadic VL cases had occurred in Teresina . In 1980 , however , the city was the site of the first large urban epidemic of VL in Brazil [17] . From 1980 to 1985 , almost 1 , 000 new cases were detected as the population increased from 370 , 000 to 460 , 000 inhabitants . The disease remained an important public health problem throughout the 1980s , although the incidence declined to less than 100 cases a year after 1985 . There was a second epidemic of 1 , 200 cases between 1993 and 1995 . During the first half of the 2000s , the incidence averaged approximately 20 cases per 100 , 000 inhabitants , and leveled off after 2005 at around 10 cases/100 , 000 inhabitants . A cluster randomized trial was carried out from January 2004 to December 2006 in ten localities ( localidades ) in seven neighborhoods ( bairros ) of the city of Teresina , that had cases of VL reported from 2000 to 2002 ( Figure 1 ) . Selection of the neighborhoods was designed to include different regions of the city , as well as a variety of land use and vegetation cover patterns . Based on detailed sketch maps routinely utilized by vector control teams , each of the ten localities was divided into blocks , each containing an average of 60 residences . The average number of blocks per locality was 30 . 9 ( range: 13–63 ) , and for each locality , four blocks were selected in a stepwise fashion as follows to minimize the risk of cross-contamination of interventions in each locality: ( i ) the first block was selected at random; ( ii ) all blocks sharing a border with the first block selected were excluded from the pool of eligible blocks for selection; ( iii ) the second block was selected at random from the pool of eligible blocks; ( iv ) steps ( ii ) and ( iii ) were repeated until four blocks were selected . Figure S1 . Schematic representation of the sampling process . Eligible participants were residents of selected blocks aged 1 year or above with no history of VL . In each block , around 25 residences were visited and one eligible person in each household was selected for the study by simple random sampling from a list of the names of the residents . Selected persons remained eligible for participation if they had no evidence of previous infection , as indicated by a negative result of a Montenegro skin test ( MST ) using 0 . 1 ml of leishmania antigen injected intradermally with reactions measured 48–72 hours later [18] . The antigen was prepared from a strain of Leishmania amazonensis and provided by the Reference Centre for Diagnostic Reagents ( Biomanguinhos—FIOCRUZ/RJ , Brazil ) . The diameter of skin induration was evaluated by two experienced and extensively trained professionals . The test was considered positive when induration measure was ≥5 mm in diameter . If the selected person was absent or refused to participate ( this occurred less than 5% of the time ) or had a positive Montenegro reaction , the next youngest resident on the list was selected instead . At the last visit ( 18 months ) a new MST was performed . At the time of the initial visit , all consenting participants had blood samples collected by venipuncture in order to test for the presence of antibodies to L . infantum by an indirect immunofluorescent serological test ( IFAT ) using the Biomanguinhos—FIOCRUZ/RJ , Brazil , kit according to the manufacturer's instructions . The original plan was to repeat the IFAT test at 6 and 12 months , but due to operational problems , data on IFAT results were not considered valid for the analysis , and serology was not used as a marker of infection in the study . Problems with serology were poor sensitivity and reproducibility . For instance , among the 951 subjects for which an IFAT result was available at baseline , only 16 ( 1 . 68% ) were positive . This result was deemed incompatible with the knowledge about VL transmission in Teresina , particularly in the studied areas in which transmission is known to occur , and inconsistent with data previously obtained indicating human seroprevalences ranging from 13 . 9% to 46 . 0% [19] , [20] . To check whether the error was in our laboratory , 827 randomly selected sera were sent to a retest at the National VL Reference Laboratory , Fundação Ezequiel Dias ( FUNED ) , in Belo Horizonte . Again , seroprevalence was also extremely low ( 1 . 33% ) and agreement between laboratories was considered poor ( kappa = 0 . 08 ) . It was unclear whether the problem with serology was due to substandard techniques for handling and storage of the collected sera , problems with test execution or problems with the kit itself . In any case , we decided not to use IFAT results in this study and relied on conversion of the MST at 18 months of follow-up as the only outcome measure , since no clinical cases of VL were detected among the studied population . Using a structured questionnaire with pre-coded questions , data were obtained on age , sex , literacy , history of migration ( ever lived outside Teresina ) , time of residence in Teresina , number of people in household , history of VL in the family , and characteristics of the household structure , peridomestic environment , and presence of domestic animals . Written consent was obtained from all participants ( or , if they were aged <18 years , written consent was obtained by one of their caregivers along with verbal assent from those above 10 years old ) . Four interventions schemes were defined: ( i ) No intervention , ( ii ) Insecticide spraying ( household and residential annexes ) , ( iii ) Culling of seropositive dogs , and ( iv ) Insecticide spraying+culling of seropositive dogs . Interventions were delivered in the selected blocks every 6 months , for three times , beginning just after each household visit . The last visit ( 18-month visit ) was not followed by any intervention . Both culling of seropositive dogs and insecticide spraying were performed according to the routine of the Visceral Leishmaniasis Control Program of the Zoonosis Control Center ( ZCC ) of the Teresina City Health Department . Teams of health workers of the ZCC with expertise in delivering such interventions were specifically recruited for this study . Interventions were performed in all houses of the blocks selected for receiving that specific intervention , not only in the houses where subjects had been recruited for the study . All domiciled dogs in the blocks under the dog culling intervention had blood samples collected by venipuncture for serological testing by indirect immunofluorescent antibody test ( IFAT ) using a canine leishmaniasis kit supplied by Bio-Manguinhos , FIOCRUZ , Rio de Janeiro . Reactions were considered positive if promastigote membrane fluorescence was observed at a serum dilution of 1∶40 . Positive sera were retested for confirmation . Dogs with a confirmed seropositive result were transported to the ZCC where they were anesthetized and killed following legal procedures [12] . Insecticide spraying was performed in all internal and external walls ( up to 3 meters of height ) of households and residential annexes located in the intervention blocks using Alpha cypermethrin 40 mg/m2 . The primary outcome was the incidence of infection by L . infantum in the eligible population after 18 months of entering the study as determined by conversion of the MST at 18 months of follow-up ( MST negative at baseline ) or diagnosis of active visceral leishmaniasis . In order to guarantee that the four selected blocks in each of the ten selected localities would have one of the four intervention schemes , allocation was performed as follows: ( a ) for each locality , a number was assigned to each block , ( b ) the intervention schemes were ordered as described above , and ( c ) using the command “sample” in Stata , the first block sampled was allocated to intervention ( i ) , the second to intervention ( ii ) and so on . At the end , each intervention scheme was allocated to a total of ten blocks throughout the ten selected localities . We estimated a cumulative incidence of infection of 35% in the non-intervention group based on data from a previous intervention study in this area [20] . We calculated that a sample size of 150 persons per intervention group would give a power of 80% to detect as significant ( p≤0 . 05 ) a difference of 15% in the incidence comparing non-intervention group with any of the intervention groups , taking into account an intraclass correlation coefficient of 0 . 03 due to the cluster sampling design . Sample size and power estimation were performed using the package “CRTSize” in R software . Cumulative incidence of infection in 18 months , crude risk ratios ( RR ) and 95% confidence intervals ( 95%CI ) were calculated for each category of intervention scheme . To assess the adequacy of randomization , we determined the distribution of selected baseline socioeconomic and environmental characteristics and the prevalence of infection ( MST positivity ) by intervention category . Those variables showing a statistical difference between any of the intervention groups in comparison to the control group at a p-value ≤0 . 2 were selected to be adjustment variables in a multivariate analysis for assessing the effects of interventions on the incidence of infection . Chi-square and t-tests were used for categorical and continuous variables , respectively . The effects of each type of intervention scheme on the incidence of infection were assessed by calculating RR and 95% CI using Poisson population-averaged models from generalized estimating equations with robust variance , an exchangeable correlation model , and designating each block as the clustering ( panel ) variable [21] . Considering that both older age and male sex have frequently been associated with VL infection in Latin America [22] , [23] , [24] , [25] , we decided to include them in the multivariate models independently of any statistical criteria . In addition , effects of interventions were controlled for the baseline prevalence of infection as assessed by the MST in each block [21] . Analyses were performed using both intent-to-treat and per-protocol approaches . Although intent-to-treat is usually a preferable approach [26] , a per-protocol analysis was considered useful in this setting since one of the interventions , namely culling of infected dogs , would only occur if a dog in a block under this intervention was found to be infected ( it might not have happened ) and the team of health workers of the ZCC could remove the dog from the environment . Failure to remove infected dogs is not uncommon , since the infected dogs are not detected immediately in the field , but only after two tests performed at the ZCC . When returning to the field , the team might not be allowed by the owner to remove the dog , the house might be closed , or the dog might not be at home . Statistical analyses were performed using Stata/MP , version 11 . 2 ( STATA Corp . , College Station , TX ) . We used sensitivity analysis to explore quantitatively the likelihood of bias due to loss to follow-up . For this , we performed the same analyses described above using simple imputation of the outcome under the assumption of random missingness . This step was implemented in Stata/MP , version 11 . 2 ( STATA Corp . , College Station , TX ) using the “mi” command . This study protocol was approved by the Committee on Research Ethics of the Institute for Public Health Studies of the Federal University of Rio de Janeiro . Written informed consent was obtained from all adult subjects and from parents or legal guardians of child participants .
Figure 2 is a flow diagram of the study with information for each intervention arm on the number of individuals initially selected , eligible for follow-up , and lost to follow-up . Baseline prevalence of infection varied from 33 . 0% to 41 . 4% ( Figure 2 ) , and no statistically significant difference was found comparing each intervention group with the control group ( all p-values >0 . 2 ) . In contrast , the 18-month cumulative incidence of infection was significantly higher in the control group as compared to the culling dog ( p = 0 . 003 ) and culling dog plus vector spraying ( p = 0 . 033 ) groups , but not as compared to the vector spraying group ( p = 0 . 128 ) . Losses to follow-up varied from 35 . 7% to 40 . 7% between intervention groups , but no statistically significant difference was found comparing each intervention group with the control group ( all p-values >0 . 3 ) . Table 1 shows the distribution of selected baseline socioeconomic and environmental characteristics for each intervention group . The dog culling group showed higher mean years of living in the residence and a smaller percentage of households with a chicken shed in the peridomestic environment as compared to the control group ( p = 0 . 015 and p = 0 . 046 , respectively ) . No other statistically significant difference with any variables or groups was detected . In addition to sex , age and baseline prevalence of infection , the variables years of living in the residence , presence of a chicken shed in the peridomestic environment , literacy of the household head ( higher in the insecticide spraying group , p = 0 . 168 ) , and the presence of a kennel ( more commonly found in the dog culling+insecticide spraying group , p = 0 . 093 ) , were selected for multivariate analysis according to the p-value <0 . 2 criterion . In the blocks under the dog-culling intervention ( solely or combined with insecticides ) , a total of 3 , 932 houses were visited during the three intervention rounds ( 1 , 275 in the first , 1 , 326 in the second , and 1 , 331 in the third ) . Seven hundred and eighty houses ( 19 . 8% ) harbored a total of 1 , 368 dogs ( 1 . 75 dogs per house with dogs ) . A total of 1 , 062 dogs ( 77 . 6% ) had blood samples collected and the global prevalence of infection was 3 . 1% ( 33 seropositive dogs ) . Prevalence by period of intervention was 4 . 8% ( round 1 ) , 2 . 2% ( round 2 ) , and 2 . 5% ( round 3 ) . Among the 33 seropositive dogs , only 21 ( 63 . 6% ) were removed from the environment . Owners of 12 dogs refused to give them for culling . Among the 20 blocks under dog-culling intervention , 5 ( 25% ) did not have any seropositive dog identified and another 3 ( 15% ) with seropositive dogs did not have them removed . In summary , only 12 ( 60% ) of the blocks under dog-culling intervention actually experienced the removal of at least one infected dog ( Figure 3 ) . In the 20 blocks under the insecticide-spraying intervention , a total of 3 , 321 houses were visited during the three intervention rounds ( 1 , 101 in the first , 1 , 108 in the second , and 1 , 112 in the third ) . Spraying coverage varied in each period , with 73 . 8% of the houses sprayed in the first , 58 . 0% in the second , and 67 . 0% in the third . The main reason for lack of universal coverage in insecticide spraying was the fact that some houses were closed or not inhabited . Table 2 shows relative risks ( RR ) and respective 95% CI for the effect of interventions on the 18-month cumulative incidence of infection , using both intent-to-treat and per-protocol analyses . In all analyses , all three intervention schemes were associated with some protection , but only the dog-culling strategy alone was significantly associated with a reduction in the incidence of infection . In the intent-to-treat analysis , individuals living in blocks under such intervention had a 38% decrease in the 18-month risk of developing infection . In the per-protocol analysis , a decrease of 52% in the 18-month risk of infection was detected for individuals living in blocks under such intervention and in which at least one infected dog was detected and removed from the environment . Table 3 shows the results of similar analyses as Table 2 , but with imputed data on the outcome . Not only the strength of all RR estimates decreased , but also no intervention was significantly associated with a reduction in the incidence of infection . These results suggest that losses to follow-up might have introduced selection bias in the study .
In this study , as in another one in the same area [20] , only dog culling showed some effect on reducing the incidence of infection , although sensitivity analysis suggests that this effect might be biased due to selective loss to follow-up . In any case , estimates of the putative effectiveness of such intervention varied between 27% and 52% , depending on the type of analysis performed . A reduction of the magnitude of the incidence of infection in this range might have an effect on the incidence rates of clinical VL , but this would be probably smaller , since susceptibility for developing clinical symptoms after infection is mediated by other factors such as age , genetics and nutrition [5] , [9] , [10] . Indeed , a mathematical model estimated that killing 2/3 of the infected dogs , as in our study , would only lead to a reduction of the incidence of human disease by less than 20% [27] . In general , the results of this study reinforce the generally accepted idea that culling seropositive dogs and insecticide spraying , the pillars of the Brazilian VL control program for at least 50 years , are not effective strategies for interrupting the spread of the disease , at least in the way they are implemented in the country [4] , [14] , [15] , [28] . In fact , the epidemiological situation leaves no doubt of the failure of both strategies . Disease counts have been on the rise since the 1980s , and VL is geographically spreading to areas in which it has not been reported before . From 1980 to 2010 , around 80 , 000 cases of VL were reported in Brazil , with around 4 , 200 deaths . The mean number of cases reported per year increased from 1 , 601 ( 1985–1989 ) to 3 , 814 ( 2006–2010 ) . In the 1990s , only 10% of cases occurred outside the Northeast region , but in 2010 the proportion reached 50% of cases . From 2009 to 2011 , autochthonous cases of VL were reported in more than 20% of the municipalities and in 21 of the 27 states of the country . The logic behind the use of such control measures in VL is the assumption that the incidence of L . infantum infection in humans is directly related to the number of infectious dogs and the vectorial capacity of the sand fly population to transmit infection from dogs to humans [27] . On the one hand , insecticide spraying decreases vector longevity , which is the major determinant of vectorial capacity . On the other hand , killing of infected dogs reduces the life expectancy of the reservoir population . Therefore , either vector control or dog culling theoretically can be effective [27] . However , many operational problems impair the effectiveness of these interventions such as the well-known inaccuracy of the serological tests used to identify canine infection by L . infantum in the field , the usual long time between identification of a seropositive dog and its removal , the fast substitution of sacrificed dogs by new , susceptible ones , insufficient knowledge about sand fly breeding sites and behavior , lack of available equipment and trained personnel for large-scale interventions , low coverage of insecticide spraying , deficiencies in quality control concerning insecticide handling , and lack of sustainability of control actions [4] , [15] , [20] , [28] , [29] , [30] , [31] . Considering all these problems , the low estimates of effectiveness for both interventions obtained in this study are not surprising , since it was designed to assess their effectiveness as implemented in practice . In this sense , our study provides some basis for comparisons of future studies that attempt to address the extent to which such operational problems affect the performance of interventions . A point that deserves further attention is that not all infected dogs become infectious and the usual serological tests used in practice do not separate infectious from non-infectious animals [4] , [32] , [33] . Control measures targeting infectious dogs could be a more effective approach since interventions focused on just those animals that contribute mostly to transmission tend to be more efficient [33] , [34] , [35] . Actually , highly infectious dogs can be distinguished from non-infectious dogs adopting quantitative PCR for detecting parasite loads in ear tissue [33] . However , a control strategy oriented to remove from the environment just the highly infectious dogs might not be sufficient to interrupt transmission , since asymptomatic dogs can also transmit Leishmania to sandflies . The relatively low effectiveness of dog culling ( 38% , considering the results from intent-to-treat analysis , a generally more accepted approach ) leads to the question of the ethics of maintaining this strategy for VL control [14] , [36] . In settings with low prevalence of canine infection , as in our study , the moderate specificity of the tests usually used in the field [37] , [38] , in particular to detect asymptomatic infection , leads to a low positive predictive value and , consequently , the sacrifice of many dogs that are actually not infected . Removing such dogs that are actually not contributing to transmission may have even an undesirable effect , since most of them will be replaced by new susceptible ones . This problem , along with the growing lack of acceptability of dog culling by the communities , makes the sacrifice of dogs an increasingly difficult control strategy to be sustained in Brazil . In light of the low effectiveness of the dog culling strategy , a further point that might also be considered is the possibility that other reservoir mammals contribute to VL transmission in urban settings . Studies have confirmed that humans , crab-eating foxes , opossums , domestic cats , and black rats can transmit L . infantum to sand flies although their importance has been minimized [4] . However , in certain scenarios these secondary reservoirs could conceivably play a role in sustaining transmission , and further studies on their infectious potential are needed . In spite of the geographic expansion and increase in number of cases of VL in Brazil in recent years , it is possible that the situation would have been even worse in the absence of these interventions . Therefore , any decision concerning the discontinuation of either of these control measures or their substitution by others , such as dog collars impregnated with insecticides , treatment of dogs , or dog vaccination , should be accompanied by a detailed monitoring of canine infection and human cases at the local level . Several limitations of this study need to be highlighted . First , since 38% of the eligible population was lost to follow-up , selection bias is a threat to the validity of the study results . Indeed , sensitivity analysis using single imputation of the outcome , assuming random missingness , generated results compatible with non-effectiveness of all interventions evaluated . It should be noted , however , that the point estimates for the dog culling strategy still showed a protective effect of 27% ( p = 0 . 073 , intent-to-treat analysis ) and 41% ( p = 0 . 056 , per-protocol analysis ) , making it difficult to conclude about the complete ineffectiveness of this control measure . Losses to follow-up were also common in other intervention trials in Brazil , ranging from 24% [39] to 44% in one year [20] , which stresses the difficulties of performing this type of study among urban population living in deprived areas . The majority of the losses in this study were due to migration to other neighborhoods within the city , as reported by the neighbors . Second , the lack of results of serological tests at each 6 months of follow-up impaired the ability to verify the potential short-term effect of the interventions , since an antibody response to infection is built rapidly after infection [40] . Third , monitoring the effect of interventions on the incidence of VL was not possible due to the relative rarity of clinical disease . Based on incidence rates of VL in these neighborhoods from 2000 to 2002 , one would have expected around 0 . 5 cases per 1 , 000 persons in a period of two years , making analysis of this outcome unfeasible in this study . This limitation needs to be taken into consideration when using the results of this study for informing decision on whether to interrupt or change the current interventions against VL , since the impact of an intervention on MST conversion and its impact on clinical VL might not be the same . Fourth , the high incidence of infection should be considered with caution , since hypersensitivity to thimerosal , used as a preservative in the Montenegro antigen , and the sensitization potential of a previous exposure to MST , might have contributed to the occurrence of false-positive cases [41] , [42] . However , there is no objective reason to believe that such error would happen differentially between the intervention areas , suggesting that the estimates of effectiveness might have well been underestimated [43] . Finally , due to the high baseline prevalence of infection , high rates of loss to follow-up and a high intraclass correlation coefficient in the actual data ( 0 . 057 ) , the final sample size provided low statistical power to detect as significant observed differences between the interventions . Indeed , power ranged from 15% ( when comparing insecticide spraying alone and no intervention ) to 53% ( when comparing dog-culling alone and no intervention ) . Such variation in the statistical power occurred mainly because the number of subjects actually followed-up and the differences observed in the incidence of infection varied between areas of intervention . Despite these limitations , this study overcomes some methodological problems of previous studies , in particular regarding the number of clusters randomly allocated to different interventions . For instance , except for one study [20] , all other controlled trials in Latin America evaluating the same control measures as our study used either a before-after approach in just one area [44] or a 1∶1 or 2∶1 comparison of intervention and control areas [39] , [45] , [46] , [47] . These approaches are inadequate for evaluating interventions , due to the high risk of lack of comparability between areas in terms of transmission intensity , but also because of the impossibility of making statistical inferences without assessing between-cluster variation [21] . A minimum of four clusters per intervention arm has been recommended for cluster randomized trials [21] . Also , some of the above studies did not evaluate the effect of interventions on human infection or disease [44] , [46] , which are the most appropriate outcome measures for public health purposes . Advantages of our study as compared to a previous one in the same area [20] , include a larger sample size and the use of clusters not restricted to just one neighborhood , which decreases the odds of contamination between interventions in neighboring clusters [21] . In summary , the continuous spread of VL in Brazil after more than 40 years of large scale deployment of insecticide spraying and dog culling indicates an urgent need for revision of the Brazilian VL control program . While waiting for the development of an effective human vaccine , effectiveness of other control measures , such as insecticide impregnated dog collars , topical insecticides for dogs , canine vaccines , and impregnated nets for humans should be evaluated in trials using solid methodologies and powered for detecting effects of such intervention on clinical outcomes . The delivery of interventions should be modified according to the different transmission scenarios , preferably targeting the areas at highest risk . Efforts to solve operational barriers to the adequate implementation of preventive measures are paramount . Finally , a broad commitment of both scientific and civil societies is necessary to interrupt the seemingly relentless progression of VL towards becoming one of the most serious infectious diseases of Brazilian urban populations . | Zoonotic visceral leishmaniasis ( VL ) constitutes a serious public health problem in the Americas , particularly in Brazil . The disease is caused by the protozoan parasite Leishmania infantum , which is transmitted by the bite of female sand flies , and dogs are the main source of infection . To decrease the risk of transmission , the Brazilian VL control program recommends residual insecticide spraying and environmental management for vector control , and culling of seropositive dogs in areas with moderate to high levels of transmission . Because there is a lack of scientific evidence supporting such interventions , we designed a study to assess the effectiveness of dog culling and residual insecticide spraying in the reduction of incidence of human VL infection . The results show that only dog culling had some statistically significant effect on reducing the incidence of infection , with estimates of effectiveness varying between 27% and 52% . In light of the continuous spread of VL in Brazil despite the large scale deployment of insecticide spraying and dog culling , the relatively low to moderate effectiveness of dog culling and the non-significant effect of insecticide spraying on the incidence of human infection , we conclude that there is an urgent need for revision of the Brazilian VL control program . | [
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"plant... | 2014 | Effectiveness of Insecticide Spraying and Culling of Dogs on the Incidence of Leishmania infantum Infection in Humans: A Cluster Randomized Trial in Teresina, Brazil |
Correct gene expression requires tight RNA quality control both at transcriptional and post-transcriptional levels . Using a splicing-defective allele of PASTICCINO2 ( PAS2 ) , a gene essential for plant development , we isolated suppressor mutations modifying pas2-1 mRNA profiles and restoring wild-type growth . Three suppressor of pas2 ( sop ) mutations modified the degradation of mis-spliced pas2-1 mRNA species , allowing the synthesis of a functional protein . Cloning of the suppressor mutations identified the core subunit of the exosome SOP2/RRP4 , the exosome nucleoplasmic cofactor SOP3/HEN2 and a novel zinc-finger protein SOP1 that colocalizes with HEN2 in nucleoplasmic foci . The three SOP proteins counteract post-transcriptional ( trans ) gene silencing ( PTGS ) , which suggests that they all act in RNA quality control . In addition , sop1 mutants accumulate some , but not all of the misprocessed mRNAs and other types of RNAs that are observed in exosome mutants . Taken together , our data show that SOP1 is a new component of nuclear RNA surveillance that is required for the degradation of a specific subset of nuclear exosome targets .
The synthesis of mRNA in eukaryotes is a complex multistep process , involving the transcription of DNA into RNA , capping , splicing of intronic sequences and maturation of the 3’ end of the messenger prior to export to the cytoplasm for translation into protein . Production of functional RNA can be impaired by either genetic mutation or incorrect processing; both can be deleterious for the cell and have been associated with various human diseases [1 , 2] . To prevent the production of potentially harmful RNA , eukaryotic cells employ numerous RNA surveillance mechanisms enabling the recognition and degradation of defective or aberrant RNA and thereby ensure quality control throughout the RNA production pipeline [3–5] . One of the principal contributors to RNA surveillance and quality control is the RNA exosome , a multi-subunit complex that provides the main 3’-5’ exoribonuclease activity in eukaryotic cells [6–8] . The exosome complex consists of a core complex of nine conserved proteins and associated ribonucleases . In addition , the exosome interacts with activator/adaptor complexes containing RNA helicases , RNA binding proteins or terminal nucleotidyl transferases that are required for exosome activity and are involved in substrate recognition . The composition of these activator/adaptor complexes varies between different intracellular compartments and also between species . In mammals , the nucleolar exosome complex interacts with the RNA helicase MTR4 , the RNA binding protein ZCCHC7 , and the terminal nucleotidyl transferase hTRF4 in a complex similar to yeast TRAMP complexes [9] . The human MTR4 is also present in the nucleoplasm where it is associated with the RNA binding proteins ZCCHC8 and RBM7 to form the so-called NEXT ( Nuclear EXosome Targeting complex ) complex [10 , 11] . NEXT targets promoter upstream transcripts , enhancer RNAs , 3’ extended small nucleolar RNAs ( snoRNAs ) and introns and is considered as a central activator/adaptor complex of exosome-mediated RNA surveillance . The core exosome and many of its cofactors are conserved in plants [12–14] . In Arabidopsis ( Arabidopsis thaliana ) , the nucleolar exosome is bound to AtMTR4 , which in turn associates with ribosome biogenesis factors [14 , 15] . The nucleoplasmic exosome associates with HUA-ENHANCER2 ( HEN2 ) , an RNA helicase closely related to MTR4 . HEN2 is part of a NEXT-like complex and required for the elimination of virtually all types of non-ribosomal exosome substrates including snoRNAs , a range of other non-coding RNAs and 3’ or 5’ extended mRNAs [14] . Downregulation of HEN2 also results in the accumulation of transcripts comprising exons and unspliced introns , suggesting that HEN2 targets also alternatively or mis-spliced mRNAs for degradation by the exosome . Hence , HEN2 appears to be the general cofactor of nuclear RNA surveillance in Arabidopsis . Here , we report the identification of SOP1 , a zinc-finger protein involved in nuclear RNA degradation . The sop1 mutation suppresses the developmental phenotype of a splice site mutation in the essential PAS2 gene . This splice site mutation results in the production of pas2-1 mRNA variants that undergo degradation by the nuclear exosome . In sop1 pas2-1 plants , selected pas2-1 mRNA variants are stabilised , thereby allowing the production of a functional PAS2 protein . In addition , loss of SOP1 results in the accumulation of splice variants generated from other gene loci , which also accumulate in hen2 and exosome mutants . Similarly to exosome mutants , loss of SOP1 counteracts the posttranscriptional silencing of a transgene ( PTGS ) , indicating that SOP1 contributes to RNA surveillance . However , only a portion of HEN2 targets accumulate in sop1 mutants suggesting that SOP1 is involved in the degradation of only a subset of nuclear exosome targets .
PAS2 ( At5g10480 ) encodes the 3 hydroxy acyl-CoA dehydratase necessary for fatty acid elongation by the elongase complex in the endoplasmic reticulum [16] . The very long chain fatty acids ( VLCFA; 20 carbons and over ) produced by the elongase complex are essential for plant growth as demonstrated by the loss of PAS2 in pas2 null mutants leading to embryo lethality [16] . However , the weak allele pas2-1 , which harbors a point mutation affecting the splicing donor site of the eighth intron , allows viable embryogenesis and seedling development of the homozygous mutants [17 , 18] . The pas2-1 homozygous mutant has a strong developmental phenotype with rod-shaped cotyledons and an enlarged hypocotyl due to an increased number of cell layers . The mutant plants also suffer from defective organogenesis with fused-organs , e . g . leaves , stems and flowers which leads to sterility [17] . During multiple rounds of mutant proliferation , we isolated a pas2-1 homozygous natural variant that still showed the severe developmental pas2 phenotype at the seedling stage , but developed into the adult stage and produced seeds . Importantly , this fertile variant , named pas2-1YaYa ( pas2-1Y ) has the same genomic sequence of the pas2-1 gene . The putative second site mutation or epigenetic phenomenon that underlies the partial restoration of the pas2-1 phenotype in pas2-1Y has not yet been identified . However , the restoration of fertility in pas2-1Y made this natural variant an ideal starting point for a genetic screen to isolate supressors of the pas2-1 seedling phenotype from an ethyl methane sulfonate ( EMS ) mutagenized population . Suppressor plants were screened from individual progeny of M1 plants at the seedling stage based on the restoration of cotyledon organogenesis of pas2-1Y ( Fig 1A and S1A Fig ) . We isolated eight suppressors of pas2 ( sop ) defining three complementation groups: four alleles for sop1 , one allele for sop2 and three alleles for sop3 ( S1A Fig ) . The three suppressors displayed almost wild type cotyledons and did not show any organ fusions , despite the presence of the splicing pas2-1 mutation . The loss of 3-hydroxy acyl-CoA-dehydratase activity in pas2-1 mutants prevents the elongation of VLCFA with an acyl chain longer than 18 carbons [19 , 20] . In addition , loss of PAS2 activity results in the accumulation of 3-OH acyl-CoA intermediates [16] ( Fig 1B and S1B Fig ) . To test if the suppression of pas2-1 developmental defects in the isolated suppressor plants was caused by restoration of VLCFA content , we compared the acyl-CoA pools in wild type , pas2-1 , pas2-1Y and the suppressor plants . As compared to pas2-1 , the pas2-1Y plants showed a partial restoration of fatty acid elongation , but with the persistence of 3-OH acyl-CoA intermediates indicating that PAS2 dehydratase activity was still impaired in these plants ( Fig 1B and S1B Fig ) . By contrast , all the sop pas2-1Y suppressor lines had wild type levels of VLCFA , associated with an absence of detectable 3-OH acyl-CoA intermediates indicating a complete restoration of the acyl-CoA dehydratase activity ( Fig 1B and S1B Fig ) . Since PAS2 provides the only acyl-CoA dehydratase activity in plants [16] , these results indicated that the suppression of the pas2-1 phenotype in sop lines was achieved by restoration of PAS2 activity . Next , we tested whether the sop1 mutation suppresses specifically the pas2-1Y phenotype or can also suppress the phenotype of other VLCFA-deficient mutants . For this purpose , we introgressed sop1-5 , a knock-out allele , that harbours a T-DNA insertion in the At5g21580 locus which encodes the SOP1 protein ( see below ) , into the original pas2-1 mutant as well as into pas1-2 , pas2-4 and pas3-1 mutants [16 , 21 , 22] . Importantly sop1-5 suppressed the bona fide pas2-1 mutant ( Fig 1E ) . Hence suppression of the pas2-1 phenotype by sop1 does not require the presence of the pas2-1yaya background and is caused by the loss of SOP1/At1g21580 function . By contrast , sop1-5 did not suppress VLCFA deficient pas1 and pas3 mutants ( Fig 1E ) , indicating that sop1 is not a general suppressor of VLCFA deficiency . Moreover , sop1-5 was also unable to suppress the embryo lethality of a pas2-4 knock-out mutant , as no homozygous pas2-4 could be recovered from 24 F3 plants from the progeny of a pas2-4 +/- sop1-5 -/- parental plant ( Fisher’s exact test , p = 0 . 0219 ) . Thus sop1 specifically suppresses the pas2-1 mis-spliced allele , but does not compensate for a complete loss of PAS2 function . Knowing that the pas2-1 allele harbors a point mutation affecting the splicing donor site of the eighth intron , we reasoned that the suppression of pas2-1 by sop mutations could be due to a restoration of the splicing defect . To test this hypothesis , we analyzed pas2-1 mRNA produced in the suppressor background by RT-PCR ( Fig 1C and 1D ) . While a single band was obtained from WT plants , three bands were detected in pas2-1 , pas2-1y and all three sop pas2-1 double mutants . This result indicates that the splicing defect of the pas2-1 mutant was not restored in pas2-1y or in the sop mutants . On the contrary , an accumulation of the largest splicing isoform was observed . When compared to pas2-1 , pas2-1Ysop suppressor plants had also slightly higher levels of the PAS2-1 mRNA of wild type size , albeit at much lower levels than WT plants ( Fig 1C and 1D ) . An identical repartition of PAS2 mRNA isoforms was observed when sop1-5 was introgressed in the pas2-1 background , while the sop1-5 mutation alone did not alter the expression of PAS2 mRNA in WT background ( Fig 1D and S2B Fig ) . These data suggested that the sop mutations affect the production or the stability of specific mRNA isoforms generated from the pas2-1 locus . To understand the splicing defects present in pas2-1 mutants , we cloned and sequenced the PAS2-1 RT-PCR products . Four different isoforms were identified ( for sequence detail see S3 Fig ) . The longest isoform ( PAS2-1LONG ) corresponded to an incompletely spliced PAS2 mRNA , which retained the 8th intron leading to the production of an mRNA with a premature termination codon ( PTC ) . The shortest ( PAS2-1SHORT ) PCR product lacked the 8th exon resulting in a direct fusion of Exon 7 to Exon 9 , which results in a frame shift leading to the loss of the stop codon . The band with a size similar to wild type corresponded to a mix of two isoforms . One corresponded to a mispliced isoform ( PAS2-1MIDb ) that used a cryptic splicing donor site ( GT ) seven nucleotides upstream of the pas2-1 mutation , and also resulted in the loss of a stop codon . The second product present in the WT-size band corresponded to a correctly spliced PAS2-1 mRNA ( PAS2-1MIDa ) which retained the point mutation present in the pas2-1 allele resulting in a single amino-acid change in the PAS2 protein sequence ( Gly199Ser ) . This latter isoform is the only isoform that is predicted to produce a full-length protein . To investigate whether the enhanced level of one of the pas2-1 splicing variants could confer suppression , we expressed the different RNA isoforms under the endogenous PAS2 promoter in a pas2-1 mutant background . Beside wild type PAS2 protein , only its closest isoform PAS2-1MIDa was able to complement pas2-1 mutant ( Fig 2A ) , suggesting that PAS2G199S encoded by PAS2-1MIDa RNA is a functional dehydratase . The relative levels of the different mRNA isoforms present in WT , pas2-1 , pas2-1y and pas2-1ysop1-1 plants were estimated with the number of RNAseq reads matching a ten nucleotide long sequence spanning the exon junction involved in each of the pas2-1 mRNA isoforms ( S3C Fig , sequences in bold ) . In agreement with the RT-PCR results ( Fig 1D ) , the quantification of RNA seq reads showed that the PAS2-1LONG isoform was the most abundant isoform in pas2-1Ysop1-1 ( Fig 2C , 7 . 5-fold increase compared to pas2-1Y ) . Interestingly , the higher levels of the PAS2-1LONG RNA were associated with a mild increase of the PAS2MIDa ( 2 . 17-fold ) , but not PAS2MIDb RNA ( 1 . 01-fold ) . While the ratio of PAS2MIDa/PAS2MIDb was about 0 . 3 in pas2-1 and pas2-1Y , it raised to 0 . 7 in pas2-1Ysop1-1 thanks to the accumulation of PAS2MIDa . These data indicate that the restoration of acyl-CoA dehydratase activity in sop1 plants was due to higher levels of the PAS2-1MIDa compared to pas2-1 and pas2-1Y plants , which in turn led to the production of a functional PAS2G199S protein . Furthermore , our data suggest that that sop1 favours the production of the PAS2MIDa either directly by affecting the efficiency of pas2-1 splicing , or indirectly by stabilising the intron-retaining RNA isoform PAS2-1LONG , which in turn would improve the production of PAS2-1MIDa isoform . To ascertain whether sop1 can affect the levels of mRNA isoforms generated from other splicing-defective loci , we crossed sop1-5 to ton2-12 , a mutant harbouring a mutation in a splicing donor site ( GT->AT of the first intron ) of the TONNEAU2 ( TON2 ) gene , encoding the regulatory subunit of the protein phosphatase 2A ( PP2A ) complex involved in the control of the orientation of the division plane [23] . The ton2-12 mutation results in the production of an mRNA isoform with similar features to pas2-1 ( retained intron with PTC ) and also leads to a strong developmental phenotype [23] . However , sop1-5 did not rescue the growth defect of ton2-12 mutants ( Fig 3A ) , and did not affect accumulation of the ton2-12 intron-retaining RNA isoform ( Fig 3B ) . We also queried intron-retention events in the sop1-1 mutant in our RNAseq data to identify other mis-spliced RNA . In addition to the expected accumulation of introns corresponding to alternative splicing events , we identified only one locus ( At5g36880 ) accumulating an intron specifically in sop1-1 background . However , this intron retention was also associated with a point mutation of its 5’ intronic splice donor site in sop1-1 ( S4 Fig ) . Similarly to the ton2-12 mutation , the intron-retaining transcript of At5g36880 did not accumulate in sop1-1 . These results suggest that sop1 influences PAS2-1LONG mRNA accumulation , but does not have a general effect on the stabilisation of incompletely spliced mRNAs . Our data indicate that the major effect of the sop mutations on pas2-1 mRNA isoforms is the accumulation of the intron containing PAS2-1LONG isoform ( Figs 1D and 1E and 2C ) , suggesting that SOP1 affects either the production or the stability of this particular isoform . The PAS2-1LONG isoform is characterised by two molecular determinants: the retained intron and the presence of a premature termination codon ( PTC , S3C Fig ) , the latter of which is known to trigger rapid RNA degradation via the non-sense mediated mRNA decay ( NMD ) pathway [24] . Therefore , we tested the hypothesis that the PAS2-1LONG isoform is a substrate for non-sense mediated mRNA decay [25–27] . The pas2-1 mutant was crossed with mutants of UPF1 ( encoding an RNA Helicase ) and UPF3 ( encoding an RNA-binding protein ) , both key components of the NMD pathway . The resulting double mutants were analysed for both growth and accumulation of the PAS2-1LONG isoform . The results showed that neither pas2-1 upf1-5 nor pas2-1 upf3-1 double mutants suppressed the pas2-1 growth phenotype ( Fig 3C ) or showed enhanced levels of the PTC containing PAS2-1LONG RNA ( Fig 3D ) . These results indicate that RNA degradation through NMD is not responsible for the low levels of PAS2-1LONG isoforms observed in pas2-1 mutants . To identify the sop mutations , we first conducted a positional cloning of the suppressor mutations with a mapping population prepared from a cross between the pas2 sop mutants ( Columbia accession ) and Landsberg erecta accession . In addition to the segregation bias on Chromosome V due to the presence of the pas2-1 mutation ( At5g10480 ) , we identified 0 . 5-1Mb segregating regions on Chromosome I for SOP1 or SOP2 and Chromosome II for SOP3 . Next generation sequencing of genomic DNA extracted from the suppressors pas2-1Ysop1-1 , pas2-1Ysop2-1 and pas2-1Ysop3-1 identified the specific polymorphisms associated with each genotype and matching the coding sequence of genes present in the mapped regions of SOP loci ( Fig 4A ) . For sop1-1 , a unique single nucleotide polymorphism ( SNP ) in At1g21580 gene fulfilled these criteria and was further confirmed by sequencing three other alleles ( all four sop1 alleles contained PTC ) . Similarly , an SNP was found in At2g06990 gene for sop3-1 and was confirmed with two other sop3 alleles ( one missense mutation and two PTC ) . For sop2-1 , a candidate SNP in At1g03360 gene was identified and confirmed by complementation of the pas2 sop2 mutant phenotype with the wild-type At1g03360 gene ( S5B Fig ) . Remarkably , all three SOP proteins are involved in RNA metabolism . SOP2 encodes Ribosomal RNA Processing 4 ( RRP4 ) , a core subunit of the RNA exosome required for the processing of rRNA , several snoRNA and the degradation of aberrant transcripts [12] . SOP3 encodes HUA-Enhancer 2 ( HEN2 ) , a RNA helicase homologous to MTR4 , identified initially as a regulator of AGAMOUS splicing [28] and more recently as interacting with the nuclear exosome for the degradation of misprocessed mRNA and other types of non-ribosomal exosome targets [14] . SOP1 encodes a recently re-annotated large protein which was formerly annotated as two genes ( At1g21570/AtC3H7 [29 , 30] and At1g21580 , unknown protein ) . SOP1 contains five zinc-finger ( ZnF ) domains at its carboxy-terminus which may bind RNA [29] . While the exosome core complex is present in both nuclear and cytosol , HEN2 was shown to be a nuclear protein enriched in nucleoplasmic foci . We therefore compared the subcellular distribution of SOP proteins by expression of functional GFP fusion proteins in stable Arabidopsis transformants ( S5A–S5C Fig ) . Confirming previous results , RRP4-GFP was detected in both the cytoplasm and nucleus , with a specific enrichment in the nucleoli ( Fig 4B and 4C ) [14 , 27] , while HEN2-GFP was detected in nucloplasmic speckles , but also diffusely distributed in the nucleoplasm ( Fig 4B and 4C ) [14] . Interestingly , SOP1-GFP was not diffused in the nucleoplasm , but predominantly localized in nucleoplasmic speckles , similar to the foci labelled by HEN2-GFP ( Fig 4B and 4C ) . Therefore co-localization of SOP1 , SOP2/RRP4 and SOP3/HEN2 was assessed by co-expression of corresponding RFP and GFP fusion proteins . This experiment revealed that SOP1 indeed colocalized with SOP3/HEN2 in nucleoplasmic speckles while SOP2/RRP4 and SOP3/HEN2 colocalized diffusely in the nucleoplasm ( Fig 4C ) . Those nucleoplasmic speckles were found throughout the nucleoplasm ( S1 Movie ) and presented a limited dynamic ( S2 Movie ) that was synchronous between SOP1 and HEN2 ( S4E and S4F Fig ) . However , speckles containing exclusively SOP1 could also be occasionally observed ( Fig 4C ) . These results reinforce the idea that SOP1 could be involved in similar functions than HEN2 , namely the degradation of nuclear exosome targets . Defects in either nuclear or cytosolic RNA quality control ( RQC ) functions generally result in increased post-transcriptional ( trans ) gene silencing ( PTGS ) . The rationale is that RQC serves as a first layer of defense to eliminate aberrant RNAs . Thus , aberrant transgene RNA bypass the RQC defenses and enter into the PTGS pathway only when the RQC machinery is dysfunctional or when it is saturated by a large excess of aberrant transgene RNA [14 , 27 , 31–34] . In particular , it was shown that mutations in the exosome core component RRP4 strongly enhance PTGS [27] . Mutations in HEN2 , but not in MTR4 , also strongly enhance PTGS , indicating that the degradation of abberant transgene RNA in the nucleus involves the nucleoplasmic fraction of the exosome [14] . The GUS tester line Hc1 , which triggers PTGS in only 20% of the population at each generation [31 , 35] , is a sensitive tool for monitoring the effect of both enhancers and suppressors of transgene PTGS . To quantify the effect of the sop mutations on PTGS , the Hc1 line was crossed to the three sop mutants and plants homozygous for both the transgene and the sop mutations were analyzed . As reported previously for rrp4 and hen2 mutants [14 , 27] , PTGS was strongly enhanced in sop2 and sop3 mutants ( Fig 5A ) . Interestingly , the sop1 mutation also increased PTGS albeit to milder levels , suggesting that SOP1 is not essential , but indeed participates to RNA quality control . To evaluate a possible role for SOP1 in RNA degradation by the nuclear exosome , we compared the accumulation of known exosome targets in sop1 , sop2 and sop3 mutants by Northern blots or qRT-PCR . In agreement with previous results [12 , 14] , only sop2/rrp4 mutants had elevated levels of 3’ extended pre-5 . 8S rRNA , a known target of the nucleolar exosome ( [14 , 15] Fig 5B ) . By contrast , sop1 did not accumulate 5 . 8S rRNA precurors similarly to sop3/hen2 indicating that SOP1 is not involved in rRNA processing ( Fig 5B ) . Among selected model targets of HEN2/SOP3 [14] , sop1 had an effect on one mis-spliced mRNA and two 3’ extended mRNAs ( Fig 5C ) . However , the effect of sop1 was weaker than the effect of hen2/sop3-1 , a result corresponding to that observed for PTGS suppression ( Fig 5A ) . Finally , unlike hen2/sop3 , sop1 mutants did not accumulate stable non-coding RNAs , precursors of snoRNAs , or transcripts generated from intergenic repeats ( Fig 5C ) . Collectively these data suggested that SOP1 is dispensable for some of the reported functions of the nuclear exosome , but could be involved in the degradation of RNAs that are also substrates of the nucleoplasmic exosome and HEN2 . To better understand the role of SOP1 in the accumulation of pas2-1 mRNA and RNA quality control , we aimed to identify other transcripts affected by sop1 mutation . Therefore , we compared the transcriptomes of WT , pas2-1Y and pas2-1Ysop1-1 plants by RNA seq . When comparing pas2-1Y to wild type plants , 424 genes were induced more than 2-fold while 414 genes were repressed . Consistent with the full restoration of the VLCFA-deficiency in pas2-1Ysop1 mutants ( Fig 1B ) , the expression of most of these genes ( 93% and 44% for induced and repressed genes , respectively ) was restored to wild type level in pas2-1Ysop1 mutants . However , our analysis identified 114 and 201 genes that were specifically up- or down-regulated in presence of the sop1 mutation ( Fig 6A ) . Unlike hen2 or exosome mutants , which were shown to accumulate a large number of non-genic transcripts [12 , 14] , the majority of the transcripts that were misregulated in sop1 were mRNAs ( S1 Table ) and likely include both direct targets of exosome-mediated degradation and secondary transcriptional responses . However , with the exception of the splicing factor SR34b ( Fig 6B ) , which was reported to modulate the splicing of IRT1 ( At4g19690 , S1 Table , [36] ) , we did not identify obvious transcriptional cascades . Interestingly , a Go-term analysis revealed that many of the misregulated mRNAs in sop1 are involved in splicing or other RNA-related processes ( Fig 6B , S1 Table ) . Since some of the upregulated RNA processing or splicing factors identified by the RNA seq analysis were predicted to undergo alternative splicing , we evaluated the levels of splicing isoforms by RT-PCR ( Fig 6C ) . For each of HEN4 and U11-48k mRNAs , only one predominant splice form was detected but appeared to be more abundant in sop1 , sop2 and sop3 mutants . For SRP30 and U2AF65a , two main RNA isoforms were detected . While the levels of the smaller isoforms were similar in all samples , the larger isoforms generated by intron retention accumulated upon mutation of SOP1 , SOP2 and SOP3 ( Fig 6C ) . These data are in line with the idea that incompletely spliced mRNAs are targeted for exosome-mediated RNA degradation , and that sop1 is involved in this process . As these alternatively spliced isoforms were not detected in NMD mutants ( S6 Fig ) , their accumulation of in sop1 , sop2 and sop3 is unlikely related to defects in non-sense mediated decay . Finally , we analysed the upregulation of some of the candidate genes identified by RNA seq analysis by qRT-PCR in sop1 , sop2 and sop3 mutants . For this experiment we used primer pairs located in the body of the mature RNA , but also primer pairs located in introns , or immediately upstream or downstream of the annotated mRNA , indicative of misprocessed mRNA with the typical features of bona fide exosome targets [14] . For all candidate targets tested , we detected a significant accumulation in sop1 , sop2 and sop3 samples ( Fig 6D ) . These data show that loss of sop1 does indeed affect the degradation of a subset of exosome substrates , including misprocessed mRNA and transcripts expressed from pseudogenes and some non-coding loci . To conclude , our data identify SOP1 as a Zn-finger protein that co-localises with the exosome-associated RNA helicase HEN2 and participates in the degradation of a selective subset of nuclear exosome targets including misprocessed mRNAs . Taken together , our results indicate that SOP1 functions as a co-factor of nuclear RNA quality control by the nucleoplasmic exosome .
In this study , we elucidated the molecular basis of the strong decrease in 3-hydroxy acyl-CoA dehydratase activity in the pas2-1 mutant . In pas2-1 plants , a mutation of the last nucleotide in the penultimate exon of PAS2 ( G1841A ) prevent correct mRNA splicing leading to the retention of the last intron and to aberrant intron splicing donor site usage . This result in a low steady state levels of four different pas2-1 mRNA isoforms , of which only PAS2MIDa encodes a protein that retains 3-hydroxy acyl-CoA-dehydratase activity . Second site mutations in the exosome subunit RRP4 ( in sop2 ) , in the nuclear exosome cofactor HEN2 ( in sop3 ) and in the Zn-finger protein SOP1/AT1G21580 ( in sop1 ) result in the accumulation of the longest PAS2-1 mRNA isoform , which still contains the unspliced 8th intron . In addition , pas2-1 sop double mutants have , relative to single pas2-1 and pas2-1y plants , higher levels of the functional PAS2-1MIDa mRNA . These findings indicate that in pas2-1 , the incompletely spliced PAS2-1LONG isoform is recognized by the nuclear RNA surveillance machinery and targeted to rapid degradation by the nuclear exosome . Therefore , impaired RNA degradation in pas2-1 sop could lead to stabilisation of PAS2-1LONG mRNA , allowing enough time for splicing to occur and resulting in an increased production of PAS2-1MIDa mRNA to eventually produce an active PAS2-1 ( Gly199Ser ) protein . In other words , slowing down degradation could allow unefficient splicing to occur , as previously reported [37 , 38] . The pas2-1 suppressor genetic screen identified two known components of the nucleoplasmic RNA surveillance machinery , HEN2/SOP3 and RRP4/SOP2 , which confirmed the role of the exosome in the degradation of mispliced mRNAs . The G55E mutation in the sop2 allele affects an evolutionary strictly conserved residue of the exosome core subunit RRP4 [39] . Based on the crystal structure of the yeast EXO9-RRP6 complex , this residue is located close to the N-Terminal Domain ( NTD ) of RRP4 which forms the interface of the core complex with RRP6 [40] . Interestingly , Arabidopsis has three RRP6 isoforms with different subcellular localizations [41] . However , none of these isoforms has yet been shown to interact with the core exosome [12 , 14] . Hence , we can only speculate that the G55E exchange found in sop2 might possibly affect the interaction of the exosome core complex ( EXO9 ) with homologues RRP6 or with other proteins that might bind to this part of the exosome surface in plants . While SOP2/RRP4 and SOP3/HEN2 are known components of the nuclear RNA surveillance machinery , SOP1 is a previously uncharacterized protein . Loss of sop1 in pas2-1 background results in accumulation of the PAS2-1LONG isoform comparable to what is observed in pas2-1 sop2/rrp4 or pas2-1 sop3/hen2 , suggesting that the underlying mechanism of pas2-1 suppression is similar in all three suppressor lines . Moreover , loss of sop1 results in accumulation of certain misprocessed mRNAs and other transcripts , all of which are also targets of HEN2 and the exosome . Lastly , sop1 enhances transgene PTGS , as previously observed for rrp4 and hen2 [14 , 27] . Collectively these findings indicate that SOP1 participates in exosome-mediated RNA degradation , which is consistent with its colocalization with HEN2 in nucleoplasmic speckles . However , not all of the targets detected in hen2 or exosome mutants accumulate also in sop1 mutants , suggesting that SOP1 participates in the degradation of only a subset of exosome targets . This idea is further supported by the fact that sop1 has a rather mild effect on PTGS when compared to sop2/rrp4 or sop3/hen2 , and that the subcellular localization of SOP1 is restricted to nucleoplasmic speckles while HEN2 and RRP4 are also detected throughout the nucleoplasm and in the entire nucleus , respectively . The recognition of RNA substrates by the yeast exosome is thought to involve so-called adaptor proteins . For example , the recognition of specific nucleolar RNA targets by the yeast exosome is mediated by the association of the HEN2-related RNA helicase MTR4 with Nop53 for the processing of pre-5 . 8S rRNA and UTP18 for the degradation of rRNA maturation by-products [42] . Similarly , two ZnF proteins have recently been shown to assist exosome-mediated RNA degradation in Schizosaccharomyces pombe . S . pombe possesses a functional homologue of Arabidopsis HEN2 , named Mtl1 ( for MTR4-like1 ) , which interacts with the large Zn Finger protein Red1 in the so-called Mtl1-Red1 core of the NURS/MTREC ( for Nuclear RNA silencing/Mtl1-Red1-core ) complex [43–46] . Another submodule of NURS is the CBCA complex comprising the Cap-binding complex and Ars2 [45 , 46] . Futhermore , NURS comprises Iss10–Mmi1 and Pab2–Rmn1-Red5 , the latter of which is also a Zn-Finger protein [44–46] . Interestingly , NURS is detected in nuclear speckles in S . pombe , resembling the localisation of HEN2/SOP3 and SOP1 in plants [44 , 45] . Similar to Arabidopsis HEN2 , S . pombe Mtl1 is required for the exosome-mediated degradation of cryptic unstable transcripts , non-coding RNAs and misprocessed mRNAs [14 , 46 , 47] i . e . virtually all types of nuclear exosome substrates . In addition , S . pombe NURS mediates the elimination of meitotic mRNAs during mitosis [43–45] . The molecular basis for the recognition of meiotic trancripts in S . pombe , called Determinant for Selective Removal ( DSR ) , has been identified as a repeated consensus sequence U ( U/C ) AAAC present in introns or 3’UTR [48 , 49] . Recently , Mmi1 has been shown to be co-transcriptionally recruited to unspliced transcripts containing the UNAAAC consensus sequence in retained introns [50] . No obvious DSR-like sequence was identified in SOP1-targets , such as PAS2-1LONG or AtU2AF65a shown to accumulate in sop1 . The accumulation of SOP1 targets was shown by qRT-PCR in oligo-dT primed cDNA , indicating that targets SOP1 are oligoadenylated , as is the case for other targets of the nuclear exosome and HEN2 [14 , 51] . However , it is still unclear whether polyadenylation is a prerequisite of target recognition , or rather a consequence of target accumulation in absence of efficient degradation . Hence , the RNA features that are recognized by SOP1 remain to be identified . Human and plant nuclear exosome targeting complexes show both common and distinct features when compared to the NURS complex in S . pombe . While humans have only a single homologue of the RNA helicase MTR4 , both S . pombe and Arabidopsis employ two related RNA helicases in nucleolar and nucleoplasmic degradation processes . In contrast , the NEXT complexes that have been co-purified from humans and plants appear to be rather similar , as they contain related Zn-knuckle and RNA binding proteins [10 , 14] , while sequence homologues of S . pombe Red1 or Red5 have not been found in plant or human exosome purifications as yet . In S . pombe , recruitment of Red1 to the exosome core complex requires RRP6 [46] . Although Arabidopsis has three RRP6-like proteins , to date none of them has been shown to interact with the exosome complex and we were not able to identify a sequence homologue of Red1 in Arabidopsis . By contrast , sequence comparison has identified SOP1 as the closest Arabidopsis homologue of S . pombe Red5 , although the sequence homology is restricted to the Zn-Finger domain . The other domains present in SOP1 do not show similarity to known proteins outside plants . Whether SOP1 associates with other protein factors involved in the degradation of nuclear exosome targets remains to be studied . The link between the exosome , targeting complexes involved in substrate recognition such as NEXT or NURS , and the CAP-binding complex is clearly conserved in S . pombe , humans and plants [10 , 14 , 45 , 47 , 52 , 53] . In humans and S . pombe , CBC is bound to Ars2 , the Arabidopsis homologue of which , named Serrate , was implicated in RNA splicing and the degradation of unspliced mRNA and introns [54 , 55] . However , in S . pombe , the physical link between the exosome and the splicing machinery could also be mediated by a direct interaction of the RNA helicase Mtl1 with the spliceosome [46] . Interestingly HEN2 , the plant homologue of Mtl1 , was co-purified with MagoNashi , a component of the exon-exon junction complex deposited by the splicing machinery , while SOP1 was not yet detected in purifications of plant NEXT-like complexes [14] . It is therefore possible that parallel mechanisms , only some of which require SOP1 , enable recognition and degradation of misspliced mRNAs in plants .
Arabidopsis thaliana Columbia ( Col 0 ) accession was used throughout this study . Seedlings were grown on Arabidopsis medium [56] supplemented with 1% sucrose in long day condition ( 16h light ) at 18–20°C . The suppressor screen was been performed on EMS-mutagenized individual pas2-1Y seeds . The progeny of 800 individual M1 plants were screened on petri dishes for restoration of cotyledons organogenesis on 7-day-old seedlings . The upf1-5 , upf3-1 and ton2-12 mutants have been described previously [23 , 27 , 57] . sop1-5 ( salk_019457 ) and pas2-4 ( GABI_700G11 ) were obtained from the Nottingham Arabidopsis Stock Center . Acyl-CoAs were extracted as described by [58] from 12-say old seedlings frozen in liquid nitrogen , and analysed using LC-MS/MS + MRM in positive ion mode . The LC-MS/MS + MRM analysis ( using an ABSciex 4000 QTRAP Framingham , MA ) was performed as described by [59] , ( Agilent 1200 LC system; Gemini C18 column ( Phenomenex , Torrance , CA ) , 2 mm inner diameter , 150 mm length , particle size 5 μm ) . For the identification and calibration , standard acyl-CoA esters with acyl chain lengths from C14 to C20 were purchased from Sigma as free acids or lithium salts . SOP1 genomic DNA was amplified from JAtY54C19 using Phusion polymerase ( Life Technologies ) and cloned in pDNR207 using Gateway Technology ( Invitrogen ) . SOP1-GFP or SOP1-RFP fusions were generated by LR recombination in pMDC83 [60] or pH7RGW2 [61] . RRP4 cDNA in pDNR201 and RRP4-GFP have been described previously [27] . RRP4-RFP has been generated by LR reaction into pH7RWG2 . HEN2-GFP has been described previously [14] . PAS2-1 isoforms were cloned by RT-PCR from pas2-1 mRNA into pDNR207 by Gateway BP reaction ( Invitrogen ) . PAS2WT cDNA was published in [16] . The various PAS2 isoforms were cloned in a modified pB7FWG2 vector [61] carrying a 2Kb PAS2 promoter cloned in place of the 35S promoter ( SpeI / HindIII ) . Plant transformations were performed using Agrobacterium C58 pMP90 by the floral dip method [62] . All primers used for construct cloning and plant genotyping are listed in S2 Table . Total genomic DNA isolated from whole 12 day old seedlings was extracted using the DNeasy Plant mini kit ( Qiagen ) according to the manufacturer’s instructions . For genome sequencing of sop1-1 , DNA was prepared into indexed fragment libraries with amplification and sequenced on an Illumina GAIIx instrument to a minimum of 30 M reads per sample , each with 76 nt read length . Using a custom Perl script , reads were trimmed to 65 nts to remove ends of biased composition and low quality . Reads were mapped to the TAIR10 genomic reference ( www . arabidopsis . org ) using GenomeMapper in the SHORE software suite [63] . Single nucleotide polymorphism ( SNP ) variants were determined using SHORE version 0 . 6 using a consensus minimum coverage of 3 reads . Overlap of SNPs with known genomic features , and functional consequences of SNPs were computed and summarized using FEATnotator [64] . Sequencing of sop2-1 and sop3-1 were performed using Illumina Technology ( The Genome Analysis Center , Norwich ) , and mutations were identified using the MutDetect pipeline [65] . Total RNA were extracted from 12-day-old seedlings using RNeasy extraction kit ( Qiagen ) according to the manufacturer’s instructions . Reverse transcriptions were performed on 1μg RNA using reverse transcriptase ( Fermentas ) . Quantitative Real-Time PCR ( RT-qPCR ) reactions were performed as in [14] and Northern Blot as in [15] . For transcriptome analysis , mRNA was enriched from total RNA using oligo ( dT ) capture ( Invitrogen ) and prepared into Illumina RNASeq libraries according to the manufacturer’s instructions . Sequencing was performed as paired reads of length 2 x 100 nt on an Illumina GAIIx instrument to minimum depth of 25 M read pairs ( 50 M reads ) per sample . These were trimmed to 88 nt as above and mapped to the Arabidopsis TAIR10 genome reference using Tophat v 2 . 0 . 5 [66] , with only uniquely mapped reads retained for further analysis . Read number aligned to annotated exon regions ( TAIR10 ) for each annotated gene was computed using a custom Perl script . For genes with multiple isoforms , exons from the representative gene model ( TAIR10 ) were used . Differential expression between samples was analyzed pairwise using NOISeq ver . 2 . 0 . 0 [67] , an R bioconductor package that uses read count data as input . NOISeq was used to simulate 5 samples within each condition ( nss parameter ) , permitting 0 . 2% of total reads in each condition for each simulated sample ( pnr parameter ) and a variability ( v parameter ) of 0 . 02 in total sequencing depth of simulated samples . Normalization ( norm parameter ) was according to the RPKM calculation , and for genes with zero read counts , a pseudo count of 0 . 5 was used ( k parameter ) for computing RPKM . Correction factor for length normalization ( lc parameter ) was set to 1 , indicating counts to be divided by a single order of length . The NOISeq pipeline was repeated for exons alone , and for full length genes ( both exons and introns included ) . RNAseq reads have been deposited in the NCBI short read archive ( SRA ) under the accession numbers listed in the BioProject PRJNA293799 . GUS activity was quantified as described before [68] using crude extracts from plant leaves and monitoring the quantity of 4-methylumbelliferone products generated from the substrate 4-methylumbelliferyl-b-D-glucuronide ( Duchefa ) on a fluorometer ( Thermo Scientific fluoroskan ascent ) . Imaging of fluorescent fusion proteins was performed on 7 day-old roots by confocal scanning laser microscopy on a Zeiss LSM710 microscope equipped with a 63X 1 . 20 NA water-immersion objective . Excitation of fluorophore were performed at 488nm for GFP and 561nm for RFP and emission settings were 500–550nm for GFP and 570–620nm for RFP . Multichannel confocal stacks were processed with ImageJ 1 . 49h for figure preparation . The raw data of sop1-1 transcriptome analysis by RNAseq have been deposited to NCBI short read archive ( SRA ) accessible in the BioProject PRJNA293799 . Data are also available in a user-friendly Jbrowse interface at http://sop1rna . inra . fr | Cells use various RNA quality control mechanisms to monitore the correct expression of their genome . Indeed , gene transcription can often generate faulty transcripts that are rapidly degraded to avoid possible deleterious effects to the cell . RNA degradation by the exosome is the main pathway for the removal of unwanted RNA in all kingdoms . Recognition of aberrant RNA involves a number of RNA binding proteins and other factors that target them for degradation by the exosome . Here , we used a genetic approach to identify proteins involved in the degradation of a mis-spliced RNA by the nuclear exosome in plants . Our screen identified two known components of nuclear RNA degradation pathway , namely the exosome core subunit RRP4 and the exosome-associated RNA helicase HEN2 that is required for the elimination of non-ribosomal RNAs by the nuclear exosome . Furthermore , we identified SOP1 as a novel putative exosome cofactor that is required for the degradation of some , but not all , of the substrates of HEN2 . | [
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... | 2016 | The Zinc-Finger Protein SOP1 Is Required for a Subset of the Nuclear Exosome Functions in Arabidopsis |
Seizures can induce endoplasmic reticulum ( ER ) stress , and sustained ER stress contributes to neuronal death after epileptic seizures . Despite the recent debate on whether inhibiting ER stress can reduce neuronal death after seizures , whether and how ER stress impacts neural activity and seizures remain unclear . In this study , we discovered that the acute ER stress response functions to repress neural activity through a protein translation-dependent mechanism . We found that inducing ER stress promotes the expression and distribution of murine double minute-2 ( Mdm2 ) in the nucleus , leading to ubiquitination and down-regulation of the tumor suppressor p53 . Reduction of p53 subsequently maintains protein translation , before the onset of translational repression seen during the latter phase of the ER stress response . Disruption of Mdm2 in an Mdm2 conditional knockdown ( cKD ) mouse model impairs ER stress-induced p53 down-regulation , protein translation , and reduction of neural activity and seizure severity . Importantly , these defects in Mdm2 cKD mice were restored by both pharmacological and genetic inhibition of p53 to mimic the inactivation of p53 seen during ER stress . Altogether , our study uncovered a novel mechanism by which neurons respond to acute ER stress . Further , this mechanism plays a beneficial role in reducing neural activity and seizure severity . These findings caution against inhibition of ER stress as a neuroprotective strategy for seizures , epilepsies , and other pathological conditions associated with excessive neural activity .
A seizure is an uncontrolled electrical disturbance in the brain . Recurrent and spontaneous seizures lead to epilepsy , a pathological condition affecting 50 million people worldwide . Despite the development of antiepileptic drugs that seek to raise the seizure threshold , one-third of epilepsy patients either respond poorly to these drugs or remain drug-resistant [1 , 2] . To improve therapeutic outcomes , it is necessary to have a substantial understanding of the molecular and cellular mechanisms which occur during seizure onset . In addition to eliciting profound neural activities and behavioral changes , seizures result in the excessive release of neurotransmitters , such as glutamate , that can cause excitotoxicity in the brain [3] . Excitotoxicity is particularly apparent in chronic epileptic brains , where neuronal degeneration and cell damage have been observed [4–6] . Among the cellular mechanisms that contribute to excitotoxicity-induced cell damage , endoplasmic reticulum ( ER ) stress has gained much attention [7 , 8] . ER stress is caused by disturbances in a cell’s growth environment , which can include viral infection , nutrient starvation , accumulation of unfolded proteins , and excitotoxicity , among many [9–12] . The cellular response to ER stress is comprised of a set of evolutionarily conserved mechanisms that serve to help the cell adapt to and remove disturbances . When the attempts to cope with the disturbances fail or when the disturbances last for an extended period of time , the ER stress response can trigger cell death . Although ER stress has been observed and suggested to contribute to cell death in various brain regions after chronic seizures or epilepsies [5] , a recent study has also proposed that ER stress can be crucial for neuronal survival after seizures [3] . Despite this controversy , it remains unknown whether and how the ER stress response might affect or modulate the hyperexcitability that occurs during seizure onset . In our current study , we utilized a multi-electrode array ( MEA ) recording system with cultured primary cortical neurons and a kainic acid-induced seizure model in mice to reveal a novel and beneficial role for the acute ER stress response in reducing neural activity and seizure severity . We subsequently showed that this phenomenon is dependent on protein translation triggered by ubiquitination and down-regulation of tumor suppressor p53 . The ubiquitination of p53 is mediated by the ubiquitin E3 ligase Mdm2 , whose expression is transcriptionally elevated upon induction of ER stress . Although Mdm2-p53 signaling has been shown to participate in cellular stress-induced apoptosis [4 , 13] , our study supports the previous study [3] and demonstrates a beneficial role for ER stress response in neural excitability homeostasis after seizures . Our findings also indicate that any attempts to ameliorate seizure-induced cell death by inhibiting ER stress may actually worsen seizure severity by diminishing the homeostatic effect on neural activity induced upon ER stress . Both the positive and negative consequences of ER stress should be taken into consideration when developing and testing the next generation of seizure therapies .
It has been previously suggested that ER stress contributes to seizure-induced neuronal damage [5 , 14] . However , it is unclear whether ER stress plays a role in counteracting or exacerbating the insult , such as hyperactivity , observed during the onset of seizures . To answer this question , we employed a kainic acid-induced seizure model in C57BL/6J mice to determine whether ER stress affects seizure severity . We first aimed to replicate the observation that kainic acid-induced seizures are accompanied by ER stress in the brain , as previously reported [3] . As shown in Fig 1A , wild-type ( WT ) mice intraperitoneally injected with kainic acid ( 60 mg/kg ) , who showed apparent seizure activity within 30 minutes , exhibited significantly elevated expression of four ER stress markers in the brain when compared to the mice injected with saline only: binding immunoglobulin protein ( BiP ) , protein disulfide isomerase ( PDI ) , protein kinase RNA-like endoplasmic reticulum kinase ( PERK ) , and the spliced isoform of X-box binding protein 1 ( XBP1s ) . Interestingly , another ER stress indicator , the cleavage of activating transcription factor 6 ( ATF6 ) , detected by a previously validated antibody [15] , was not observed after seizures . These results suggest an acute induction of selective ER stress pathways upon the onset of seizures . To determine whether acute ER stress can affect neuronal hyperactivity during seizures , we next asked whether inducing ER stress prior to seizure onset could affect seizure severity . To this end , we intraperitoneally injected littermate mice with saline or Thapsigargin ( Tg , 2 mg/kg ) [16] , a commonly used drug which induces ER stress through inhibition of ER Ca2+ ATPase , for three hours . This dosage , administration route , and treatment duration were based upon a previous study that demonstrated successful induction of ER stress in the brain [16] . Three hours after injecting saline or Tg , the mice were injected with kainic acid at 30 mg/kg or 60 mg/kg . The use of these relatively high dosages of kainic acid is due to the fact that mice of C57BL/6 background are relatively resistant to kainic acid-induced seizures [17–20] . Immediately following injections , ER stress markers and seizure behavior were quantified . As shown , the mice who received Tg showed an elevation of ER stress markers ( S1 Fig ) and a significant delay in the onset of the stage 4 seizures ( rearing and falling ) and an extended time between stage 4 and stage 5 seizures ( tonic-clonic seizures ) , as compared to the mice receiving saline ( Fig 1B ) . The same results were also observed with older , 12-weeks old , mice ( S2 Fig ) , suggesting the effect is likely independent of development stages . To determine whether a similar effect can be seen after a chronic induction of ER stress , we injected littermate mice with saline or Tg at a lower dose ( 0 . 5 mg/kg ) for 48 hours [21] . As shown ( S3 Fig ) , a significant delay in seizure activities was also observed after the chronic induction of ER stress . These results indicate that the effect of ER stress on reducing seizure severity is likely a long-lasting event . We then asked whether this reduction in seizure severity upon induction of ER stress occurred as a result of activation of ER stress response pathways . To this end , we employed an inhibitor of the ER stress response , Salubrinal ( 2 mg/kg ) , which acts through inhibition of eukaryotic translation initiation factor 2α ( eIF2α ) dephosphorylation [22] . As shown , mice receiving Salubrinal three hours before kainic acid showed a reduction of ER stress markers ( S1 Fig ) and an earlier onset of stage 4 seizures and a reduction in the time spent between stage 4 and stage 5 seizures , when compared to mice receiving saline , following injections of kainic acid at 30 mg/kg or 60 mg/kg ( Fig 1C ) . Together , our results indicate that inducing the acute ER stress response reduces seizure severity in mice . To study the mechanisms by which ER stress reduces seizure severity , we asked whether the acute response to ER stress is able to modulate neural activity . To this end , we employed a MEA recording system to record extracellular spontaneous spikes of electrical activity in primary cortical neuron cultures prepared from WT mice . To determine the most appropriate time point for recordings , we measured the spontaneous spike frequency in cultures starting from days-in-vitro ( DIV ) 10 until DIV 18 . As shown ( S4 Fig ) , the spontaneous spike frequency peaks at DIV 14 and gradually goes down after DIV 16 , which is consistently observed by another study [23] , with no obvious changes in the number of active electrodes after DIV 14 . These results suggest the network is likely most active and stable at DIV 14 in our cultures , as we have observed previously [18] . We therefore chose DIV 14 , similar to other previous studies [24–27] , to perform our recordings . To induce ER stress , we treated the cultures at DIV 14 with DMSO or Tg ( 1 μM ) for one hour . This dosage and treatment duration were chosen based on previous studies showing effective induction of ER stress without triggering cell death [28–30] . The treatment duration was selected also because of our intent to focus on the acute response after the onset of ER stress . As shown in Fig 2 , we observed a reduction in frequency , but not in amplitude , of spontaneous spikes in cultures treated with Tg in comparison to cultures treated with DMSO . When analyzing the firing pattern of spontaneous spikes , we observed no significant difference in burst activity ( as demonstrated by the duration of qualifying bursts ) . The numbers of active electrodes were not changed after DMSO or Tg treatments ( S5 Fig ) . These results suggest reduced spontaneous spikes in cortical neuron cultures during the early phase of ER stress . Previous studies have linked multiple translational regulators to epileptogenesis [31] and neural activity regulation [32] . Because we confirmed that seizures can induce ER stress ( Fig 1A ) and it has been shown previously that a subset of proteins can be selectively translated during acute ER stress response [33] , we asked whether the ER stress-induced reduction of spontaneous spikes are dependent on protein translation . As shown in Fig 2 , pre-treatment with cycloheximide ( 60 μM ) [34] , a translation inhibitor , blocked the reduction of spontaneous spike frequency , without changes to spike amplitude or burst activity , upon the induction ER stress . The numbers of active electrodes were again not changed after drug treatments ( S5 Fig ) . Altogether , our results indicate that ER stress , through a protein translation-dependent mechanism , reduces spontaneous spike frequency , which suggest a role for the ER stress response in reducing neural activity . To determine whether and how acute ER stress modulates protein translation , we studied murine double minute-2 ( Mdm2 ) , a ubiquitin E3 ligase known to participate in various cellular stress responses [35 , 36] . As shown in Fig 3A , cortical neuron cultures treated with Tg ( 1 μM ) for one hour show significantly elevated Mdm2 protein levels . However , this elevation was blunted by pre-treatment with either the translational inhibitor cycloheximide ( 60 μM ) or a transcription inhibitor actinomycin-D ( 20 μM ) , indicating that the elevation of Mdm2 upon induction of ER stress likely occurs at the transcription level . To test this possibility , we performed real-time reverse transcription and quantitative PCR ( real-time RT qPCR ) to measure the relative levels of Mdm2 mRNA upon induction of ER stress . As shown in Fig 3B , cultures treated with Tg exhibit significantly elevated levels of Mdm2 mRNA . Together , our results indicate that induction of ER stress elevates Mdm2 expression . The activity , substrate recognition and subcellular distribution of Mdm2 are known to be regulated by phosphorylation at serine-163 ( serine-166 in human Mdm2 ) [37 , 38] . To determine whether ER stress elevates the levels of phosphorylated Mdm2 , we measured the phosphorylation of Mdm2 at serine-163 in WT cortical neuron cultures and found it was significantly elevated during ER stress; however , the relative level of phosphorylated Mdm2 ( p-Mdm2/t-Mdm2 ) was unchanged ( Fig 3C ) . These data suggest that the signals leading to Mdm2 phosphorylation are likely unchanged . Instead , it is that phosphorylated Mdm2 is proportionally increased with the up-regulation of total Mdm2 , thus increasing the availability of phosphorylated Mdm2 during the early phase of the ER stress response . Because Mdm2 phosphorylation at serine-163 leads to its nuclear distribution [37] , we asked whether Mdm2 accumulates in the nucleus upon ER stress . As shown in Fig 3D , we found significant elevation of Mdm2 in the nuclear fraction upon ER stress induction for one hour in WT cortical neuron cultures . This observation was confirmed with immunocytochemistry where acute ER stress significantly elevated Mdm2 in the nucleus , which was identified by the staining of 4′ , 6-diamidino-2-phenylindole ( DAPI ) ( Fig 3E ) . Altogether , our data show that acute ER stress elevates expression and nuclear accumulation of Mdm2 . To examine whether Mdm2 modulates protein translation during ER stress , we employed a conditional Mdm2 knockdown mouse model by crossing Mdm2 floxed mice ( Mdm2f/f ) with Emx1-Cre mice to obtain Mdm2f/+-Emx-Cre+ and Mdm2f/+-Emx-Cre- mice . Emx1-Cre can confer Mdm2 reduction in the cortex and hippocampus , primarily in excitatory neurons , beginning as early as embryonic day 10 . 5 ( E10 . 5 ) [39 , 40] . We used heterozygous mice ( Mdm2f/+ ) to avoid potential apoptosis caused by complete Mdm2 knockout [41 , 42] . The knockdown efficiency of Mdm2 in Mdm2f/+-Emx-Cre+ cortical neuron cultures at DIV 14 is approximately 42% in comparison to the Mdm2f/+-Emx1-Cre- cultures , when cultures are prepared on postnatal day 0 ( Fig 4A ) . We then determined global protein translation in Mdm2f/+-Emx1-Cre+ and Mdm2f/+-Emx1-Cre- cultures after the induction of ER stress for one hour , with the use of puromycin to label newly synthesized proteins followed by western blotting with an anti-puromycin antibody . As shown in Fig 4B , induction of ER stress in Mdm2f/+-Emx1-Cre- cultures slightly elevated global protein translation . Because the typical cellular response to ER stress is traditionally known to repress protein translation , we suspect that such elevation , or maintenance , of translation only occurs during the early phase ( 1 hour ) of ER stress . To test this hypothesis , we treated the cortical neuron cultures with DMSO or Tg for 4 hours , a duration of treatment known to repress protein translation [43 , 44] . As shown in S6 Fig , the cultures that received Tg for 4 hours , with puromycin labeling occurring only during the fourth hour , exhibit a reduction in global protein translation . These results confirm an elevation , or maintenance , of protein translation during the early phase of the ER stress response in primary cortical neuron cultures . In contrast to Mdm2f/+-Emx1-Cre- cultures , we observed basally elevated translation in Mdm2f/+-Emx1-Cre+ cultures ( Fig 4B ) , consistent with our previous study showing that Mdm2 acts as a translational suppressor [34] . Of note , the acute ER stress-induced maintenance of protein translation was not observed in Mdm2f/+-Emx1-Cre+ cultures ( Fig 4B ) . Instead , a significant down-regulation of protein translation was observed . Altogether , our data suggest that Mdm2 is required to promote or maintain protein translation during the early phase of ER stress in cortical neuron cultures . Elevated Mdm2 upon ER stress was initially speculated to reduce protein translation , since Mdm2 has previously been found to interact with ribosomes and repress translation [34] . Because we instead observed an elevation of protein translation upon ER stress , we suspected that Mdm2 regulates translation through a different mechanism . Because we observed nuclear accumulation of Mdm2 upon ER stress and because Mdm2 is known to ubiquitinate its substrate tumor suppressor p53 in the nucleus [45 , 46] , we asked whether induction of ER stress leads to ubiquitination and down-regulation of p53 through Mdm2 . As shown in Fig 4C , an elevation of p53 ubiquitination was observed in Mdm2f/+-Emx1-Cre- cultures after Tg treatment for one hour . In Mdm2f/+-Emx1-Cre+ cultures , we instead saw a slight reduction of p53 ubiquitination . These results of p53 ubiquitination were consistent with the total protein level of p53 , where a significant reduction of p53 was seen in Mdm2f/+-Emx1-Cre- cultures but a slight trend toward elevation of p53 was seen in Mdm2f/+-Emx1-Cre+ cultures after Tg treatments ( Fig 4D ) . These results confirmed Mdm2-dependent p53 down-regulation during the early phase of the ER stress response . In contrast , elevated expression of Mdm2 and the subsequent down-regulation of p53 were not observed in cultures treated with Tg for 4 hours ( S7 Fig ) despite the continuous ER stress response shown by elevated phosphorylation of eIF2α ( S8 Fig ) . These results suggest that Mdm2-triggered ubiquitination and down-regulation of p53 only occur during the early phase of the ER stress response . Because our observation of Mdm2-dependent p53 down-regulation ( Fig 4C and 4D ) is consistent with our data on protein translation during early phase of the ER stress response ( Fig 4B ) , we suspect that impaired down-regulation of p53 is responsible for the failure to maintain protein translation during the early phase of ER stress in Mdm2f/+-Emx1-Cre+ . To test this possibility , we employed a widely used p53 transcriptional inhibitor Pifithrin-α ( 1 μM ) [45 , 47–49] in Mdm2f/+-Emx1-Cre+ cultures to mimic inactivation of p53 when degraded in Mdm2f/+-Emx1-Cre- cultures ( Fig 4D ) . As shown in Fig 4E , Mdm2f/+-Emx1-Cre+ cultures pre-treated with Pifithrin-α were able to maintain protein translation upon induction of ER stress , as seen in Mdm2f/+-Emx1-Cre-cultures ( Fig 4B ) , supporting our conclusion that ER stress-induced down-regulation of p53 allows for maintenance of protein translation . It is unclear how stabilized p53 can disrupt protein translation during ER stress in Mdm2f/+-Emx1-Cre+ cultures . One possibility is through reduction of ribosome biogenesis as previously described [50] . To test this possibility , we characterized ribosome biogenesis through the measurement of 47S pre-rRNA , as shown previously [51 , 52] , of Mdm2f/+-Emx1-Cre- and Mdm2f/+-Emx1-Cre+ cortical neuron cultures after the induction of ER stress for one hour . However , as shown in S9 Fig , we did not detect significant changes in the levels of 47S pre-rRNA that could explain the protein translation phenotypes observed in Mdm2f/+-Emx1-Cre+ cultures . These data suggest that Mdm2-p53 signaling likely acts through a novel , and yet unidentified , mechanism to maintain protein translation during the early phase of ER stress response . Our previous data suggest that the neuronal response to acute ER stress leads to a reduction in spontaneous spike frequency through a protein translation-dependent mechanism ( Fig 2 ) , and we have now shown that ER stress induces Mdm2-p53 signaling-dependent protein translation ( Figs 3 and 4 ) . To validate the role of Mdm2-p53 signaling in reduced neural activity triggered by acute ER stress , we performed MEA recording using Mdm2f/+-Emx1-Cre+ cultures . As shown in Fig 5A , Tg-induced reduction of spontaneous spike frequency was absent in Mdm2f/+-Emx1-Cre+ cultures , similar to WT cultures treated with the translation inhibitor cycloheximide ( Fig 2 ) . Spontaneous spike amplitude and burst activity were not affected ( Fig 5B and 5C ) . These observations suggest that Mdm2f/+-Emx1-Cre+ cultures have lost the ability to reduce neural activity upon ER stress induction . However , most importantly , the deficits of Mdm2f/+-Emx1-Cre+ cultures in spontaneous spike frequency can be restored by pre-treatment with p53 inhibitor Pifithrin-α , thereby confirming the role of Mdm2-p53 signaling in these effects . The spontaneous spike amplitude and burst activity were not affected by Pifithrin-α ( Fig 5B and 5C ) . To validate that our observation with the use of Pifithrin-α is specific to p53 and to provide a secondary method to confirm the role of p53 in the ER stress-induced reduction of neural activity , we lentivirally transduced a shRNA against p53 or a control non-target shRNA into Mdm2f/+-Emx1-Cre+ cultures . As shown ( S10 Fig ) , knocking down approximately 40% of p53 was able to restore the Tg-induced reduction of spontaneous spike frequency in Mdm2f/+-Emx1-Cre+ cultures , again without changes in spontaneous spike amplitude or burst activity . Altogether , our results confirm the role of the Mdm2-mediated p53 down-regulation in ER stress-induced maintenance of protein translation and reduction of neural activity . Our data show that inducing acute ER stress prior to kainic acid stimulation can reduce seizure severity in mice ( Fig 1 ) . Based on our data which demonstrate the necessity of the Mdm2-p53 signaling pathway in the ER stress-induced reduction of neural activity ( Fig 5 ) , we next sought to determine whether the acute ER stress-induced reduction of seizure severity is altered in Mdm2f/+-Emx1-Cre+ mice and whether any deficits can be corrected by Pifithrin-α . We first confirmed an approximately 20% reduction in Mdm2 in the cortex of Mdm2f/+-Emx1-Cre+ mice when compared to that in Mdm2f/+-Emx1-Cre- mice ( S11 Fig ) . This moderate knockdown efficiency is likely due to the restrictive expression of Emx1-Cre to excitatory neurons while Mdm2 is also expressed in other cell types [53 , 54] . Next , we intraperitoneally injected Mdm2f/+-Emx1-Cre- or Mdm2f/+-Emx1-Cre+ mice with saline , Tg ( 2 mg/kg ) , Pifithrin-α ( 2 mg/kg ) , or Tg + Pifithrin-α . Three hours after initial injection , the mice were injected with kainic acid ( 60 mg/kg ) . Immediately following , seizure behavior was closely monitored and quantified . As shown in Fig 6A and 6B , the Mdm2f/+-Emx1-Cre- mice who received Tg exhibited significantly delayed onset of stage 4 seizures and an extended time between stage 4 and stage 5 seizures , as we had observed in WT mice ( Fig 1 ) . Pifithrin-α slightly delayed the seizure activity , as we observed previously [17] . As we expected , the Mdm2f/+-Emx1-Cre+ mice who received Tg did not exhibit the delayed onset of stage 4 seizures or elevated duration between stage 4 and 5 seizures that were observed in Mdm2f/+-Emx1-Cre- mice . Importantly , Mdm2f/+-Emx1-Cre+ mice receiving Tg + Pifithrin-α exhibited delayed onset of stage 4 seizures and enhanced duration between stage 4 and 5 seizures , supporting our previous observation that inhibition of p53 restores Tg-induced reduction of neural activity in Mdm2f/+-Emx1-Cre+ cortical neuron cultures ( Fig 5 and S10 Fig ) . Basally elevated seizure latency was also observed in Mdm2f/+-Emx1-Cre+ mice in comparison to Mdm2f/+-Emx1-Cre- mice , suggesting a possibility that Mdm2-p53 signaling pathway may regulate brain excitability through certain p53 target genes , such as potassium channel KCNKs [55] , or through other unidentified mechanisms . Altogether , our data demonstrate that ER stress triggers Mdm2-p53 signaling-dependent protein translation which functions to reduce neural activity in cultures and seizure severity in mice ( Fig 6C ) .
Our study revealed that acute responses to ER stress serve to reduce neural activity through a protein translation-dependent mechanism . Because we ( Fig 1 ) and others [14] have shown that seizures can induce ER stress , our findings suggest that acute ER stress can initiate cellular responses that possess a beneficial function to counteract seizure activity . The data further support the notion that the cellular response to ER stress enables the neuron to deal with insults rather than passively waiting for the removal of the insults . A similar beneficial function has been reported in the past through the Mdm2-p53 signaling-dependent up-regulation of CCAAT/enhancer-binding protein homologous protein ( CHOP ) in the brain [3] . This study demonstrated an elevation of CHOP in the hippocampus of epileptic mice , and showed that knocking down CHOP expression was able to promote seizure-induced neuronal death and cognitive decline [3] . Both this previous study and our current work caution against inhibition of ER stress or Mdm2-p53 signaling as a neuroprotective strategy , especially in pathological conditions where excessive neural activity is observed . Both the positive ( neural activity homeostasis ) and negative ( neuronal death ) consequences of ER stress would need to be considered when developing therapies against hyperexcitability or excitotoxicity . Because our results show a beneficial effect of ER stress response toward reducing seizure severity after a chronic induction of ER stress , it would be of particular interest to determine how a disruption or promotion of ER stress may affect the duration or frequency of subsequent seizures in individuals with reoccurring seizures . To address this important issue , it would be useful to measure long-term seizure activity in a model of reoccurring seizures with an electrocephalogram ( EEG ) , which provides more detailed and sensitive measurements of seizure activity than behavioral studies due to the unpredictable and often behaviorally unnoticeable nature of spontaneous reoccurring seizures . Related to this question , it remains unknown whether the intensity of ER stress and the beneficial function of Mdm2-p53 signaling remain the same each time a seizure occurs . It is assumed that ER stress and Mdm2-p53 signaling can be induced during each seizure . However , it is unknown whether there are any compensatory mechanisms or whether desensitization occurs after repeated seizures . In addition , because the duration and severity of seizures are extremely variable in patients , how long ER stress lasts after seizures and how long it takes for the neuron to recover from seizure-induced ER stress are likely variable , as well . Presumably , based on our results , promoting ER stress could help reduce excitability in the brain . However , given the complexity of human epilepsies , our limited knowledge about ER stress in the nervous system , as well as the multitude of functions of Mdm2-p53 signaling in the neuronal cells , manipulating ER stress to reduce brain excitability may not be ideal at this moment . The full effect of ER stress and a detailed time course of the ER stress response in neurons needs to be further explored in order to better understand excitability homeostasis , especially in epilepsy patients . Our study showed that ER stress-mediated neural activity reduction depends on protein translation . ER stress is traditionally known to activate pathways which repress translation , such as phosphorylation of eIF2α , with the rationale that doing so conserves energy consumption [56] . Our results demonstrated a maintenance in protein translation mediated by Mdm2-p53 signaling during the early phase of the ER stress response , suggesting the possibility that Mdm2-p53 signaling offsets the translational suppression mediated by eIF2α phosphorylation or other pathways . When Mdm2-p53 signaling is turned off during the late phase of the ER stress response , translational suppression dominates and is then apparent . If this prediction is true , it would suggest Mdm2-p53 signaling is an important “switch” for turning protein translation on and off during different phases of ER stress . Studies have also indicated that certain proteins , particularly those associated with cellular stress response such as heat shock proteins , continue to be translated during ER stress [33] . Based on this rationale , it is likely that certain proteins associated with the reduction of neuronal or synaptic excitability could be preferentially translated during the early phase of ER stress in order to reduce neural activity . These proteins could include ion channels , neurotransmitter receptors , and many others . A comprehensive proteomic profiling is the logical next step to identify the proteins being translated within neurons during the early phase of ER stress . Alternatively , the use of slice electrophysiology , which allows for wash-out experiments with drugs , could also help to dissect the relative contribution of synaptic transmission or intrinsic excitability to ER stress-induced reduction of neural activity , as well as the associated ionic mechanisms . Such analyses will provide novel insights into the cellular stress response in terminally differentiated neuronal cells . Our current results indicate a novel , although indirect , mechanism by which Mdm2 regulates translation: through ubiquitination and down-regulation of its nuclear substrate p53 to maintain translation . Our data have ruled out ribosome biogenesis as the mechanism underlying p53-dependent protein translation during ER stress , but the precise mechanism remains unidentified . A previously published unbiased genome-wide study has identified several target genes of p53 that could potentially modulate protein translation through the ribosomal protein S6 kinase ( S6K ) pathway [55] , including Notch1 , TGF-α and FGF-2 [57–59] . A prominent future direction would be studying the expression and regulation of these genes , and their roles in modulating S6K activity during the ER stress response . Our previous study identified Mdm2 as a translational suppressor that acts through directly binding to ribosomes in the cytoplasm [34] . It is logical to speculate that if ER stress becomes unresolved after a long period of time and translational suppression is required , Mdm2 might re-distribute from the nucleus to the cytoplasm to increase Mdm2-ribosome interaction . This would also allow for stabilization of p53 in the nucleus , where it could suppress cellular growth and be ready to elicit apoptosis when needed . This prediction is supported by our data showing diminished elevation of total Mdm2 and nuclear Mdm2 after chronic induction of ER stress ( S5 Fig ) . It is also supported by studies which have shown that the nuclear export of Mdm2 requires dephosphorylation by the protein phosphatase PP2A [37] and ER stress is known to activate PP2A and its downstream signaling pathways [60 , 61] . Based on our current study showing the connection between Mdm2 and ER stress-dependent regulation of neural activity and our previous studies showing a role for Mdm2-dependent signaling in homeostatic plasticity [45 , 46] , we believe Mdm2 is a crucial molecule in brain excitability homeostasis and should be studied further . Given that many therapeutic agents targeting Mdm2 or Mdm2-p53 signaling are clinically available or being developed , a better understanding of Mdm2 would benefit future therapeutic development for seizures , epilepsies , and other neurological conditions associated with excessive neural activity .
All experiments using animal data followed the guidelines of Animal Care and Use provided by the Illinois Institutional Animal Care and Use Committee ( IACUC ) and the guidelines of the Euthanasia of Animals provided by the American Veterinary Medical Association ( AVMA ) to minimize animal suffering and the number of animals used . This study was performed under an approved IACUC animal protocol of University of Illinois at Urbana-Champaign ( #17075 to N . -P . Tsai . ) The WT ( C57BL/6J ) and Emx1-Cre mice were obtained from The Jackson Laboratory . The Mdm2-floxed mice were obtained from Frederick National Laboratory for Cancer Research . Cycloheximide was from Santa Cruz Biotechnology . Actinomycin-D was from Sigma . MG132 was from Selleck Chemical . Puromycin was from MP Biomedicals . Kainic acid and Thapsigargin were from Alomone Lab . Pifithrin-α was from Adipogen Corporation . Salubrinal was from Enzo Life Sciences . Dimethyl sulfoxide ( DMSO ) was from Fisher Scientific . DMSO was used as a vehicle in this study . The antibodies used in this study were purchased from Santa Cruz Biotechnology ( anti-Mdm2 , anti-Lamin A/C , anti-HSP-90 ) , GenScript Corporation ( anti-Gapdh ) , Millipore ( anti-puromycin ) , and Cell Signaling ( anti-ubiquitin , anti-p-Mdm2 , anti-p53 , anti-BiP , anti-PDI , anti-PERK , anti-ATF6 , anti-XBP1s , anti-eIF2α and anti-p-eIF2α ) . HRP-conjugated secondary antibodies were from Santa Cruz Biotechnology and Cell Signaling . The lentiviral control non-target shRNA and p53 shRNA were from Santa Cruz Biotechnology . Primary neuron cultures were made from mice aged at p0-p1 as described previously [34] and maintained in Neural Basal-A medium ( Thermo Fisher ) supplemented with B27 supplement ( Thermo Fisher ) , GlutaMax ( final concentration at 2 mM; Thermo Fisher ) , and Cytosine β-D-arabinofuranoside ( AraC , final concentration at 2 μM; Sigma ) . AraC was added 6 hours after plating to prevent glial overgrowth . To prevent unwanted synchronous activity triggered by Penicillin [62–64] , no antibiotics were added to our cultures . The culture medium was changed 50% on DIV 2 and every 3–4 days thereafter until the experiments on DIV 14 . MEA recordings were performed as previously described [65] . In brief , each MEA plate was coated with poly-D-lysine for 30 minutes and plated with 2x105 cells counted using a hemocytometer . Recordings were done at DIV 13–14 in the same culture medium using an Axion Muse 64-channel system in single well MEAs ( M64-GL1-30Pt200 , Axion Biosystems ) inside a 5% CO2 , 37°C incubator . Field potentials ( voltage ) at each electrode relative to the ground electrode were recorded with a sampling rate of 25 kHz . After 30 min of baseline recording , the MEA was treated with the drugs indicated in each experiment and recorded for another 30 min . Because of the changes in network activity caused by physical movement of the MEA when starting each recording , only the last 15 min of each recording were used in data analyses . AxIS software ( Axion Biosystems ) was used for the extraction of spikes from the raw electrical signal obtained from the Axion Muse system . After filtering , a threshold of ±7 standard deviations was independently set for each channel; activity exceeding this threshold was counted as a spike . Only MEAs with more than 2 , 000 spikes during the last 15 minutes of recording were included for data analysis [17 , 66] . The total spikes obtained from each MEA culture was normalized to the number of electrodes , as described in a previous study [67] . The settings for burst detection in each electrode were a minimum of 5 spikes with a maximum inter-spike interval of 0 . 1 sec as described previously [17] . The burst duration and number of spikes per burs were analyzed by AxIS software . To ensure consistency when acquiring MEA data , all experiment procedures , including the animal dissection , cell counting and plating , medium changing , and recordings are conducted by the same individual in each experiment . Throughout culture maturation and before recording , each MEA is visually inspected under the microscope and any MEA with poor growth is excluded . Recordings of each experiment were alternate between treatments or genotypes . For all before and after drug treatment comparisons , to minimize the variability between cultures , the recording from each MEA culture after treatment was compared to the baseline recording from that same culture . Male mice at age 3-weeks or 12-weeks old were intraperitoneally injected with kainic acid , prepared in saline solution ( Hannas Pharmaceutical ) , at doses of 30 mg/kg or 60 mg/kg as indicated in each figure . The total injection volume was kept close to 0 . 1 ml . After injection , mice were closely observed in real time for 1 hour . The intensity of seizures was assessed by a modified Racine’s scoring system [68] . To clearly determine seizure activity , only stage 4 ( rearing and falling ) and stage 5 ( tonic-clonic activity ) were considered positive for seizures , as previously performed [17 , 18 , 69] . The mortality rate after seizures is summarized in S1 Table . For immunoprecipitation ( IP ) , cell lysates were obtained by sonicating pelleted cells in IP buffer ( 50 mM Tris , pH 7 . 4 , 120 mM NaCl , 0 . 5% Nonidet P-40 ) . Eighty μg of total protein mixtures were incubated for one hour at 4°C with 0 . 5 μg primary antibodies . Protein A/G agarose beads were added for another hour followed by washing with IP buffer three times . For western blotting , after SDS-PAGE , the gel was transferred onto a polyvinylidene fluoride membrane ( Santa Cruz Biotechnology ) . After blocking with 1% Bovine Serum Albumin in TBST buffer ( 20 mM Tris pH 7 . 5 , 150 mM NaCl , 0 . 1% Tween-20 ) , the membrane was incubated with primary antibody overnight at 4°C , followed by three 10-min washings with TBST buffer . The membrane was then incubated with an HRP-conjugated secondary antibody for 1 hour at room temperature , followed by another three 10-min washings . Finally , the membrane was developed with an ECL Chemiluminescent Reagent [70] . All the western blot results were semi-quantitatively normalized to the control groups within the same set of sister cultures for analysis . Nuclear/cytoplasmic fractionation was conducted using the NE-PER Nuclear and Cytoplasmic Extraction Reagents Kit ( Pierce Chemical ) as described previously [37] and according to manufacturer’s protocol . Lamin A/C and HSP-90 served as nuclear and cytoplasmic markers , respectively . After drug treatment , the total RNA from cortical neurons in cultures was obtained with TRIzol reagent ( Life Technologies ) . Reverse transcription was performed with Photoscript reverse transcriptase ( New England Biolab ) and the real-time PCR was performed with Thermo Scientific Maxima SYBR Green reagent . The primers used in this study were: Mdm2 , 5’- AGC AGC GAG TCC ACA GAG A -3’ and 5’- ATC CTG ATC CAG GCA ATC AC -3’; Actin , 5’- CCT GTG CTG CTC ACC GAG GC -3’ and 5’- GAC CCC GTC TCT CCG GAG TCC ATC -3’; 47S , 5’- CGT GTA AGA CAT TCC TAT CTC G-3’ and 5’- GCC CGC TGG CAG AAC GAG AAG-3’ . Immunocytochemistry was done as previously described [18] . In brief , primary neurons grown on poly-D-lysine coated coverslips were fixed at DIV 14 with ice-cold buffer ( 4% paraformaldehyde and 5% sucrose in PBS ) . After washing and permeabilization with an additional incubation with 0 . 5% Triton X-100 in PBS for 5 min , an incubation with anti-Mdm2 antibody was performed overnight . After washing three times with PBS , fluorescence-conjugated secondary antibodies were applied to the cells at room temperature for 1 hour . After washing the cells an additional three times with PBS , the coverslips were mounted using a mounting medium ( Fisher Scientific ) supplied with DAPI and observed under Zeiss LSM 700 Confocal Microscope with 40X magnification . Pinhole was set to 1 airy unit for all experiments . Confocal microscope settings were kept with the same laser and scanning configurations to allow for comparison across conditions . Quantification was done by first circling the nuclei using the line tools in ImageJ ( National Institute of Health ) based on the DAPI signal . Subsequently , the Mdm2 signal in circled regions was measured also using ImageJ software before subtracting background signal . The final Mdm2 signal from the Tg-treated group is then normalized to that from the vehicle-treated group . All the numerical data underlying graphs were summarized in S1 Appendix . The data presented in this study have been tested for normality using Kolmogorov-Smirnov Test . Statistical methods to determine significance along with sample numbers were indicated in each figure legend . In brief , ANOVA with post-hoc Tukey HSD ( Honest Significant Differences ) test was used for multiple comparisons between treatments or genotypes ( Figs 2 , 3A , 4B–4E , 5 , 6A and 6B , S1 Fig and S5 Fig ) . Student’s t-test was used for the conditions where only two treatment groups were performed ( Figs 1 , 3B–3D , 4A , S2 Fig , S3 Fig , S6 Fig , S7 Fig , S8 Fig , S9 Fig , S10 Fig and S11 Fig . ) . Each “n” indicates an independent culture . Differences are considered significant at the level of p < 0 . 05 . | One-third of epilepsy patients respond poorly to current anti-epileptic drugs . Thus , there is an urgent need to characterize cellular behavior during seizures , and the corresponding molecular mechanisms in order to develop better therapies . Seizures are known to induce ER stress but how the ER stress response functions to modulate seizure activity is unknown . Our study provides evidence to demonstrate a novel and beneficial role for the ER stress response in reducing neural activity and seizure severity . Mechanistically , we found that these beneficial effects are mediated by elevated protein translation , which is triggered by the activation of Mdm2-p53 signaling , during the early ER stress response . Our findings suggest that therapeutic attempts to reduce ER stress in epilepsies may result in worsening seizure activity and therefore caution against inhibition of ER stress as a neuroprotective strategy for epilepsies . | [
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"... | 2019 | Novel roles of ER stress in repressing neural activity and seizures through Mdm2- and p53-dependent protein translation |
Differences between noninfective first-stage ( L1 ) and infective third-stage ( L3i ) larvae of parasitic nematode Strongyloides stercoralis at the molecular level are relatively uncharacterized . DNA microarrays were developed and utilized for this purpose . Oligonucleotide hybridization probes for the array were designed to bind 3 , 571 putative mRNA transcripts predicted by analysis of 11 , 335 expressed sequence tags ( ESTs ) obtained as part of the Nematode EST project . RNA obtained from S . stercoralis L3i and L1 was co-hybridized to each array after labeling the individual samples with different fluorescent tags . Bioinformatic predictions of gene function were developed using a novel cDNA Annotation System software . We identified 935 differentially expressed genes ( 469 L3i-biased; 466 L1-biased ) having two-fold expression differences or greater and microarray signals with a p value<0 . 01 . Based on a functional analysis , L1 larvae have a larger number of genes putatively involved in transcription ( p = 0 . 004 ) , and L3i larvae have biased expression of putative heat shock proteins ( such as hsp-90 ) . Genes with products known to be immunoreactive in S . stercoralis-infected humans ( such as SsIR and NIE ) had L3i biased expression . Abundantly expressed L3i contigs of interest included S . stercoralis orthologs of cytochrome oxidase ucr 2 . 1 and hsp-90 , which may be potential chemotherapeutic targets . The S . stercoralis ortholog of fatty acid and retinol binding protein-1 , successfully used in a vaccine against Ancylostoma ceylanicum , was identified among the 25 most highly expressed L3i genes . The sperm-containing glycoprotein domain , utilized in a vaccine against the nematode Cooperia punctata , was exclusively found in L3i biased genes and may be a valuable S . stercoralis target of interest . A new DNA microarray tool for the examination of S . stercoralis biology has been developed and provides new and valuable insights regarding differences between infective and noninfective S . stercoralis larvae . Potential therapeutic and vaccine targets were identified for further study .
Strongyloides stercoralis is a parasitic nematode endemic to the tropics and subtropics that infects an estimated 30–100 million people worldwide . Chronically infected individuals have the potential to develop hyperinfection syndrome or disseminated disease , clinical entities that carry a very high ( 87–100% ) mortality if unrecognized [1] . Free-living S . stercoralis infective third stage ( L3i ) larvae residing in the soil penetrate intact skin and blood vessels , ultimately developing to adults in the small intestine . Adult females , typically residing in the duodenum of the host , produce eggs by mitotic parthenogenesis that develop into first-stage ( L1 ) larvae that are excreted into the stool . L1 larval progeny of parasitic females develop into free-living adults unless triggered by genetic , environmental , or host factors to develop directly into L3i larvae [2] , [3] . Despite sharing many characteristics , L1 and L3i larvae can be distinguished by their behavior and morphology . L1 larvae have a short , trilobed pharynx and expend much of their energy on feeding and growth [3] . L3i larvae , by contrast , can survive in harsh environmental conditions , enabled by a comparatively thickened cuticle , constricted gastrointestinal tract , and closed mouth . These larvae are developmentally arrested , non-feeding , stress resistant , and long lived [3]–[5] . A high degree of specificity between these stages has been suggested by expressed sequence tag ( EST ) based analysis of free living L1 and L3i larvae for S . stercoralis [6]–[8] . These comparisons , however , are based on short reads of cDNA libraries and assumptions about abundance . There remain many unanswered questions about the basic molecular features underlying the apparent morphologic and behavioral differences between these larval stages . An improved understanding of these differences can provide insights into what defines infectivity and may ultimately prove useful in defining targets for the development of vaccines and therapeutics against this parasite . In order to answer these questions , a DNA microarray tool for S . stercoralis – the species causing the vast majority of human infection worldwide - is needed . Although a DNA microarray has recently been developed for Strongyloides ratti , the natural parasite of brown rats ( Rattus norvegicus ) [9] , previous work has suggested little conservation of gene expression profiles between these two species [10] , underscoring the need for a DNA microarray specific to this species . The availability of a S . stercoralis DNA microarray enables comparative analyses across nematodes , which can be utilized to further our understanding of the biologic determinants of parasitism . The free-living , non-parasitic , nematode C . elegans has been used as a model species for comparison with S . stercoralis . C . elegans dauer stage larvae and S . stercoralis L3i larvae share many morphologic and physiologic characteristics . The ‘dauer hypothesis’ recognizes these similarities and suggests that the same molecular genetic mechanisms control the morphogenesis of these stages [11] . Comparative genomics of gene expression based on EST abundance data for S . stercoralis suggests a higher degree of similarity between S . stercoralis L1 and C . elegans non-dauer expressed genes [6] . By contrast , a robust ‘dauer-L3i expression signature’ has not been found [6] . A comparative analysis based on microarray expression data for these species could prove useful not only in identifying a ‘dauer-L3i expression signature’ should it exist , but also in uncovering potentially significant determinants of S . stercoralis L3i infectivity . The purpose of this study was to: 1 ) develop and optimize a DNA microarray tool for S . stercoralis , 2 ) utilize this microarray to examine differences in gene expression between L3i and L1 larvae and 3 ) perform a comparative microarray analysis between parasitic S . stercoralis and non-parasitic C . elegans in order to develop further insights into the biologic determinants of parasitism .
Animal handling and experimental procedures were undertaken in compliance with the University of Pennsylvania's Institutional Animal Care and Use Committee ( IACUC ) guidelines . Ethical approval was obtained for the study ( protocol number 702342 ) from IACUC ( University of Pennsylvania , Philadelphia , PA ) . All larvae used in this analysis were obtained from laboratory dogs infected with S . stercoralis , UPD strain [12] . Fecal samples from dogs were processed using the charcoal coproculture followed by Baermann funnel technique , as outlined elsewhere [13] . Post parasitic L1 larvae were recovered from freshly deposited stool samples; L3i larvae were recovered after 7 days of stool incubation at 25°C . L3i larvae underwent surface decontamination by migration through low-melting-point agarose . L1 larvae were decontaminated by 3 washes with phosphate buffered saline ( PBS ) containing an antibiotic cocktail . Decontaminated parasites were subsequently stored in Trizol reagent ( Invitrogen , San Diego , CA ) at −80°C . Using this method , 30 , 700 post-parasitic L1 and 50 , 000 L3i larvae were collected . Total RNA was extracted by thawing pooled samples of L1 and L3i larvae at 37°C in a warm water bath and centrifuging the samples at 4°C ( 805× g ) for 10 minutes to obtain a pellet . The pellet was frozen in liquid nitrogen , ground thoroughly with an autoclaved mortar and pestle and then purified using an RNeasy mini kit ( Qiagen , Valencia , CA ) following the manufacturer's protocol . A Nano Drop-1000 spectrophotometer ( NanoDrop Products , Wilmington DE ) was used to determine the RNA concentration in each sample . RNA was more precisely quantified and quality assessed using the 2100 Bioanalyzer ( Agilent , Santa Clara , CA ) . RNA samples from L1 and L3i stage larvae were co-hybridized using Cy3 and Cy5 labels to discriminate the relative level of target bound to the microarray probe . Fluorescent-labeled cDNA targets were prepared from total RNA using the Ovations amino-allyl kit ( NuGEN , San Carlos , CA ) according to the manufacturer's protocol . The kit utilizes an oligo dT primer for selective amplification of mRNA transcripts . Labeled samples were combined with blocking components poly ( dA ) , yeast tRNA , and human Cot-1 , in hybridization buffer composed of 25% formamide/5× saline-sodium citrate ( SSC ) /0 . 2% ( w/v ) sodium dodecyl sulfate ( SDS ) to a total volume of 60 µl . After heating the sample ( 95°C for 3 minutes ) , it was centrifuged ( 20 , 000× g ) for 3 minutes . Fifty eight µl of the sample ( 1 . 6 µg of labeled cDNA ) was loaded onto the microarray chip . The microarray chips were hybridized overnight at 45°C using the MicroArray User Interface ( MAUI ) hybridization system ( BioMicro Systems , Inc . , Salt Lake City , UT ) . The following day , the chips were washed twice in 1× SSC/0 . 05% ( w/v ) SDS buffer ( 3 minutes each wash ) and twice in 0 . 1× SSC buffer ( 5 minutes each wash ) . For the present study , four technical replicate experiments using pooled L1 and L3i larvae were performed , including one dye swap . The microarray chips were imaged using a GenePix 4000 B scanner ( Molecular Devices , Sunnyvale , CA ) . Agilent Feature Extraction software was used for image analysis , protocol GE2-v5 10 Apr08 . The data discussed in this publication have been deposited in the National Center for Biotechnical Information ( NCBI ) Gene Expression Omnibus ( GEO ) and are accessible through GEO Series accession number GSE24735 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE24735 ) . ESTs ( 11 , 335 ) were identified from L1 and L3i cDNA libraries created as part of the nematode EST project [6] , [7] . ESTs were organized into 3 , 571 contigs by bioinformatics analysis [14] . Oligonucleotide probes designed to hybridize with these contigs were used to develop early versions ( V1 and V2 ) of chips manufactured by Combimatrix ( Irvine , CA ) based on a variety of algorithms for oligonucleotide design . Versions 1 and 2 were assessed for performance using RNA from L1 and L3i larvae . After testing the performance of these two versions of the arrays , an optimized version ( V3 ) was developed . The best probe for each target was selected based on the average signal intensity for all arrays and the number of arrays with detectable signal . The spot density was 22K spots per array . Of the six oligonucleotides designed per target , one was designed using the Array Designer program ( Premier Biosoft International , Palo Alto , CA ) , two were designed using E-Array ( Agilent , Santa Clara , CA ) using the “base composition” method ( replicated twice ) , two were designed using E-array “best Tm” method , and the last was a 40-mer designed using Array Designer . Probes were selected to avoid cross-hybridization to other sequences in the target ( contig ) dataset manufactured by Agilent SurePrint . The probes designed to make the V3 microarray are found in Table S1 in Supporting Information Text S1 . All data were exported into the cDNA Annotation System ( dCAS ) [14] , [15] . This tool enabled annotation of each S . stercoralis contig based on Basic Local Alignment Search Tool ( BLAST ) alignments against multiple databases ( NCBI nr protein database ( NR ) , Gene Ontology ( GO ) , euKaryotic Orthologous Groups ( KOG ) , Pfam protein families database ( PFAM ) , Simple Modular Architecture Research Tool ( SMART ) , Wormbase ( CELEG ) , and Saccharomyces genome database ( YEAST ) and provided the corresponding E-values . The database was also annotated manually with a composite categorization that summarized the findings across databases . The entire annotated database , with hyperlinks to the NIAID exon website , is accessible for download at: http://exon . niaid . nih . gov/transcriptome/S_stercoralis/SS-Supp-Web . zip . A stand-alone version can also be accessed and downloaded at: http://exon . niaid . nih . gov/transcriptome/S_stercoralis/SS-Supp-StandAlone . zip . Extract the excel file and the links directory to your own computer for browsing the hyperlinks locally . Spot values were calculated using a linear lowess dye normalization . Further , the 50th percentile of a set containing all the ribosomal genes in the array was applied to all spot values . In cases of multiple spots for the same S . stercoralis contig , the average of the log2 signal was calculated for each array . The mean signal ratio ( log2 L3i/L1 ) was calculated from the signals for all 4 arrays . No surrogate values were applied . A single group t-test analysis was calculated on the data set . Variance shrinkage was not used when calculating p-values for differential expression . Differentially expressed genes were identified using a ‘cutoff’ of 2 fold expression difference or greater for log2 L3i/L1 signal ratios , and p<0 . 01 for microarray signal data ( false discovery rate ( FDR ) = 2 . 5% ) . A functional analysis was performed based on annotations provided by each database ( Pfam , SMART , KOG , etc . ) . The number of genes per functional category ( e . g . transcription , cytoskeleton , metabolism , etc . ) was compared between L1 and L3i differentially expressed genes ( as defined by the above cutoff ) . To ascertain whether genes belonging to certain functional classes were more likely to be highly expressed in one stage or another , we used a statistical test for one proportion using Normal approximation . Assuming a null proportion of 0 . 5 ( i . e . , that there is no difference in the number of genes of that category for the two classes ) , p values were calculated for deviation from 0 . 5 using Normal approximation . P values were adjusted for multiple comparisons using the Bonferroni criterion . Gene Set Enrichment Analysis ( GSEA ) is a robust method for analyzing molecular profiling data examines the clustering of a pre-defined group of genes ( gene set ) across the entire microarray database ( all 3 , 571 contigs ) in order to determine whether the gene set has biased expression in one larval stage versus another [16] . GSEA was used in this study to complement our use of single gene methods and determine whether S . stercoralis gene sets grouped according to various putative categories ( for example , putative extracellular matrix genes ) showed biased expression in either larval stage . For this analysis , the entire list of contigs on the microarray was sorted by mean log2 L3i/L1 signal ratios . The distribution of genes from an a priori defined gene set throughout this ranked list was then determined using GSEA . Based on this distribution , the expression difference for each gene in the set is aggregated and a p-value for significance of the gene set as a whole is calculated using the Kolmogorov-Smirnoff test . Gene sets were compiled by first downloading GO categories from Wormbase ( www . wormbase . org ) for C . elegans genes . Definitions for each GO category used can be found at http://www . wormbase . org/db/ontology/gene . S . stercoralis orthologs for C . elegans genes were determined by dCAS based on BLAST alignments to the C . elegans gene . BLAST matches with E values>0 . 05 were excluded . Gene sets with fewer than 5 S . stercoralis contig matches were excluded from GSEA analysis . Using these criteria , 18 S . stercoralis gene sets were created ( see Figure 1A ) . Additional manually compiled gene sets included the group of S . stercoralis genes whose products have been shown to be immunoreactive in humans infected with S . stercoralis [17]–[19] , and a group of putatively identified heat shock proteins . Microarray expression data for S . stercoralis L3i and C . elegans dauer larvae were compared using several methods as follows: 1 ) We defined three gene sets comprising the S . stercoralis orthologs of “dauer-enriched” C . elegans genes derived from either C . elegans microarray expression data alone , both serial analysis of gene expression ( SAGE ) and microarray expression data or from the Gene Ontology category dauer larval development ( Figure 1A ) [20] , [21] . We then used GSEA to determine whether these gene sets showed significant L3i enrichment . 2 ) We examined whether a correlation exists between C . elegans dauer/L1 microarray expression data obtained by Wang and colleagues [20] with our S . stercoralis L3i/L1 microarray expression data . The previously obtained C . elegans microarray expression data can be found at http://cmgm . stanford . edu/~kimlab/dauer/ExtraData . htm , Table S1 in Supporting Information Text S1 , column “AdjD/L1_Ratio” which corresponds to the average log2 expression values for C . elegans dauer larvae at time 0 relative to L1 larvae [20] . 3 ) Using these data , we calculated the absolute value of the difference between fold change values for C . elegans genes and their S . stercoralis orthologs ( C . elegans dauer/L1 fold change - S . stercoralis L3i/L1 fold change ) . Only those genes with robust microarray expression data ( p values<0 . 01 ) were included . In order to identify those genes that are expressed differently by S . stercoralis L3i and C . elegans dauer larvae , a list was generated of all S . stercoralis-C . elegans orthologs with the greatest differences in fold change values ( absolute value >2 ) . The list was further narrowed to include only those S . stercoralis-C . elegans gene pairs where gene expression was regulated in opposite directions between the two nematodes ( Table 1 ) . The sequences of L3i biased genes ( contigs 24 , 25 , 65 , 243 , 2136 ) and L1 biased genes ( contigs 55 , 222 , 387 , 2328 ) were used to create primer-probe sets designed and manufactured by Applied Biosystems ( Foster City , CA ) . The sequences for these primer probes are listed in Table S2 in Supporting Information Text S1 . The S . stercoralis control genes for qPCR analysis was S . stercoralis glyceraldehyde 3 phosphate dehydrogenase ( GAPDH; GenBank accession number BI773092; contig_90; log2L3i/L1 = −0 . 28179 ) . Post-parasitic L1 and L3i larvae ( distinct from those hybridized onto the microarray ) were collected and total RNA made as described above . Total RNA ( 1 µg ) from L1 and L3i larvae was used to synthesize cDNA . qPCR was performed using all 9 primer probe sets in separate reactions with L1 cDNA and also with L3i cDNA . The reaction was performed using 10× RT buffer ( 10 µl ) , 25 mM MgCl2 ( 22 µl ) , dNTP ( 20 µl ) , random hexamers ( 5 µl ) , RNase inhibitor ( 2 µl ) , and multiscribe reverse transcriptase ( 50 U/µl; 6 . 25 µl ) in a microamp 96-well reaction plate ( Applied Biosystems ) . De-ionized , distilled water was added to total volume of 65 . 25 µl . Cycling conditions were: 25°C for 10 minutes , 37°C for 60 minutes , 95°C for 5 minutes , then 4 . 0°C . Each experiment was performed in triplicate . The mean negative delta threshold cycle ( delta CT ) was calculated for each sample . The data generated by performing qPCR using primer probes for 9 contigs on L1 and L3i cDNA ( n = 18 ) was plotted against the average L1 and L3i intensity signals for each gene ( Figure 2 ) .
A total of 3 , 571 distinct contigs were studied by this microarray analysis ( Table S3 in Supplemental Information Text S1 ) . Using pre-defined cutoffs , 935 contigs were identified as differentially expressed as shown in the volcano plot ( Figure 3 ) . Of these , 466 genes were L1 biased ( Table S4 in Supporting Information Text S1 ) and 469 genes were L3i biased ( Table S5 in Supporting Information Text S1 ) . Among the 25 most highly expressed L3i genes were the S . stercoralis orthologs of fatty acid/retinol binding protein-1 ( contig 1151; 11 fold expression difference ) , a ferritin chain homolog ( contig 94; 14 fold expression difference ) , and one of four putative trehalases ( contig 68; 14-fold expression difference ) . Among the 25 most highly expressed L1 genes were electron transport chain proteins such as NADH dehydrogenase ( contig 371; 0 . 13-fold change ) ; cytochrome b ( contig 2328; 0 . 19 fold change ) and cytochrome c oxidase subunit 1 ( contig 55; 0 . 29 fold change ) . The 25 most highly expressed L1 or L3i genes are listed in Table S6 in Supporting Information Text S1 . A greater number of L1 ( n = 40 ) than L3i biased ( n = 18 ) genes were putatively involved in transcription ( p = 0 . 004 , not Bonferroni adjusted; see Figure 4A , B ) . A complete listing of these genes is shown in Figure 4B . This finding was also noted in an analysis of classifications based on GO categories ( p = 0 . 01 for ‘transcription’ ) , and manual annotations ( p = 0 . 007 for ‘transcription machinery’ ) , although p values were not <0 . 05 when Bonferroni-adjusted for multiple comparisons . BLAST matches to SMART and Pfam databases both indicated that the sperm-containing glycoprotein ( SCP ) domain was found exclusively in the L3i-group ( n = 13 genes; see Table S7 in Supporting Information Text S1 for the complete list; p value based on matches to Pfam = 0 . 003 , Bonferroni-adjusted for multiple comparisons ) . Of the entire 3 , 571 contigs , 1 , 351 S . stercoralis genes ( 37 . 8% ) were of unknown function ( manual annotation ) . S . stercoralis orthologs were matched to 35 sets of C . elegans genes grouped by various categories ( e . g . negative regulation of vulval induction , oviposition , heat shock proteins , etc . ) . Eighteen of 35 gene sets queried met criteria for inclusion into the GSEA analysis ( based on minimum size of 5 genes; see Figure 1A ) . Of these 18 gene sets , only 2 gene sets were significantly enriched in the L3i phenotype at nominal p value<5% . The most significantly enriched genes were those with immunoreactive gene products recognized by sera from infected individuals ( Figure 1B; nominal p-value<0 . 0001; FDR<0 . 0001 ) . Heat shock proteins were the next most highly enriched ( nominal p value = 0 . 034 , FDR = 0 . 56 ) . For an annotated list of the individual genes enriched in each of these categories , refer to Tables S8 and S9 in Supporting Information Text S1 . None of the 18 gene sets were enriched in the L1 phenotype . Four hundred and twenty two of 3 , 571 S . stercoralis contigs had C . elegans orthologs for which robust microarray signal data were available . When C . elegans and S . stercoralis microarray signals were plotted against each other , a poor and non-significant correlation was found ( Spearman rank = 0 . 06; p = 0 . 2444 , graph not shown ) . No significant L3i enrichment of S . stercoralis orthologs of C . elegans ‘dauer enriched’ genes was found by GSEA ( nominal p-value = 0 . 10 ) . On the contrary , 25 orthologs expressed in opposite directions by dauer and L3i larvae relative to their respective L1 stage larvae were identified ( see Table 1 ) . A statistically significant positive correlation was found between microarray expression data and EST abundance data ( p<0 . 0001; max R2 = 0 . 26; graph not shown ) . A positive correlation was found ( Spearman rank = 0 . 4778; p = 0 . 0449 ) between average L1 or L3i microarray intensity signals and mean negative delta CT of qPCR ( Figure 2 ) .
Analogous to their non-dauer C . elegans counterparts , actively growing S . stercoralis L1 larvae are thought to have higher rates of transcription relative to L3i-stage larvae . This supposition is based on comparisons between C . elegans non-dauer biased genes and S . stercoralis L1-biased genes that suggest transcriptional conservation of genes involved in early larval growth [6] . Consistent with this finding , we found L1 biased expression of genes putatively involved in transcription . Among the S . stercoralis L1-biased genes involved in transcription were transcription initiation factors ( contigs 3245 , 1037 , 686 ) , transcription factors ( contigs 1905 , 1277 , 891 , 2023 , 2446 , 1036 , 1794 , 592 , 2210 ) , and subunits of RNA polymerase ( contigs 1505 , 3218 , 1020 , 2917 ) . By contrast , the L3i-biased genes involved in transcription though fewer , included transcriptional regulators ( contigs 446 , 445 , 156 ) as well as transcription factors ( contigs 1521 , 519 , 836 , 167 , 1478 ) , implying that L3i larvae are not transcriptionally inactive and may regulate transcription differently . This would be consistent with what is known of C . elegans dauer larvae , which express distinct sets of dauer-specific genes at certain time points ( dauer exit , for example ) [20] , [21] . Not surprisingly , genes encoding S . stercoralis antigens known to produce robust antibody responses in infected humans were found to have L3i biased expression by GSEA [17]–[19] . Two of these genes , IgG immunoreactive antigen ( SsIR ) and NIE antigen , have been recently employed in serodiagnostic assays with some advantage over crude antigen [19] . The finding that genes with products capable of inducing protective immunity demonstrate stage-biased gene expression supports the further investigation of these genes as vaccine candidates . Heat shock proteins have been shown to play a critical role in determining parasite survival during stressful conditions because they can bind denatured or misfolded proteins [22] , [23] . Biased expression of genes encoding heat shock proteins in the S . stercoralis L3i relative to L1 larvae , as suggested by GSEA , is consistent with this role . Hsp-90 in particular has been identified as a parasitism-central gene based on changes in S . ratti gene expression during high immune pressure [22] and is similarly abundantly expressed by S . stercoralis L3i larvae . The SCP domain , found exclusively in L3i biased genes , is a conserved domain of unknown function present in a wide range of organisms [24] . Interestingly , it has been found to be present in activation-associated secreted proteins that have been studied as potential vaccine targets in other nematodes [24] , [25] . Whether overrepresentation of the SCP domain in the L3i group is related to the presence of these secreted proteins is unclear , but activation-associated secreted proteins have been found to be important in many parasitic nematodes in which they have been studied to date . Consistent with previous findings , a striking L3i-C . elegans ‘dauer expression signature’ was not uncovered in this comparative microarray analysis [6] . We instead identified genes that are regulated in apparently opposite manners by C . elegans dauer and S . stercoralis L3i larvae which offer useful clues about the biology of S . stercoralis parasitism . L3i biased expression of the putative nmy-2 gene ( encoding the myosin heavy chain ) is consistent with the highly motile nature of L3i larvae which , unlike their dauer counterparts , seek out and initiate infection in a host . Although dauer and L3i larvae both contain a cuticle that enables survival in the environment , the parasitic cuticle has been associated with the ability of infective stages to evade the immune response of the host , and its structure varies from one species to another [26] . Biased expression of genes putatively encoding particular collagens ( col-37 , col-119 ) in the L3i but not the C . elegans dauer , points to differences in the composition of the parasitic cuticle that could potentially have a role in this regard . In fact , a recent microarray based analysis of the response of the S . ratti transcriptome to host immunologic environment notes upregulation of collagen genes by S . ratti which is believed to play a protective role for the parasite [27] . C . elegans dauer and S . stercoralis L3i larvae can survive in the environment even in the absence of a steady source of food . One way by which this occurs is by the development of electron-dense intestinal granules that store non-lipid products [11] . The gene lmp-1 plays an essential role in this regard for dauer larvae as suggested by RNA interference studies [28] . It is likely that L3i larvae similarly utilize these granules while in the environment . The presence of these granules may additionally explain the darkened color of the radially constricted intestines of L3i larvae , an appearance shared by its dauer counterpart . A key feature shared by dauer and L3i larvae is the ability to extend the lifespan while in the free-living state . In both C . elegans and S . stercoralis , the forkhead transcription factor DAF-16 plays a role in regulating dauer diapause , longevity and metabolism [11] , [29] , [30] . A downstream target of DAF-16 , egl-10 , is known to be negatively regulated by DAF-16 in C . elegans [29] . By contrast , this gene was found to have biased L3i larval expression in S . stercoralis . Such discordance is consistent with findings from a prior study that failed to detect a transcriptional profile typical of down-regulated insulin-like signaling in long-lived parasitic females of S . ratti [31] . Although the downstream targets of insulin-like signaling have not been fully elucidated in Strongyloides species , the apparent upregulation of Ss-egl-10 in the L3i potentially highlights adaptations at a molecular level that likely underlie the evolution to parasitism . Such adaptations could include alterations in genes controlling metabolic and developmental functions , adaptations of pre-existing genes to encode new functions , and gene duplication and diversification [32] . The apparent lack of a C . elegans dauer-like transcriptional profile in S . stercoralis L3i is also consistent with published findings on the expression of transcripts encoding the orthologs of DAF-7 in this parasite [33] and in S . ratti and Parastrongyloides trichosuri [34] . DAF-7 is the ligand that activates TGF-β-like signaling and thereby promotes continuous ( i . e . non-dauer ) development in C . elegans . Its expression is biased towards C . elegans first-stage larvae fated for continuous development rather than dauer third-stage larvae [34] , [35] . By contrast , messages encoding DAF-7 orthologs in S . stercoralis , S . ratti and P . trichosuri all show biased expression in the L3i , which has been characterized heretofore as dauer-like [33] , [34] . These facts notwithstanding , outright rejection of the ‘dauer hypothesis’ of developmental regulation in the L3i of parasitic nematodes on the basic of transcriptional data alone is likely to be premature [36] . It is particularly noteworthy in this regard that key signal transducing elements such as DAF-16 that directly regulate C . elegans dauer development are constitutively transcribed and their functions governed not at the transcriptional level but rather by posttranslational modifications such as phosphorylation [37] , [38] . The true value in identifying these and other genetic determinants of S . stercoralis parasitism lies in whether the products of these genes can induce protective immunity . Indeed , one of the genes identified in our list , the S . stercoralis ortholog of eat-6 Na+k+ATPase , has already been identified as a potential vaccine candidate based on animal experiments [39] . Contig 1872 , a gene with L3i biased expression , encodes an ortholog of C . elegans core subunit of the cytochrome bc1 complex , UCR 2 . 1 ( E-value = 1E-014 ) . This subunit has been shown to be a potential target for antiparasitic drugs based on the finding that in C . elegans , UCR 2 . 1 is essential for viability and is less related to mammalian UCR-1 than to mitochondrial processing peptidases from other organisms [40] . S . stercoralis transgenesis experiments [41] may prove useful in investigating the question of whether this gene is similarly essential for S . stercoralis larval survival . In our microarray analysis of S . stercoralis , we found abundant L3i expression of the S . stercoralis ortholog of hsp-90 , contig_77 ( 3 fold expression difference ) . Interestingly , the hsp-90 inhibitor geldanamycin has been shown to have a macrofilaricidal effect on filarial nematode Brugia pahangi [42] . Hsp-90 has been identified among S . ratti parasitism central genes critical for survival and further studies investigating it as a chemotherapeutic target are warranted . Contig 1151 , which was among the 25 most highly biased L3i genes ( 11-fold expression difference ) , corresponds to fatty acid and retinol binding protein-1 ( FAR-1; E-value = 1E-016 ) . FAR-like proteins are major secreted products of parasitic nematodes that allow the parasite to scavenge essential nutrients from its host [43] . Depletion of host lipids is thought to be necessary for parasite survival and may additionally impair the host immune response [44] . These proteins have additionally demonstrated stage and gender specificity in other nematodes , most notably in the hookworm Ancylostoma ceylanicum [45] . The immunodiagnostic potential of FAR-like proteins has been assessed in other nematodes , such as Onchocerca volvulus , in a serologic assay based on Ov-20 ( FAR-1 ) [45] , [46] , [47] . FAR-1 proteins have been successfully used in a vaccine in animals infected with A . ceylanicum [45] . These microarray data identify S . stercoralis far-1 as an L3i-biased target that may be a potential vaccine candidate or immunodiagnostic antigen . Approximately one-third of S . stercoralis genes are of unknown function . This finding is consistent with a previous EST analysis that revealed a similar percentage ( 25% ) of S . stercoralis clusters with no significant BLAST alignments [8] . This finding is also consistent with functional genomics analyses of the C . elegans and human genomes where significant numbers of genes of unknown function were identified [48] , [49] . Some of these unknown sequences may derive from 3′ untranslated mRNA regions , which are common in polydT-primed libraries [50] . The complete genome sequence of S . stercoralis is not available to date . Inferred functional annotations of an analogous nematode C . elegans , while useful , may not be directly applicable to S . stercoralis , as suggested by interspecies differences uncovered in the present comparative microarray analysis . Because a number of C . elegans genes did not have S . stercoralis orthologs that were also differentially expressed according to our predefined ‘cutoffs , ’ it was difficult to formulate gene lists organized into functional categories with at least 5 contigs . This limited our ability to analyze biochemical or metabolic pathways of potential importance . As our knowledge of the S . stercoralis genome increases , these microarray analyses will likely gain in usefulness and a more direct approach using annotation based on known S . stercoralis gene functions would be even more informative . DNA microarrays allow for simultaneous analysis of large numbers of genes from two or more biologic conditions . This powerful method of analysis has revolutionized our understanding of the immunopathogenesis of schistosomiasis [51] , for example , and has advanced the development of vaccine discovery and therapeutics in parasitology [52] , [53] . Until now , studies of S . stercoralis have been limited to the analysis of ESTs rather than the full genome sequence . Development of a novel DNA microarray tool for the study of S . stercoralis represents an exciting step forward in our understanding of this parasite . | Strongyloides stercoralis is a soil-transmitted helminth that affects an estimated 30–100 million people worldwide . Chronically infected persons who are exposed to corticosteroids can develop disseminated disease , which carries a high mortality ( 87–100% ) if untreated . Despite this , little is known about the fundamental biology of this parasite , including the features that enable infection . We developed the first DNA microarray for this parasite and used it to compare infective third-stage larvae ( L3i ) with non-infective first stage larvae ( L1 ) . Using this method , we identified 935 differentially expressed genes . Functional characterization of these genes revealed L3i biased expression of heat shock proteins and genes with products that have previously been shown to be immunoreactive in infected humans . Genes putatively involved in transcription were found to have L1 biased expression . Potential chemotherapeutic and vaccine targets such as far-1 , ucr 2 . 1 and hsp-90 were identified for further study . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"infectious",
"diseases/helminth",
"infections",
"infectious",
"diseases/tropical",
"and",
"travel-associated",
"diseases"
] | 2011 | Microarray-Based Analysis of Differential Gene Expression between
Infective and Noninfective Larvae of Strongyloides
stercoralis |
ClinicalTrials . gov NCT01223716
The most frequent cause of permanent visual loss in childhood is amblyopia ( “lazy eye” ) [1] , [2] , a developmental disorder associated with early abnormal visual experience that disrupts neuronal circuitry in the visual cortex and results in abnormal spatial vision . It is generally believed that adult amblyopia is irreversible beyond the sensitive period of brain development . However , new studies , both in humans [3]–[12] and in rodents [13]–[15] , suggest that the mature amblyopic brain retains a substantial degree of plasticity . In particular , human adults with long-standing amblyopia show substantial improvements in performing a visual task , following perceptual learning ( extended practice ) of the task . Playing video games results in enhancement of a broad range of visual tasks in adults with normal vision , including light sensitivity [16] , contrast sensitivity [17] , visual crowding [18] , and visual attention [19] . However , while it is now clear that video-game play can strengthen some aspects of normal vision , it is not clear whether video-game play can induce functional plasticity in the mature visual system following a prolonged period of abnormal development . Moreover , while video-game play improves the spatial resolution of attention in normal participants , it does not improve visual acuity ( with isolated targets ) . Since reduced visual acuity is the sine qua non of amblyopia , it is crucial that video-game play can improve visual acuity if it is to be a useful tool for visual rehabilitation in patients with reduced spatial vision . In the present study , we aimed to assess with a small pilot group whether playing video games with an amblyopic eye can induce cortical plasticity and improve spatial vision in adults with amblyopia , well beyond the “sensitive period” of brain development . We hypothesized that the intense sensory-motor interactions while immersed in video-game play might push brain functions to the limit , enabling the amblyopic visual system to learn , on the fly , to recalibrate and adjust , providing the basis for functional plasticity . Moreover , game playing requires the allocation of spatial attention , detection , and localization of low contrast , fast moving targets , and aiming ( in first-person shooter games ) . Thus , we speculated that video games may include several essential elements for active vision training to boost visual performance , and thus could potentially be useful in improving amblyopic vision . We tested a range of visual functions to examine the neural alternations , if any , following video-game play in a small group of adults ( Figure 1 ) . These visual functions , ranging from low-level to high-level vision , included visual acuity ( letter acuity ) , positional acuity ( Vernier acuity ) , visual counting ( spatial attention ) , and stereoacuity ( 3-D binocular vision ) . In order to understand the neural mechanisms that underlie the video-game experience induced visual plasticity , we measured and modeled a positional acuity task in noise . While action video games are reported to be useful in enhancing visual function in normal humans , non-action video games are not effective [19] . Playing action video games may not be ideal for patients with amblyopia , particularly children . Therefore , in another set of pilot experiments , we also examined whether non-action video games may be effective for recovering amblyopic visual functions . Our participants played video games for 40 h with their fellow eye patched . One might argue that the visual improvements , if any , might have resulted from the eye patching alone . To address this point , we used a cross-over treatment design in which a group of amblyopes first underwent occlusion therapy , i . e . patching the fellow sound eye , for a period of time before the video-game phase . With this experimental design , we can compare the efficacy of the two treatment approaches ( passive patching and video-game playing ) . Our study had several limitations: small sample size , lack of randomization , and differences in numbers between groups . A large-scale randomized clinical study is needed to confirm the therapeutic value of video-game treatment in clinical situations . Nonetheless , taken as a pilot study , this work suggests that video-game play may help guide future treatment of amblyopia .
To evaluate how video-game play alters amblyopic vision , we monitored the changes , if any , in visual acuity in 10 adults with amblyopia while they played a first-person shooter game—Medal of Honor: Pacific Assault ( MOH ) —using their amblyopic eye , with the fellow sound eye patched with a black eye patch . Visual acuity ( VA ) is a standard clinical procedure to quantify spatial vision by determining the smallest letter on a chart that can be identified at a given viewing distance . In amblyopia , vision is often substantially poorer when the target letter is presented with surrounding letters than when it is presented alone , a phenomenon known as crowding [20] . Therefore we measured both crowded line-letter acuity and uncrowded single-letter acuity so as to provide a comprehensive evaluation of visual acuity . Surprisingly , playing video games rapidly reversed their amblyopia . After 40 h of video-game play ( 2 h/d ) , acuity improved , on average , by 1 . 6 and 1 . 4 lines on a LogMAR letter chart for crowded letters and single letters , respectively ( Figure 2a , top panels ) , representing 31 . 2%±3 . 1% ( crowded: t = 10 . 154 , p<0 . 0001 ) and 27 . 2%±3 . 2% ( uncrowded: t = 8 . 598 , p<0 . 0001 ) improvements in minimum angle of resolution ( MAR—bottom panels ) . Two mild amblyopes ( SA2 & SA4 ) completely “normalized” according to a criterion of 20/20 ( LogMAR = 0 , dotted line ) . It might be argued that the improvements could be due to learning the letter charts . Therefore , instead of taking measurements every 10 h , we tested observer SS1's acuity only before and after the video-game intervention , and similar to what we observed in other observers , his acuity improved substantially ( ≈2 . 5 letter-lines or ≈44% for both measurements ) . While it has been clearly demonstrated that playing action video games improves a broad array of visual functions in adults with normal vision , non-action games are not effective [17] , [18] . For example , playing action video games resulted in enhanced crowded resolution acuity in normal vision , while playing a non-action video game did not . However , action games may not be ideal for patients with amblyopia , particularly younger patients . Therefore , in the next experiment , we asked another three amblyopic patients to play a non-action video game—SimCity Societies ( SIM ) . Interestingly , we found that similar to the action game group , all three non-action game players showed enhanced vision ( Figure 2b , phase 1: 0 to 40th h ) , and one , a mild amblyope ( SA6 ) normalized to ≈20/16 . On average , this group was able to read 1 . 5 more letter-lines ( 28 . 4% improvement ) for crowded-letter acuity and 0 . 8 more lines ( 15 . 1% improvement ) for single-letter acuity . These findings suggest that non-action games share useful properties for enhancing amblyopic vision . To determine the limits of plasticity , the three players who participated in the SimCity experiments were then asked to play MOH for another 40 h ( phase 2: 40th to 80th h ) . Additional improvements of about one letter-line ( SB2 & SA5 , crowded: 18% ) were observed . Note that SA6's amblyopia was completely normalized at the end of phase I and no further significant improvement was observed . Since our participants played video games with the fellow eye patched , the vision enhancement we observed could have been the result of wearing an eye patch alone . Thus , in a control experiment , another group ( OT ) of seven amblyopic adults wore a patch , but instead of playing video games they were required to engage in other visually demanding activities , such as watching television , reading books , knitting , and surfing the Internet , using the amblyopic eye . After 20 h , however , no significant change in acuity was observed ( Figure 2c , phase 1: 0–20th h ) ; the dashed line in the bottom panels shows the mean data ( OT20: crowded: mean improvement = 0 . 4%±3 . 0% , t = 0 . 1317 , p = 0 . 8995; uncrowded: mean improvement = −3 . 7±3 . 2% , t = 1 . 136 , p = 0 . 2991 ) . In contrast , for the same amount of time , the video-game group ( n = 9 ) showed a marked improvement in acuity of ≈20% ( Figure 2a , MOH20>OT20: crowded: t = 4 . 337 , p = 0 . 0007; uncrowded: t = 3 . 74 , p = 0 . 0022 ) . Five of the seven participants who completed the patching experiment continued to the video-game phase for another 40 h . In this phase of the experiment , we used both action ( all except SC1 ) and non-action ( SC1 ) games . Although none of the five showed any significant change in acuity in the patching phase , all improved substantially in the video-game phase ( OT-VG20: ≈1 . 7 letter-lines , ≈29% improvement in both measurements; OT20<OT-VG20: crowded: paired t = 5 . 712 , p = 0 . 0046; uncrowded: paired t = 2 . 785 , p = 0 . 0495 ) . From this small-scale “cross-over” experimental design , we can conclude that it is the video-game experience , and not simply the patching , that enhances amblyopic vision . Figure 2d summarizes all the acuity data from the above experiments . The mean improvement in visual resolution across all 18 participants who completed the video-game training from the three experiments was ≈30% ( crowded acuity: 1 . 8 letter-lines , 33 . 4%±2 . 4% and isolated acuity: 1 . 5 letter-lines 27 . 4%±3 . 5% ) . The effect sizes ( Cohen's d value ) at the 20th h were 3 . 03 and 1 . 33 for crowded acuity and isolated acuity , respectively . The recovery of crowded acuity was slightly faster than uncrowded acuity . An exponential fit y = yo+a ( 1−e−bx ) to the data revealed time constants ( b ) of 0 . 064 and 0 . 054 h−1 for crowded acuity and uncrowded acuity , respectively . It is worthwhile noting that the recovery rate we observed here in adults is ≈5-fold faster when compared with the conventional occlusion therapy in children . It would take >200 h to obtain comparable treatment effects in children ( ≈0 . 1 logMAR unit/120 h ) [21] , and it would be reasonable to expect a much longer treatment course for adults [22] . There was no significant correlation between the amount of acuity improvement and the baseline acuity ( Figure 2d bottom left ) . The mean crowding index , crowded acuity ( MAR ) / uncrowded acuity ( MAR ) , was slightly , but not significantly , reduced ( by 5 . 9%±4 . 3% ) , indicating that video-game play improved crowded acuity slightly more than uncrowded acuity ( Figure 2d bottom right ) . While visual acuity represents one important limit to spatial vision , positional acuity , which represents the ability to localize visual objects , is another important aspect of spatial vision . While positional acuity is remarkably acute in normal vision ( often referred to as hyperacuity ) , it is often severely impaired in amblyopia . We found that positional acuity ( the ability to detect a misalignment between the two line segments—Figure 3a ) improved significantly following video-game play ( on average 16 . 0%±4 . 0%; n = 16 [MOH40: 12+SIM40: 4]—Figure 3b , black solid line , zero external noise: t = 3 . 963 , p = 0 . 0012; non-amblyopic eye: 1 . 0%±10% , t = 0 . 1057 , p = 0 . 9179 [ns] ) . To understand the neural mechanisms underlying this improvement , we introduced positional noise [23] to mimic the spatial distortions ( internal spatial noise ) existing in the visual system and applied a positional averaging model to the data ( see Materials and Methods ) . Figure 3c shows that the ability to extract and process information from the visual stimuli for positional averaging was actually boosted by 33 . 1% , with mean sampling efficiency improved from 6 . 8% to 9 . 0% . Some observers also showed reduced spatial distortion ( on average internal noise decreased by 7%—Figure 3d , from 0 . 185 λ to 0 . 172 λ ) , indicating that the distorted retinotopic cortical mappings were recalibrated and less distorted . Fig 3e summarizes the different neural mechanisms ( SB2: sampling efficiency enhancement; SA5: spatial distortion reduction; SS3: combination of both ) that underlie the improvement in positional acuity . Video-game play also appears to increase visual attention in amblyopia . We used a visual counting task to determine how many visual locations the brain can direct attention to in a very brief time period , 200 ms ( Figure 4a ) . Previous work has shown that some amblyopes show severe deficits in visual counting [24] and that action game play can enhance counting in normal vision [19] . In general , participants who initially showed the largest deficits in counting performance also showed the most improvement ( Figure 4c ) . A subgroup of five participants ( symbols surrounded by dotted circles in Figure 4b ) showed significant undercounting ( Figure 4d , blue line ) . For example , when 10 dots were displayed , the mean number of dots reported was 7 ( undercounted by 3 dots or 30% ) . Undercounting is thought to reflect high-level neural deficits in amblyopia [24] . Following video-game play , for the range of 7–10 dots , undercounting decreased significantly by 8 . 4% ( from 25 . 3% to 16 . 9%—Figure 4d , two-way RM ANOVA: F = 33 . 022 , p = 0 . 005; non-amblyopic eye: pre 5 . 6%→post 5 . 0% , two-way RM ANOVA: F = 0 . 609 , p = 0 . 492 [ns] ) . The mean counting threshold ( the number of dots that can be reliably counted ) increased significantly by 37% , from 3 . 3±0 . 3 to 4 . 4±0 . 4 dots ( Figure 4e , paired t = 4 . 508 , p = 0 . 0108; non-amblyopic eye: pre 7 . 7±0 . 3 dots→post 8 . 0±0 . 3 dots , paired t = 1 . 161 , p = 0 . 3102 [ns] ) and the mean response latency decreased by 16 . 5% ( Figure 4f , N = 1–10 ) , though not significantly ( two-way RM ANOVA: F = 0 . 839 , p = 0 . 424 ) . In short , video-game play increases the number of items the amblyopic brain can direct attention to simultaneously , reduces undercounting deficits , and increases the processing speed of visual counting . Amblyopia is associated with abnormal binocular vision and reduced or absent stereopsis ( binocular depth perception or 3-D ) . With improved monocular vision following video-game play , for some amblyopes binocular vision also recovered to a substantial extent . Five of the six anisometropic amblyopes ( with straight eyes ) were tested for stereopsis following the training . All five showed improved stereopsis ( Figure 5a , n = 5 [MOH40: 3+SIM40: 2] ) . Mean improvement in stereoacuity was 53 . 6%±8 . 4% ( Figure 5b , t = 6 . 410 , p = 0 . 003 ) , noting that SA6 failed the stereo test and had no recordable stereopsis in the baseline session . Three participants ( SA2 , SA3 , and SA4 ) fully regained normal stereoacuity ( 20 arc sec ) as measured by this test , and were basically “cured” in this aspect of vision .
Here we provide evidence from a pilot study of a small number of people that video-game play can induce a substantial degree of visual plasticity in adults with amblyopia . After a brief period of video-game play , a wide range of spatial vision functions improve very rapidly and substantially , reflecting normalization of both low-level ( visual acuity , positional acuity ) and high-level ( spatial attention , stereoacuity ) visual processing . Importantly , we provide preliminary characterization of the time course , limits , and underlying mechanisms of video-game experience-dependent cortical plasticity . The findings of our “cross-over control” experiment show that the treatment effects cannot be simply explained by eye patching , suggesting that it is indeed the video-game experience which improves amblyopic vision . The visual plasticity stimulated through video-game training has been well documented in the “normal” visual system , however the neural mechanisms are not yet clear . Using positional noise , we are able to reveal the underlying mechanisms . As we previously reported , repeatedly practicing a Vernier task in positional noise , with response feedback , can improve sampling efficiency and re-calibrate the distorted retinal topographical mappings of the amblyopic visual field [11] , [17] . Here we show that video-game play also results in a substantial increase in the ability to extract visual information ( increased sampling efficiency ) , without specific direct training , and we found that spatial distortion ( or internal positional noise ) can also be reduced to a certain extent through video-game play . Our findings also provide insights into which levels of processing in visual cortex can be modified . Counting deficits in amblyopes are thought to reflect a higher level limitation in the number of features ( and missing features ) the amblyopic visual system can individuate [24] . We speculate that the reduction in undercounting deficits in our amblyopic participants may represent the normalization of these higher level cortical areas . Recent work suggests that the ability to apprehend numbers may reflect a primary sensory attribute [25] , possibly reflecting the responses of neurons in parietal cortex that are tuned to number . From this perspective , the response characteristics of these affected numerosity processing neurons might be modifiable with video-games experience . While it is possible that low-level factors such as crowding [26] may result in improved counting in amblyopes , we can safely exclude this cause since our observers did not show any significant recovery in crowding . Our regression analysis suggests that changes in crowded acuity account for 3% of the variance in counting threshold and changes in isolated acuity account for 77% . Perhaps most importantly , we show that playing video games can indeed improve visual acuity and sharpen amblyopic vision . Note that visual acuity is the gold standard for examining spatial vision in clinical situations . To our knowledge , our work is the first to report that uncrowded visual acuity can be improved through video-game training . Green and Bavelier [18] reported that 30 h of video-game play did not result in improved visual acuity in normal adults , perhaps because there is little room for improvement in the normal visual system , or because 30 h is simply not long enough to improve a function as fundamental as normal visual acuity . Here we find that video-game play , both action and non-action , can result in a substantial improvement of amblyopic visual acuity . This is especially important because reduced visual acuity is the sin qua non of amblyopia . Playing a non-action game for 30 h has been found to be ineffective in enhancing attentional performance in participants with normal vision [19] . However , our results suggest that not only action but also non-action video games might be effective in improving amblyopic spatial vision . Although non-action games do not impose the same intense pressure on the player to respond to sudden pop-up targets from somewhere in the visual field , and to track fast moving objects , they do require the player to pay attention to fine and small spatial details and to different visual features in the visual scene—which may be a very demanding visual task for someone with reduced vision . In fact we noted that during game play , some deep amblyopes initially required more time than normal participants and had to get closer to the screen in order to identify targets or read instructions . In some sense , this is essentially similar to training spatial resolution [27] . A long period of sustained attention in seeing fine visual details might play an important role in triggering neural plasticity . It is worth noting that we had fewer participants , altogether four ( three from Group 2 and an extra one from the cross-over group SC1 ) , for the non-action video game . We recognize that the treatment effects could vary from individual to individual . A much larger sample size is necessary for future studies to investigate which type , action or non-action , is more effective in treating amblyopia . Perceptual learning has shown to be useful in improving amblyopic vision [28] . It is worthwhile noting that the visual recovery , e . g . visual acuity and positional acuity , we observed here with video-game play , although substantial , is somewhat smaller when compared with perceptual learning [4] . However , it is not too surprising that direct training can produce greater improvements , as it usually involves a large number of practice trials ( for example , deep amblyopes might need more than 50 , 000 trials to reach the plateau levels [11] ) in which the task difficulty is very challenging , most of the time around the observers' threshold limits . In contrast to perceptual learning , video games provide a visually enriched and stimulating environment , demanding different fundamental visual skills . Animal studies have highlighted the importance of environmental enrichment in promoting cortical plasticity [13] , [14] . We postulate that the intense sensory-motor interactions while immersed in video-game play might push brain functions to the limit , enabling the visual system to learn , on the fly , to recalibrate and adjust , providing the basis for functional plasticity . Treatment of adult amblyopia has recently received considerable attention ever since the introduction of perceptual learning techniques in the past few years [28]–[30] . There have been numerous attempts to find an effective treatment for amblyopia . These attempts include subcutaneous injection of strychnine [31] , flashing red and blue lights [32] , [33] , and rotating gratings [34] . Other more recent studies have attempted to use electric stimulation [35] , direct transcranial magnetic stimulation [36] , and pharmacological approaches [37] to induce brain plasticity . Some of these techniques seem promising , but the others lack repeatable clinical evidence . Before a video-game-based approach is used to treat amblyopia clinically , there are still many questions to be addressed ( e . g . , dose-response , prognosis for different ages of onset , types and depths of amblyopia ) . The current study serves as a “pilot” trial and , as such , has several design limitations: lack of randomization , small study size , and differences in numbers between arms . The lack of randomization and differences in numbers between arms may have resulted in potentially imbalanced makeup of the study arms on baseline characteristics . For example , the action game group was much more likely to be male and younger than the other groups . In addition , the small number of participants ( four ) in the non-action game group makes it difficult to draw strong conclusions . A much larger sample size is necessary for future studies to investigate which type , action or non-action , is more effective in treating amblyopia . Specifically , a large-scale randomized double-blind clinical trial ( with equal numbers in each group ) is needed to eliminate differences between people , placebo effects , and measurement differences . Despite these limitations , the present pilot study provides new insights into how video-game play sharpens visual functions in adult amblyopia and , most importantly , reveals that video-game play may provide important principles for improving treatment in amblyopia , and perhaps other clinical abnormalities .
The experimental procedures were approved by the University Committee for the Protection of Human Subjects , and the research was conducted according to the principles expressed in the Declaration of Helsinki . Informed consent was obtained from each participant . There was no known risk involved in the experimental procedures . Altogether 20 adults with amblyopia participated in three video-game experiments ( age range: 15–61 y , mean age: 31 . 4±3 . 5 y ) . They were recruited through advertisements in newspapers and through the Internet websites . Thorough eye examination was carried out by an experienced optometrist ( first author , RWL ) . Our participant inclusion criteria included: ( 1 ) age >15 years; ( 2 ) all forms of amblyopia , e . g . strabismic , anisometropic , refractive , deprivative , and meridional amblyopia; and ( 3 ) interocular visual acuity difference of at least 0 . 1 LogMAR . Exclusion criteria included any ocular pathological conditions ( e . g . , macular abnormalities ) and nystagmus . All of our participants had a difference in crowded visual acuity of two lines or more between the two eyes , and had normal vision in the sound eye ( ∼20/12–20/16 ) . The maculae of all participants were assessed as normal , and they all had clear ocular media ( as assessed by direct ophthalmoscopy ) . Their clinical data are summarized in Table 1 . The study took place in our research laboratory at the University of California , School of Optometry in Berkeley , California , from December 2004 to December 2009 . Participants were allocated into three intervention groups—two video-game treatment groups and one conventional occlusion therapy cross-over control group ( Figure 1a ) . The first 10 enrolled patients participated in the action video game group , the subsequently enrolled three patients participated in the non-action videogame group , and then another seven patients were recruited in the cross-over intervention group of which participants were allowed to choose between the two types of video games ( MOH: n = 4; SIM: n = 1 , SC1 ) in phase 2 . The two video games used were Medal of Honor Pacific Assault and SimCity Societies ( Electronic Arts , Inc . ) . Since there has been no previous clinical evidence indicating that video games can modify vision in adult amblyopia in any way , in this pilot trial we decided to recruit participants for the video game treatment groups in the beginning , in order to evaluate the feasibility of this treatment approach . It is important to note that the participant allocation was not based on the clinical characteristics of participants . In the main experiments , participants were required to play the assigned video games in our research laboratory for 40 or 80 h ( 2 h/d ) using the amblyopic eye , with the fellow eye occluded with a black eye patch . They were given full optical correction for the viewing distance . A battery of vision function tests listed below was used to examine the effects of video-game experience on amblyopic vision ( Figure 1b ) . All visual stimuli were displayed on a 21 in flat Sony F520 monitor screen at 1800×1440 resolution and 90 Hz refresh rate . Not all participants completed every visual function testing ( visual acuity , n = 20; positional acuity , n = 16; visual counting , n = 14; stereoacuity , n = 5 ) . Those participants in the control experiment ( OT group ) were given a log sheet to keep track of the patching hours and the visual tasks performed during patching . | Early abnormal visual experience disrupts neuronal circuitry in the brain and results in reduced vision , known as amblyopia or “lazy eye , ” the most frequent cause of permanent visual loss in childhood . It is generally believed that adult amblyopia is irreversible beyond the sensitive period of brain development during childhood . In this study , we examine whether playing video games , both action and non-action , has an effect on the vision of adults with amblyopia . We assessed visual acuity ( visual resolution ) , positional acuity ( the ability to localize object's relative position ) , spatial attention ( the ability to direct visual attention to various locations in the visual field ) , and stereoacuity ( stereo-vision / 3-D depth perception ) in a small group of teenagers and adults . We found that they tended to recover vision much faster than we would have expected from the results of conventional occlusion therapy in childhood amblyopia . Additional experiments and modelling suggest that the improvements are a result of decreasing spatial distortion and increasing information processing efficiency in the amblyopic brain . Thus , video games may include essential elements for active vision training to boost visual performance . Most importantly , our findings suggest that video-game play may provide important principles for treating amblyopia , a suggestion that we are pursuing with larger scale clinical trials . | [
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] | 2011 | Video-Game Play Induces Plasticity in the Visual System of Adults with Amblyopia |
Sodium antimony gluconate ( SAG ) unresponsiveness of Leishmania donovani ( Ld ) had effectively compromised the chemotherapeutic potential of SAG . 60s ribosomal L23a ( 60sRL23a ) , identified as one of the over-expressed protein in different resistant strains of L . donovani as observed with differential proteomics studies indicates towards its possible involvement in SAG resistance in L . donovani . In the present study 60sRL23a has been characterized for its probable association with SAG resistance mechanism . The expression profile of 60s ribosomal L23a ( 60sRL23a ) was checked in different SAG resistant as well as sensitive strains of L . donovani clinical isolates by real-time PCR and western blotting and was found to be up-regulated in resistant strains . Ld60sRL23a was cloned , expressed in E . coli system and purified for raising antibody in swiss mice and was observed to have cytosolic localization in L . donovani . 60sRL23a was further over-expressed in sensitive strain of L . donovani to check its sensitivity profile against SAG ( Sb V and III ) and was found to be altered towards the resistant mode . This study reports for the first time that the over expression of 60sRL23a in SAG sensitive parasite decreases the sensitivity of the parasite towards SAG , miltefosine and paramomycin . Growth curve of the tranfectants further indicated the proliferative potential of 60sRL23a assisting the parasite survival and reaffirming the extra ribosomal role of 60sRL23a . The study thus indicates towards the role of the protein in lowering and redistributing the drug pressure by increased proliferation of parasites and warrants further longitudinal study to understand the underlying mechanism .
Leishmaniasis is a neglected tropical disease , affecting almost , more than 10 million people around the world and ranks itself second to malaria in terms of mortality and morbidity . It is caused by an obligatory intracellular protozoan parasite of the genus Leishmania and has varied clinical spectrum from self healing skin ulcers to fatal visceral infection if left untreated . As vaccine against visceral form is still a distant proposition , treatment against Visceral Leishmaniasis ( VL ) solely relies on chemotherapy . Unfortunately , during the last decade Sodium Antimony Gluconate ( SAG ) which had a traditional background of sixty years of chemotherapy has been worn out due to the resistance developed against this drug . This has become a major obstacle to the treatment , especially in India , where more than 60% of VL patients are unresponsive to SAG treatment . Although , new drugs have become available in recent years for treatment of VL , they are also far from satisfactory [1] . This is due to increased relapse cases , lack of cost effectiveness and emerging resistance against them , as reported earlier [2] . Therefore , understanding the resistance mechanism could only strengthen the search for safe and wise chemotherapeutic strategies against VL . SAG having Sb ( V ) is a pro-drug and requires biological reduction to active form i . e . Sb ( III ) in macrophage and/or amastigotes . Sb ( III ) has been reported to interact with several targets . Resistance in general has been understood as interplay among uptake , efflux and/or sequestration of active molecule and modulating gene expression levels [3] . Most of the drug resistance studies were done on the laboratory mutants as compared to the clinical isolates . Although some studies emphasized on field isolates but these were based on biochemical , biophysical and immunological analysis of resistant isolates but several questions remains unrequited regarding the parasite's modulation for SAG at molecular level [4] , [5] . Actual scenario could be more lucid by exploring clinical isolates and characterizing the up regulated as well as down regulated proteins in the resistant strains . Several differential studies revealed many proteins under several metabolic pathways , proteins involved in maintaining redox balance , transporters and signaling pathways along with a large number of translational/ribosomal proteins indicating its possible role in resistance mechanism in the resistant clinical isolates [6] , [7] , [8] . Some of them are well characterized for their possible role in SAG resistance mechanism e . g . Trypanothione reductase , γ glutamyl cysteine synthtase , ornithine decarboxylase and ABC transporters but ribosomal proteins are yet to be explored for its involvement in drug resistance against VL [9] . 60s ribosomal L23a ( 60sRL23a ) is one such over-expressed protein in SAG resistant strain of Leishmania donovani ( Ld ) identified through differential proteomics indicating its possible involvement in SAG resistance in L . donovani [10] . 60sRL23a encoded protein is a component of 60s subunit of large subunit of ribosome . In eukaryotes , ribosome biogenesis is a coordinated assembly involving four ribosomal rRNA molecules and more than seventy ribosomal proteins . It was believed that ribosome were previously consists of RNA only and ribosomal protein appeared later in the evolution to facilitate the protein synthesis . However , ribosomal proteins have also been reported to regulate cell growth and apoptosis apart from their regular translational apparatus activity [11] . There are some circumstantial evidences available regarding ribosomal proteins acting as modulators and effectors of changes [12] . Ribosomal proteins may be meant for ribosome , but could be recruited for extra ribosomal functions [13] . In the present study for the first time involvement of over-expressed 60sRL23a in in vitro SAG resistance has been explored in clinical isolates of L . donovani .
Clinical isolates used in this study were isolated from patients at the Kala –Azar Medical Research centre , Institute of Medical Sciences , Banaras Hindu University , Varanasi , India , and at its affiliated hospital at Muzaffarpur , Bihar , India . Clinical isolates were obtained prior to drug treatment from VL patients who had responded to chemotherapy by SAG and were designated as SAG-S ( SAG-sensitive ) , whereas VL patients who did not respond to SAG were designated as SAG-R ( SAG-resistant ) . Promastigotes of corresponding strains were harvested by transformation of amastigotes from the splenic aspirates of kala-azar patients . SAG-S isolates used in this study include 2001 ( S1 ) whereas the three SAG-R isolates were 2039 ( R1 ) , 1216 ( R2 ) , 761 ( R3 ) . The isolates used in this study were anonymized . The Dd8 ( S2 ) strain ( MHOM/IN/80/DD8 ) was used as a reference strain in this study . The isolates were maintained in RPMI-1640 medium containing 10%FCS at 26°C ( Sigma , USA ) in 75 cm2 culture flask ( Nunc ) . The virulence have been retained in parasites through regular passage through hamster , so as to maintain their chemosensitivity profiles that was measured periodically by amastigote macrophage J774A . 1 infectivity assay as described elsewhere [14] . L . donovani genomic DNA was isolated from 108 cultured promastigotes [19] . Genomic DNA was spooled and subjected to RNase ( 100 µg/ml ) treatment . 60sRL23a gene was amplified using primers: forward 5′GGTACCATGCCTCCTGCTCAGAAG3′ and reverse5′ AAGCTTGACAAGACCGATCTT3′ and Taq DNA polymerase ( Sigma Aldrich ) lacking 3′-5′ exonuclease activity in a termocycler ( Bio-Rad ) under conditions at one cycle of 95°C for 5 min , 30 cycles of 95°C for 45 s , 54°C for 30 s , 72°C for 45 s , and finally one cycle of 72°C for 10 min . Amplified PCR product was electrophoresed in agarose gel and eluted from the gel by Gen Elute columns ( Qiagen ) . Eluted product was cloned in pTZ57R/T ( T/A ) cloning vector ( Fermentas ) and transformed into competent DH5α cells . The transformants were screened for the presence of recombinant plasmids with 60sRL23a insert by gene specific PCR under similar conditions as previously mentioned . Isolated positive clones were sequenced from Chromous Biotech Pvt Ltd . ( Bangalore ) and submitted to the National Centre for Biotechnology Information http://www . ncbi . nlm . nih . gov/nuccore/GU121098 . 1 ( accession no . GU121098 . 1 ) . 60sRL23a was further sub cloned at the KpnI and HindIII site of bacterial pTriEx4 ( Novagen ) . The expression of 60sRL23a was checked in bacterial cell by transforming the 60sRL23a+pTriEx4 in Escherichia coli Rosetta Strain . The transformed cells were inoculated into 5 ml test tube culture medium ( Luria Bertani ) and allowed to grow at 37°C in a shaker at 220 rpm . Cultures in logarithmic phase ( at OD600 of ∼0 . 5–0 . 6 ) were induced for 3 hrs with 1 mM isopropyl-ß-D-thiogalactopyranoside ( IPTG ) at 37°C . After induction cells were lysed in SDS- Sample buffer using 5× stock ( 0 . 313 M Tris-Hcl ( pH 6 . 8 ) , 50% glycerol , 10%SDS ) [20] . Uninduced control culture was analyzed in parallel . These separated proteins from the polyacrylamide gel were transformed onto a nitrocellulose membrane in a semidry blot apparatus ( Amersham ) as described elsewhere [21] . Membrane was incubated for 1 h in blocking buffer followed by a 2 h incubation at room temperature with mouse anti-His antibody ( Novagen ) as primary antibody ( 1∶2500 dilution ) and then incubated with goat anti-mouse HRP conjugate antibody ( 1/10 , 000: Bangalore Genie ) for 1 h at room temperature . The blot was developed using an ECL kit ( GE Biosciences ) . For purification of 60sRL23a 200 ml of LB medium containing 35 µg/mL of chloramphenicol and 35 µg/mL ampicillin were inoculated with E . coli Rosetta strain transformed with pTriEx4-Ld60sRL23a and grown to an O . D . 600 of , 0 . 6 and then induced by addition of 1 mM ( IPTG , Sigma ) then further incubated for an additional 4–5 h at 37°C . The rLd60sRL23a was purified by affinity chromatography using Ni2+ chelating resin to bind the His6- Tag fusion peptide derived from the pTriEx4 vector . The cell pellet was resuspended in 5 mL of lysis buffer ( 10 mM Tris-HCl ( pH 8 . 0 ) , 200 mM NaCl , ) containing 1∶200 dilution of protease cocktail inhibitor ( Sigma ) and incubated for 30 mins on ice and the suspension was sonicated for 10×20 s ( with 30 s interval between each pulse ) on ice . The sonicated cells were centrifuged at 15 , 000 g for 30 min , and the supernatant was incubated at 4°C for 1 h with the 2 ml of Ni-NTA Superflow resin ( Qiagen , Hilden , Germany ) previously equilibrated with lysis buffer . After washing with buffer ( 10 mM Tris-HCl , 200 mM Nacl ) containing different concentrations of imidazole i . e . 10 , 20 , 30 and 50 mM , the purified rLd60sRL23a was eluted with elution buffer ( 10 mM Tris-HCl , 200 mM NaCl , and 200 mM imidazole , pH 7 . 5 ) . The eluted fractions were analysed by 12% SDS-PAGE and the gels were stained with Coomassie brilliant blue R-250 ( Sigma-Aldrich , St . Louis , USA ) . The protein content of the fractions was estimated by the Bradford method using bovine serum albumin ( BSA ) as standard . The purified rLd60sRL23a was used for raising antibodies ( Ab ) in swiss mice . Swiss mice were first immunized using 25 µg of rLd60sRL23a in Freund's complete adjuvant . Twelve days after the first dose the mice were given 2 booster doses of 15 µg of the recombinant protein each in incomplete Freund's adjuvant at 15 days interval and blood was collected after the last immunization by sacrificing the mice for serum collection . For immunoblotting experiment , purified rLd60sRL23a protein and whole cell lysate ( WCL ) were resolved on 12% SDS-PAGE and transformed onto nitrocellulose membrane using a semi-dry blot apparatus ( Amersham ) [21] . After overnight blocking in 5% BSA , the membrane was incubated with antiserum to the rLd60sRL23a protein at a dilution of 1∶3000 for 120 min at room temperature ( RT ) . The membrane was washed three times with PBS containing 0 . 5% Tween 20 ( PBS-T ) and then incubated with Rat anti-Mouse IgG HRP conjugate ( Invitrogen , Carlsbad , USA ) at a dilution of 1∶10 , 000 for 1 h at room temperature . Blot was developed by using diaminobenzidine+imidazole+H2O2 ( Sigma ) . 10 million log phase parasites each of S1 , S2 , R1 , R2 , R3 were taken for RNA extraction . The freshly harvested promastigotes were immediately resuspended in Tri reagent ( Sigma , Aldrich ) . RNA was isolated according to the manufacture protocol . Isolated RNA were treated with DNase and quantified in Gene-quant ( Biorad ) . Total RNA ( 1 µg/reaction ) was reverse transcribed using first-strand cDNA synthesis kit ( Fermentas ) and then cDNA was treated with RNase . For qRT-PCR primers were designed using Beacon Designer software ( Biorad ) . qRT-PCR was carried out with 12 . 5 ml of SYBER green PCR master mix ( TAKARA ) , 1 µg of cDNA , and primers at a final concentration of 200 nM in a final volume of 25 µl . PCR was conducted under the following conditions: initial denaturation at 95°C for 10 min followed by 40 cycles , each consisting of denaturation at 95°C for 1 min , annealing at 52°C for 1 min and extension at 70°C for 1 min followed by 80°C for 10 sec . 87 cycles of melt curve was set at 52°C for 10 sec . All quantification was normalized to the Ld-actin gene . A no-template control cDNA was included to eliminate contaminations or nonspecific reactions . The cycle threshold ( CT ) value was defined as the number of PCR cycles required for the fluorescence signal to exceed the detection threshold value ( background noise ) . Difference in gene expression was calculated by comparative CT method [24] . This method compares test samples to the comparator sample and uses results obtained with a uniformly expressed control gene ( Ld-actin ) to correct for differences in the amounts of RNA present in the two samples being compared to generate a ΔCT value . Results are expressed as the degrees of difference between ΔCT values of test and comparator sample ( S1 ) to get ΔΔCT i . e . [25] . Then the normalized expression ratio was calculated as 2−ΔΔCT [26] . 60sRL23a gene was amplified from 60sRL23a+pTriEx4 construct using primers: forward 5′ GGATCCATGCCTCCTGCTCAGAAGACC3′ and reverse 5′ GATATCGACAAGACGGATCTTGTTGGCAG3′ and then cloned into Leishmania expression vector pXG-'GFP+ at BamHI and EcoRV site [27] . Late log phase S1 promastigotes were washed with transfection buffer ( 21 mM HEPES , pH 7 . 5 , 137 mM NaCl , 5 mM KCl , 0 . 7 mM Na2HPO4 , 6 mM glucose ) . Transfection of the parasites with 20 µg of 60sRL23a+pXG-'GFP+ and pXG-'GFP+ alone , were carried out in 0 . 2 cm electroporation cuvet using a Gene Pulsar ( Bio-Rad ) . Transfectants were allowed to recover for 24 h and then were selected for resistance to G418 at 5 , 10 , 20 , 50 µg/mL . 2×106 parasites ( promastigote ) were seeded . Counting of parasites ( promastigote ) for each transfectants was done for 8 days . Growth curve was plotted as number of parasites versus number of days . The proliferative potential of transfectants at amastigote stage was studied in macrophage amastigote ( 1∶8 ) system in chamber slide . Parasites in macrophages were counted by geimsa staining at 0 h , 6 h , 12 h , 24 h , 48 h . Sb ( III ) and Sb ( V ) sensitivity profile of Transfectants growing at 0 µg , 20 µg , 50 µg were determined in the same procedure as it was done for clinical isolates . Transfectants ( promastigotes ) growing at 50 µg of G418 were assessed for their sensitivity towards miltefosine ( SynphaBase ) , paramomycin ( Sigma ) and amphotericin B ( Sigma ) through flow-cytometry as described earlier [29] .
The in vitro and in vivo SAG sensitivity was assessed in clinical isolates isolated from SAG responsive and unresponsive patients from Muzaffarpur . The in vitro chemotherapeutic profile of clinical isolates was summarized in ( Table 1 ) . The in vitro SAG sensitivity assay was studied with both Sb ( V ) and Sb ( III ) and has been found to be correlated to each other as confirmed by the resistance index ( Table 1 ) . The chemotherapeutic sensitivity profiles of clinical isolates tested in hamster model showed the successful treatment of hamsters infected with S1 and S2 with standard dose of SAG ( 80 mg/kg×5 i . p . ) with a percent inhibition ( PI ) of 94 . 97±2 for S1 and 96 . 915±0 . 84 for S2 . SAG still exerted leishmanicidal action at 20 mg/kg×5ip with PI of 65 . 67±3 . 75 for S1 and 70 . 640±1 . 24 for S2 . PI was 79 . 98±2 . 41 for S1 and 84 . 87±2 . 313 for S2 at 40 mg/kg×5 i . p . whereas SAG failed to inhibit the multiplication of R1 , R2 and R3 even at higher doses ( Figure 1 ) . Since 60sRL23a was found to be over-expressed in the SAG resistant strains as identified through differential proteomics study it was further investigated for its expression profile in several resistant and sensitive strains of L . donovani through real-time PCR ( Figure 3A ) . The study revealed the difference in the expression levels of 60sRL23a between the sensitive and resistant strains of L . donovani . There was ∼two fold increase in the expression of protein in resistant parasite ( Figure 3B ) . Expression profile of 60sRL23a in protein level was further confirmed through western blot analysis with anti-r60sRL23a antibody and was found to replicate the response of real-time study as confirmed by the densitometric study through chemidoc software ( BIORAD ) using Ld-actin as internal control [31] . 60sRL23a gene was further sub-cloned in Leishmania expression vector pXG-'GFP+ ( Figure 4A ) and was transfected in S1 to check whether this protein can alter the sensitivity profile of S1 . The western blot analysis of the whole cell lysate of S1 [60sRL23a+pXG-'GFP+] parasite with anti-60sRL23a antibody revealed the identification of the protein at ∼43 kDa and ∼16 kDa mol wt . ( Figure 4B ) , whereas with anti-GFP antibody the protein bands were detected at ∼43 kDa and ∼27 kDa ( Figure 4C ) . The S1 lysate having vector alone [pXG-'GFP+] exhibited a band at mol wt of 27 kDa when analyzed with GFP antibody and at ∼16 kDa when analyzed with r60sRL23a antibody . This indicated towards the expression of GFP alone ( Figure 4C ) . The increased expression pattern of the protein in S1 [60sRL23a+pXG-'GFP+] parasite has been observed with increased pressure of G418 at 0 µg/mL , 20 µg/mL and 50 µg/mL . ( Figure 4D ) . In vitro SAG sensitivity of transfectants maintained at 0 , 20 , 50 µg of G418 were assessed with both Sb ( V ) in macrophage amastigote model and with Sb ( III ) in promastigotes . The sensitivity profile of the transfectants to Sb ( V ) and Sb ( III ) is depicted in ( Figure 5A and 5B ) . S1 containing episomal over expressed 60sRL23a growing at 50 µg/mL of G418 has IC50 i . e . 158 . 066±4 . 28 for Sb ( V ) than of S1 expressing vector alone i . e . 92 . 506±5 . 7 . S1 [60sRL23a+pXG-'GFP+] growing at 20 µg/mL of G418 also exhibited higher IC50 ( 123 . 2±5 . 117 ) as compared to S1 ( 78 . 55±6 . 5 ) containing vector alone . Whereas S1 [60sRL23a+pXG-'GFP+] growing at 0 µg/mL demonstrated the IC50 comparable to S1[pXG-'GFP+] and S1 . The sensitivity of transfectants to Sb ( III ) and Sb ( V ) patterns were similar to each other . The Sb ( III ) IC50 of S1 [60sRL23a+pXG-'GFP+] i . e . 61 . 58±7 . 23 was found to be greater than S1 [pXG-'GFP+] i . e . 17 . 67±1 . 71 at 50 µg/mL of G418 , whereas at 20 µg/mL of G418 it was 47 . 34±2 . 54 and S1 having empty vector has similar IC50 at all concentrations of G418 . In absence of G418 the IC50 of transfectants were comparable . The transfectants were further checked for their sensitivity towards other antileishmanial compounds ( miltefosine , paramomycin and amphotericin B ) ( Figure 6 ) . S1 [60sRL23a+pXG-'GFP+] showed ∼7 fold decreased sensitivity towards miltefosine ( 67 . 38±9 . 64 µg/ml ) and ∼2 . 4 fold towards paramomycin ( 142 . 16±8 . 76 µg/ml ) . S1 [pXG-'GFP+] demonstrated IC50 of 8 . 6±1 . 44 for miltefosine and 57 . 11±11 . 58 for paramomycin . Whereas the transfectant showed comparable IC50 for amphotericin B ( 0 . 201±0 . 003 µg/ml for S1 [pXG-'GFP+] and 0 . 1748±0 . 093 µg/ml for S1 [60sRL23a+pXG-'GFP+] ) .
The emergence of SAG resistance and the limited knowledge of the mechanism by which parasite acquire resistance are the major obstacle for the control of VL . Several SAG resistance studies were done on the basis of differential proteomics or/and transcriptomics and microarray to understand the parasite strategy to escape the drug pressure . These studies led to the identification of several proteins , playing crucial role in liberating the drug pressure , including ribosomal proteins [6]–[8] . . The enhanced expression of ribosomal proteins has been reported in tumors such as breast cancers , prostate cancers and hepatocellular cancers [32] . These ribosomal proteins have not been studied in detail in relation to SAG resistance in Leishmania . In this study , therefore , we have evaluated the involvement of 60sRL23a in SAG resistance using five clinical isolates which were isolated from kala-azar patients ( Muzaffarpur ) and their SAG sensitivity was further verified in vitro ( macrophage and amastigote model ) and in vivo ( hamster model ) . SAG sensitivity profile of sensitive and resistant isolates in vitro and in vivo was comparable . S2 ( Dd8 ) , a WHO reference laboratory strain of L . donovani was used in this study to assess the resistance index of isolates . Resistance index of different resistant clinical isolates ( R1 , R2 , R3 ) revealed that their response to Sb ( V ) and Sb ( III ) were similar . In vivo response of the resistant isolates to SAG in hamster model exhibited no inhibition even at higher concentration of SAG where as the S1 , S2 exhibited 94 . 97±2 . 0% and 96 . 915±0 . 84% inhibition . The sensitive isolates responded even at the lower doses of SAG ( 20 and 40 mg/kg×5i . p . ) . In vitro and in vivo SAG sensitivity profile of all the isolates replicated the patients' response and confirmed the persistence of SAG response of clinical isolates even after several passages in hamster . In order to assess the association of 60sRL23a in SAG resistance we cloned , expressed and purified the protein which exhibited very close homology with L . major 60sRL23a to the tune of 95% and 45% identity with humans indicating towards the difference among the humans and parasite's entity . The protein's mismatched homology with humans can present the protein as a potential drug target . Immunoblot study of L . donovani promastigote lysate with the polyclonal anti-rLd60sRL23a antibody has revealed one dominant protein of 16 kDa mol wt . This protein was identified earlier at lower molecular weight range in proteomic studies which is approximately identical to its observed molecular mass [10] . The protein has been observed to have a cytosolic localization in the parasite , though earlier in the proteomic study it was identified in the membrane fraction . The protein is a well known member of large subunit of ribosomes so it is obvious for its attachment to endoplasmic reticulum thus it would have eventually been identified in the membrane fraction in the proteomic study . To study the expression level of 60sRL23a in different clinical isolates real time and immunoblot analysis was done and it revealed the 2 fold expression of the transcript and protein in the resistant strain as compared to the sensitive one , verifying the differential proteomics finding [10] . Differential regulation of ribosomal proteins has been utilized by cells to cater their needs such as replication , transcription , delaying apoptosis and proliferation , thus helping the cell to escape the stress conditions [33] . In this study whether 60sRL23a could modulate the SAG sensitivity profile of parasite , protein has been over expressed in S1 in pXG-'GFP+ vector having GFP tag in C-terminus end of the protein . Immunoblot analysis with 60sRL23a polyclonal antibody revealed the ∼43 kDa protein in the lysate of S1 transfected with 60sRL23a gene containing vector , verifying over expression of GFP tagged 60sRL23a . The immunoblot analysis with the GFP antibody identified two protein bands at ∼43 kDa and ∼27 kDa , indicating the detachment of GFP protein from combined entity of 60sRL23a+pXG-'GFP+ after expression ( Figure 4C ) . This ensures the expression of protein returns in its original length after fusion protein expression . Transfectants expression profile in increasing G418 concentration indicated the increased protein expression pattern with increasing amount of G418 ( Figure 4D ) i . e . ∼ two fold expression of GFP tagged 60sRL23a in parasites growing in 50 µg/mL G418 as compared to parasites residing with 20 µg/mL G418 , whereas no expression of GFP tagged 60sRL23a has been seen in 0 µg/mL of G418 . Transfectants growth profile was analyzed by counting parasites per day for each transfectants and a clear proliferation has been observed in 60sRL23a over expressing parasites . The transfectants S1 ( 60sRL23a+pXG-'GFP+ ) growing at 50 µg/mL of G418 has more proliferative potential as compared to transfectants growing at 0 µg/mL and 20 µg/mL of G418 , indicating the increasing expression of protein leading the parasite proliferation . As the growth curve results exhibited greater number of parasites in transfectants over expressing 60sRL23a , thus the cell cycle progression of a synchronized transfected and control parasite population was analyzed to identify its relation with 60sRL23a expression . Parasites exhibited the tendency towards ‘G2/M’ phase as increasing number of parasites are seen in this phase wherein parasite progresses at different time intervals in compared to the control S1 ( pXG-'GFP+ ) one . The different proportion of both parasites [S1 ( pXG-'GFP+ ) and S1 ( 60s+pXG-'GFP+ ) ] in S phase were approximately same at different time interval . In G0/G1 phase number of parasites were decreased in S1 ( 60s+pXG-'GFP+ ) as compared to S1 ( pXG-'GFP+ ) revealing no arrest and a smooth progression to G2M phase . Whereas in G2M phase at 8 hrs and 12 hrs the S1 ( 60s+pXG-'GFP+ ) progressively increased in comparison to S1 ( pXG-'GFP+ ) leading to rapid proliferation of parasites . Cell cycle progression pattern of transfectants reconfirmed the proliferation of S1 ( 60sRL23a+pXG-'GFP+ ) . Cellular proliferation as one of the extra ribosomal functions of ribosomal proteins have been reported to alter the cell cycle by interacting with cyclin-dependent kinases ( Cdk ) and regulatory molecules of cell cycle check points [34] , [35] . Although Cdk are absent in Leishmania still apoptosis like cell death has been evidenced in Leishmania [36]–[38] . Transfectants were checked for their SAG sensitivity in vitro in macrophage-amastigote model Sb ( V ) as well as in the promastigotes for Sb ( III ) directly . Results revealed the in vitro SAG sensitivity of transfectants maintained at 0 , 20 , 50 µg of G418 were assessed with both SbV in macrophage amastigote model and with Sb ( III ) in promastigotes . The sensitivity profile of the transfectants to Sb ( V ) and Sb ( III ) is depicted in ( Figure 5A , 5B ) . S1 containing episomally over-expressed 60sRL23a growing at 50 µg/mL showed 1 . 7 fold higher IC50 ( 158 . 066±4 . 28 ) for Sb ( V ) than IC50 ( 92 . 506±5 . 7 ) of S1 expressing vector alone . S1 ( pXG-'GFP+60sRL23a ) growing at 20 µg/mL of G418 also exhibited higher IC50 ( 123 . 2±5 . 117 ) which is 1 . 5 fold as compared to S1 ( 78 . 55±6 . 5 ) . Whereas S1 ( pXG-'GFP+60sRL23a ) growing at 0 µg/mL of G418 demonstrated the IC50 comparable to S1 ( pXG-'GFP+ ) and S1 . The sensitivity of transfectants to Sb ( III ) and Sb ( V ) patterns were similar to each other . The Sb ( III ) IC50 ( 61 . 58±7 . 23 ) of S1 ( pXG-'GFP+60sRL23a ) was 3 . 5 fold higher than S1 ( pXG-'GFP+ ) ( 17 . 67±1 . 71 ) at 50 µg/mL of G418 , whereas at 20 µg/mL of G418 it was 2 . 7 fold higher . In absence of G418 the IC50 of transfectants were comparable . The IC50 of S1 ( pXG-'GFP+60sRL23a ) growing at 50 µg/mL was 1 . 2 fold higher than the IC50 of transfectants growing at 20 µg/mL . This sensitivity profile of transfectants to Sb ( V ) /Sb ( III ) and increasing expression pattern of 60sRL23a in response to varying G418 concentration revealed the SAG sensitivity profile and 60sRL23a expression pattern is inversely correlated . The IC50 values of transfectants , were ∼1 . 3 to 1 . 8 fold higher for Sb ( V ) and ∼1 . 2 to 1 . 4 fold higher for Sb ( III ) to all the three resistant isolates , depicting the comparable resistance acquired by the transfectants to that of resistant isolates . Since SAG helps in sustaining innate as well as adaptive immunity against Leishmania by generating ROS and NO , higher proliferating capacity would increase the chances of the parasite to survive the intracellular host killing mechanism and combating the drug pressure [39] . Cellular proliferative potential of 60sRL23a and decreased SAG sensitivity of transfectants further emphasized the need to check its sensitivity profile for other antileishmanial drugs such as miltefosine , paramomycin and amphotericin B ( Figure 6 ) . Results revealed the decreased sensitivity of transfectants towards miltefosine and paramomycin . Whereas transfectants retained unaltered sensitivity towards amphotericin B . Paramomycin in general is known to inhibit protein synthesis by targeting ribosomal proteins and resistant strains of paramomycin revealed upregulated translational/ribosomal proteins to combat the drug pressure [8] . On the other hand the resistance mechanism of miltefosine involves several defect in inward translocation and increased efflux of drugs [40] . Since paramomycin is known to inhibit protein synthesis and the exact working mechanism of miltefosine and SAG is still unknown , the pathways of these drugs may do the cross talk somewhere or the toxicity of these drugs could have been overtaken by the parasite through increased cellular proliferation . In the light of above observation increased proliferative potential may strengthen the parasite to redistribute or lower the drug pressure hence providing a prospect to escape the drug pressure . Amphotericin B being the most successful drug among these and no resistance cases reported till date , further revealed its unique mechanism unbeatable by the parasite . Despite of its peerless therapeutic results , its toxicity and cost factors further compelled us to rejuvenate the safe traditional drugs . Down regulation of 60sRL23a could validate the finding of the present study but as RNAi machinery is absent in L . donovani and only knockout remains the only way to study the down regulation effect of the gene , but knockout 60sRL23a would be futile due to multicopy of the gene present in the genome of Leishmania . Presence of multicopy of 60sRL23a again indicates the protein to be an essential component of parasite that could be used by the parasite in different ways as and when so ever needed . Hence this study could only confirm the after effects of up regulation of 60sRL23a . Study further revealed that parasite could use its usual protein to perform an unusual function such as cellular proliferation to combat pressure of different drugs carrying out different anti-parasitic pathway . Indian subcontinent is now relying on several other drug combinations other than SAG , but parasites had developed resistance against these drug combinations under laboratory conditions [41] . To win the battle against Leishmaniasis searching new drugs or combinations against Leishmania is not sufficient but our understanding for the resistance mechanism has to be explored enough to strengthen the new chemotherapeutic strategy . SAG has not been in use for some time on the Indian subcontinent , and although the removal of drug pressure is expected to allow the return of the sensitive parasites by natural selection , although this is not universally accepted [42] , [43] , [4] , [5] . Our understanding regarding resistance mechanism is in its infancy , this study will help to focus on the substantial role played by the ribosomal proteins in disease progression by assisting the parasite to escape the drug pressure . | Visceral Leishmaniasis ( VL ) is the most fatal form in Indian subcontinent . Till last few years , the treatment of the disease was done with Sodium antimony gluconate ( SAG ) , the first line drug against VL . This , however , was severely eroded by the resistance developed by the parasite against it . In order to understand the underlying mechanism , earlier a proteomic analysis of SAG sensitive as well as SAG resistant isolates of L . donovani ( Ld ) was done in which 60s ribosomal L23a ( Ld60sRL23a ) protein , one of the essential member of translational machinery , was found to be over-expressed . To examine its role in SAG resistance mechanism , which is hitherto not known , 60sRL23a was characterized and over-expressed in the sensitive isolate of L . donovani . The sensitivity of these transfectants , was found to be decreased towards SAG . The growth curve of transfectants clearly showed its proliferation potential in both promastigote and amastigote forms . Cell cycle analysis of the transfectants further assured its rapid progression towards the G2/M phase . The above studies , thus , indicate that 60s RL23a regulates proliferation of L . donovani parasites and represents a unique strategy to resist SAG . 60sRL23a could be further explored as a potential drug target to strengthen the chemotherapy strategy against L . donovani . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Over-Expression of 60s Ribosomal L23a Is Associated with Cellular Proliferation in SAG Resistant Clinical Isolates of Leishmania donovani |
The E6 oncoprotein from high-risk genus alpha human papillomaviruses ( α-HPVs ) , such as HPV 16 , has been well characterized with respect to the host-cell proteins it interacts with and corresponding signaling pathways that are disrupted due to these interactions . Less is known regarding the interacting partners of E6 from the genus beta papillomaviruses ( β-HPVs ) ; however , it is generally thought that β-HPV E6 proteins do not interact with many of the proteins known to bind to α-HPV E6 . Here we identify p300 as a protein that interacts directly with E6 from both α- and β-HPV types . Importantly , this association appears much stronger with β-HPV types 5 and 8-E6 than with α-HPV type 16-E6 or β-HPV type 38-E6 . We demonstrate that the enhanced association between 5/8-E6 and p300 leads to p300 degradation in a proteasomal-dependent but E6AP-independent manner . Rather , 5/8-E6 inhibit the association of AKT with p300 , an event necessary to ensure p300 stability within the cell . Finally , we demonstrate that the decreased p300 protein levels concomitantly affect downstream signaling events , such as the expression of differentiation markers K1 , K10 and Involucrin . Together , these results demonstrate a unique way in which β-HPV E6 proteins are able to affect host-cell signaling in a manner distinct from that of the α-HPVs .
Human papillomaviruses ( HPVs ) are a large family of DNA tumor viruses that infect both cutaneous and mucosal epithelium , and lead to a range of pathologies , from benign papillomas to cancerous lesions . Over 130 different HPV types have been identified and divided into a number of genera based on DNA sequence homologies [1] . The best-studied HPVs are those of the alpha genus ( α-HPVs ) , and include both low-risk ( HPV types 6 and 11 ) and high-risk ( HPV types 16 and 18 ) viruses . Low-risk HPVs have been most often associated with genital warts and non-cancerous papillomas , whereas the high-risk HPVs have been shown to be the etiologic agent in cervical cancer , as well as other anogenital carcinomas and a subset of head and neck cancers [2] , [3] . Recently , another group of HPVs , the beta-HPVs ( β-HPVs ) have become the subject of interest due to their possible involvement in squamous cell skin carcinoma ( SCSC ) [4] , [5] , [6] , [7] . All HPVs encode the E6 and E7 oncoproteins , which are responsible for numerous physiological changes within the infected host cell [reviewed in 8 , 9] . However , E6 and E7 proteins differ functionally among different HPV genera , species and types . In the α-HPV genus , some E6 functions are conserved between both high- and low-risk HPV types , including association with the E3 ubiquitin ligase E6AP [10] , [11] , [12] , [13] and degradation of the pro-apoptotic protein Bak [14] , [15] . Conversely , many E6 functions are manifested primarily by high-risk α-HPVs , including the activation of telomerase [16] , [17] , [18] and the degradation of a number of proteins including p53 [19] and PDZ domain containing proteins such as hDlg [20] , [21] , hScrib [22] , MAGI [23] , [24] , MUPP1 [25] , and PTPN3 [26] , [27] . Not surprisingly , some of these functional differences can be attributed to variations in the E6 amino-acid sequence [24] , [28] . For example , only high-risk α-HPVs harbor a PDZ domain , which explains why they are able to associate with PDZ proteins and low-risk E6 proteins are not . While the functions of E6 from both the high- and low-risk α-HPVs have been well studied , little is known about how E6 from β-HPVs contribute to viral pathogenicity . Recent studies have demonstrated that like α-HPV E6 , some β-HPV E6 proteins are capable of activating telomerase , and interacting with Bak and E6AP [29] , [30] , however other well-documented E6 functions are not conserved between these two genera . For example , as β-HPV E6 proteins ( like the low-risk α-HPV E6 proteins ) lack a PDZ domain , they are unable to interact with and disrupt key polarity signaling pathways , as do high-risk α-HPV E6 proteins . Moreover , while some β-HPV E6 proteins , like 38E6 , have been shown to perturb p53 signaling through transcriptional activation of deltaNp73 [31] , [32] , most β-HPV E6 proteins are unable to bind p53 , making it unclear as to whether they are capable of inactivating p53 signaling; a crucial step in carcinogenesis for the high-risk α-HPVs . It is unclear what other protein interactions occur between the β-HPV E6 proteins and host-cell proteins , the subsequent signaling pathways that may be disrupted due to these interactions , and the role these interactions may play in the development of SCSC . One protein that has garnered interest due to its ability to interact with E6 from both α- and β-HPVs , Bovine Papillomavirus ( BPV ) , and Cottontail Rabbit Papillomavirus ( CRPV ) is the histone acetyltransferase p300 [33] , [34] , [35] , [36] , [37] , [38] . p300 is a central hub in numerous signaling pathways , and consequently has been shown to associate with over 100 different proteins [Reviewed in 39 , 40] , thus the potential for E6 to disrupt important signaling pathways via association with p300 is vast . E6 from high-risk α-HPVs has been shown to bind to three distinct regions of p300; the C/H1 domain , the C/H3 domain and the C-terminus , and disrupt important p300-dependent signaling events such as p53 and NFΚB transactivation [34] , [36] , [38] . Conversely , association of E6 from low-risk α-HPVs appears to be confined to the C/H1 region , and conflicting evidence has been reported as to whether or not this association alters p300-mediated signaling [34] , [36] , [38] . Interestingly , as seen with E6 from the high-risk α-HPVs , BPV 1E6 and β-HPV 8E6 have both been shown to bind to the C/H3 region of p300 , while 8E6 is also capable of binding to the C/H1 and C-terminal domains [33] , [35] . This association has been shown to attenuate p53 transactivation in the case of BPV 1E6 [35] , however it is unknown if 8E6 causes the same effect . Most recently , CRPV E6 and β-HPV 38E6 were both shown to interact with full-length p300 , and these interactions also attenuated p53 signaling [37] . As can be seen , E6 proteins from different HPVs associate with different domains of p300 with different effects . Likewise , at least two distinct regions of E6 have been implicated in this interaction; the region encompassing the second zinc-finger domain corresponding to aa 100-147 of 16E6 [33] , [34] , [38] , and a more N-terminal region corresponding to aa 75–84 of 38E6 [37] . The region of 8E6 that bound p300 was mapped to residues 132–136 [33] . Thus , even when associating with the same protein , E6 from different HPVs handle this interaction in a unique manner . In order to better understand what signaling pathways may be altered by β-HPV E6 , we set out to identify host-cell proteins that might interact with β-HPV E6 proteins . GST-pulldowns were performed using recombinant GST-E6 proteins from a number of β-HPVs and lysates from human foreskin keratinocytes ( HFKs ) The isolated complexes were then analyzed by mass spectrometry . Here we describe p300 , as a protein that interacts with all of the E6 proteins tested , but with different strengths and in turn different consequences . Importantly , we found that β-HPV 5 and 8E6 bound to p300 very strongly , which in turn led to the proteasomal mediated degradation of the p300 protein . We provide evidence that degradation is mediated by E6 occluding the AKT-phosphorylation site on p300 , which , when phosphorylated , maintains the stability of p300 within the cell . Finally , we demonstrate that lower p300 levels in HPV 5 and 8E6 expressing cells , in turn , affects normal p300-dependent signaling pathways .
In order to identify the cellular interacting partners of various β-HPV E6 proteins , N-terminally GST-tagged E6 proteins from β-HPV types 5 , 8 , and 38 and α-HPV type 16 ( as a control ) were purified and incubated with whole cell lysates from primary human foreskin keratinocytes ( HFKs ) . Interacting proteins were extracted using glutathione beads , and identified by mass spectrometry following SDS-PAGE separation . This resulted in the identification of hundreds of potential E6 interacting proteins , including those that interacted with all of the purified E6 proteins tested , and those that interacted with only select E6 proteins ( see Table S1 ) . Importantly , we were able to identify E6AP , hScrib , and hDlg peptides with purified 16E6 , thus validating the efficacy of our pulldown and identification protocol . Of particular interest , 9–12 unique peptides corresponding to the histone acetyltransferase p300 were identified in the lysates incubated with either HPV5 or 8E6 . Four peptides from the related protein , CBP , were also identified from lysates incubated with 8E6 . Surprisingly , no p300 or CBP peptides were identified from lysates incubated with HPV 16 or 38E6 , even though an association with p300 has previously been shown with E6 from these HPV types [34] , [36] , [37] , [38] . To verify the interaction with p300 , lysates from parallel GST-pulldown assays were subjected to immunoblot analysis , in which membranes were probed using an antibody for p300 . Interestingly , p300 co-immunoprecipitated with each of the β-HPV types tested , as well as with α-HPV type 16E6 ( Figure 1A ) . However , the interaction between p300 and E6 from β-HPV types 5 and 8 was much stronger than that between β-HPV 38E6 or α-HPV 16E6 , as evidenced by the relative signal intensities on the immunoblot . Importantly , differences in the magnitude of the interaction of E6 and p300 among different types has not been previously reported and suggests that some E6 proteins may interact with p300 in a unique manner to affect p300 functions . To verify the interaction between p300 and E6 in vivo , C-terminally HA-tagged E6 proteins were expressed in HFKs , and E6 expression verified by immunoblot ( Figure 1B “In” lanes ) . We were unable to express 16E6-HA at sufficiently high levels needed for comparative binding assessment , therefore only 5- , 8- and 38E6-HA are shown in Figure 1B and C . E6-HA expressing cell lysates were then incubated with an anti-HA antibody to immunoprecipitate complexes that bound to E6 . Subsequent immunoblotting demonstrated that 5 , 8 and 38 E6 all interacted with p300 in vivo , and verified that the interaction of 5 and 8E6 with p300 was many times stronger than that seen with 38E6 ( Figure 1B ) . Co-immunoprecipitations were repeated in the reverse direction , by pulling down with an anti-p300 antibody , followed by immunoblot analysis against HA . Once again , p300-E6 interactions were seen in 5 , 8 and 38E6-HA expressing cells , with 5 , and 8 E6 interactions being the highest ( Figure 1C ) . Taken together , these data demonstrate that p300 interacts with the E6 protein from multiple β-HPV types . Moreover , the observed differences in the magnitude of this interaction suggests there may be different consequences to p300-mediated signaling events in cells expressing each of these E6 proteins . Given the observation that 5 and 8E6 interacted with p300 to such a great extent , we wished to determine the consequences of this interaction in E6 expressing cells . Importantly , the interaction of 16E6 with p300 has previously been shown to alter a number of signaling pathways , including p53 activation [34] , [36] , [38] . Moreover , while 8E6 has previously been shown to interact with p300 , the consequences of this interaction with respect to host-cell signaling has not been examined [33] . We first wished to determine if E6 expression had an effect on p300 protein levels within the cell , as the stability of many proteins known to interact with high-risk E6 is altered . Cell lysates from vector control LXSN , or E6-expressing HFKs were analyzed by immunoblotting for p300 . Surprisingly , levels of p300 protein were decreased in cells expressing 5 and 8E6 as compared to cells expressing either 38E6 , 16E6 or vector alone ( Figure 2A ) . Importantly , expression of a p300-binding deficient 8E6 mutant harboring a 5aa deletion between residues 132–136 ( designated here as Δ8E6 , [33] , see Table S2 for E6 alignment at these residues ) did not lead to decreased levels of p300 , indicating that association between the two proteins is necessary for this response . Real-time RT-PCR demonstrated that levels of p300 mRNA remain unchanged in each of these cells ( Figure 2B ) , suggesting that the lower levels of p300 seen in 5 and 8E6 expressing cells may be due to degradation of the p300 protein . To ensure that 5E6 or 8E6 expression was required for p300 degradation , siRNAs for each respective E6 protein were transfected into LXSN , 5E6 or 8E6 expressing cells , and lysates harvested for RNA and protein . E6 mRNA knockdown was verified by real-time RT-PCR for the respective E6 ( Figure 2C ) . Examination of p300 protein levels in these cells revealed that p300 protein levels increased upon E6 knockdown for both siRNAs targeting 5E6 and one siRNA targeting 8E6 ( Figure 2D ) . The second 8E6-specific siRNA did not lead to increased p300 expression , but was also the siRNA with the least efficient knockdown of E6 , indicating a possible dose-response effect of E6 expression toward p300 degradation ( Figure 2C ) . High-risk E6 proteins such as 16E6 are well known for their ability to promote protein degradation in a proteasomal-dependent manner . To test if the decreased levels of p300 protein observed in 5 and 8E6 expressing cells was due to proteasomal degradation , proteasomal inhibitors MG132 and Lactacystin were used . LXSN control and E6 expressing cells were incubated with either MG132 or Lactacystin for 2 hrs prior to cell lysis . Cell lysates were then harvested and analyzed by immunoblot to determine the levels of p300 protein ( Figure 3A and 3B ) . As a control , p53 levels were also analyzed , as p53 is known to be degraded by 16E6 in a proteasomal-mediated fashion [19] . In the absence of inhibitor , p53 was degraded in 16E6 expressing cells; however , in the presence of either MG132 or Lactacystin , p53 levels rebounded . Similarly , in cells expressing 5 or 8E6 , p300 levels were much lower than in control cells in the absence if inhibitor; however upon pre-incubation with either MG132 or lactacystin p300 levels increased . Thus , the lower levels of p300 protein seen in 5 and 8E6 expressing cells is mediated by the proteasome . The proteasome inhibitors also caused a slight increase in p300 levels in the LXSN , 38E6 and 16E6 expressing cells , suggesting a general mechanism for regulating p300 stability . It should be noted that although we have demonstrated that the proteasome is involved in the modulation of p300 levels , we can not rule out the possibility that this is occurring through the proteasomal degradation of another factor involved in p300 translation , rather than via direct degradation of p300 itself . 16E6 is known to mediate the proteasomal degradation of many proteins through an interaction with the E3 ubiquitin ligase E6AP [11] . Importantly , E6AP has been shown to interact weakly with many of the β-HPV E6 proteins [30] . We therefore wished to determine if E6AP was involved in HPV 5 and 8E6 mediated degradation of p300 . LXSN control and E6 expressing cells were transfected with pools of 4 individual siRNAs directed against E6AP or non-targeting controls . Cells were harvested 72 hr post-transfection and assayed by immunoblot for E6AP , p53 , and p300 ( Figure 3C ) . Importantly , in all cells transfected with the pooled siRNAs targeting E6AP , E6AP protein levels were drastically reduced . This was confirmed at the mRNA level using real-time RT-PCR ( data not shown ) . Very low basal levels of E6AP were present in 16E6 cells , consistent with previous observations that E6AP is auto-ubiquitinated and degraded in 16E6-expressing cells [41] . As above , p53 was used as a control , as it is known to be degraded in an E6AP-dependent manner . In 16E6 cells transfected with the pooled control siRNA , p53 levels were decreased as compared to LXSN control cells . Conversely , when these cells were transfected with a pool of siRNAs targeting E6AP , p53 levels increased . Surprisingly , the E6AP targeting siRNA pool had no effect on the levels of p300 in either 5 or 8E6 expressing cells . p300 levels were lower than control cells in the presence of the control siRNA pool , and remained unchanged upon transfection with the E6AP targeting pool , indicating that p300 degradation was E6AP independent in these cells . To verify these results and minimize any potential off target effects from a pool of 4 siRNAs we repeated these experiments using 2 of the 4 siRNAs from the pool individually . Similar to the results seen with the pooled siRNAs , each individual siRNA was able to knockdown the majority of E6AP in the cell ( Figure 3D ) . Likewise , the effects on p53 and p300 were similar to that seen above . In 16E6 expressing cells p53 protein levels rebounded in cells transfected with either targeting siRNA , while the siRNAs had no effect respect to p300 levels in either 5 or 8E6 expressing cells . Taken together these data demonstrate that while p300 degradation is indeed dependent on the proteasome , it is independent of the E3 ubiquitin ligase E6AP . As p300 degradation was shown to be proteasome-dependent , but E6AP-independent , we wished to determine the mechanism by which E6 may affect degradation . Importantly , p300 stability has previously been shown to be modulated by AKT activation [42] . A consensus AKT phosphorylation site is located within the p300 C-terminus , between amino acids 1829–1834 , near the CH3 and Q regions of the protein [43] , [44] ( Figure 4A ) . Phosphorylation of the S1834 within this site has been shown to be required for stabilization of the p300 protein; if this site is mutated or AKT signaling inhibited , then p300 protein is targeted for degradation in a proteasomal-dependent manner . In support of a role for AKT in β-HPV 5 and 8E6 mediated p300 degradation , we found that p300 levels were not lowered in 5- and 8E6 expressing HT1080 cells , which harbor constitutively active AKT [45] ( Figure 4B ) . Importantly , p300 protein levels in these samples were analyzed with an antibody specific only to p300 , and not cross-reactive to its related protein , CBP . In contrast , an antibody specific to CBP showed equal CBP protein levels in all samples from both HFK and HT1080 . Additionally , there were no differences in AKT or pAKT levels between LXSN control or any of the E6 expressing cells within their respective cell lines ( Figure 4C ) , indicating that E6 expression itself was not perturbing cell-wide AKT activation . Thus , while AKT activation appears to be involved in 5 and 8E6 mediated degradation of p300 , AKT activation itself is not altered by E6 expression . To further demonstrate the importance of AKT in E6 mediated p300 degradation , we employed the use of an activator of AKT signaling , Ro 31-8220 [46] , and an inhibitor of AKT signaling , Ly294002 [47] . We hypothesized that activating AKT with Ro 31-8220 would increase p300 levels in 5 and 8E6 expressing cells , as the protein would no longer be targeted for proteasomal degradation . Conversely , inhibiting AKT with Ly294002 would destabilize p300 across all of the cell lines , and total p300 protein levels would be lower than their corresponding non-drug treated controls . Indeed , when cells were pre-treated with Ro 31-8220 to activate AKT , p300 levels were increased in all cells , but most dramatically in 5 and 8E6 expressing cells , which initially harbored the least amount of p300 to begin with ( Figure 4D ) . Conversely , when cells were pre-treated with Ly294002 to inhibit AKT , p300 levels were depleted in both LXSN control , and E6 expressing cells ( Figure 4E ) . The observation that p300 protein levels also change in non-E6 expressing cells is expected as this is a normal mechanism of p300 regulation in the absence of E6 . Also of note , the levels of pAKT in the lysates from 5 and 8E6 expressing cells used in Figure 4D and E suggest that 5 and 8E6 may be activating AKT , differing from the data shown in Figure 4C where no such difference was seen . Over most experiments we consistently saw no differences in AKT activation , as stated above . Finally , utilizing a pCMV-HA-p300 expression vector , we generated two mutants of p300 at the AKT phosphorylation site , S1834 . These mutants included S1834A , which should not be able to be phosphorylated by AKT and thus shouldn't be affected by E6 expression; and S1834E , in which the serine is replaced by glutamic acid , and thus represents a phospho-mimic which should also not be affected by E6 expression , and additionally should stabilize the protein leading to higher levels when assessed by immunoblotting . Both of these mutants have been previously described and tested with respect to AKT mediated effects on p300[43] . LXSN , 8E6 and Δ8E6 expressing HFKs were transiently transfected with either WT-HAp300 , HAp300-S1834A , or HAp300-S1834E . 48 hours post-transfection , samples were harvested and assessed by western blot with an antibody to HA , endogenous p300 , and Actin as a loading control ( Figure 4F ) . WT-HAp300 mimicked the endogenous p300 in that both proteins were present in lower levels in 8E6 , but not Δ8E6 expressing cells , when compared to LXSN . HAp300-S1834A and HAp300-S1834E both showed minimal decrease in their respective protein levels in 8E6 expressing cells , as compared to LXSN or Δ8E6 expressing cells , indicating that mutation at this site significantly abrogates 8E6-mediated degradation of p300 . Moreover , total levels of HAp300-S1834E were much higher in all three cell lines than that of WT or the S1834A mutant p300 , indicating that this protein has been stabilized due to the glutamic acid acting as a phospho-mimic at this site . Finally , all cell lines were re-probed with a p300 antibody to gauge the levels of endogenous p300 protein within each sample . In all cases , regardless of which p300 expression vector was used for transient transfection , endogenous p300 levels were lower in the 8E6 cell lysates as compared to LXSN or Δ8E6 expressing lysates . Taken together , these data further confirm that 5 and 8E6 mediated p300 degradation involves AKT signaling . The AKT phosphorylation site in the p300 C-terminus lies within a well-defined region of p300 that has been shown to be the binding site of numerous other p300-interacting proteins , including E6 . 8E6 interaction with p300 at this region has been finely mapped , and has been shown to bind to the region encompassing aa 1770–1814 [33] , which is directly adjacent to the AKT phosphorylation site at aa 1829–1834 . We hypothesized that due to the proximity of the AKT and E6 binding sites on p300 , strong binding of 8E6 would in turn prevent AKT from binding , phosphorylating and in turn stabilizing p300 , thus leading to p300 degradation . To test this hypothesis a competitive binding assay was performed using purified GST-tagged 8E6 , FLAG-tagged p300 and His-tagged AKT . Beginning with equimolar amounts of p300 and AKT , increasing molar amounts of 8E6 were introduced . The complexes were then pulled down using anti-FLAG agarose beads , and eluted samples subjected to SDS-PAGE and immunoblot analysis for each of the proteins ( Figure 5A and B ) . As can be seen , as greater amounts of E6 was added , more E6 was pulled down with p300 , and correspondingly less AKT was recovered . The experiment was then repeated by starting with equimolar amounts of p300 and 8E6 , followed by the addition of increasing molar amounts of AKT . Once again , complexes were pulled out using anti-FLAG agarose beads and analyzed as above ( Figure 5C and D ) . Similarly , as greater amounts of AKT were added , more AKT was found to be associated with p300 , and correspondingly less E6 was recovered . Taken together , these results demonstrate that E6 and AKT compete for binding to p300 , and imply that this competition could preclude AKT from phosphorylating the activation site within the p300 C-terminus , thus leading to de-stabilization of p300 protein levels . Reduced levels of p300 protein in 5 and 8E6-expressing cells has the potential to affect a multitude of signaling pathways . One such pathway that has recently been shown to be specifically abrogated by p300 knockdown is keratinocyte differentiation [48] . Introduction of shRNAs targeting p300 has been shown to attenuate both an early differentiation marker , K1 and a late differentiation marker , filaggrin , in both calcium-induced and raft-culture model systems of keratinocyte differentiation [48] . Additionally , the expression of another differentiation marker , involucrin , has previously been demonstrated to be regulated by p300 at the transcriptional level [49] , [50] . We examined the protein levels of three differentiation markers; K10 , K1 and involucrin in both untreated , and calcium-differentiated cells harboring either 8E6 , Δ8E6 , 38E6 or LXSN vector ( Figure 6A ) . As expected , as calcium-induced differentiation was allowed to proceed ( see Figure S1 for AKT/pAKT levels during differentiation , and Figure S2 for cell morphology during differentiation ) , the levels of K1 , K10 and involucrin all increased substantially in LXSN control cells . Importantly however , while a slight increase of each protein was observed in 8E6-expressing cells over the timecourse of differentiation , the levels of K1 , K10 and involucrin in 8E6 cells relative to that in control LXSN cells was dramatically lower; not only in untreated cultures , but also during each timepoint during differentiation . Strikingly , this abrogation of differentiation was not seen in 38E6 expressing cells , indicating that this effect is not simply a general response to HPV-E6 expression . Moreover , the attenuated expression of differentiation markers was also not seen in Δ8E6-expressing cells , indicating that p300 binding and/or degradation are necessary to achieve this effect . To determine if attenuation of differentiation marker expression was at the level of transcription or translation , we examined the same cultures for mRNA expression using real-time RT-PCR ( Figure 6B ) . As seen with the levels of protein expression , the levels of K1 , K10 and involucrin mRNA all increased in LXSN control cells following the induction of calcium-mediated differentiation . Likewise , while a slight increase in the expression of all three genes was discernable in 8E6 expressing cells , the relative levels of each gene was drastically reduced by 8E6 expression when compared to the corresponding LXSN control sample . Finally , as seen with the protein levels , both this attenuation of expression was almost completely absent in both 38E6 and Δ8E6-expressing HFKs . Thus , 8E6 expression attenuates the mRNA levels of three different markers of differentiation; K1 , K10 and involucrin . As p300 has been shown to associate with the involucrin promoter and enhance its transcription [49] , [50] , we hypothesized that p300 occupancy at the involucrin promoter would be attenuated in 8E6 expressing cells , as these cells harbor significantly less p300 protein . We performed ChIP analysis using antibodies to p300 and primers to amplify the region of the involucrin promoter known to be bound by p300 ( Figure 6C ) . Using occupancy at the RPL30 promoter ( control provided in the ChIP kit ) , and binding of IgG as controls , we verified that p300 is approximately 6-fold enriched at the involucrin promoter in LXSN cells , and over 10-fold enriched at the involucrin promoter in 38E6 cells . Conversely , p300 was almost completely absent from the involucrin promoter in 8E6 cells . While p300 has been shown to play a role in the transcriptional regulation of involucrin [49] , [50] , a direct role for the involvement of p300 in the transcriptional regulation of K1 and K10 has not been described . Therefore , to more thoroughly demonstrate that p300 degradation by itself is directly responsible for the altered expression of each of the differentiation marker examined in Figure 6 , we employed the use of siRNA pools and two individual siRNAs to knockdown p300 in non-E6 expressing cells , and subsequently examine both the mRNA and protein from the resulting cell lysates . In undifferentiated cell lysates , knockdown of p300 by either an siRNA pool or two individual siRNAs resulted in lower relative levels of K1 , K10 and involucrin mRNA ( Figure 7A ) . When extended to a calcium-differentiation model , this trend was maintained following 24hr treatment with calcium-media; while the mRNA ( Figure 7B ) and protein ( Figure 7C ) levels for each gene increased dramatically during calcium treatment in control cells , levels of K1 , K10 or involucrin mRNA and protein either stayed the same or increased only slightly in cells transfected with p300 siRNA . The inability to see dramatically lower levels of K1 , K10 or involucrin mRNA or protein in undifferentiated cells upon knockdown of p300 ( Figure 7B and C ) could be due to an inability to detect a difference in samples treated with siRNA for only 48 hrs ( as opposed to the 72 hrs used for Figure 7A ) , or may be due to a dose-effect from the level of p300 knockdown itself in this particular experiment . Under more standard siRNA conditions however ( as used for Figure 7A ) , decreased mRNA expression of each gene is consistently reproducible . Additionally , the effect of p300 siRNA on IVL protein expression in Figure 7C is not as dramatic as that seen with 8E6 expression in Figure 6A . As above , this may be due to inadequate knockdown of p300 at this particular time , or it may indicate that additional functions of 8E6 other than p300 degradation may affect IVL levels . Taken together , these data demonstrate that 8E6-expressing cells are attenuated in their ability to undergo calcium-mediated differentiation , and this attenuation is directly mediated by the decreased levels of p300 .
In order to identify host-cell proteins that interact with β-HPV E6 , we performed GST-pulldowns using GST-tagged E6 proteins and whole cell HFK lysates , followed by mass spectrometry analysis of the interacting complexes . We identified p300 as a protein that interacted with all of the E6 proteins tested; albeit with different relative strengths and different consequences to downstream signaling . While p300 binding to 16E6 , 8E6 and most recently 38E6 , has previously been demonstrated [33] , [34] , [36] , [37] , the relative strengths of the two interactions had not been examined , and the consequences of 8E6-p300 binding had not been studied with respect to effects on host cell signaling . Here we demonstrate not only that other β-HPV E6 proteins are also able to interact with p300 , but also show that β-HPV 5 and 8E6 bind to p300 at relatively high levels , while β-HPV 38E6 and α-HPV 16E6 bind to the protein at much lower levels . The importance of p300 for a myriad of signaling and regulatory pathways has been well documented . The protein plays a role in bridging other transcription factors to the basal transcriptional machinery; acetylating histones to facilitate chromatin remodeling; regulating DNA repair , cell growth , differentiation and cell death; is involved in embryogenesis; and functions as a tumor suppressor [Reviewed in 39 , 40] . Not surprisingly , p300 has been shown to be mutated in a number of cancers , and targeted by many viruses . p300 has been shown to be inactivated by C-terminal truncation in a small percentage of cancers of epithelial origin , including colorectal , gastric , breast , pancreatic , cervical and ovarian , as well as in the human diffuse B-cell lymphoma cell line RC-K8 [51] , [52] , [53] , [54] , [55] , [56] . Mutations in p300 are even more common in colorectal cell lines , where 4/17 were found to harbor homozygous or heterozygous mutations [54] . Moreover , ectopically expressing p300 in cancer lines harboring biallelic mutations of p300 slows cell growth [57] , and p300 knockout mice have been shown to develop histiocytic sarcomas [58] . Viral inactivation of p300 has been shown to be mediated by oncoproteins from five different viruses; adenovirus E1A , SV40 large T antigen , E6 and E7 from HPV , Tax protein from HTLV-1 and A238L protein from African Swine Fever Virus [59] , [60] , [61] , [62] , [63] , [64] , [65] . Importantly all of these oncoprotein/p300 interactions interfere with the acetyltransferase and/or transactivation ability of p300 , which in turn mediates tumorigenicity . Interestingly , recent data has shown that the promoters for p300 regulated genes vary greatly with respect to their affinities for p300 [66] , thus lowering levels of p300 ( even slightly ) may have drastic consequences for the expression of genes with a relatively low affinity site for p300 , and possibly no effect on genes/promoters with high affinity sites . Thus , even slight alterations of this pathway by viruses or mutation can have profound effects on cellular signaling . Importantly , we demonstrate that the strong binding observed between p300 and either β-HPV 5 or 8E6 does indeed have a functional consequence for the host-cell , as the interaction inhibits the association of AKT with its normal binding site at the p300 C-terminus , thus targeting the p300 protein for proteasomal degradation . We provide additional evidence that altering the levels of E6 expression via siRNA knockdown reverses this affect in somewhat of a dose-response dependent manner , suggesting that differences in 5 or 8E6 expression may alter the degree of p300 degradation observed . This is important because it is still controversial as to what the physiological levels of β-HPV E6 proteins may be in HPV infected individuals . Regardless , the general effects of p300 knockdown in both primary and immortal cell lines has been well studied . In primary HFKs , p300 knockdown with shRNA has been shown to delay differentiation , allow differentiated HFKs to re-enter the cell-cycle , increase cell proliferative capacity , extend the lifespan of cells in culture , and regulate the acetylation and expression of p53 [48] . Additionally , cells lacking p300 have been shown to exhibit changes characteristic of epithelial to mesenchyme transition , including gene expression changes , loss of cell-cell adhesion , defects in cell-matrix adhesion and increased migration through collagen and matrigel [67] . With respect to cell death , p300 has been shown to regulate the sensitivity of cells to irradiation , and has a pro-apoptotic function in the DNA damage response . Thus mutations in tumor cells that attenuate p300 function confer resistance to ionizing radiation and other genotoxic agents [68] . Finally , in the context of 16E6 , p300 knockdown by shRNA was shown to induce transcription of hTERT mRNA and induce telomerase activity [69] . Thus the discovery of p300 degradation in HPV 5 and 8 expressing cells has the potential to have far-reaching consequences . Indeed , our finding that the decreased p300 levels in 8E6 expressing cells leads to the attenuated expression of multiple structural markers of differentiation has profound implications for the HPV replicative cycle , as the ability of HPV to replicate depends on continued cell proliferation and inhibition of terminal differentiation . Recent studies have focused on K10 as a possible tumor suppressor , as overexpression of this particular cytokeratin has been shown to both inhibit cell proliferation and suppress tumor development [70] , [71] , [72] . Thus , by inhibiting the expression of K10 via p300 degradation , 8E6 functions to remove this blockade . Moreover , in a transgenic mouse model , K1 and K10 expression were shown to be lost during skin tumor progression [70] . Interestingly , loss of K1 expression and altered K10 localization have been previously demonstrated both in cultured cells expressing the HPV 8 early region [73] , and from papillomous lesions taken from EV patients [74] , however the mechanisms by which these events occurred were not known . Importantly , our data has now identified a link between E6 expression , p300 degradation and decreased expression of important differentiation markers within keratinocytes . Another important function of p300 that is of particular relevance to HPV infection is that of regulating the activity of p53 , and as a co-factor in p53-dependent transactivation of a number of genes . Following DNA damage , p300 acetylates p53 on Lys 382 , which stabilizes the p53/DNA complex at target promoters [75] , [76] , [77] . Additionally , p300 can be recruited by p53 to certain promoters , where it can act as a bridge for other transcription factors , or act to acetylate histones [78] , [79] . Importantly , E6 from both high-risk and low-risk α-HPVs , as well as BPV-1 and most recently β-HPV 38E6 have been shown to inhibit the ability of p300 to transactivate p53 [34] , [35] , [36] , [37] , [38] . This function of E6 is independent of E6AP and does not require p53 degradation . Rather it is thought that the interaction between E6 and p300 may inhibit the association of p53 with p300 [34] . Thus , while only high-risk α-HPV E6 proteins are able to disrupt p53 signaling by degrading the p53 protein itself , E6 from low-risk α-HPVs are capable of interfering with this pathway simply through association with p300 . It is therefore tempting to speculate that the degradation of p300 by β-HPV 5 and 8E6 represents yet another way in which this pathway may be disrupted by the E6 oncoprotein . Preliminary evidence from our lab suggests that HPV 5 , 8 and 38E6 are all capable of attenuating p300-mediated acetylation of p53 , and further studies are underway to examine other effects these oncoproteins may have on p53 signaling . In summary , we have identified p300 as a protein that appears to broadly interact with the E6 oncoprotein from both alpha and beta genera of HPV . Importantly , due to the observed differences in the strength of this association among HPV types , different consequences to p300 signaling occur . The relatively weak association with 16E6 has previously been shown to allow for inhibition of p53 signaling [34] , [36] , [38] , however does not perturb the steady state levels of p300 protein within the cell . Conversely , 5 and 8E6 bind to p300 at relatively high levels , causing the stability of the p300 protein becomes perturbed , leading to an overall decrease of the p300 levels within the cell . As a result , certain proteins that depend on p300 for their regulation , like K1 , K10 and IVL become de-regulated , altering their expression level , and in turn leading to additional consequences with regard to downstream signaling events . Importantly , given the broad role that p300 plays in orchestrating the enhancement or repression of transcriptional processes within the cell , the consequences of p300 degradation are likely to have far-reaching effects with regard to HPV infection .
Antibodies to GST ( B-14 ) , Nucleolin ( C-23 ) , E6AP ( H-182 ) , His ( H-15 ) , EGR-1 ( 588 ) , p300 ( N-15 ) Actin ( I-19 ) , Cytokeratin 10/13 ( DE-K13 ) and Involucrin ( SY8 ) were purchased from Santa Cruz Biotechnology; antibodies to AKT , phospho AKT ( Ser 473 ) and CBP were purchased from Cell Signaling Technology; antibodies to HA ( HA . 11 ) and Keratin 1 were purchased from Covance; antibodies to p300 ( NM11 ) were purchased from BD Pharmingen; antibodies to p53 ( DO-1 ) were purchased from Calbiochem; non-cross reactive p300 antibodies ( RW128 ) were purchased from Millpore . siRNA pools and individual siRNAs specific for E6AP , p300 and controls were purchased from Dharmacon RNA Technologies . siRNA's designed to target 5E6 and 8E6 were designed and purchased from Invitrogen ( see Table S3 for sequences ) . Proteasome inhibitors Lactacystin and MG132 were purchased from Calbiochem and used at a final concentration of 20 µM . AKT inhibitor LY294002 was purchased from Cell Signaling Technology and used at a final concentration of 10 µM . AKT activator Ro 31-8220 was purchased from Sigma and used at a final concentration of 10 µM . Purified recombinant FLAG-p300 and recombinant His-AKT1 were purchased from Active Motif . EZ-view Red anti-FLAG M2 affinity gel was purchased from Sigma . All HA-tagged and untagged E6 constructs have been described previously [29] , [30] , with the exception of Δ8E6 , which was constructed by performing site-directed mutagenesis to obtain a deletion of aa 132–136 [33] ( see Table S3 for mutagenesis primers ) . Expression of E6 was verified at the mRNA level following selection ( see below ) by real-time RT-PCR analysis . All primers have been previously described [29] . New primers were designed to assess levels of Δ8E6 ( see Table S3 ) , as the original 8E6 primers flanked the deletion site . HA-p300-pCMV was purchased from Upstate Biotech , and used to construct S1834A/E mutants via site directed mutagenesis ( see Table S3 for mutagenesis primers ) . Primary human foreskin keratinocytes ( HFKs ) were derived from neonatal human foreskins and grown in EpiLife medium supplemented with calcium chloride ( 60 µM ) , human keratinocyte growth supplement ( Cascade Biologics , Portland , OR ) and penicillin-streptomycin . 293T cells were grown in Dulbecco's modified Eagle's medium ( Gibco-BRL ) containing 10% fetal bovine serum ( FBS ) and penicillin-streptomycin . For calcium-induced differentiation , confluent monolayers of HFKs were treated by withdrawal of growth factors and addition of media containing 1 . 5 mM CaCl2 . Stable E6-expressing cell lines were produced using transient vesicular stomatitis virus G ( VSV-G ) pseudo typed virus as previously described [29] . Briefly , E6 proteins cloned into an LXSN vector were co-transfected with VSV-G helper plasmids into 293T cells using Fugene 6 ( Roche ) , and retrovirus collected at 12 , 24 , 36 and 48 hrs post transfection . Transiently produced virus was concentrated by ultracentrifugation and used to infect HFK monolayers ( 50 to 60% confluent ) in the presence of Polybrene ( 8 µg/mL ) . Four hours after infection , cells were washed with PBS and the media replaced . The cells were expanded when confluent and were placed under neomycin-G418 selection ( 50 mg/liter ) for 7 days . HA-p300WT , HA-p300S1834A and HA-p300S1834E were all transiently transfected into LXSN , 8E6 and Δ8E6 cells using TransIT Keratinocyte transfection reagent ( Mirus ) , and assessed 72 hr later for levels of both endogenous p300 and the transfected HA-p300 construct . Generation of N-terminally GST tagged E6 vectors was described previously [29] . GST-E6 constructs were transformed into BL21-AI Escherichia coli , and grown overnight at 37°C on LB plates containing 50 µg/ml ampicillin . Isolated colonies were used to inoculate 20 ml LB broth containing 200 µg/ml carbenicillin and grown overnight with shaking at 37°C . 10 ml of the overnight culture was added to 1 L of fresh LB-carbenicillin and incubated at 37°C with shaking for 2 . 5 hrs . Cultures were then transferred to room temperature and incubated for 30 min with shaking , after which the optical density at 600 nm of all cultures was between 0 . 4 and 0 . 6 . L-Arabinose was added to each culture at a final concentration of 0 . 2% to induce protein expression , followed by growth for 4 hr at room temperature with shaking . Bacterial cells were then harvested by centrifugation at 6 , 000 rpm for 15 min at 4°C , and the resulting pellets were stored at −20°C . Bacterial pellets were resuspended in PBS-P ( phosphate-buffered saline , 50 mM EDTA and protease inhibitor tablets ) and lysed via two passages through a microfluidizer , followed by a 30 min incubation with 0 . 1%TritonX-100 at 4°C with end-over-end rotation . Bacterial lysates were centrifuged at 14 , 000 rpm in a JA17 rotor for 15 min at 4°C , and the pellets discarded . The resulting supernatants were added to pre-equilibrated glutathione-sepharose bead slurries , and incubated at 4°C for 1 hr with end-over-end rotation . The bead slurries were then washed four times with PBS-P , followed by elution of the bound GST proteins with 20 mM GSH/50 mM Tris-CL for 1 hr at 4°C with end-over-end rotation . Eluted proteins were collected by centrifugation of the bead slurry , and aspiration of the protein-containing supernatant . A total of two elutions were carried out in this manner . The two elutions for each GST-E6 protein were then combined , and dialyzed using Zeba Desalt Spin Columns ( Pierce , Rockford , IL ) and protein buffer ( 5 mM Tris , 100 mM KCl , 0 . 5 mM EDTA , 1 mM DTT , 5% glycerol , 0 . 1%NP40 , and protease inhibitor tablets ) . For GST pulldown assays , equal amounts of GST-tagged proteins were incubated with pre-cleared whole cell HFK lysates in dialysis buffer ( 5 mM Tris-HCL pH 7 . 4 , 100 mM KCL , 0 . 5 mM EDTA , 1 mM DTT , 5% glycerol , 0 . 1% NP-40 , and protease inhibitor tablets ) and gently agitated for one hour at 4°C . Glutathione sepharose 4B beads were added to each pulldown , incubated at 4°C for two hours , washed in binding buffer , and recovered by boiling in 2X sample buffer . The samples were separated on SDS-polyacrylamide gels , and analyzed by immunoblot or for mass spectrometry at our proteomics facility ( FHCRC ) . HA-tagged E6 expressing HFKs were harvested in NP-40 lysis buffer ( 1x PBS , 0 . 5% NP-40 , 10% glycerol , 10 µM zinc chloride , 2 mM dithiothreitol , 80 mM β-glycerophosphate , 50 mM sodium fluoride , 1 mM sodium orthovanadate , and a COMPLETE protease inhibitor tablet [Roche , Alameda , CA] ) . Cells were lysed by sonication for 1 min at 50% duty . Cell debris was pelleted at 14 , 000 rpm for 15 min and lysates were precleared by rotating at 4°C with 50 µL of protein G agarose ( Roche , Alameda , CA ) . After centrifugation to remove the beads , lysates were incubated with the appropriate antibody for 1–2 h at 4°C and purified by adding protein G agarose and rotating for another hour at 4°C . Immunocomplexes were washed three times with lysis buffer and eluted by heating for 10 min at 70°C in 2x sample buffer . Elutions were electrophoresed on NuPAGE 4%–12% Tris-Bis gradient gels ( Invitrogen , Carlsbad , CA ) to resolve HA-tagged E6 proteins and immunoblotted for p300 or HA as described . RNA was isolated with Trizol reagent ( Invitrogen , Carlsbad , CA ) as previously described [29] . 1 µg of total RNA was reverse transcribed to generate cDNA , using the iScript cDNA synthesis kit ( BioRad , Hercules , CA ) . As a negative control , parallel samples were run without reverse transcriptase . Non-quantitative PCR amplification was then performed to identify 100 bp amplicons with E6 and 36B4 primers as previously described [29] . For real-time RT-PCR , RNA was isolated and reverse transcribed as above , and quantitative real-time PCR was performed using an ABI 9700 sequence detection system ( Applied Biosystems , Foster City , CA ) . Amplification was carried out using TaqMan master mix and the following pre-designed Taqman primer/probes: GAPDH ( 4333764F ) , p300 ( Hs00914223_m1 ) , Krt1 ( Hs00196158_m1 ) , Krt10 ( Hs00166289 ) and IVL ( Hs00846307_s1 ) according to the manufacturer's instructions ( Applied Biosystems , Foster City , CA ) . Reactions were performed in triplicate in a 25 µl volume , with the following cycle parameters: enzyme activation ( 10 min at 95°C ) , followed by 40 cycles ( each cycle consisting of 15 seconds at 95°C and 1 in at 60°C ) . Data analysis was performed using the comparative threshold cycle method ( Applied Biosystems , Foster City , CA ) to determine relative expression levels . Whole-cell lysates were prepared by mechanically detaching cells in cold PBS and resuspending in WE16th lysis buffer ( 50 mM Tris-HCL at pH 7 . 5 , 250 mM NaCl , 5 mM EDTA , 1% NP-40 , 0 . 1% sodium dodecyl sulfate , 20% glycerol , 80 mM β-glycerophosphate , 50 mM sodium fluoride , 1 mM sodium orthovanadate , and a COMPLETE protease inhibitor tablet [Roche , Alameda , CA] ) . Lysates were then sonicated and clarified by centrifugation . The DC protein assay ( Biorad , Hercules , CA ) was used to determine protein concentrations . For immunoblotting of differentiation markers K1 , K10 and involucrin , cells were lysed directly in 2X sample buffer ( 100 mM Tris pH 6 . 8 , 4% SDS , 20% glycerol , 0 . 8% bromophenol blue ) . Equal amounts of protein lysates ( 15 to 30 µg ) were electrophoresed on SDS-polyacrylamide gels and transferred to Immobilon-P membranes ( Millipore , Billerica , MA ) . For quantification of western blot data , the membranes were scanned and bands were analyzed by densitometry using ImageJ ( NIH ) . In-vitro competition assays were performed using a protocol modified from [80] . Briefly , 250 ng of recombinant FLAG-p300 was pre-incubated with equimolar amounts of either His-AKT1 or GST-E6 in modified HAT buffer ( 50 mM Tris-HCL , pH 8 . 0 , 10% glycerol , 1 mM DTT , 0 . 1 mM EDTA , 100 mM KCL , 0 . 1% NP40 , Complete protease inhibitor tablet ( Roche ) ) for 1 hr at 4°C with rotation . Increasing concentrations ( molar excess 5x–20x ) , of GST-E6 or His-AKT1 ( 5x–10x ) , were added , and further incubated for 1 hr before the addition of 50 µl of anti-FLAG M2 affinity gel . Samples were then rotated at 4°C for 2 hr , washed 4 times with modified HAT buffer , and eluted with 2x SDS-PAGE sample buffer for 5 min at 100°C . Chromatin immunoprecipitations were performed using the Enzymatic Chromatin IP ( Magnetic bead ) kit ( Cell Signaling Technology ) , as per the manufacturer's instructions , with minor modifications . Briefly , chromatin from fixed cells was digested to a size range of 150–1000 bases with micrococcal nuclease , followed by brief sonication to disrupt the nuclear membrane . Solubilized chromatin was immunoprecipitated with antibodies to p300 ( N-15 ) or IgG control . Antibody-chromatin complexes were pulled-down using ChIP-grade protein-G magnetic beads , washed and then eluted . After cross-link reversal and proteinase K treatment , immunoprecipitated DNA was extracted with phenol-chloroform , and ethanol precipitated . Real-time RT-PCR was performed using SYBR green and primers to the distal AP1 site of the involucrin promoter with the following sequences: 5′-GCTCACACATACCATCTTCTCCTTA-3′ ( forward ) and 5′-CACCGGTCTTATGGGTTAGCA-3′ ( reverse ) . Standard curves were calculated using serial dilutions of the input sample , and used to calculate the relative amount of product amplified in each reaction . Results were calculated based on the relative enrichment of protein over that seen with the RPL30 control . All statistics calculations were performed using a two-tailed student's T-test . | Human papillomaviruses ( HPVs ) are a family of more than 100 different viruses that cause a wide range of pathologies , from benign warts to cervical cancer . One subgroup of HPVs , the beta-HPVs , have recently become a topic of interest due to their potential involvement in squamous cell skin cancer . However , unlike the HPVs involved in cervical cancer , little is known with regards to how the beta-HPVs may facilitate cellular changes that would allow cancerous lesions to develop . Here we have identified a host-cell protein , p300 , which interacts strongly with the E6 oncoprotein from two beta-HPVs , HPV 5 and HPV 8 . We show that this interaction subsequently blocks another cellular protein , AKT , from binding to and stabilizing p300 . By blocking this association , p300 is targeted for degradation , and thus is present in lower amounts than in normal cells . Importantly , because p300 is involved in numerous cell processes such as DNA repair , cell growth , and differentiation , the potential for E6 disrupting a number of cellular signaling pathways is vast . Taken together , our findings shed new light on how the beta-HPVs may facilitate carcinogenesis . | [
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... | 2011 | Beta-HPV 5 and 8 E6 Promote p300 Degradation by Blocking AKT/p300 Association |
The tumor suppressor p53 has been implicated in multiple functions that play key roles in health and disease , including ribosome biogenesis , control of aging , and cell cycle regulation . A genetic screen for negative regulators of innate immunity in Caenorhabditis elegans led to the identification of a mutation in NOL-6 , a nucleolar RNA-associated protein ( NRAP ) , which is involved in ribosome biogenesis and conserved across eukaryotic organisms . Mutation or silencing of NOL-6 and other nucleolar proteins results in an enhanced resistance to bacterial infections . A full-genome microarray analysis on animals with altered immune function due to mutation in nol-6 shows increased transcriptional levels of genes regulated by a p53 homologue , CEP-1 . Further studies indicate that the activation of innate immunity by inhibition of nucleolar proteins requires p53/CEP-1 and its transcriptional target SYM-1 . Since nucleoli and p53/CEP-1 are conserved , our results reveal an ancient immune mechanism by which the nucleolus may regulate immune responses against bacterial pathogens .
The relatively simple innate immune system of the nematode Caenorhabditis elegans and the number of traits that facilitate genetic and genomic analysis using this organism have led to the discovery of several pathways that regulate innate immune responses to pathogen infections . Interestingly , many of the C . elegans innate immune pathways integrate responses to pathogens , oxygen , and various stresses [1] , [2] , [3] , [4] . This suggests that multiple stress-sensing mechanisms are activated in response to bacterial infection . In addition to their role as ribosome factories , nucleoli also function in maturation of non-nucleolar RNAs or ribonucleoproteins , senescence and regulation of telomerase function , regulation of cell cycle , tumor suppressor and oncogene activities , and cell stress sensing [5] , [6] , [7] , [8] . The stress-sensing function of the nucleolus , which involves the tumor suppressor p53 , is one of its most important newly identified roles . Although there are several ways in which p53 is regulated in mammals , changing the balance between its synthesis and degradation seems to be one of the most important . Under normal conditions , p53 is synthesized and then quickly degraded to maintain a very low level of the protein . The abundance of p53 is primarily regulated by the interplay of two proteins , MDM2 and ARF . In addition to binding to the transactivation domain of p53 [9] , [10] , MDM2 functions as an E3 ubiquitin ligase which targets p53 for export to the cytoplasm and/or proteasome-mediated degradation [11] , [12] , [13] . This auto-regulatory feedback loop likely acts to restrain p53 function in normal cells , in the absence of stress . ARF associates with MDM2 to inhibit the ubiquitination , nuclear export , and subsequent degradation of p53 [14] , [15] , [16] . The finding that ARF is primarily localized in the nucleolus [15] , [17] , [18] suggests that the nucleolus functions as a subnuclear compartment in which p53-activating proteins are sequestered in the absence of stress . Additionally , MDM2 has been shown to bind ribosomal protein L5 and 5S rRNA before export into the cytoplasm [19] , [20] , providing further evidence that nucleolar proteins are involved in the regulation of p53-regulating proteins . Even though there is no clear MDM2 orthologue in nematodes , the levels of active p53/CEP-1 are also known to be regulated at the translational and posttranscriptional levels in C . elegans . For example , GLD-1 controls the levels of p53/CEP-1 by binding to the 3-UTR of cep-1 mRNA to repress its translation [21] . In addition , the Skp1/cullin/F-box ( SCF ) E3 ubiquitin ligase FSN-1 appears to negatively regulate endogenous CEP-1 protein phosphorylation levels [22] . In response to DNA damage , p53 levels rise as a consequence of activation of several kinases that phosphorylate the N-terminus of p53 preventing binding to MDM2 . In response to cellular stress , such as DNA damage , heat shock , or hypoxia , p53 becomes stabilized and accumulates in the nucleus , leading to elevated transcriptional activity [23] , [24] , [25] , [26] . In addition to the aforementioned stresses , it has been proposed that aberrant ribosome biogenesis may also cause “nucleolar stress” leading to stabilization of p53 in mice and human cells [27] , [28] , [29] . Disruption of the nucleolus , either by direct interference of ribosomal proteins [28] , [29] or chemical inhibitors of ribosome biogenesis [29] , [30] causes the release of p53-stabilizing proteins , and thus results in elevated levels of active p53 . These findings indicate that the nucleolus may act as a stress sensor responsible for maintaining low levels of active p53 which become elevated upon impairment of nucleolar function . Different genetic studies have led to the identification of several pathways involved in C . elegans innate immunity . However , while gene inactivation cripples innate immunity by affecting a variety of different pathways , only gene depletions that result in higher DAF-16 activity appear to promote innate immunity . DAF-16 is a FOXO transcription factor which regulates a wide variety of genes involved not only in immunity but also in stress-response , development , and longevity [3] , [31] , [32] . To identify genes that when mutated may enhance innate immunity without extending the life span of the nematodes , we undertook a comprehensive forward genetic analysis of nematodes exhibiting wild-type life span and enhanced resistance to the human Gram-negative pathogen Salmonella enterica . The study resulted in the isolation of a strain carrying a mutation in nol-6 , which encodes a nucleolar RNA-associated protein . In addition , we demonstrate that RNAi-mediated depletion of nol-6 as well as other nucleolar genes leads to an enhanced resistance to S . enterica-mediated killing that correlates with a reduction of pathogen accumulation . The results also show that animals deficient in nol-6 are more resistant to infections by Gram-negative pathogen Pseudomonas aeruginosa and Gram-positive pathogen Enterococcus faecalis , indicating that nucleolar disruption activates immunity against different bacterial pathogens . Further studies indicate that nucleolar disruption through RNAi ablation of ribosomal genes results in an increased pathogen resistance that requires p53/cep-1 . This study suggests that nucleolar disruption may be a mechanism by which C . elegans activates innate immunity against bacterial infection in a p53/cep-1-dependent manner .
Until now , only gene depletions that result in higher activity of the FOXO transcription factor DAF-16 appear to promote innate immunity . DAF-16 is positively regulated by heat shock [33] and negatively regulated by the insulin-like receptor DAF-2 [34] . Thus , mutations in daf-16 not only suppress the enhanced longevity of daf-2 mutants , but also their enhanced resistance to pathogens [35] , [36] . To identify genes involved in the regulation of innate immunity that do not affect the life span of C . elegans , we took a forward genetic approach . An important limitation of these types of studies is that resistant mutants would not be identified until much beyond the time frame for fertility , thus requiring the transfer of individual mutants in order to maintain each mutant line . This is tedious and time consuming , greatly reducing the number of animals that can be studied , particularly in the case of the various slow-killing pathogens of C . elegans [37] . For example , in the case of infections by S . enterica , hermaphrodite nematodes initially exposed to the pathogen need to be transferred each day to fresh plates to avoid losing track of these initial nematodes in the morass of progeny . Thus , we took advantage of the ability of S . enterica to cause a persistent colonization and luminal distension of the C . elegans intestine that correlate with the premature death of the animals [38] , [39] , [40] . To identify C . elegans mutants which exhibit reduced pathogen accumulation ( Rpa ) , EMS-mutagenized nematodes grown to one day old gravid adults on the laboratory food Escherichia coli strain OP50 were transferred to S . enterica strain SMO22 expressing GFP . A population of approximately 15 , 000 second generation mutants was screened for an Rpa phenotype after 48 hours of feeding on S . enterica/GFP . Out of 287 isolated rpa mutants , 43 mutants that generated progeny were further studied . Of these 43 rpa mutants , 9 mutants exhibited enhanced resistance to S . enterica-mediated killing ( not shown ) . Five mutants exhibited stunted development and were therefore excluded from further analysis . Of the remaining 4 rpa mutants , rpa-9 was chosen for mapping and further analysis based on the strength of its resistance to pathogen infection and lack of extended life span . Compared to wild-type nematodes , rpa-9 mutant nematodes exhibit a reduced accumulation of S . enterica/GFP after 48 hours of feeding ( Figures 1A , 1B , 1E , and S1 ) . In addition , distension of the intestinal lumen 48 hours after initial exposure to S . enterica strain 1344 is completely suppressed in rpa-9 mutants compared to wild type ( Figure 1C and 1D ) . The reduced pathogen accumulation and intestinal distention of rpa-9 mutants correlate with enhanced resistance to S . enterica-mediated killing ( Figure 1F ) . As shown in Figure 2 , rpa-9 exhibits resistance to accumulation and killing by other bacterial pathogens . Specifically , the intestinal lumen of rpa-9 is not distended when infected with the Gram-negative pathogen Pseudomonas aeruginosa strain PA14 ( Figure 2A and 2B ) as well as the Gram-positive pathogen Enterococcus faecalis strain OG1RF ( Figure 2C and 2D ) . In contrast , when fed relatively non-pathogenic E . coli , intestinal distension is absent in both wild-type nematodes and rpa-9 mutants ( Figure 2E and 2F ) . Consistent with previous studies indicating that intestinal distension and bacterial accumulation can correlate with nematode death [39] , [41] , [42] , rpa-9 exhibits enhanced resistance to killing by P . aeruginosa and E . faecalis ( Figure 2G and 2H , respectively ) . These results suggest that , like daf-2 , rpa-9 acts as an inhibitor of innate immunity against different bacterial pathogens . However , unlike daf-2 , the life span of rpa-9 mutants is comparable to that of wild-type animals grown on E . coli lawns ( Figure 2I ) . These results indicate that the resistance to pathogen infection of rpa-9 mutants is not simply a consequence of an effect on life span extension and that immune mechanisms can be uncoupled from effects on aging . In addition to enhanced resistance to pathogen infection , rpa-9 nematodes also exhibit temperature sensitive reduced fertility and larval lethality of progeny when raised at the restrictive temperature of 25°C . The study of sterile mutants that are temperature sensitive demonstrated that sterility may result in enhanced resistance to pathogen through a DAF-16-dependent mechanism [43] . Since the C . elegans infections are performed at 25°C , it is conceivable that the enhanced resistance to pathogens of rpa-9 mutants is a consequence of the reduced fertility at 25°C . However , rpa-9 mutants are also resistant to pathogen-mediated killing when the infections are performed at the permissive temperatures of 20°C and 15°C ( Figure S2 ) , indicating that their enhanced resistance to pathogens is not simply a consequence of reduced fertility . DAF-16 activation by reduction of daf-2 function causes not only resistance to pathogen infection , but also entry into an alternative larval stage and dramatic increase in longevity when the animals are grown on the laboratory food E . coli [44] , [45] , [46] . In contrast to daf-2 mutants , rpa-9 mutants exhibit a life span that is comparable to that of wild-type animals when grown on plates containing E . coli ( Figure 2I ) , suggesting that the enhance resistance to pathogens of rpa-9 mutants is caused by a mechanism that is independent of the DAF-16 effects on longevity . Consistent with this idea , a daf-16 mutation known to suppress the enhanced resistance to pathogen phenotype of daf-2 mutants does not suppress the enhanced resistance to S . enterica of rpa-9 mutants ( Figure 3A ) . To provide insight into the mechanism underlying the enhanced immunity of rpa-9 mutants , we utilized gene expression microarrays to find clusters of genes upregulated or downregulated in rpa-9 mutants relative to wild-type animals grown on S . enterica . Overall , the microarray data show poor overlap with genes previously known to be regulated by pathways involved in C . elegans innate immunity ( Figure 3D and Table S1 ) . Out of 247 upregulated genes in rpa-9 nematodes relative to wild-type nematodes , only seven genes have been linked to innate immune pathways in C . elegans . As shown in Figure 3D , only six upregulated genes are regulated by DAF-16 and one upregulated gene is regulated by the C . elegans p38 MAP kinase , PMK-1 , which like DAF-16 , plays a crucial role in innate immunity [38] , [47] , [48] , [49] , [50] . The lack of major enrichment in DAF-16 and PMK-1-regulated genes is consistent with the lack of suppression of the enhanced resistance to S . enterica of rpa-9 nematodes by loss of DAF-16 or PMK-1 ( Figure 3A and 3B ) . The microarray data show that the rpa-9 mutation results in a significant enrichment in genes regulated by the C . elegans homologue of p53 , CEP-1 , which plays a role in apoptosis , meiosis , and stress resistance [21] , [51] , [52] , [53] , [54] ( Figure 3E , 3F , Table S1 , Table S2 , and Table S3 ) . Quantitative real-time polymerase chain reaction ( qRT-PCR ) confirmed the up-regulation of CEP-1-regulated genes in rpa-9 mutants ( Figure S3 ) , suggesting that higher CEP-1 activity is responsible for the enhanced immunity against S . enterica of rpa-9 animals . Thus , we studied whether a loss-of-function mutation in cep-1 ( gk138 ) nematodes [55] suppresses the enhanced resistance to S . enterica-mediated killing of rpa-9 nematodes . Since cep-1 ( gk138 ) nematodes exhibit an Egl phenotype , the animals that die from matricide were censored . As shown in Figure 3C , cep-1 ( gk138 ) mutation suppresses the enhanced resistance to S . enterica-mediated killing of rpa-9 nematodes , indicating that higher CEP-1 activity is required for activation of immunity against S . enterica . Whole genome sequencing of rpa-9 mutants and analysis of the RFLP-SNP mapped 109-kilobase region revealed a single mutation within this region . The rpa-9/nol-6 ( ac1 ) allele is a G to A substitution in the third exon of the C . elegans gene nol-6 resulting in a glycine to glutamic acid substitution at amino acid position 151 ( Figure S4 ) . Since glycine is the smallest of the amino acids and can be either positively or negatively charged depending upon the environment , it is likely that substitution with a large , highly polar amino acid such as glutamic acid will alter the folding pattern of the protein and potentially hinder its function . To study whether nol-6 acts as a suppressor of innate immunity , we first compared S . enterica intestinal accumulation of wild-type nematodes to that of nematodes in which nol-6 gene expression was depleted by RNAi . As shown in Figure 4A–4C , nol-6 RNAi in wild-type nematodes results in a significant decrease in the percentage of nematodes exhibiting intestinal accumulation of S . enterica/GFP 48 hours after the infection . Additionally , the number of intestinal S . enterica colony forming units in nol-6 RNAi and rpa-9 ( ac1 ) nematodes is lower than that in control animals ( Figure S5 ) . Consistent with the idea that the enhanced resistance to pathogen infection of rpa-9 animals is due to a mutation in nol-6 , nol-6 RNAi enhances nematode resistance to S . enterica-mediated killing ( Figure 4D ) . Furthermore , nol-6 RNAi in rpa-9 mutant nematodes results in no significant change in intestinal accumulation of S . enterica/GFP , as expected if rpa-9 is allelic to nol-6 ( Figure 4A ) . RNAi-mediated depletion of nol-6 in rpa-9 nematodes results in no significant change in survival further supporting the idea that rpa-9 is allelic to nol-6 ( Figure 4D ) . Consistent with the observation that daf-16 ( mu86 ) does not suppress the enhanced resistance to S . enterica of rpa-9 mutants ( Figure 3A ) , daf-16 ( mu86 ) does not suppress the enhanced resistance to S . enterica of nol-6 RNAi nematodes ( Figure 4E ) . In addition , nol-6 RNAi phenocopies the reduced fertility of rpa-9 mutants when raised at 25°C ( Figure S6 ) , providing further evidence that rpa-9 is allelic to nol-6 . C . elegans nol-6 encodes a nucleolar RNA associated protein ( NRAP ) that is conserved across eukaryotic organisms and involved in early stages of ribosome biogenesis [56] . The first step of generating a ribosome subunit requires the initial transcription of rDNA genes by RNA polymerase I ( Pol-I ) . Inhibition of Pol-I by actinomycin D , an inhibitor of ribosome biogenesis [57] , [58] , leads to an enhanced resistance to S . enterica-mediated killing in wild-type nematodes without significantly affecting S . enterica virulence ( Figure 5A ) . However , actinomycin D treatment in rpa-9 mutant nematodes has no effect ( Figure 5A ) , suggesting that the ribosomal stress caused by the mutation cannot be further enhanced by drug treatment . Even though the nucleoli of S . enterica-infected animals are slightly larger than that of animals grown on E . coli and the nucleoli of rpa-9 mutants are also larger than the nucleoli of wild type nematodes when fed S . enterica ( Figure S7 ) , the small changes observed suggest that the overall structure of the nucleoli is not extensively affected . In order to elucidate whether general disruption of ribosomal proteins can lead to enhanced pathogen resistance , we used RNAi to knock down individual ribosomal protein subunit ( rps ) genes and the percentage of live nematodes was determined five days after the infection by S . enterica . As shown in Figure 5B , five days after the infection only 30% of control nematodes remained alive , while 72% to 98% of nematodes in which individual rps genes were depleted by RNAi remained alive . To address whether the germline may affect the enhanced resistance to pathogen infection of nol-6 or rps RNAi nematodes , RNAi was performed in germline-deficient animals glp-4 ( bn2 ) . Inhibition of nol-6 or rps genes by RNAi enhances the median survival of glp-4 ( bn2 ) nematodes infected with S . enterica by 25–33% ( Table S4 ) , indicating that loss of ribosomal proteins activates innate immunity even in the absence of a fully developed germline . Since loss of ribosomal proteins enhances resistance of wild type animals to S . enterica-mediated killing by 41–64% , it is possible that the germline responds to nucleolar stress and contributes to the activation of innate immunity in wild-type animals . Taken together , these results provide the first indication that the ribosome acts as a negative regulator of innate immunity and that reduced ribosomal function by mutation or RNAi boosts innate immunity . Elevated p53 transcriptional activity in response to various cellular stresses such as DNA damage , heat shock and hypoxia has previously been reported [23] , [24] , [25] , [26] . In addition , aberrant ribosome biogenesis can lead to stabilization of p53 in mice and human cells [27] , [28] , [29] . Therefore , we hypothesized that higher p53 activity , as a consequence of aberrant ribosome biogenesis and nucleolar stress , in nol-6 and rps RNAi animals results in enhanced resistance to S . enterica . To test this hypothesis , we compared S . enterica-mediated killing of loss-of-function cep-1 ( gk138 ) nematodes [55] to that of cep-1 ( gk138 ) nematodes in which nol-6 and rps RNAi gene expression was depleted by RNAi . As shown in Figure 5C , cep-1 mutation suppresses not only the enhanced resistance to S . enterica-mediated killing of nol-6 RNAi nematodes but also that of rps RNAi nematodes , indicating that derepression of CEP-1 transcriptional activity by nol-6 or rps RNAi activates immunity against S . enterica . After development , CEP-1 is highly expressed in the pharynx [51] , which we have recently demonstrated plays a key role in C . elegans immunity against S . enterica [2] , [59] . Interestingly , not only CEP-1 , but also PMK-1 and DAF-16 appear to be required for the enhanced resistance to P . aeruginosa of rpa-9 nematodes ( Figures S8 , S9 , and S10 ) . These results are consistent with previous studies that showed that different mechanisms mediate innate immunity to S . enterica and P . aeruginosa [59] , [60] . Consistent with the idea that higher CEP-1 activity is responsible for the enhanced immunity against S . enterica of NOL-6-deficient animals , the microarray data show a significant enrichment in CEP-1-regulated genes in rpa-9 mutants ( Figure 3E and Table S1 ) . The expression analysis of five studied genes that belong to the cluster of CEP-1-regulated genes that are induced in rpa-9 mutants ( Figure 3F ) shows that they are also upregulated in nol-6 RNAi nematodes compared to control wild-type nematodes ( Figure 6A ) . An additional known CEP-1 target , egl-1 [61] , [62] , [63] was also found to be upregulated in nol-6 RNAi nematodes compared to vector control wild-type nematodes ( Figure 6A ) . Taken together , these results suggest that higher CEP-1 activity is responsible for the enhanced resistance to S . enterica-mediated killing in animals with impaired ribosomal function due to mutation or RNAi of nol-6 , and suggest that the nucleolus suppresses innate immunity in a CEP-1-dependent manner ( Figure 6D ) . The most highly upregulated gene in rpa-9 or nol-6 RNAi animals was the leucine rich repeat ( LRR ) encoding gene sym-1 , which is one of twelve genes that are signature of C . elegans response to infections by different pathogens , including S . enterica ( Table S2 , and Aballay and Tenor unpublished data ) . To investigate the importance of sym-1 during S . enterica infection , we compared the survival of sym-1 ( mn601 ) null mutants [64] with that of wild-type nematodes . Indeed , sym-1 ( mn601 ) mutant nematodes exhibit enhanced susceptibility to S . enterica-mediated killing ( Figure 6B ) . In addition , the sym-1 ( mn601 ) mutation completely suppresses the resistance phenotype conferred by nol-6 RNAi ( Figure 6B ) . It should be noted that a significant number of sym-1 ( mn601 ) nematodes die from matricide during the early time point of the assay . However , when matricide is censored , nol-6 RNAi still fails to enhance resistance to S . enterica-mediated killing in sym-1 ( mn601 ) animals ( Figure S11 ) . In addition to providing protection from S . enterica-mediated killing , sym-1 is also required to prevent S . enterica invasion of the pharynx ( Figure 6C ) . Taken together , these data indicate that higher CEP-1 activity results in the expression of genes important for proper immune response to the bacterial pathogen S . enterica .
Increasing evidence indicates that the nucleolus plays a role as a coordinator of cellular stress responses by regulating the activity of p53 . However , the relationship between nucleolar proteins and p53 in response to bacterial infections has not been studied . In this study , we provide evidence indicating that nucleolar proteins suppress innate immunity against bacteria by preventing the transcriptional activity of p53 . Animals lacking NOL-6 and other nucleolar proteins were found to be resistant to infections by bacterial pathogens . Importantly , whole-genome microarray analyses and subsequent qRT-PCR studies demonstrated that inhibition of the nucleolar protein NOL-6 by mutation or RNAi results in higher activity of the C . elegans homologue of p53 , CEP-1 . Furthermore , we found that CEP-1 and SYM-1 , which is induced by UV irradiation in a CEP-1-dependent manner [55] , are required for the enhanced resistance to pathogen infection of animals lacking NOL-6 . The results indicate that nucleolar stress , which may be caused by loss of nucleolar proteins , pathogen infection , or UV irradiation , enhances innate immunity by activating the transcriptional activity of CEP-1 . To date , the only identified suppressor of innate immunity is DAF-2 , which acts through inhibition of the FOXO transcription factor DAF-16 [35] . Until now , all the known suppressors of C . elegans innate immunity act through DAF-16-dependent mechanisms and , as a consequence , increase C . elegans longevity [35] , [43] . Therefore , enhanced resistance to pathogen infection by loss of nucleolar proteins represents the first mechanism by which enhanced innate immunity does not result in enhanced longevity in C . elegans . Even though DAF-16 is not required for the enhanced resistance to S . enterica-mediated killing of rpa-9 mutants ( Figure 3A ) , it is required for the enhanced resistance to P . aeruginosa-mediated killing ( Figure S10 ) . These results indicate that different mechanisms mediate innate immunity to S . enterica and P . aeruginosa . Further analysis will be required to understand the role of DAF-16 in the enhanced resistance to P . aeruginosa by loss of nucleolar proteins . Nucleolar RNA-associated proteins like NOL-6 are largely conserved across eukaryotic organisms and have been shown to associate closely with condensed chromosomes during mitosis , suggesting an involvement in ribosomal RNA ( rRNA ) processing during the early stages of ribosome biogenesis [56] . Our results show that disruption of ribosomes via treatment with actinomycin D or by RNAi-mediated knockdown of individual ribosomal protein subunit ( rps ) genes leads to an enhanced resistance to S . enterica infection . Ribosomal proteins are required for proper germline development [65] , [66] and CEP-1 plays a role in stress-induced germline apoptosis in C . elegans [67] . To address whether the germline may affect the enhanced resistance to pathogen infection of nol-6 or rps RNAi nematodes , RNAi was performed in germline-deficient animals glp-4 ( bn2 ) . Inhibition of nol-6 or rps genes by RNAi enhances the median survival of glp-4 ( bn2 ) nematodes infected with S . enterica by 25–33% ( Table S4 ) , indicating that loss of ribosomal proteins activates innate immunity by a mechanism that does not require CEP-1 expression in the germline . The nucleolus has been linked to the regulation of p53 via sequestration of p53-activating proteins [15] , [17] , [18] . In addition , it has been demonstrated that nucleolar disruption due to the effects of DNA-damaging agents is the cause of p53 accumulation [29] . These findings further support the function of the nucleolus as a stress sensor responsible for maintaining low levels of active p53 which become elevated upon impairment of nucleolar function . Our studies show that disruption of the C . elegans nucleolar protein NOL-6 leads to increased transcriptional levels of CEP-1-regulated genes and a significant enhanced resistance to the bacterial pathogen S . enterica . A comparison of genes that are misregulated in rpa-9 mutant animals and genes that require cep-1 for proper regulation following ultraviolet irradiation [55] revealed a significant overlap between the two gene sets ( Figure 3E and Table S1 ) . These findings suggest that disruption of the nucleolus by mutation in nol-6 leads to enhanced resistance to S . enterica through increased p53 activity . Further studies show that the enhanced resistance to S . enterica imposed by rpa-9 mutation or nol-6 ( RNAi ) is suppressed in a cep-1 ( gk138 ) mutant background ( Figures 3C and 5C ) , indicating that an increase in CEP-1 activity is required for the protective effect . The suppression of the enhanced resistance to S . enterica of rpa-9 and nol-6 RNAi animals by cep-1 mutation suggests that nucleolar disruption by loss of nucleolar proteins results in the activation of a CEP-1-dependent immune response . Consistent with this idea , sym-1 , which has been shown to be regulated by CEP-1 in response to UV irradiation [55] and is the most highly induced gene in rpa-9 and nol-6 RNAi animals , was found to be required for the enhanced immunity of nol-6 RNAi nematodes ( Figure 6B and 6C ) . Like Toll receptors that function in both development and immunity in Drosophila , SYM-1 may regulate the two processes in C . elegans . Although sym-1 ( mn601 ) mutation does not cause a discernible phenotype , in combination with mutations that affect a key regulator of alternative splicing it results in deficient muscle attachment to the cuticle during development [64] . Thus , subtle developmental deficiencies due to lack of sym-1 may weaken C . elegans , increasing its susceptibility to pathogen infection . Interestingly , sym-1 encodes a leucine rich repeat ( LRR ) which is found in the majority of pattern recognition receptors involved in innate immunity . LRRs , are found in proteins ranging from plant resistance ( R ) genes [68] to Toll or Toll-like receptors in species ranging from insects to mammals [69] . Recent work indicates that TOL-1 is required for C . elegans immunity against S . enterica and for the correct expression of abf-2 , an antimicrobial peptide encoding gene , and hsp16 . 41 [59] , which is part of the heat shock pathway required for immunity in C . elegans [36] , [70] . Additionally , a recent screen of candidate LRR receptors in C . elegans has led to the identification of FSHR-1 as an essential component of innate immunity [71] . Further studies will be required to address whether the LRR-containing proteins , TOL-1 , FSHR-1 , and SYM-1 function as pathogen recognition receptors or play different roles in C . elegans defense against bacterial pathogens . In summary , using forward and reverse genetics we have identified a new mechanism by which innate immunity is regulated . Our results provide evidence that nucleolar proteins and p53/CEP-1 transcriptional activity play a role in defense response against infections by bacterial pathogens . In animals lacking nucleolar proteins and infected with bacterial pathogens , nucleolar stress leads to the activation of a p53/CEP-1-mediated immune mechanism . Given the conserved functions of nucleoli and p53/CEP-1 , our findings provide a mechanism by which the nucleolus may regulate antibacterial responses across metazoans .
C . elegans strains were cultured and maintained using standard procedures [72] . The following strains were kindly provided by the Caenorhabditis Genetics Center ( University of Minnesota , St . Paul , Mn , USA ) : wild-type var . Bristol ( N2 ) , Hawaiian mapping strain ( CB4856 ) , daf-16 ( mu86 ) , cep-1 ( gk138 ) , sym-1 ( mn601 ) , pmk-1 ( km25 ) , and glp-4 ( bn2 ) . rpa-9/nol-6 ( ac1 ) animals were generated in this study and backcrossed to wild type 4 times before analysis . EMS ( ethane methyl sulfonate ) mutagenesis was performed as previously described [73] . Briefly , the wild-type strain Bristol N2 was mutagenized with 50 mM EMS for 4 hours at 20°C . This is expected to generate ∼220 G/C→A/T transition mutations per haploid genome , ∼50 of which cause amino acid mutations in protein coding genes [73] , [74] . Mutagenized progeny were harvested and allowed to self-fertilize in order to fix induced mutations . Mutagenized nematodes were grown to one day old gravid adults on E . coli strain OP50 [72] before transfer to S . enterica/GFP strain SMO22 [75] for 48 hours . Nematodes were visualized using a Leica MZ FLIII fluorescence stereomicroscope and mutants which displayed little or no GFP within the intestine were isolated and propagated on individual plates . C . elegans strains were grown exactly as described for C . elegans killing assays . For S . enterica , E . faecalis and E . coli , C . elegans were fed the bacteria for 48 hours before being harvested and transferred to an agar pad on microscope slides in sodium azide for visualization . For P . aeruginosa , C . elegans were exposed for 24 hours prior to visualization . C . elegans were imaged under a 40× oil immersion objective and processed with a Zeiss Axioscope epifluorescence microscope equipped with a Hamimatsu CCD camera and processed with Axiovision v3 . 0 imaging software . C . elegans wild-type Bristol N2 animals and mutants were maintained as hermaphrodites at 20°C , grown on modified nematode growth medium ( NGM ) agar plates , and fed with E . coli strain OP50 as described [72] . S . enterica strain SL1344 [76] , Pseudomonas aeruginosa strain PA14 [41] , and Enterococcus faecalis strain OG1RF [77] cultures were grown in Luria–Bertani ( LB ) broth at 37°C . S . enterica and P . aeruginosa bacterial lawns used for C . elegans killing assays were prepared by spreading 25 µl of an overnight culture of bacteria on modified NGM agar ( 0 . 35% instead of 0 . 25% peptone ) in plates 3 . 5 cm in diameter . E . faecalis bacterial lawns were prepared by spreading 25 µl of an overnight culture on brain-heart infusion ( BHI ) agar on plates 3 . 5 cm in diameter . Nematodes were scored and transferred once a day to fresh plates . Nematodes were considered dead when they failed to respond to touch . For killing assays involving actinomycin D , 0 . 5 µg/mL actinomycin D ( Sigma-Aldrich ) was added to the NGM agar and plates were inoculated with 25 µl of an overnight culture of S . enterica . All assays were performed at 25°C unless otherwise noted . All the experiments were performed in triplicate unless otherwise indicated . C . elegans strains were grown exactly as described for C . elegans killing assays . After 48 hours of feeding on S . enterica/GFP strain Smo22 , nematodes were transferred to E . coli strain OP50 and visualized using a Leica MZ FLIII fluorescence stereomicroscope . In each case , graphs represent combined data from three independent experiments . Differences between bar graphs were considered statistically significant when p<0 . 05 using a two-tailed t-test in PRISM 4 . 0 . Five rpa-9 hermaphrodites were placed with 10 Hawaiian CB4856 males for mating at 20°C overnight . After 24 hours , males were removed and hermaphrodite rpa-9 nematodes were isolated to separate plates . Thirty F1 progeny were isolated from a single successful mating and allowed to self-fertilize . Twelve F2 progeny were collected from each F1 progeny ( a total of 360 ) and were allowed to egg-lay on two sets of plates . One set of plates was maintained at 20°C and kept as stocks . The second set of plates was transferred to 25°C . Because the Rpa phenotype is not 100% penetrant , cross progeny which displayed the temperature sensitive larval lethal phenotype of rpa-9 mutants were isolated and screened for enhanced resistance to P . aeruginosa . Recombinants that displayed the temperature sensitive larval lethality also displayed enhanced resistance to P . aeruginosa ( data not shown ) . 96 positive recombinants were used for genotyping . RFLP-SNPs and surrounding sequences were obtained from the C . elegans SNP database ( http://genome . wustl . edu/genome/celegans/celegans_snp . cgi ) . Primers were designed using the Primer 3 program ( http://frodo . wi . mit . edu/cgi-bin/primer3/primer3_www . cgi ) . Oligonucleotides were synthesized and HPLC purified by MWG Biotech ( Ebersberg , Germany ) . DNA lysates were generated by suspending 50 nematodes in 100 µl lysis buffer ( 50 mM NaCl , 10 mM Tris-Cl pH 7 . 5 , 2 . 5 mM MgCl2 , 0 . 45% Tween 20 , 0 . 01% gelatin , 0 . 2 mg/ml proteinase K ) and lysed at 65°C for one hour . 2 µl of this crude lysate was used as the PCR template . The PCR reactions contained 100 µM of each the forward and reverse primer , 125 µM dNTPs , 0 . 5 U Choice-Taq DNA polymerase and 1× Choice-Taq PCR buffer ( Denville Scientific Inc . , Metuchen , NJ , USA ) , in a 25 µl total volume . All reactions were performed in 96-well PCR plates sealed with an adhesive cover as follows: initial denaturation at 95°C for 2 minutes , followed by 30 cycles of 95°C denaturation for 30 seconds , 55°C annealing for 30 seconds , 72°C extension for 30 seconds , and ending with a 7 minute final extension at 72°C . All restriction digests were performed using New England BioLabs , Inc ( NEB ) enzymes and the recommended corresponding NEB buffer in a total reaction volume of 20 µl . A 2× digestion master mix was made as follows: 2× NEB buffer , 2× BSA , 3 U NEB enzyme to a total volume of 1 mL . 10 µl of each PCR reaction was added to 10 µl of 2× digestion master mix in a new 96-well plate and incubated at the appropriate temperature for 2 hours . Digested PCR fragments were resolved on 2% agarose gels and visualized with ethidium bromide staining and the genotype determined . As the region containing rpa-9 became narrower , SNPs which did not disrupt a restriction site were sequenced using traditional Sanger sequencing techniques for analysis . Mutant rpa-9 was crossed to the Hawaiian SNP mapping strain CB4856 and approximately four hundred second generation cross progeny were isolated and screened for an rpa-9 phenotype . DNA from a total of 96 rpa-9 positive recombinant progeny was isolated and used for RFLP-SNP mapping . SNP data from the two most informative recombinants identified a 109-kilobase region on chromosome II between positions 1 , 407 , 386 and 1 , 516 , 505 containing the rpa-9 mutation ( Figure S4 ) . Recently , whole genome sequencing has been validated as a powerful new method of detecting lesions caused by chemical mutagens in C . elegans [78] , [79] , [80] . Whole genome sequencing of rpa-9 mutants and analysis of the RFLP-SNP mapped 109-kilobase region revealed a single mutation within this region . This G to A mutation occurred in the third exon of the C . elegans gene nol-6 resulting in a glycine to glutamic acid substitution at amino acid position 151 . Since glycine is the smallest of the amino acids and can be either positively or negatively charged depending upon the environment , it is likely that substitution with a large , highly polar amino acid such as glutamic acid will alter the folding pattern of the protein and potentially hinder its function . In an attempt to rescue the mutation by transgenic complementation , different constructs carrying nol-6 cDNA or genomic nol-6 were used to express wild-type nol-6 in rpa-9 mutants . In the cases where the reporter gene gfp was used , GFP expression was observed in embryos and larvae of F1 animals . However , GFP expression steadily declined during development and was not observed in the progeny . Overall , animals expressing different transgenes failed to produce progeny , suggesting potential toxicity of the transgene . Because microinjection involves the generation of multicopy extrachromosomal transgene arrays , it is possible that increased dosage of nol-6 is deleterious during early embryogenesis , resulting in lethality . Sequence sample preparation was performed via the standard Illumina Genome Analyzer genomic sample preparation protocol . In brief , this entails beginning with 5 µg of high quality genomic DNA and fragmenting this DNA via nebulization to sizes of less than 800 bp . The fragment ends are repaired and an ‘A’ base is added to the 3′ ends . Adapters containing a single ‘T’ overhang at their 3′ end are then ligated to the fragments . A fragment size of approximately 200–250 bp is isolated and purified via agarose gel purification . Finally , a short , ten cycle PCR is performed to enrich those DNA fragments that have adapter molecules on both ends and to amplify the amount of DNA in the library without skewing the representation of the library . Following Illumina's standard sequencing protocol , the resultant DNA library was sequenced to a depth of 6× across the entire genome . Solexa genome analyzer single-end reads were produced at a size of 36 base pairs and aligned to the wild-type reference genome ( NC_003279-84 ) at an average depth-coverage of 5× . The data was analyzed using the Mapping and Assembly with Quality ( MAQ ) software which performs read alignment and SNP prediction [81] . A 197 base pair fragment was amplified using forward primer 5′-tcaggtcgaccattgaaattccgccaaaagc-3′ and reverse primer 5′-tcagggtaccatccaattcgaactccatcg-3′ . The fragment was cloned into the SalI and KpnI sites of pL4440 ( Open Biosystems ) and transformed into E . coli HT115 ( DE3 ) cells . We used the RNA interference technique to generate loss-of-function RNAi phenotypes by feeding nematodes with E . coli expressing double-stranded RNA that is homologous to a target gene [82] , [83] . The E . coli strain HT115 ( DE3 ) harboring the appropriate vectors was grown in LB broth containing 100 µg/ml ampicillin and 10 µg/ml tetracycline at 37°C overnight . Bacteria were plated onto NGM plates containing 100 µg/ml carbenicillin and 2 mM isopropyl β-D-thiogalactoside ( IPTG ) and were allowed to grow overnight at 37°C . For knockdown of nol-6 , eggs were harvested by treatment of gravid adults with alkaline hypochlorite [84] and synchronized to L1 stage overnight in S-basal buffer . Nematodes were grown on plates containing E . coli expressing dsRNA for 4 days at 20°C to gravid adult stage before being transferred to S . enterica strain SL1344 . For knockdown of rps genes , eggs were harvested by bleaching gravid adult nematodes and synchronized to L1 stage for 22 hours in S-basal buffer . L1 larvae were plated onto NGM plates seeded with E . coli strain OP50 and grown for 2 days at 20°C to L4 stage before being transferred to RNAi plates as previously described . Nematodes were fed RNAi expressing bacteria for 24 hours at 20°C before being transferred to S . enterica strain SL1344 . Bacteria strains expressing double-stranded RNA to inactivate the C . elegans genes other than nol-6 were obtained from Wellcome/Cancer Research ( Cambridge , U . K ) and Open Biosystems ( Huntsville , AL ) . The identity of all clones was confirmed by sequencing . Gravid adult wild-type nematodes were lysed using a solution of sodium hydroxide and bleach , washed , and the eggs were synchronized for 22 hours in S basal liquid medium at room temperature . Synchronized L1 animals were placed onto NGM plates containing 2 mM IPTG and 100 ug/mL carbenicillin seeded with E . coli HT115 expressing double stranded RNA against nol-6 or empty vector and grown until L4 ( 5 days at 15°C ) . The L4 animals were fed S . enterica for 24 hours at 25°C and then harvested . The nematodes were collected by washing the plates with M9 buffer , and RNA extracted using Trizol reagent . Genomic DNA was removed by treating the RNA samples with DNase using the DNA-free kit according to manufacturer's instruction ( Ambion ) . qRT-PCR was conducted using the Applied Biosystems TaqmanOne-Step Real-time PCR protocol using SYBR Green fluorescence ( Applied Biosystems ) on an Applied Biosystems 7900HT real-time PCR machine in 96 well plate format . Fifty nanograms of RNA were used for real-time PCR . Twelve microliter reactions were set-up and performed as outlined by the manufacturer ( Applied Biosystems ) . Gene expression for three independent isolations of nol-6 RNAi nematodes were compared to vector control nematodes using the comparative Ct method after normalization to act-1 , -3 , -4 ( pan-actin ) . Primer sequences are available upon request . Nematode survival was plotted as a nonlinear regression curve using the PRISM 4 . 00 computer program . Survival curves are considered significantly different from the control when p<0 . 05 . Prism uses the product limit or Kaplan–Meier method to calculate survival fractions and the logrank test , which is equivalent to the Mantel–Heanszel test , to compare survival curves . In each case , curves represent combined data from at least three independent experiments . Mann-Whitney test and Student's exact t test were used to analyze bacterial accumulation and qRT-PCR results , respectively . Gravid adult wild-type and rpa-9 nematodes were lysed using a solution of sodium hydroxide and bleach , washed , and the eggs were synchronized for 22 hours in S basal liquid medium at room temperature . Synchronized L1 animals were placed onto NGM plates seeded with E . coli OP50 and grown until L4 ( 40 hours at 20°C ) . The L4 animals were exposed to S . enterica for 12 hours at 25°C and then harvested by washing the plates with M9 buffer . RNA was extracted using Trizol reagent for two independent isolations for wild type nematodes and three independent experiments for rpa-9 nematodes . cDNA was generated and hybridized to Affymetrix C . elegans Genome Array following the manufacturer's instructions at the Duke Microarray Facility . Detailed protocols are available on the Duke Microarray Facility Web site ( http://microarray . genome . duke . edu ) . GeneSpring Software 9 . 0 ( Agilent Technologies ) was used to perform normalizations and fold change analysis . Gene lists for pmk-1 regulated genes [50] , daf-16 regulated genes [3] and genes misregulated by cep-1 in response to UV [55] have been described . Briefly , microarrays used for pmk-1 regulated genes [50] were from Affymetrix while the microarrays used for daf-16 regulated genes [3] and genes misregulated by cep-1 in response to UV [55] were custom made . Overall , RNA samples were obtained from synchronized by hypochlorite treatment L1 animals that were grown to young adults or L4-yound adults . Statistical significance of enrichment was determined by using a program for comparing two sets of genes ( http://elegans . uky . edu/MA/progs/overlap_stats . html ) . P values are calculated using a method that is essentially the same as EASE [85] . P values are calculated using an exact hypergeometric probability or its binomial approximation where appropriate , using a jackknife adjustment . Either a Holm-Bonferroni or Bonferroni correction for multiple testing is applied . This assay was performed essentially as previously described [2] , [59] but with minor modifications . Nematodes were synchronized by treatment of gravid adults with sodium hydroxide and bleach . Synchronized L1 larvae were grown on NGM plates seeded with E . coli for 4 days at 20°C . One hundred 1 day old adult hermaphroditic nematodes were placed on lawns of S . enterica ( Smo22 ) or E . coli ( DH5α ) expressing GFP for 48 hours at 25°C . Nematodes exhibiting infected pharynxes were quantified using fluorescence microscopy . | Innate immunity comprises a variety of defense mechanisms used by metazoans to prevent microbial infections . These nonspecific defense responses used by the innate immune system are governed by interacting and intersecting pathways that control not only immune responses but also longevity and responses to different stresses . Increasing evidence highlights the plurifunctional nature of the nucleolus , which appears to control various cellular processes involved in health and disease , from ribosome biogenesis to regulation of the cell cycle and the cellular stress response . We provide evidence indicating that the nucleolus suppresses innate immunity against bacteria by preventing the transcriptional activity of the tumor suppressor p53 . We found that animals lacking nucleolar proteins are highly resistant to infections by bacterial pathogens . We also found that the activation of innate immunity by inhibition of nucleolar proteins requires potential immune effectors whose expression in response to stress is regulated by p53 . Our study links the nucleolus , p53 , and innate immunity against bacterial infections for the first time , and highlights a new mechanism that can potentially be exploited to alleviate bacterial infections . | [
"Abstract",
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"Methods"
] | [
"immunology/immunity",
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] | 2009 | Nucleolar Proteins Suppress Caenorhabditis elegans Innate Immunity by Inhibiting p53/CEP-1 |
Naturally acquired immunity against invasive pneumococcal disease ( IPD ) is thought to be dependent on anti-capsular antibody . However nasopharyngeal colonisation by Streptococcus pneumoniae also induces antibody to protein antigens that could be protective . We have used human intravenous immunoglobulin preparation ( IVIG ) , representing natural IgG responses to S . pneumoniae , to identify the classes of antigens that are functionally relevant for immunity to IPD . IgG in IVIG recognised capsular antigen and multiple S . pneumoniae protein antigens , with highly conserved patterns between different geographical sources of pooled human IgG . Incubation of S . pneumoniae in IVIG resulted in IgG binding to the bacteria , formation of bacterial aggregates , and enhanced phagocytosis even for unencapsulated S . pneumoniae strains , demonstrating the capsule was unlikely to be the dominant protective antigen . IgG binding to S . pneumoniae incubated in IVIG was reduced after partial chemical or genetic removal of bacterial surface proteins , and increased against a Streptococcus mitis strain expressing the S . pneumoniae protein PspC . In contrast , depletion of type-specific capsular antibody from IVIG did not affect IgG binding , opsonophagocytosis , or protection by passive vaccination against IPD in murine models . These results demonstrate that naturally acquired protection against IPD largely depends on antibody to protein antigens rather than the capsule .
Streptococcus pneumoniae is a leading cause of infectious disease related death , responsible annually for up to a million child deaths worldwide [1] . Pneumonia represents the greatest burden of disease caused by S . pneumoniae [2] , and despite current vaccination strategies the burden of pneumococcal pneumonia remains high . Invasive pneumococcal disease ( IPD ) is the most severe form of S . pneumoniae infection and mainly affects very young children and older adults . This is attributed to an underdeveloped adaptive immune system in infants , and to waning natural immunity combined with co-morbidities in the older adult . A clear understanding of the mechanisms of natural-acquired adaptive immunity to S . pneumoniae is essential to characterise why both the young and elderly are at high risk of disease and for the development of effective preventative strategies . Vaccines based on the polysaccharide capsule of S . pneumoniae are highly protective against the capsular serotypes included in the vaccine preparation [3–5] , and protection correlates with the level of anti-capsular antibody responses . It has generally been assumed that the type-specific anti-capsular antibodies that can develop in response to colonisation or episodes of infection are also the main mechanism of natural adaptive immunity against IPD [6 , 7] . However , there is little good evidence supporting the concept that levels of anti-capsular antibodies predict risk of IPD in unvaccinated individuals . As well as causing symptomatic disease , S . pneumoniae asymptomatically colonises the nasopharynx , affecting at least fifty percent of infants and approximately ten percent of adults [8] . Colonisation is an immunising event . In humans , it leads to antibody responses to capsular polysaccharide [9] , but also induces both antibody [10–14] and cellular immune responses to protein antigens [15 , 16] . Serum levels of antibody to multiple pneumococcal surface proteins rise in the first few years of life [13] , and have been show to fall in older age for a limited number of antigens [17] . Similar adaptive immune responses are observed in mouse models of nasopharyngeal colonisation [11 , 18–25] . In animal models , these anti-protein responses alone can be protective , with T-cell mediated immunity preventing re-colonisation and non-invasive pneumonia[15 , 24 , 25] and anti-protein antibody responses protecting against IPD [19 , 20 , 22 , 24] . Recent human data suggests that Th17-cell mediated responses to protein antigens also play an important role in protection against colonisation in humans [26] with implications for vaccine design [27] . There are several converging lines of evidence from human studies which support the concept that naturally-acquired anti-protein antibodies can also protect against S . pneumoniae infections . Lower serum IgG levels to a range of pneumococcal proteins correlate with susceptibility to acute otitis media [28 , 29] and respiratory tract infections in children [30] . Passive transfer of human serum from experimentally challenged human volunteers protected mice against invasive challenge with a different capsular serotype of pneumococcus [20] , providing proof of concept that ‘natural’ antibodies against bacterial proteins induced through nasopharyngeal exposure can protect against IPD . Furthermore , the incidence of IPD falls after infancy for all serotypes of S . pneumoniae , irrespective of how commonly the serotype is carried in the nasopharynx [31] suggesting that naturally-induced adaptive immune mechanisms are serotype-independent . If the protection against IPD that develops naturally through colonisation requires anti-protein antibody responses rather than serotype-specific anti-capsular antibody , this would represent an important readjustment in our understanding of immunity to S . pneumoniae . It would have major implications for identifying subjects with an increased risk of infection , understanding mechanisms of immunosenescence that increase susceptibility to S . pneumoniae with age , and for guiding future vaccine design . Passive transfer of pooled human immune globulin ( IVIG ) is an established treatment to prevent infections in individuals with primary antibody deficiency [32 , 33] , in whom S . pneumoniae is a leading cause of disease [34] . Previous investigations in mice have indicated that IVIG may protect against experimental IPD [35 , 36] . Commercially-manufactured IVIG is pooled immunoglobulin G ( IgG ) from >1000 different donors [37] , and therefore represents the pooled antibody responses acquired through natural exposure across a population . We have used IVIG to determine the targets of natural acquired immunity to S . pneumoniae and the relative functional importance of anti-capsular and anti-protein responses for prevention of IPD .
ELISAs using the whole S . pneumoniae cell as the antigenic target confirmed that IVIG contained significant titres of IgG that recognised S . pneumoniae ( Table 1 ) . Polysaccharide-specific ELISAs demonstrated that IVIG contained IgG that recognised common S . pneumoniae capsular serotypes and cell wall polysaccharide ( CWPS ) ( Table 1 ) . To assess whether IVIG contained IgG that bound S . pneumoniae proteins , immunoblots were performed against lysates of several S . pneumoniae strains of differing capsular serotypes . Multiple protein antigens were recognised by IVIG with a largely similar pattern of bands for all strains , suggesting the major protein targets of IVIG are generally conserved between capsular serotypes of S . pneumoniae ( Fig 1A ) . Competitive inhibition was used to assess which antigens contributed significantly towards the whole cell ELISA titres for the TIGR4 strain . Pre-incubation of IVIG with a soluble bacterial lysate reduced whole cell ELISA IgG titres in a dose-dependent manner , which was partially reversed by pre-treating the soluble lysate with the protease trypsin ( Fig 1B ) . In contrast , neither purified capsular polysaccharide nor CWPS affected whole cell ELISA IgG titres ( Fig 1C ) . The whole cell ELISA assays were repeated for four different S . pneumoniae serotypes with competitive inhibition by encapsulated and unencapsulated bacterial lysates ( Fig 2A–2D ) . The results demonstrated that for two of four strains lysates of encapsulated and unencapsulated bacteria equally reduced the IgG binding titre in the whole cell ELISAs . For the D39 ( serotype 2 ) and serotype 3 strain whole cell ELISA titres were inhibited to a greater extent by lysates of encapsulated bacteria compared to unencapsulated bacteria . Further whole cell ELISAs for these two strains demonstrated that the unencapsulated mutants blocked IgG binding to unencapsulated mutants ( Fig 2E and 2F ) , indicating the reduced inhibition in the whole cell ELISAs against the wild-type strain is likely to be due to the effects of anti-capsular antibody . These data show that IVIG contains antibodies to both capsular , CWP and protein antigens , but which class of antigens made the dominant contribution to IVIG recognition varied to an extent between S . pneumoniae strains when assessed using whole cell ELISAs . To identify protein targets for IgG in IVIG , lysates of S . pneumoniae mutants lacking specific surface proteins were probed with IVIG . The results showed that IgG in IVIG recognised the cell wall proteins PspA , PspC and PhtD and at least two lipoproteins ( shown using the lipoprotein deficient strain Δlgt ) , including PiaA ( Fig 3A ) . Immunoblotting of recombinant proteins confirmed that IVIG contains IgG that recognises multiple ( but not all ) S . pneumoniae protein antigens tested ( Fig 3B ) . To assess whether protein targets for naturally acquired IgG to S . pneumoniae were conserved between donors from different geographical regions we performed immunoblots against S . pneumoniae lysates with a further commercially available IVIG preparation ( Vigam ) obtained from the USA , and with sera pooled from 20 Malawian subjects . The results showing an almost identical band pattern for each source of IgG ( Fig 3C ) , suggesting a high degree of consistency for the major protein antigen targets for IgG obtained from different geographical regions . A Luminex assay of antibody binding to 19 different S . pneumoniae surface proteins conjugated to xMAP beads was used to semi-quantify responses from different sources of pooled human IgG to specific protein antigens . The Luminex assay confirmed that IgG in IVIG recognised multiple protein antigens including PsaA , PpmA , PhtD , PhtE , PspA , pneumolysin ( Ply ) and PspC ( Fig 3D ) . Overall , the strength of IgG binding to individual S . pneumoniae protein antigens between the different sources of antibody correlated strongly , with PhtD and PspC as the dominant antigens in all three sources of pooled human IgG ( Fig 3D , and for correlation of Vigram versus Intratech R2 = 0 . 966 ) . To assess whether there is significant variation between individuals in which S . pneumoniae antigens are recognised by naturally acquired IgG , whole cell ELISAs to four S . pneumoniae serotypes , immunoblots against S . pneumoniae lysates , the Luminex assay of protein antigen responses , and capsular serotype antibody ELISAs were repeated using sera from six young adult HIV negative Malawian individuals ( mean age 29 years , range 21 to 36 , 3 male , 3 female ) . The results showed all the individuals investigated have significant anti-protein antibody responses ( Fig 4 ) . However , there were variations between individuals in whole cell ELISA titres to different S . pneumoniae strains ( Fig 4A ) and the levels of antibodies to some protein antigens as shown by variations in band strengths in the immunoblot ( Fig 4B ) and in the results for the Luminex bead assay ( Fig 4C ) . For all the strains tested whole cell ELISA titres from individuals correlated with the mean anti-protein antigen responses , whereas there was no correlation to anti-capsule antibody levels except for the serotype 1 strain ( S1 Fig ) . These data support the hypothesis that anti-protein responses dominate IgG recognition of S . pneumoniae in human sera . To investigate whether anti-protein antigen responses could be affected by age , an electrochemiluminescence-based multiplex assay based on MesoScale Discovery ( MSD , Rockville , MD , USA ) technology [13] was used to measure responses to 27 protein antigens in sera from 10 individuals aged over 62 years ( mean 67 . 2 years ) and 10 young adult individuals ( mean age 31 . 2 years ) . In general , mean anti-protein antigen responses were slightly lower for the aged subjects ( Fig 3D ) , with the most marked differences being for PspC ( Fig 3E ) and PcpA ( Fig 3F ) . The difference between older and younger sera reached statistical significance for PspC . Functionally important IgG responses to S . pneumoniae were assessed using a flow cytometry assay to measure total IgG binding to intact live bacteria from different S . pneumoniae strains . Incubation in IVIG resulted in significant IgG binding to four different strains of S . pneumoniae . The level of IgG binding was either increased or unaffected when the assay was repeated using otherwise isogenic unencapsulated mutant derivatives of each strain , indicating that most of the IgG was binding to non-capsular antigens ( Fig 5A and 5B ) . Conversely , pre-treatment with Pronase to degrade surface protein antigens ( Fig 5C ) , using D39 mutant strains with reduced expression of dominant surface proteins ( Δlgt , missing all lipoproteins , and ΔpspA/pspC missing the corresponding choline binding proteins ) ( Fig 5D ) , or pre-incubation of IVIG with an unencapsulated TIGR4 strain ( Fig 5E ) , reduced the amount of IgG binding to the TIGR4 strain suggesting proteins were the target antigens . To further demonstrate that capsular polysaccharide was not the target for IgG binding , the assay was repeated using Streptococcus mitis strains genetically manipulated to express the serotype 4 S . pneumoniae capsule [37] . There was some binding of IgG in IVIG to the surface of the S . mitis strain indicating the presence of antibodies to surface antigens . However , there was no increase in IgG binding to the S . mitis strain expressing the S . pneumoniae serotype 4 capsule compared to wild-type S . mitis ( Fig 5F ) . Conversely , expression by S . mitis pspC , one of the dominant S . pneumoniae protein antigens recognised by IgG in IVIG ( Figs 3B , 3D , 4C and 4D ) , resulted in a large increase in IgG binding ( Fig 5G ) . These results indicate that protein antigens ( including lipoproteins , PspA and PspC ) rather than capsular polysaccharide are the major surface targets for IgG binding to live S . pneumoniae . To further assess whether immune recognition of live S . pneumoniae is dependent on IgG recognition of protein antigens , IgG from IVIG was selectively enriched for responses to S . pneumoniae protein antigens using antibody affinity purification columns coated with unencapsulated S . pneumoniae lysates . The enriched IVIG ( eIVIG ) preparation made using either the TIGR4 or D39 unencapsulated strains had a markedly higher whole cell ELISA titres to both the TIGR4 and D39 encapsulated S . pneumoniae strains compared to untreated IVIG ( Fig 6A–6D ) . Despite the eIVIG preparation IgG concentration being only 30 μg/ml , approximately 1/150 the concentration in IVIG , incubation in eIVIG still resulted in IgG binding to S . pneumoniae in the flow cytometry assay ( Fig 6E–6F ) . These data confirm that IgG targeting S . pneumoniae protein antigens can mediate IVIG immune recognition of S . pneumoniae . IgG binding to S . pneumoniae can cross-link bacteria to form bacterial aggregates that are more susceptible to complement opsonisation [38] . Microscopy showed addition of IVIG to S . pneumoniae TIGR4 resulted in the formation of bacterial aggregates ( Fig 7A ) , the relative size of which could be measured by flow cytometry using increases in forward scatter ( Fig 7B ) . Both encapsulated and unencapsulated TIGR4 S . pneumoniae formed bacterial aggregates in IVIG , indicating these did not require recognition of capsular antigen ( Fig 7A and 7B ) . Furthermore addition of IVIG restricted the increase in OD580 over time for different S . pneumoniae strains cultured in THY broth , and this effect was particularly noticeable for unencapsulated strains ( Fig 7C–7F ) . Vigorous pipetting raised the OD580 to similar levels for both encapsulated and unencapsulated TIGR4 strains ( Fig 7G ) , with no significant differences in numbers of bacterial CFU between the strains ( log10 CFU / ml for the TIGR4 strain 7 . 60 SD 0 . 14 , for the TIGR4Δcps 7 . 85 SD 0 . 11 after 6 h incubation in THY plus 10% IVIG ) . These results indicated that the reduction in the increase in OD580 over time in THY containing IVIG was due to formation of bacterial aggregates . When IVIG was pre-treated with papain to yield monovalent Fab fragments , the majority of the inhibitory effect of increase in OD580 effect was lost , confirming that bacterial aggregation was caused by cross-linking of bacterial cells via the Fab portions of IVIG ( Fig 7H ) . Overall , the aggregation data demonstrate that the dominant target antigen for functionally important IgG binding to S . pneumoniae incubated in IVIG was not the polysaccharide capsule . In vitro assays were used to assess the effects of IVIG on interactions of encapsulated and unencapsulated S . pneumoniae TIGR4 with phagocytes . Opsonisation with IVIG enhanced the association of S . pneumoniae with a murine macrophage cell lines ( Fig 8A ) and with fresh purified human neutrophils ( Fig 8B ) , and enhanced neutrophil killing of S . pneumoniae ( Fig 8C ) . For all three assays , IVIG had a proportionally greater effect on the unencapsulated strain than the encapsulated strain . These data support the hypothesis that anti-capsular IgG is not important in mediating the opsonophagocytic effects of natural human IgG present in IVIG . Passive vaccination was used to investigate the protective efficacy of IVIG in different murine models of S . pneumoniae TIGR4 infection . Mice were given a total of 12 . 8mg of IVIG ( Intratect , Germany , 40 g/L ) in two separate i . p . injections 3 h and immediately before challenge with S . pneumoniae . This IVIG dose was selected as it is equivalent to the doses used in replacement therapy in primary immunodeficiency . In a test dose experiment , human IgG was readily detectable in the sera of IVIG-treated mice three h following the second intraperitoneal injection at approximately 1 . 5 g/L , within the same order of magnitude of circulating IgG levels in humans ( 7+ g/L ) ( Fig 9A ) . Human IgG was not detectable in mouse bronchoalveolar lavage fluid ( BALF ) in uninfected mice . Following S . pneumoniae lung infection by i . n . inoculation of 5x106 CFU of TIGR4 , human IgG concentrations increased in BALF over time ( Fig 9B ) and correlated with BALF murine albumin levels , a marker of serum leak into alveolar spaces ( Fig 9C ) . IVIG treatment had no effect on the inflammatory response to S . pneumoniae pneumonia , both in terms of inflammatory cell numbers ( S2 Fig ) or levels of the pro-inflammatory cytokine TNF-α in BALF post-infection ( control group 2828 SEM 670 versus IVIG group 2665 SEM 506 pg/ml ) in the lavage fluid following infection ) . IVIG treatment also had no effect on bacterial CFU in lavage fluid 2 . 5 hours after low dose inoculation with TIGR4 ( Fig 9D ) , a time point and inoculum dose when alveolar macrophages are the main effector cell [38] . However , at 24 h following challenge , infected mice that had been pre-treated with IVIG were strongly protected against the development of bacteraemia ( present in 100% of controls but only 17% of IVIG treated mice ) and partially protected against lung infection , with 2 log10 fewer S . pneumoniae CFU in lung tissue compared to controls ( Fig 9E ) . Pre-treatment with IVIG also protected mice against developing bacteraemia 4 h following direct i . v . bacterial challenge ( Fig 9F ) . Protection against bacteraemia required macrophages , since their depletion by pre-treatment with liposomal clodronate ( Fig 9G ) reduced IVIG-dependent S . pneumoniae clearance from the blood ( Fig 9H ) . The partial protection provided by IVIG within the lungs was lost when mice were depleted of neutrophils before infection by treatment with anti-Ly6G antibody ( Fig 9I ) . Mice depleted of neutrophils failed to develop bacteraemia even without passive vaccination with IVIG . These data confirm that passive vaccination with IVIG strongly protects mice against IPD , and that protection was dependent on phagocytes . To directly demonstrate that the protection afforded by IVIG is not mediated via anti-capsular antibody , IVIG was pre-treated to deplete anti-capsular antibody prior to testing its protective effects against IPD in vivo . Selective depletion of capsular serotype 4 specific antibody depletion was achieved by incubating IVIG with the S . mitis strain expressing the S . pneumoniae serotype 4 capsule . This process had no effect on the pattern and level of IgG binding to protein antigens in immunoblot and in ELISA for at least two specific proteins ( Fig 10A ) . Whilst the depletion process almost completely removed serotype 4 anti-capsular IgG from the IVIG ( Fig 10B ) , it had no effect on total IgG binding to the surface of S . pneumoniae when assessed by flow cytometry ( Fig 10C ) . Passive transfer of IVIG depleted of type 4 serotype specific antibody to mice still protected against bacteraemia developing after i . n . inoculation of TIGR4 S . pneumoniae ( Fig 10D ) , and after i . v . inoculation of TIGR4 restricted blood CFU to similar levels seen in mice given untreated IVIG ( Fig 10E ) . These data confirm that IVIG does not require IgG to capsular polysaccharide to protect against invasive infection due to S . pneumoniae .
The bimodal distribution of S . pneumoniae infections in the very young and elderly suggests there is a significant degree of naturally-acquired immunity that evolves in early life and then wanes in later life . This naturally-acquired immunity is probably acquired through multiple episodes of nasopharyngeal colonisation with S . pneumoniae that repeatedly affect all humans rather than solely after disease episodes [16 , 18–20 , 24 , 31] . Human epidemiological and experimental evidence from mouse models of infection suggest naturally-acquired immunity has a serotype-independent component [20 , 28 , 29 , 31] , yet the assumption remains that antibody to capsular antigen is the dominant mechanism of protection against IPD [6 , 7] . As a consequence , clinical assessment of susceptibility to IPD is dependent on measuring anti-capsular IgG levels . IVIG is a source of pooled IgG that contains naturally-acquired antibody to S . pneumoniae . We have used in vitro and in vivo experiments to compare the relative functional importance of the anti-capsular and anti-protein antigen IgG in mediating protection against S . pneumoniae . Overall , the data show greater importance for anti-protein rather than anti-capsular IgG , summarised as follows: ( 1 ) For both surface binding of IgG measured by flow cytometry and in vitro aggregation capsular antigen was not the main target for the four serotypes investigated . Data from the whole cell ELISAs were more mixed , with evidence of some contribution of anti-capsular IgG for two of the four strains assessed . However , IgG surface binding to live bacteria has been show to be a better surrogate for protection than ELISA titre [18] . ( 2 ) Enzymatic degradation of surface proteins , absorbtion of anti-protein antibody by incubation with unencapsulated TIGR4 strain , or reduced expression of some classes of surface proteins due to mutation all reduced total IgG binding to S . pneumoniae . ( 3 ) Expression by S . mitis of an immunodominant protein antigen but not the serotype 4 capsule increased IgG recognition when incubated in IVIG . ( 4 ) A low concentration of an IVIG derivative enriched for anti-protein responses to S . pneumoniae recognised heterologous S . pneumoniae strains in whole cell ELISA and flow cytometry IgG binding assays . ( 5 ) Loss of the capsule did not impair the protective effects of IgG in functional assays of neutrophil and macrophage phagocytosis of the TIGR4 S . pneumoniae strain . ( 6 ) Specific depletion of serotype 4 anti-capsular antibodies from IVIG had no effect on IgG binding to intact bacteria and did not abrogate the ability of IVIG to protect against IPD when tested in mouse models of infection . These data form the first evidence to our knowledge demonstrating the redundancy of naturally-acquired human IgG against capsular antigens in protection against IPD , with protection afforded by anti-protein antibody instead . By necessity , the four strains investigated represent only a proportion of the 97 S . pneumoniae capsular serotypes currently known [39] , and we have only been able to deplete anti-capsular antibody for the serotype 4 strain as this is the only available S . pneumoniae capsular serotype expressed in S . mitis . Hence , although the in vitro aggregation and IgG binding data suggest capsule antigen is not functionally relevant for the four serotypes investigated , it remains possible that for selected serotypes anti-capsular antibody has a greater role in mediating protection against IPD than we have identified here . In addition , as we have not been able to make a sufficient quantity of an IVIG derivative effectively depleted of anti-protein antigen responses , we have not been able to explicitly demonstrate in the mouse model of infection that protection is dependent on anti-protein responses rather than to other potential non-capsule non-protein antigens . Our data also do not preclude an important role for naturally-acquired antibody to capsular antigens at other body sites , for example for prevention of nasopharyngeal colonisation [40] . Despite these caveats , the different strands of data we have presented here provide strong support for the hypothesis that the protection in humans against IPD mediated by naturally acquired IgG is not dependent on capsular antibodies . Instead protection seems to require recognition of bacterial surface proteins . Protection against S . pneumoniae infection depends on phagocytes , with different cell types having dominant roles at different anatomical sites and at different time points . Alveolar macrophages are important for bacterial clearance during early lung function [38] , whereas recruited neutrophils are important for controlling bacterial numbers in the lung at later time points [41] . In mice at least , protection against S . pneumoniae bacteraemia and therefore IPD is highly dependent on splenic and reticuloendothelial macrophages [42] . In the mouse model of S . pneumoniae lung infection , passive vaccination with IVIG did not reduce BALF CFU , even at early time points after low dose infection . These results suggest that alveolar macrophages did not mediate the protective effect , although this has not been formally confirmed by infections in mice depleted of alveolar macrophages . Depletion of neutrophils prevented the protective efficacy of IVIG within the lung , whereas systemic depletion of macrophages prevented its protective efficacy in the blood . Unexpectedly , depletion of neutrophils prevented septicaemia in the mouse model of pneumonia , preventing this model from being used to assess whether there is a role for neutrophils in IVIG-mediated protection against bacteraemia . Investigating this would require using neutrophil depletion in the systemic infection model , which we have not assessed . IVIG therapy has been used for immunomodulation , but in our model did not affect cellular recruitment to lavage fluid or TNFα responses . These results suggest that IVIG had no major effects on the inflammatory response to S . pneumoniae , although they do not exclude potentially beneficial effects on other aspects of the inflammatory response . We have demonstrated that IgG in IVIG recognises a large number of S . pneumoniae protein antigens , several of which were identified using immunoblots and a Luminex assay and these include current protein vaccine candidate antigens [43] . There was a striking similarity between which protein targets were quantitatively dominant in binding IgG in IVIG from different geographical sources , with PspA , PhtD , PsaA and PpmA having the strongest antibody recognition in all IgG sources investigated . These similarities suggest that the immunodominance of certain protein antigens is largely independent of human genetic variation . Our protein target identification was biased towards existing well-described antigens , and further non-biased assessment is needed to identify all the antigens recognised by naturally acquired antibody . Several of the immunodominant surface proteins such as PspC and PspA are antigenically variable , and as only a single variant was represented on the Luminex assay it is unclear whether antibody recognition of these antigens is specific to certain alleles . Expression of PspC did increase IgG binding to live S . mitis , and for the D39 strain deletion of surface lipoproteins or both PspA and PspC both reduced IgG binding . These data suggest that PspC , PspA and lipoproteins may contribute towards IgG recognition of S . pneumoniae , but further investigation is necessary to identify which protein antigens are required for the protective IgG responses . This will be technically challenging as it is highly likely there is functional redundancy for IgG binding to S . pneumoniae surface proteins , and using mutants lacking specific protein antigens to identify functionally important targets for IgG in mouse infection models will be confounded by the importance for virulence of many of the potential protein antigens ( e . g . PspA , PspC , Ply , PhtD ) . We also demonstrated IgG binding to S . mitis itself , which may be due to cross-recognition of S . mitis and S . pneumoniae surface antigens , or specific responses to S . mitis induced by natural oropharyngeal colonisation . These data demonstrating that antibodies to S . pneumoniae capsular polysaccharide are not the major target of protective naturally acquired IgG have several important clinical implications . Firstly , measuring levels of anti-capsular antibody may not identify those patients at risk of IPD . Instead , measurement of antibodies to a range of protein targets or to whole S . pneumoniae by flow cytometry may be more relevant . Secondly , it may explain why individuals with specific-deficiencies in anti-polysaccharide antibody production , who are at increased risk of sino-pulmonary infection do not have the same high risk for invasive IPD as subjects with complete agammaglobulinaemia [44 , 45] . Thirdly , the exponential rise in the incidence of S . pneumoniae infection with increasing age is thought to be related to immunosenescence . Antigen responses to a small number of protein antigens have been shown to be lower in the elderly [17] , and we have also shown reduced responses to PspC in a small number of older subjects . These data suggest that one reason for the increased incidence of S . pneumoniae with age could be waning anti-protein antibody levels . Further investigation of the effects of age on anti-protein antigen responses and the functional consequences of any changes is needed to establish whether this hypothesis is correct . Fourthly , if there is reduction in S . pneumoniae colonisation in infants as a result of future vaccines with greater serotype coverage , this could potentially reduce anti-protein mediated natural immunity and perhaps lead to a paradoxical increase in adult disease , as has been postulated for the effects of Bordetella pertussis vaccination [46] . Finally , by identifying the mechanisms of naturally acquired immunity to S . pneumoniae , we can design vaccination strategies to improve these . For example , a multivalent protein vaccine using the dominant protein antigens should provide effective protection against IPD . To conclude , we present multiple lines of supporting evidence that the protective benefits of human naturally acquired IgG against IPD is not , as previously thought , largely dependent on antibody to capsular polysaccharide antigen . Instead , natural human IgG-mediated protection against IPD seems to be dependent on IgG against protein antigens that are highly conserved between different geographical sources of IgG . These findings have important implications for identifying patients at risk of IPD , understanding relevant mechanisms of immunosenescence , and for novel vaccine development .
Wild-type S . pneumoniae serotype 4 strain TIGR4 and its unencapsulated mutant were kind gifts of J . Weiser ( Univ . Pennsylvania ) . D39 and its unencapsulated mutant D39-DΔ were kind gifts of J . Paton ( Univ . Adelaide ) . The ΔpspC , ΔpspA , ΔppmA , Δlgt , ΔphtD , ΔpiaA , and Δply mutant strains have been previously described [47–51] . Serotype 19F strain EF3030 was a kind gift of D . Briles ( Univ . Alabama ) , and the serotype 6B stain ST6B , serotype 14F strain ST14 , and serotype 23F strains were kind gifts from B . Spratt ( Imperial College ) . The unencapsulated mutant strains of 0100993 and ST23F were made by replacing the cps locus ( Sp_0346 to Sp_0360 ) with the Janus cassette [52] . The S . mitis strain expressing the S . pneumoniae TIGR4 serotype 4 capsule has been previously reported [53] . To construct the S . mitis pspC+ mutant strain , the TIGR4 pspC gene was amplified by PCR and integrated between S . mitis flanking DNA using PCR ligation before transformation into the S . mitis strain , similar to the mutagenesis strategy as described [22] . Bacteria were cultured overnight at 37°C in 5% CO2 on Columbia agar ( Oxoid ) supplemented with 5% horse blood ( TCS Biosciences ) . Working stocks were made by transferring one colony of S . pneumoniae to Todd-Hewitt broth supplemented with 0 . 5% yeast extract ( THY ) , grown to an OD of 0 . 4 ( approximately 108 CFU/ml ) and stored at -80°C in 10% glycerol as single use aliquots . CFU were confirmed by colony counting of log10 serial dilutions of bacteria cultured overnight on 5% Columbia blood agar . To partially digest surface proteins , bacteria were suspended in 500μl PBS with or without 100μg Pronase ( Roche ) , incubated for 20min at 37°C shaking at 150rpm , followed by addition of 20μl of 25X Complete Mini-Protease Inhibitor ( Roche ) . Bacteria were then washed twice in PBS and re-suspended in PBS+10% glycerol . Bacterial lysates were prepared as described previously [48] . When required , 20μl of lysate ( 1500 μg/ml ) was treated with 10μl trypsin ( 2 . 5mg/ml , Gibco , Invitrogen ) or PBS ( control lysates ) and incubated overnight , before the addition of 10μl 25X Complete Protease Inhibitor ( Roche ) . Intratect was a kind gift of Biotest Pharma GmbH , Dreieich , Germany . Vigam ( Bioproducts Laboratories Ltd , Elstree , UK ) was obtained commercially . Both contain 5% pooled human intravenous immunoglobulin . Dilutions of IVIG described for experimental data refer to dilutions of the 5% product rather than the resulting IgG concentration . Individual sera were collected from HIV-negative healthy adults in Malawi ( age range 19 to 49 years , mean 29 years , 16 male and 4 female ) who had not been immunised against S . pneumoniae . Serum from elderly subjects ( age range 62 to 78 years , 6 males , 4 females ) and young adult controls subjects ( age range 24 to 33 years , 4 males , 6 females ) was a kind gift from Dr Elizabeth Sapey , University of Birmingham . Specific antibody was depleted from IVIG by bacterial surface absorption with either unencapsulated TIGR4 or S . mitis expressing the serotype 4 capsule [53] . Bacteria were grown to OD580 0 . 4 , washed and re-suspended to OD 1 . 0 using PBS , and 4mls were pelleted by centrifugation before re-suspension in 1 . 8mls of IVIG ( Intratect ) . The suspension was incubated for 1hr at 37°C , shaking at 100rpm . The antigen-depleted IVIG was recovered by centrifugation and the process repeated . Mock absorbed IVIG was prepared by following the same process but without addition of bacteria . IVIG was pre-treated with papain to yield monovalent Fab fragments using the Pierce Fab Preparation Kit according to the manufacturer’s instructions and confirmed by immunoblot . Enriched ( e ) IVIG was prepared by affinity chromatography as previously described [54] . For the affinity resin , unencapsulated TIGR4 or D39 cultures were grown for 16h , pelleted and resuspended in 1 volume of coupling buffer ( 0 . 1 M Sodium bicarbonate , 0 . 5 M Sodium chloride; pH 8 . 3 ) . Cells were pressure lysed at 200 MPa using a pressure cell homogeniser ( Stansted ) and the resulting lysates were 0 . 2 μm filtered and dialysed against 5L of coupling buffer for 4 h at RT . Lysates were concentrated using Vivaspin 20 centrifugal concentrators with a molecular weight cut of 10 kDa ( GE healthcare ) and coupled to cyanogen bromide activated agarose ( Sigma-Aldrich ) at a concentration of approximately 1 mg/ml according to the manufacturer’s instructions . Whole cell , or specific antigen ( individual proteins , capsular polysaccharide or cell wall polysaccharide ) ELISAs were performed as previously [18 , 55–57] . Recombinant PhtD was a kind gift of C . Durmort [58] and PsaA was a kind gift from J . Paton [59] . IgG binding to a panel of bacterial proteins and multiple capsular serotypes were assessed using Luminex assays [55] and electrochemiluminescence-based multiplex assay based on MesoScale Discovery ( MSD , Rockville , MD , USA ) technology as previously described [13 , 55 , 60] . For immunoblotting , bacterial lysates were separated by SDS-PAGE and transferred on to nitrocellulose membranes as previously described [36] . Membranes were probed with IVIG ( Intratect ) or pooled human sera ( 1:1000 ) . To assess IgG binding to the bacterial surface , flow cytometry was performed as previously described [57 , 61 , 62] . Effects of IVIG on bacterial aggregation during growth were assessed by inoculating THY with 1x106 of S . pneumoniae and measuing the OD580 over an 8 h period in the presence of 10% IVIG ( Intratect , 40mg/ml IgG ) or PBS . Following 8 h growth , cultures were fixed onto polylysine slides ( VWR ) , stained with rapid Romanowsky staining ( Diff-Quick ) and imaged under light microscopy ( Olympus , BX40 ) at 100X using Q capture pro software . Bacterial aggregation was directly assessed by incubating bacteria diluted in PBS to 1X106 CFU/ml at 37°C in 5%CO2 for 1 hr with 0% , 1% , 5% , 10% , IVIG ( Intratect 40mg/ml IgG ) . After fixation in 50μl 10% NBF , particle size was asessed by flow cytometry using a FACSCalibur with Cellquest and Flowjo software ( BD Bioscience , UK ) as a change in forward-scatter ( FSC ) . Bacterial phagocytosis was measured as previously described as the association of FAM-SE labelled bacteria with either RAW 264 . 7 macrophages ( MOI 10 ) [38 , 53] or freshly isolated human neutrophils [57] . Briefly , RAW 264 . 7 murine cells were grown in RPMI supplemented with 10% heat-inactivated foetal calf serum . After washing , they were infected with FAM-SE labelled bacteria at an MOI of 10 which had been pre-incubated with IVIG or PBS for 30 mins at 37 C . After 45 min , cells were harvested with trypsin , fixed with paraformaldehyde ( PFA ) and fluorescence assessed using a FACS Calibur flow cytometer with Cellquest and Flowjo software ( BD Bioscience , UK ) . For neutrophil phagocytosis , similarly opsonised labelled bacteria were incubated with freshly isolated human granulocytes for 30 min at MOI 20 , after which they were fixed with PFA and assessed by flow cytometry . To assess bacterial killing by human neutrophils , pre-opsonised bacteria were incubated with freshly isolated granulocytes for 45 min after which they were serially diluted , plated and incubated overnight prior to colony counting . For passive immunisation experiments with IVIG , 6 to 8 week old age-matched outbred CD1 mice ( Charles River , UK ) received two i . p . injections of IVIG totalling 12 . 8mg IgG or the equivalent volume of PBS 3 h prior and immediately before S . pneumoniae TIGR4 . Challenges were given either i . n . with 50μl of PBS containing 1x107 CFU or i . v . with 100μl of PBS containing 5x105 CFU . To ensure aspiration of the IN inoculum , mice were anaesthetised using 4% halothane ( Vet-Tech ) . At the designated time points after inoculation , mice were culled and BALF , lung homogenates , and blood obtained for plating to calculate bacterial CFU as described previously [19 , 48] . BALF was collected by instilling the lungs with 1ml PBS via an incision in the trachea . This was recovered by aspiration repeated three times . Splenic macrophages were depleted by i . v . administration of 100ul of 5mg/ml liposomal clodronate ( controls were given PBS liposomes ) [38 , 63] . Macrophage depletion was confirmed by a 50% reduction in F4/80+ splenocytes by flow cytometry using anti-F4/80-phycoerythrin ( Caltag ) . To deplete Ly6G+ neutrophils , 600 μg anti-Ly6G monoclonal antibody ( 1A8m , Bioxcell ) was administered by i . p . injection 24 hours prior to infection challenge depletion , as previously [24] , resulting in a 94 . 8% decrease in neutrophils recruited to lavage fluid 24 hours after infection . Murine albumin was measured by ELISA using a commercially available kit following manufacturer’s instructions ( Bethyl Laboratories ) . Murine TNF-alpha was measured by ELISA and BALF cell counts in cytospins as previously described [24] . Human IgG was measured in murine samples using a commercially available ELISA kit following manufacturer’s instructions ( Cambridge Bioscience ) . Data are presented as group means with error bars representing standard deviations ( SDs ) . Student’s unpaired T-test was used to compare the mean of two groups or analysis of variance ( ANOVA ) for comparisons between multiple groups , using Bonferroni post-test comparisons . F tests were used to assess if the slope of linear regressions were statistically different to 0 . Statistical tests were performed using Graph Pad Prism software , and P values < 0 . 05 were considered significant . Experiments were approved by the UCL Biological Services Ethical Committee and the UK Home Office ( Project Licence PPL70/6510 ) . Experiments were performed according to UK national guidelines for animal use and care , under UK Home Office licence . Blood samples were taken from human volunteers in Malawi with approval of the University of Malawi College of Medicine Research and Ethics Committee and the Liverpool School of Tropical Medicine Research Ethics Committee ( Ref: 00 . 54 ) . | Streptococcus pneumoniae is a major global killer . Invasive pneumococcal disease ( IPD ) is the most severe form of infection . Surprisingly , the natural mechanisms of immunity to IPD in healthy individuals are unclear . The success of vaccines stimulating anti-capsular antibodies have led to the belief that the same mechanism lies behind natural protection . Using studies with pooled human immunoglobulin , we demonstrate that this is not the case and instead IgG recognising the bacterial surface proteins appears to have the dominant functional role . This finding supports efforts towards protein antigen-based vaccines , and opens the possibility of stratifying potential risk for individuals of IPD . | [
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"... | 2017 | Naturally Acquired Human Immunity to Pneumococcus Is Dependent on Antibody to Protein Antigens |
Previous studies have shown substantial differences in Sodalis glossinidius and trypanosome infection rates between Glossina palpalis palpalis populations from two Cameroonian foci of human African trypanosomiasis ( HAT ) , Bipindi and Campo . We hypothesized that the geographical isolation of the two foci may have induced independent evolution in the two areas , resulting in the diversification of symbiont genotypes . To test this hypothesis , we investigated the symbiont genetic structure using the allelic size variation at four specific microsatellite loci . Classical analysis of molecular variance ( AMOVA ) and differentiation statistics revealed that most of the genetic diversity was observed among individuals within populations and frequent haplotypes were shared between populations . The structure of genetic diversity varied at different geographical scales , with almost no differentiation within the Campo HAT focus and a low but significant differentiation between the Campo and Bipindi HAT foci . The data provided new information on the genetic diversity of the secondary symbiont population revealing mild structuring . Possible interactions between S . glossinidius subpopulations and Glossina species that could favor tsetse fly infections by a given trypanosome species should be further investigated .
Tsetse flies are medically and agriculturally important vectors that transmit African trypanosomes , the causative agents of sleeping sickness in humans ( human African trypanosomiasis – HAT ) and Nagana in animals . This debilitating disease still affects a wide range of people in sub-Saharan Africa [1] and is invariably fatal if untreated . Nagana is estimated to cost African agriculture US$4 . 5 billion per year [2] . The drugs currently used are unsatisfactory: some are toxic and all are difficult to administer in humans [3] . Furthermore , drug resistance is increasing [4] . Therefore , investigations for novel drugs and/or novel disease control strategies are urgently needed . The biological process leading to transmission of the trypanosomes from one mammalian host to another is complex . Prior to being transmitted , the parasite must first establish in the tsetse fly midgut following an infective blood meal . Then it must mature either in the salivary glands or in the mouthparts , depending on the trypanosome species [5] , [6] . This ability to acquire the parasite , favor its maturation , and transmit it to a mammalian host is known as vector competence . It depends on both Glossina and trypanosome species . Tsetse flies harbor three different symbiotic microorganisms [7] . One of them , Sodalis glossinidius [8] , [9] , a maternally transmitted secondary endosymbiont , is suspected of being involved in the vector competence of Glossina [10]–[12] . The reported full-length sequencing of the complete genome [13] and extrachromosomal DNA [14] showed that S . glossinidius displays active mechanisms of cellular interactions and is an intermediate between free-living and obligate intracellular bacteria evolving toward a specific interaction with Glossina [13] , [14] . G . palpalis gambiensis ( palpalis group ) and G . morsitans morsitans ( morsitans group ) were shown to harbor genetically distinct populations of S . glossinidius [15] . Neutral evolution is likely to explain this result , but , interestingly , G . palpalis gambiensis and G . morsitans morsitans transmit preferentially different trypanosomes species . The ability of Trypanosoma brucei gambiense and Trypanosoma brucei brucei to establish in G . palpalis gambiensis midgut was further linked to the presence of S . glossinidius-specific genotypes in the insectarium [16] . This suggests that vector competence might be linked to given genotypes of S . glossinidius rather than a mere presence/absence of the symbiont . Given that flies multiplying in the insectary may undergo specific selective pressures that differ from those in the natural environment , the possibility that field environmental conditions of HAT foci may lead to alternative results could not be excluded . Therefore , an epidemiological investigation was conducted in two HAT foci in the south of Cameroon . This study took into account a large panel of criteria to test the existence of interactions between the three Glossina-trypanosome-symbiont partners . The results showed that the trypanosome infection was not randomly distributed between subpopulations of field flies harboring S . glossinidius or free of S . glossinidius . Statistical analyses confirmed the association between the presence of the symbiont and the field flies' infection by trypanosomes: the parasite prevalence was nearly threefold higher within the populations of flies harbouring the symbiont than within those that did not harbour the symbiont [17] . The results obtained from fly trapping , the prevalence of S . glossinidius and trypanosomes , and the different symbiont/parasite associations also showed significant differences between the fly populations from the two HAT foci . We hypothesized that the geographical isolation of the two foci may have induced independent evolution of fly and symbiont populations in each area , resulting in a diversification of symbiont genotypes . If so , it may be assumed that such genotypes may interact differently with Glossina species and may favor fly infection by a given trypanosome species in different ways . The foregoing assumption necessitated a large-scale analysis of the genetic diversity of S . glossinidius and the distribution of the different genotypes within the fly populations of the different sampling areas . Following preliminary assays , microsatellite markers seemed to be well suited to perform this analysis . To the best of our knowledge , the present study is the first large-scale genetic population investigation on the tsetse fly symbiont , S . glossinidius , from Glossina palpalis palpalis flies sampled in Campo and Bipindi , two sleeping sickness foci in Cameroon . The aim was to analyze their genetic diversity in order to determine the genetic structure of Sodalis and to study possible rates of gene flow at various spatial scales of Cameroonian S . glossinidius populations .
Glossina palpalis palpalis flies were collected in two HAT foci ( Bipindi and Campo ) situated in the Ocean Division of the South Region of Cameroon . The Campo focus ( 2°20′N , 9°52′E ) presents several biotopes ( farmland , marshes , swampy areas , and equatorial forest ) . The Bipindi focus ( 3°2′N , 10°22′E ) has a typical forest bioecological environment including equatorial forest and farmland along roads and around villages . Both foci contain highly diversified wild fauna [18] , [19] . The Bipindi focus has been known since 1920 [20] and is still active since 60 new patients were detected between 1998 and 2002 [20] and two in 2006–2007 ( V . Ebo'o Eyenga , pers . comm . ) . Bipindi covers several villages , mainly located along roads . It is surrounded by hills and has a dense network of rivers crossing cocoa farms , offering suitable habitats for tsetse flies . Campo is located on the Atlantic coast and extends along the Ntem River [21] . It is characterized by an equatorial rain forest zone with a network of several rivers , swampy areas , and marshes . During the epidemiological survey conducted in 2006–2007 , ten cases of sleeping sickness were detected ( V . Ebo'o Eyenga , pers . comm . ) . The two HAT foci ( Bipindi and Campo ) are located on different river basins . Entomological surveys were conducted in 2007 in Bipindi and Campo . The geographical positions of the sampling sites were determined using the global positioning system . Tsetse flies were captured using pyramidal traps [22] planted in suitable tsetse fly biotopes . Each trap remained deployed for four consecutive days and flies were harvested twice a day . The different Glossina species were first identified and then sorted into teneral and non-teneral flies , according to morphological criteria [23] . Tenerals are young flies that have never taken a blood meal and that have never got the opportunity to get infected by trypanosomes; thus they were discarded from the experiment design . The non-teneral flies were dissected in a drop of sterile 0 . 9% saline solution , and their midgut separately transferred into microfuge tubes containing ethanol ( 95° ) for further symbiont analyses . The instruments used were carefully cleaned after the dissection of each fly to prevent contamination . During field manipulations , the microfuge tubes were maintained at room temperature; thereafter , they were stored in the laboratory at −20°C until use . DNA was extracted from tsetse fly midguts using cetyl trimethyl ammonium bromide ( CTAB ) as described by Navajas et al . [24] and processed according to the previously published methodological report [25] . Briefly , tissues were homogenized with a pestle in a CTAB buffer ( CTAB 2%; 0 . 1 M Tris , pH 8; 0 . 02 M EDTA pH 8; 1 . 4 M NaCl ) and incubated at 60°C for 30 min . The DNA was extracted from the lysis mixture with chloroform/isoamylic alcohol ( 24/1; V/V ) and precipitated by adding isopropanol ( V/V ) to the DNA containing phase . After centrifugation ( 10 , 000 g , 15 min ) , the pellet was rinsed with 70% ethanol , air-dried , and resuspended in distilled sterile water . The DNA samples were stored at −20°C until PCR amplification . This step was processed as in Farikou et al . [25] , with however several modifications . We used the three microsatellites ( ADNg 5/2; ADNg 21/22 and ADNg 15/16 ) described in Farikou et al . [25] . Besides , we amplified a fourth microsatellite ( ADNg 12/13 ) from the sequence published in Genbank ( GenBank accession number AP008232 ) , according to its potentiality to generate high level of polymorphism [26]; its primers were designed using the software Primer3 ( http://frodo . wi . mit . edu/primer3/ ) ( Table 1 , Table 2 ) . For each of the four couples of primers , one of the primers was 5′ end labelled with an infrared dye ( IRD700 or IRD800 ) for sizing the PCR products with an automatic sequencer . Primers were synthesized by MWG ( Ebersberg , Germany ) . Specific primers amplifying tsetse fly mitochondrial DNA were used to control the quality of the extracted DNA , as previously described [8] . Specific polymerase chain reaction ( PCR ) detection of S . glossinidius was performed on midgut-extracted DNA , as previously described [17] . Midguts showing specific detection of S . glossinidius were further processed for S . glossinidius microsatellite genotyping . The method was adapted from Farikou et al . [25] . The amplification reaction mixture consisted of 10 mM Tris–HCl ( pH 8 . 3 ) , 50 mM KCl , 1 . 5 mM MgCl2 , 0 . 2 mM dNTPs ( QBiogene ) , 4 pmol of each primer , 0 . 6 U Taq DNA polymerase ( QBiogene ) , and 3 µl of fivefold diluted DNA in a 20-µl reaction volume . Amplifications were carried out as follows: 3 min at 94°C for initial denaturation , 35 cycles of denaturation at 94°C , annealing at 58°C for the marker ADNg5/2 and 55°C for the markers ADNg15/16 , ADNg12/13 , and ADNg21/22 , and extension at 72°C for 30 s . The final cycle was followed by an additional 10 min at 72°C to complete polymerization . Primer sets for each locus were tested to ensure that they specifically amplified S . glossinidius , and not host ( fly ) DNA . In order to assess whether the amplicons corresponded to S . glossinidius microsatellites and to determine the number of microsatellite repeats , the different alleles were cloned into PGEM-T Easy ( Promega , Charbonnières , France ) . For each different allele , one recombinant plasmid was then sequenced ( GenBank accession numbers JN032317–JN032335 ) and compared with the reference sequence of S . glossinidius [13] ( GenBank accession number AP008232 ) . The number of the repeat elements was determined by sequence analysis . Negative controls , consisting of extraction reagents without tsetse fly material , were used throughout the isolation procedures and included in PCR assays along with several template blanks ( water ) to ensure the absence of contamination in typing experiments . After specific amplification , infrared dye-labeled ( IRD700 or IRD 800 ) PCR products were diluted to 1/5 , 1/10 , or 1/50 in loading buffer ( 95% deionized formamide , 20 mM ethylenediaminetetraacetic acid ( EDTA ) , pH 8 . 0 , and 1 mg/ml bromophenol blue ) . Then they were denatured for 3 min at 95°C , and transferred to ice before loading . The sample-loading volume was 1 . 22 µl . Each mixture was separated , in a 1- to 2-h run at 1500 V , on a 6 . 5% ( wt/vol ) Long Ranger polyacrylamide gel , using 1× Tris-borate-EDTA buffer ( Bio-Rad , Hercules , CA , USA ) , on a two-dye , model 4300 LI-COR-automated DNA sequencer . Infrared images of the patterns were analyzed using the semiautomated scoring program Quantar ( version 1 . 05; KeyGene products B . V . , Wageningen , The Netherlands ) . Measurement of allele length on polyacrylamide gels was automated using molecular size markers .
The complete dataset included multilocus genotypes for the 244 S . glossinidius strains from the 244 G . palpalis palpalis sampled in HAT foci in Cameroon ( 113 from Bipindi , 131 from Campo ) . The genome of these 244 S . glossinidius samples carried the four loci investigated in full length . The four microsatellite loci were polymorphic , and a total of 19 alleles were detected , ranging from three ( ADNg 12/13 ) to six ( ADNg 21/22 , ADNg 15/16 ) alleles per locus ( Table 1 ) . Over all populations , the mean number of alleles was 2 . 25 for the ADNg 12/13 locus , 3 . 25 for the ADNg 5/2 locus , 3 . 75 alleles for the ADNg 21/22 locus , and 4 . 25 for the ADNg 15/16 locus ( Table 3 ) . The mean heterozygosity ( HE ) ( Table 3 ) was quite different between loci , ranging from 0 . 07 ( ADNg 12/13 ) to 0 . 59 ( ADNg 15/16 ) . Per population over the four loci , the heterozygosity ( HE ) varied from 0 . 35 to 0 . 41 , corresponding to the villages Ebimimbang and Campo Beach/Ipono , respectively . The combination of the microsatellite alleles yielded a total of 35 haplotypes ( Table 3 ) . The four populations were polymorphic , showing 12–20 haplotypes . Their haplotypic diversities were 0 . 84 , 0 . 86 , 0 . 91 , and 0 . 92 for Ebimimbang , Mabiogo , Akak , and Campo Beach/Ipono , respectively , with haplotypic richness of 10 . 14 , 9 . 51 , 10 . 56 , and 14 , respectively . The mean haplotypic diversity was 0 . 87 . The population structure of S . glossinidius was explored at different hierarchical levels using AMOVA ( Table 4 ) . On haplotypic frequencies , AMOVA revealed that most of the variation was found among individuals within populations ( 97 . 8% ) . The fixation index reflecting the nested design of the samples indicated no overall differentiation between populations within the Campo focus ( FSC = 0 . 003 , P = 0 . 33 ) and a slight but not significant differentiation at the foci level ( FCT = 0 . 019 , P = 0 . 25 ) . The genetic differentiation among the four populations was low ( FST = 0 . 022 ) but significant ( P = 0 . 006 ) . Pairwise population comparisons of genetic differentiation ( FST ) are shown in Table 5 . No differentiation was shown between the villages Akak and Campo Beach/Ipono ( FST = −0 . 005 ) nor between Campo Beach/Ipono and Mabiogo ( FST = −0 . 001 ) . A positive but not significant differentiation ( FST = 0 . 009 ) was recorded between Mabiogo and Akak . Even though FST values were relatively low , significant differences were shown between Ebimimbang and Mabiogo , Akak and Campo Beach/Ipono , with FST of 0 . 018 ( P = 0 . 023 ) , 0 . 022 ( P = 0 . 027 ) , 0 . 032 ( P = 0 . 014 ) , respectively . This indicates a significant differentiation between the Bipindi ( Ebimimbang ) population and those of the three villages ( Akak , Campo Beach/Ipono , and Mabiogo ) in the Campo focus . The neighbor-joining ( NJ ) tree calculated from the haplotype frequency using the usual Euclidian distance is shown in Figure 1 . The NJ tree merges ( 1 ) the Ebimimbang S . glossinidius population ( Bipindi HAT focus ) with the Mabiogo population ( Campo focus ) and ( 2 ) the Akak and Campo Beach/Ipono populations ( Campo focus ) , but the node is mildly supported by the bootstrap resampling ( bootstrap value 63% ) . The four main haplotypes ( H11 , H14 , H27 , H30 ) , showing overall frequencies above 0 . 05 , were shared by the populations from the four sampling areas ( Table S1 and Figure 2 ) and were present at high frequencies within the populations studied ( except H27 in Ebimimbang , present at a low frequency ) . These haplotypes were separated by several mutation steps . The median-joining network resulted in a complex haplotype network and did not show a clear pattern of phylogeographic evolution ( Figure 2 ) . Global NST was estimated at 0 . 015 and was not significantly different from global GST ( P = 0 . 72 ) . Pairwise NST between populations are shown in Table 5 and were not significantly different from GST , but the NST estimated between Akak and Mabiogo was almost significantly larger than GST ( P = 0 . 059 ) . In addition , GenGIS was used to draw a georeferenced pattern of haplotype diversity ( Figure 3 ) . Figure 3 clearly shows that haplotypes with high frequencies are shared between populations . Moreover , the Akak and Campo Beach/Ipono populations displayed more haplotypes with frequencies above 0 . 05 ( eight and five haplotypes , respectively ) than Mabiogo ( four haplotypes ) and Ebimimbang ( four haplotypes ) , reflecting the large genetic diversity of the first two populations .
The present study was conducted within our investigations on sleeping sickness and most particularly on the tripartite interactions between the vector ( the tsetse fly ) , its secondary symbiont ( Sodalis glossinidius ) , and the parasite ( the trypanosomes ) the vector transmits to humans and animals . This study was the first to take an interest in the population genetics of S . glossinidius in the field . Its aim was to analyze the genetic diversity of S . glossinidius populations from two human African trypanosomiasis foci , Bipindi ( one village ) and Campo ( three villages ) , in South Cameroon , in order to detect possible differentiation / gene flow within or between the HAT foci . The S . glossinidius analyzed were those harbored by the flies ( 244 in total ) sampled in the different areas of the two foci . Genotyping was performed using the variable-number-of-tandem-repeats ( microsatellites ) approach , not yet used to investigate S . glossinidius genetic diversity in the field . Four polymorphic loci were analyzed , the robustness and resolving power of this approach being maximized when large strain collections are analyzed using multiple loci [39] , [40] . The microsatellite markers were polymorphic and had sufficiently high resolution to estimate the genetic structure of S . glossinidius isolated from G . palpalis palpalis . The populations , corresponding to the four villages analyzed , had high levels of genetic diversity , as indicated by the allelic richness and the proportion of heterozygotes ( HE ) , whereas one locus ( ADNg 12/13 ) was nearly nonpolymorphic . The combination of the four markers into haplotypes led to substantial overall diversity ( HEh = 0 . 87 ) . However , genetic diversity was lower in Mabiogo and Ebimimbang than in Akak and Campo Beach/Ipono . The lower genetic diversity of the Ebimimbang and Mabiogo S . glossinidius population may be associated with a lower effective population size in these villages . This could be due to the lower effective population size of its host , G . palpalis palpalis , or to the existence of a selective pressure exerted by the tsetse flies on the symbiont S . glossinidius in the populations concerned . It should be noted that a lower apparent fly density per trap and per day was observed in Ebimimbang in comparison with the other three villages , and particularly Akak [17] , and in Mabiogo in comparison with Akak and Campo Beach/Ipono . However , this observation should be taken with caution because differences in the apparent fly density may not reflect differences in effective population sizes . Within the Campo HAT focus , differentiation between populations was not significant . The village of Ebimimbang , located in the Bipindi HAT focus , showed significant FST with the three villages in the Campo focus , with the foci 150 km apart and located on different river basins . The differentiation analysis , based on the pairwise FST between populations and the AMOVA , revealed that the S . glossinidius populations presented a slight but significant differentiation between the Bipindi and Campo HAT foci . The network and the georeferenced haplotype analysis showed that three frequent ancestral haplotypes were shared between the four populations and that there was not a geographic pattern of haplotypic diversity . These data suggest either that the gene exchange between populations occurred repeatedly or that the haplotypes derived from a common ancestral population . The information provided by the NST did not show an impact of the alleles' phylogeography on the structure of genetic diversity , except perhaps for the relation between haplotypes from the Mabiogo and Akak populations . However , the absence of information on the mutation process of microsatellite markers in S . glossinidius does not allow inferring the liability of NST/GST comparison in this species . Finally , these results tend to show that the Akak and the Campo Beach/Ipono S . glossinidius populations may be considered as a single population , suggesting that gene flow occurred within the Campo HAT focus . Between Campo and Bipindi HAT foci , differentiation existed but was low . This could be explained by the fact that genes flow between the Ebimimbang population and the Campo focus is ongoing or has been maintained until recently at a level preventing strong differentiation . As a symbiont of Glossina , with mainly a vertical transmission but also perhaps a horizontal transmission among matrilines of tsetse flies [41] , the genetic diversity of S . glossinidius depends on its host . Our results suggest that gene flow exists between tsetse flies within the Campo HAT focus and that structuring may exist between the two foci , implying a limited gene flow , at least of female flies . The slight local differentiation among the S . glossinidius populations might be related to the fly migration rate between the HAT foci . The two HAT foci are located on different river basins ( see Figure 3 ) , but tsetse flies could move from place to place and form a continuous belt , which could be promoted by the presence of a large number of rivers and stream habitats , combined with suitable host availability allowing good dispersal conditions and a less confined spatial distribution of flies [42] . Finally , all these results suggest that the S . glossinidius populations of the two Cameroonian foci may be considered to belong to a lineage from which subgroups are genetically differentiating . Genetic diversity was previously observed in S . glossinidius strains from insectary Glossina palpalis gambiensis species [15] , [16] and was hypothesized to reflect differential host-driven selective pressures . In a previous study [17] , sizeable differences between the sampled population of flies from Campo and Bipindi were recorded for the prevalence of S . glossinidius and trypanosome infections . Nevertheless , a significant association was found between the presence of S . glossinidius and the Trypanosoma infections of field populations of tsetse flies [17] . In conclusion , these results provide new information on the genetic diversity of S . glossinidius populations . They evidence the existence of differences between symbiont populations according to the flies' origin , the Campo or the Bipindi HAT focus . The evidence of a slight gene flow ( or gene flow maintained up to very recently ) between the two foci located about 150 km from each other was unexpected . This means that tsetse fly migration occurs despite this rather large distance . This finding is important in the context of sustainable vector control . Accurately estimating to what extent the genetic diversity of S . glossinidius populations depends on the population genetics of its host G . palpalis palpalis deserves to be studied: the genetic diversity analysis of tsetse fly populations will have to be undertaken within the same foci . Moreover , further investigations will consist in looking for a possible association between field tsetse fly infections by a given trypanosome species and the presence of S . glossinidius-specific haplotypes . These investigations could contribute to understanding the differences in the prevalence of S . glossinidius and trypanosomes between foci . The identification of S . glossinidius haplotypes potentially associated with vector competence could be included as diversity markers in epidemiological surveys , risk mapping and management , and vector control strategies . | Human African trypanosomiasis remains a threat to the poorest people in Africa . The trypanosomes causing the disease are transmitted by tsetse flies . The drugs currently used are unsatisfactory: some are toxic and all are difficult to administer . Furthermore , drug resistance is increasing . Therefore , investigations for novel disease control strategies are urgently needed . Previous analyses showed the association between the presence of Glossina symbiont , Sodalis glossinidius , and the fly infection by trypanosomes in a south-western region in Cameroon: flies harbouring symbionts had a threefold higher probability of being infected by trypanosomes than flies devoid of symbionts . But the study also showed substantial differences in S . glossinidius and trypanosome infection rates between Glossina populations from two Cameroonian foci of sleeping sickness . We hypothesized that the geographical isolation of the two foci may have induced the independent evolution of each one , leading to the diversification of symbiont genotypes . Microsatellite markers were used and showed that genetic diversity structuring of S . glossinidius varies at different geographical scales with a low but significant differentiation between the Campo and Bipindi HAT foci . This encourages further work on interactions between S . glossinidius subpopulations and Glossina species that could favor tsetse fly infections by a given trypanosome species . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"biology",
"veterinary",
"science"
] | 2011 | Genetic Diversity and Population Structure of the Secondary Symbiont of Tsetse Flies, Sodalis glossinidius, in Sleeping Sickness Foci in Cameroon |
There is great scientific and popular interest in understanding the genetic history of populations in the Americas . We wish to understand when different regions of the continent were inhabited , where settlers came from , and how current inhabitants relate genetically to earlier populations . Recent studies unraveled parts of the genetic history of the continent using genotyping arrays and uniparental markers . The 1000 Genomes Project provides a unique opportunity for improving our understanding of population genetic history by providing over a hundred sequenced low coverage genomes and exomes from Colombian ( CLM ) , Mexican-American ( MXL ) , and Puerto Rican ( PUR ) populations . Here , we explore the genomic contributions of African , European , and especially Native American ancestry to these populations . Estimated Native American ancestry is in MXL , in CLM , and in PUR . Native American ancestry in PUR is most closely related to populations surrounding the Orinoco River basin , confirming the Southern America ancestry of the Taíno people of the Caribbean . We present new methods to estimate the allele frequencies in the Native American fraction of the populations , and model their distribution using a demographic model for three ancestral Native American populations . These ancestral populations likely split in close succession: the most likely scenario , based on a peopling of the Americas thousand years ago ( kya ) , supports that the MXL Ancestors split kya , with a subsequent split of the ancestors to CLM and PUR kya . The model also features effective populations of in Mexico , in Colombia , and in Puerto Rico . Modeling Identity-by-descent ( IBD ) and ancestry tract length , we show that post-contact populations also differ markedly in their effective sizes and migration patterns , with Puerto Rico showing the smallest effective size and the earlier migration from Europe . Finally , we compare IBD and ancestry assignments to find evidence for relatedness among European founders to the three populations .
The 1000 Genomes project [1] released sequence data for 66 Mexican-American ( MXL ) , 60 Colombian ( CLM ) , and 55 Puerto Rican ( PUR ) individuals using an array of technologies including low-coverage whole genome sequence data , high-coverage exome capture data , and OMNI 2 . 5 genotyping data . These data provide a unique window into the settlement of the Americas that complement archeological and the more limited genetic data previously available . Here we interpret these data to answer basic questions about the pre- and post-Columbian demographic history of the Americas . People reached the Americas by crossing Beringia during the Last Glacial Maximum , likely between 16–20 kya ( see e . g . [2] , [3] , [4] , [5] ) . The presence of early South American sites such as Monte Verde [6] suggests a rapid occupation of the continent , which is supported also by recent mitochondrial DNA studies [7] . A coastal route has been proposed to explain this rapid expansion ( e . g . , [8] , [6] , [7] ) , but other migration routes , possibly concurrent , have also been proposed ( see . e . g . , [5] , [9] , and references therein ) . This original peopling of the Americas , followed by European contact starting in 1492 and substantial African slave trade starting in 1502 , have created a diverse genetic heritage in American populations . The initial settlement of the Caribbean has been much debated ( e . g . [10] , [11] , [12] and references therein ) . People reached the islands around 7 kya , probably from a Mesoamerican source [11] . Around 4 . 5 kya , a second wave of migrants probably reached the islands , likely coming from the Orinoco Delta or the Guianas in South America and speaking Arawakan languages ( see [13] and references therein ) . By approximately 1 . 3 kya , they had established large Taíno communities through the Greater Antilles , including Puerto Rico . The earliest available account reports 600 , 000 Native Americans in Puerto Rico at the time of European arrival , not counting women and children ( Vázquez de Espinosa 1629 ) . More conservative estimates suggest 110 , 000 individuals [14] , and as few as 30 , 000 inhabitants in 1508 [15] . All references agree that the Native American population was subsequently largely decimated through disease , forced labor , emigration , and war . Despite the bottleneck at contact , admixture and the subsequent population growth on the Island resulted in a Native American genetic contribution averaging of the modern population of million [16] . The MXL were sampled in Los Angeles , USA and the CLM in Medellin , Colombia . These panels represent urban populations , but recent urbanization means that they derive ancestry from larger geographic areas . Among respondents to the 2005 Colombia Census in Medellin , were born in the city , and were born in another part of Colombia , with a sizable proportion from the surrounding Department of Antioquia . Given this high rate of within-country migration , but a relatively low rate of migration from outside Colombia , we can think of the sample as representing a diverse sample from Antioquia . Similarly , the 1 . 2M Angelenos of Mexican origin in the 2010 US census represent the added contributions of multiple waves of migrations starting with the city's foundation in 1781 and received contributions from diverse states . The use of genetic data to study Native American history is well established . The bulk of these studies rely on Y chromosome [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] and mitochondria DNA ( mtDNA ) [25] , [26] , [27] , [28] , [29] , [30] , [31] , [22] , [32] , [7] , [33] , [34] , [35] , with a number of studies using increasingly dense sets of autosomal markers [22] , [36] , [37] , [38] , [39] , [40] . Such studies provided evidence for a bottleneck recovery into the Americas 16–12 kya ( e . g . , [34] , [35] ) , and for complex models of migrations and admixture within Native groups [40] . In this article , we use the 1000 Genomes data and a diversity of population genetic tools to delve deeper in the founding of the Puerto Rican , Mexican , and Colombian populations . To propose models for Native American demography , we must first quantify the African , European , and Native American contributions to these populations . Because of strong sex-asymmetric migrations , autosomal and sex-linked markers exhibit substantial differences in ancestry proportions [41] , [42] , [43] , [44] , [45] , [46] . Focusing on the autosomal regions , we infer the locus-specific pre-Columbian continental ancestry in each sample , and estimate the timing and intensity of different migration waves that contributed to these populations . Using identity-by-descent analysis , we identify relatedness among the different ancestral groups and estimate recent effective population sizes . We also propose a three-population model based on the diffusion approximation to study the distribution of allele frequencies across the Native American ancestors of the MXL , PUR , and CLM . We present statistical methods that take advantage of admixture linkage patterns to disentangle the histories of each continental group . The large sample of sequence data allows for the joint inference of split times and effective population sizes among the Native ancestors to the three panels . Finally , through an expectation maximization ( EM ) framework , we estimate genome-wide allele frequencies in the inferred Native components of MXL , CLM , and PUR genomes . A broad summary of the data and analysis pipelines used in this article are displayed in Figure 1 .
To estimate the global proportions of African , European , and Native American ancestry in the CLM , MXL , and PUR , we combined them with YRI , CEU , and a panel of Native American samples [40] and performed an admixture [47] analysis ( Figure 2 ( a ) ) and principal component analysis ( Figure S1 ) . Dense genotyping arrays allow for inference of ancestry at the level of individual loci , using software such as RFMix [48] . Trio-phased OMNI data was used to generate such locus-specific ancestry calls for 66 CLM , 68 MXL , and 64 PUR individuals , including all sequenced individuals , as part of the 1000 Genomes Project . Summing up the local ancestry contribution inferred by RFMix provides an alternate estimate of ancestry proportions . Using admixture , we find Native American proportions being in PUR , in CLM , and in MXL ( Figure 2a ) . RFMix finds values falling within percentage points of these values , and within one percentage point of the values inferred in the 1000 Genomes project through related methods [1] . Estimates of African ancestry showed a larger difference across methods , with admixture ( RFMix ) estimates at in PUR , in CLM , and in MXL . The inferred Native American ancestry proportions are in good agreement with results from the GALA study [49] , which reported proportions of in Puerto Rico and in Mexico . The PUR result is also comparable to the of Native ancestry inferred in a different Puerto Rican sample [16] . By contrast , none of the populations from Colombia in [37] show median ancestry proportions quite similar to the CLM sample from Medellin , the closest being the sample from the surrounding Department of Antioquia , with Native , African and European . Figure 2 ( c–d ) shows a principal component analysis restricted to segments of inferred Native ancestry [50] . We find that the MXL individuals cluster primarily with southern Mexican Native groups ( mostly Mixe ) , and the CLM cluster primarily with the Embera , Kogii , and Wayu , all of which were sampled in Colombia North-West of the Andes , where Medellin is also located . The PUR clusters principally with populations South-East of the Andes , surrounding the Guyanas and the Orinoco River basin ( Ticuna , Guahibo , Palikur , Jamamadi , Piapoco ) , although a few populations from further south are also close in PCA space , particularly the Guaraní and the Chané , together with some Kaqchikel , Toba , and Wichi individuals . The Piapoco and the Palikur speak Arawakan languages . The other groups with known Arawakan-speaking ancestors in our panel are the Chané , whose ancestors spoke Arawakan and likely originated in Guiana [51] , and the Guarani , through gene flow from the Chané [52] . Taken together , these clustering patterns support a demic diffusion of the Arawakan/Taínos into Puerto Rico from a southern American route , and reduced gene flow between Native Americans groups living in the Andes or to the west , and groups living east of the Andes . Because continuous tracts of local ancestry are progressively broken down by recombination , the length distribution of continuous ancestry tracts can reveal details of the timing and mode of the migration processes . We used RFMix to infer ancestry tracts ( Text S1 ) , and the software tracts [53] to infer the migration rates and model likelihoods under different scenarios . Tracts can predict the distribution of ancestry block length for arbitrary models of time-varying migration , under the assumptions that the migrants are themselves not admixed , and that the admixed population follows Wright-Fisher reproduction . Since admixture only begins after two populations are in contact , the admixed population is founded when the second population arrives . Tracts determines the time and ancestry proportions at the onset of admixture and the time and magnitude of subsequent migrations by maximum likelihood . Because of limited statistical power , we start with a simple model in which each population contributes a single pulse of migration . We then progressively introduce models with additional periods of migration when justified by information criteria , as described in Text S1 . The models that best describe the data are shown in Figures 3 and S2 . Parameters for these , together with confidence intervals obtained through bootstrap over individuals , are provided in Table S1 in the Text S1 file . For MXL , we considered a model introduced in [54]: three populations start contributing migrants at the same time , but Europeans and Native Americans keep contributing at a constant rate . The best-fitting model has an onset of admixture 15 . 1 generations ago ( ga ) , with a CI of , in good agreement with [54] despite a different genotyping chip and local ancestry inference method . In PUR , we found evidence for two periods of European and African migration , the first ga ( CI ) and the most recent period at ga ( CI 5 . 9–8 . 8 ) . This model is in excellent agreement with historical records , which suggest that isolated Native populations contributed little gene flow to the colony after the initial contact period , and that substantial slave trade and European immigration continued until the second half of the 19th century . We do not mean to imply that migrations actually occurred in exactly two distinct pulses-we do not have the resolution to distinguish more than two pulses per population . However , the inference of a migration pulse 6 . 8 ga indicates that migrations occurred during a period spanning this date . This complex scenario , with multiple waves of migration from African and European individuals , is consistent with the observation that European and African ancestries vary across the island , whereas no evidence of such variation was found in Native ancestry [16] . The inferred onset of admixture in CLM is 13 . 0 ga ( CI ) , significantly later than that in both MXL and PUR and consistent with later European settlement in western Colombia compared to Mexico and Puerto Rico . We also find evidence for a small but statistically significant second wave of Native American migration , 4 . 8 ga ( CI 4–6 ) . As above , this does not necessarily indicate a single , punctual event , but probable contact between an admixed population and Native American individuals during that period . By contrast , we find no evidence for continuing African gene flow in CLM . We used germline [55] and the trio-phased OMNI data above to identify segments identical-by-descent ( IBD ) within and across populations ( see Text S1 ) . Not surprisingly , we found more IBD segments within populations ( 23936 ) compared to across populations ( 1440 ) , and within-population segments were longer ( Figure S3 ) . The MXL population exhibits significantly less within-population IBD compared to the other two panels ( Figure 4 ) . The amount of IBD among unrelated individuals can be used to infer the underlying population size under panmictic assumption: the larger a population , the more distant the expected relationship between any two individuals [56] . Using IBD segments longer than 4 cM , we infer effective population sizes of 140 , 000 in MXL , 15 , 000 in CLM , and 10 , 000 in PUR . As we will show , these largely reflect post- admixture population sizes . We expect long IBD segments to be inherited from a recent common ancestor , and therefore to have identical continental ancestry . Comparing the RFMix ancestry assignments on chromosomes that have been identified as IBD by germline thus provides a measure of the consistency of the two methods ( see [57] for a related metric ) . Rates of IBD-Ancestry mismatch ranged from in segments of to less than for segments longer than 40 Mb ( Figure S4 ) . Patterns of ancestry in IBD segments within a population differ markedly from those across populations ( Figure 5 ) : IBD segments within populations contain many ancestry switches . This indicates that many common ancestors lived after contact , and that the effective population sizes estimated using IBD largely reflects post-contact demography . The IBD patterns in cross-population IBD segments exhibited fewer ancestry switches than a random control ( Figure S5 ) , as may be expected if common ancestors often predate the onset of admixture . Cross-population IBD segments were also found to be overwhelmingly of European origin: among the 120 longest cross-population IBD segments , 117 are in European-inferred segments , two are among Native segments , and one is among African segments . This is not due to overall ancestry proportions , as can be observed by considering the alternate ( non-IBD ) haplotypes at the same positions ( Figure S5 ) . This is likely a result of the colonization history , in which European colonists rapidly spread from a relatively specific region over a large continent . This interpretation is supported by the admixture analysis ( Figure S6 ) , showing a common cluster of ancestry for the European component dominant in PUR , CLM , MXL , and Andean populations , but not in CEU , Eskimo-Aleut , and Na-Dene . Finally , we were interested in testing whether the relationship between IBD and ancestry can be used to date recombination events . The ancestry within an IBD segment represents the ancestry state of the most recent common ancestor . The shorter the IBD segment , the older the ancestor , and the less time available since the onset of admixture to create ancestry switch points through recombination . Indeed , we find that the density of ancestry switch-points on IBD tracts increases with IBD tract length in PUR ( bootstrap , see Text S1 ) and in MXL ( bootstrap ) , whereas the results are not significant in CLM . Thus we can use ancestry patterns in admixed populations not only to recognize recombination events but also to help date most recent common ancestors and recombination events ( see Text S1 for details ) . The small amount of cross-population IBD among Native American tracts tells us that the ancestral Native populations were not as closely related as European founders , consistent with historical and anthropological data . To infer split times and population sizes of the Native ancestors , we consider the joint site frequency spectrum ( SFS ) . The SFS is informative of demography because stochastic differences in allele frequencies accumulate over time and at a rate that depends on population sizes . We use the diffusion-approximation framework implemented in [58] to perform the inference . We focus on synonymous sites in the 1000 Genomes exome capture data of 60 CLM , 66 MXL , and 55 PUR individuals because the high coverage reduces sequencing artifacts and synonymous sites are less affected by selection compared to non-synonymous sites . A complete model with admixture would require at least one European , one African , and three Native American populations , which is beyond the 3-population limit of We therefore wish to focus on variants within Native American backgrounds . Unfortunately , trio-phased sequencing data was not available for most samples . Because of phasing uncertainty , the actual ancestry assignment for variants at ancestry-heterozygous loci is uncertain . To overcome this , we introduce a negative ascertainment scheme , in which we only consider variable sites that have not been observed in any of the non-Native populations in the 1000 Genomes data set . The effect of this ascertainment scheme is to remove the majority of variants that predate the split of Native Americans from the rest of the populations . An additional benefit of this approach is that the impact of European and African tracts incorrectly assigned as Native American will be substantially reduced . We hypothesized that the effect of negative ascertainment could be approximately modeled by a strict bottleneck at the Native/non-Native split time . This was confirmed through simulations ( see S1 ) . We considered a simple 3-population demographic model starting with a constant population of size . At time the population size changes to . From this population of size , population diverged with size at time and populations and diverge at a later time with respective sizes and . We considered all three split orderings , with . In the optimal model , illustrated on Figure 6 , we have , , . This model is a vast oversimplification of the historical demographic processes . However , given the limited statistical power to reconstruct time-dependent demographic histories using allele frequency data ( e . g . [59] ) , such simple models with step-wise constant population sizes provide useful coarse-grained pictures of human demography . The population sizes in this model are effective population sizes: they are the size of Wright-Fisher populations that best explain the observed patterns of polymorphism . They differ from census sizes because of population size fluctuations , overlapping generations , sex bias , offspring number dispersion , and other departures from the Wright-Fisher assumptions . The ratio is expected to converge to large values to reflect both the negative ascertainment scheme ( see Methods ) and the expansion post-founding of the Americas . The current data does not enable us to model these two effects separately , so the recovery time can be thought of as an interpolation between the two events . When performing likelihood optimization , tended to slowly increase without bound . Beyond a value of 100 , this had minimal impact on the likelihood function and other parameter estimates . We therefore fixed this value to to facilitate optimization and prevent numerical instabilities . All other parameters , and the order of population splits , were chosen to maximize the model likelihood . We find dramatic differences in the inferred population sizes of the Native Ancestors to the MXL , CLM , and PUR ( see Table 1 ) , with the MXL showing by far the largest effective population size at 64 , 000 , times larger than the CLM and 32 times larger than the PUR . Given the many sources of uncertainty and model limitations , these ratios are in good qualitative agreement with pre-Columbian populations estimated at 14M in central Mexico [60] , 3M in Colombia [60] , and somewhat over 110 , 000 in Puerto Rico [61] . This could largely be a coincidence , given that the Native ancestors to the MXL and CLM were not panmictic populations over present-day political divisions . Another possible explanation for the differences in effective population sizes is a serial founder model after the crossing of Beringia: CLM and PUR would have experienced stricter and longer bottlenecks compared to MXL due to greater distances traveled from Beringia . The crossing to Puerto Rico is likely to have introduced intense bottlenecks in PUR , resulting in a smaller recent effective population size . The model suggests that PUR and CLM ancestral populations did not share serial founding events past the split with the MXL ancestors and split well before the expected arrival of the Arawak people of the Caribbean . Indeed , the first and second split times ( and , respectively ) are remarkably close to each other , with ( bootstrap CI: , see S1 , Figure S7 , and Table 1 ) . This corresponds to a difference of about 500 years , 12 , 000 years ago . In fact , the splits are so close that it is impossible to distinguish which population split first , with bootstrap instances supporting all three orderings: the Taíno ancestry does not appear much more closely related to either CLM or MXL Native ancestors . This is also consistent with the PCA results shown in Figure 2 , showing a clear distinction between Native American groups in eastern and western Colombia . Despite strong historical evidence for extensive population bottlenecks suffered by Native American populations following the arrival of Europeans [62] , we could not detect the presence of such bottlenecks through allele frequency analysis . However , the presence of such bottlenecks may affect our interpretation of effective population sizes . To quantify this , we fixed the timing and magnitudes of bottlenecks using non-genetic sources , and re-inferred model parameters . Dobyns [62] proposed a maximum population reduction of in the Native American population after European contact , but this number is expected to vary from location to location . Because we are studying admixed populations , the size of the bottleneck is related to the number of individuals that contributed to the admixed population , thus Dobyns' estimate may not apply . In PUR , where the decline was particularly abrupt , we considered a decline of spanning years ( see S1 ) . We found that inferred parameters were little affected by the existence of such a bottleneck , with the exception of the effective population size in the pre-bottleneck PUR population , which would be 3 . 9 times larger than in the no-bottleneck model . Assuming an additional bottleneck in the CLM population led to similar 4-fold increase in inferred pre-bottleneck CLM population size , with little effect on inferred split times . These are significant effects , but are less than the inferred differences in effective population sizes . Thus , in the absence of extreme differences in the recent bottlenecks experienced by the three populations , the observed differences in population sizes likely point to differences in pre-Columbian demography . By calibrating our results using , towards the most recent end of the range of plausible values for the peopling of the Americas ( see e . g . , [6] and references therein ) , we find a mutation rate of ( bootstrap CI: ) , within the range of recently published human mutation rates [63] . The narrowest confidence interval reported in [63] was , obtained from a de novo exome sequencing study [64] . Our sampling confidence interval is narrower than this value , but the main source of uncertainty here is the degree to which the bottleneck in our model reflects the bottleneck at the founding of the Americas , or the earlier split with the ancestors to the Chinese ( CHB ) and Japanese ( JPT ) sample , as well as uncertainty with respect to the timing of these two events ( see Figure 7 ) . The effect of changing the founding time or mutation rate assumptions would be to scale all parameters and confidence intervals according to Thus the absolute uncertainty on individual parameters is larger than the sampling uncertainty suggests . There is scarce publicly available , genome-wide data about Native American genomic diversity . The 1000 Genomes dataset offers the opportunity to provide a diversity resource for Native American genomics by reconstructing the genetic makeup of Native American populations ancestral to the PUR , CLM , and MXL . This is particularly interesting in the case of the Puerto Rican population , where such reconstruction may be the only way to understand the genetic make-up of the pre-Columbian inhabitants of the Islands . Using the expectation maximization method presented in the Methods section , we estimated the allele frequencies in the Native-American-inferred part of the genomes of the sequenced individuals . These estimates are available at http://genomes . uprm . edu/Taino/ . Figure 8 shows the distribution of the number of Native American haplotypes per site and the resulting confidence intervals for allele frequency in each population for exome capture target regions . Absolute confidence intervals are narrow for rare variants , and reach a maximum for SNPs at intermediate frequency; the leftmost peak in the bimodal distribution corresponds to the large number of rare variants , whereas the right most peak encompasses a broader range of frequencies . Focusing on the variants with observations in all populations and within the exome capture regions , where coverage and accuracy were highest , the most significantly different among Native groups is rs11183610 on chromosome 12 , with an estimated frequency of in MXL Native ancestry , in CLM Native ancestry , and in PUR Native Ancestry . The MXL-PUR difference remains significant after Bonferroni correction ( bootstrap , see Methods ) . The bulk of the differentiation among populations is likely due to genetic drift , but such sub-continental ancestry informative markers are also interesting candidates for further selection scans .
The bottleneck at the founding of the Americas provides a unique opportunity to obtain precise estimates of the human autosomal mutation rate , as reported in Table 1 and Figure 7 . One remaining challenge in interpretation is whether the ‘founding time’ studied here corresponds to the bottleneck at the founding of the Americas , or the split time of the Native Americans with the Asian populations . Fortunately , this uncertainty can be addressed by sequencing either trio-phased populations from the Americas , or individuals of Native American ancestry without large amounts of recent European and African ancestry . In either case , the dramatic events that led to the initial peopling of the Americas , together with the early dates of South American archaeological sites , provides us with estimates of the human mutation rate that are more precise than pedigree-based estimates . A more thorough study of the robustness of these estimates to model assumptions is therefore desirable . We find substantially larger effective population size in Mexico than in the other two populations through IBD-based and allele-frequency based estimates . These methods are sensitive to different time-scales: IBD analysis largely reflects post-Columbian events , as evidenced by the large number of mixed ancestry IBD segments in Figure 5 ( a ) . Allele frequencies reflect older events as well , and we showed that recent bottlenecks alone are unlikely to be responsible for the much larger effective MXL population size . To interpret the population size differences , we must consider the recent histories of the populations studied here . The MXL panel was recruited in Los Angeles among Mexican-American individuals , who may come from different regions in Mexico , a much wider geographical region than Puerto Rico , thus likely more populated . A natural question is whether the larger effective population sizes in MXL reflect a large panmictic population in Mexico , or a large number of small , previously isolated populations . Figure 2 and references [65] , [40] provide compelling evidence that there is substantial population structure within Native groups of Mexico . However , Figure 2 also shows that the Native component of the MXL forms a relatively homogeneous cluster together with populations from southern Mexico . The much larger Native populations in central and southern Mexico are likely to have contributed the most to the Native American ancestry of Mexican mestizos , and thus Mexicans-Americans . Even though the MXL may have ancestors in different parts of Mexico , their Native genetic origins likely reflect the demographic history of the areas in Mexico with the highest Native American population sizes . Because Puerto Rico is an island , building a relatively complete population genetic model for the population may be more tractable . Clearly , our model of a single idealized pre-Columbian Native American , European , and African populations , joining to form a panmictic admixed population , is an oversimplification . African and European ancestry proportions vary along the island [16] and eastern parts of Puerto Rico , with elevated proportions of African ancestry , are underrepresented in this study . By contrast , we do not have evidence for variation in the amount or composition of the Native American ancestry across the island , and it is likely that the conclusions about the pre-Columbian Native American fraction of the population are robust to sampling ascertainment . Interestingly , we find that the distribution of ancestry tract length in a sample of individuals of Puerto Rican descent in south Florida gave very similar results , despite different location , sequencing platform , and local ancestry inference method [50] . Historical gene flow inference using individuals of Colombian descent in south Florida provided comparable estimates of the time of admixture onset , but different patterns of recent gene flow–as is typical in demographic inference , inference of recent events is more sensitive to population structure . Our analyses largely rely on accurate estimates of local ancestry patterns along the genome obtained through RFMix . This method has been shown to provide more than accuracy on three-way admixture using comparable reference panels [48] , an accuracy level that enables accurate estimation of genome-wide diversity [54] . To ensure that our results are robust to residual errors , we further took into account the difficulty of calling short ancestry tracts in our migration estimates , and performed negative ascertainment of non-Native American alleles in the demographic inference . Some of these results can be independently verified by independent sequencing of contemporary or ancient individuals with more uniform ancestry . However , understanding the genetic history of admixed populations will continue to rely on statistically picking apart the contributions of different ancestral populations , and the development of improved statistical methods , particularly for admixture that is ancient or between closely related populations , remains highly desirable . The genetic heterogeneity in continental ancestry proportions among populations of the Americas is well appreciated [66] , [67] , [43] . Our results emphasize more fine-scale aspects of this diversity: because of the similarity between European founders of different populations and the high divergence among the Native American ancestors , populations that appear similar under classical tests such as or principal component analysis may still harbor population specific Native American haplotypes that must be carefully accounted for when performing rare-variant association testing in cosmopolitan cohorts . Similarly , the choice of a replication cohort for an identified risk variant should be guided by the ancestral background on which the variant is found . The PUR may be an excellent replication cohort for a result found in CLM if the background is European . If the background is Native American , a different cohort with related Native Ancestry would likely be much more appropriate . Understanding the genetics of the different ancestral populations of the Americas , and the relatedness among these ancestral groups , will therefore facilitate the development of association methods that account for and take advantage of this rich diversity .
Ideally , we would have been able to directly model the joint site-frequency spectrum ( SFS ) of all the ancestral populations to the PUR , CLM , and MXL . However , because we are interested in distinguishing the Native American ancestries to the three populations , this would require modeling at least 5 populations , which is beyond the scope of current methods . We would like to use the inferred local ancestry to focus on the Native American ancestry only , but this is difficult because most Native American haplotypes are in segments heterozygous for ancestry . Because of phasing errors , allele-specific ancestry can be incorrectly assigned . To minimize the impact of such mis-assigned ancestry and to ensure that we focused on variants of genuine Native American ancestry , we discarded all variants observed in 1000 Genomes individuals of African , European , and Asian ancestry , as well as variants observed in Hispanic/Latino populations in segments with no Native American ancestry inferred . We then considered all remaining variable sites that were assigned Nat/Nat diploid ancestry and Nat/Eur ancestry , and calculated the expected frequency distribution under the assumption of perfect negative ascertainment , that is , that all remaining variants were on the Native American background . Because the European backgrounds are expected to carry a number of singletons , this would result in an overestimate of the number of singletons in the Native Ancestry . Fortunately , this bias is easy to estimate empirically: we first choose segments of Eur/Eur ancestry to mimic the European haplotypes in our sample . After performing the negative ascertainment scheme on these genotypes , we can directly estimate the bias in the negative ascertainment scheme . In practice , this correction is very low except for singletons , as expected . The number of excess singletons was 129 for CLM , 73 for PUR , and 40 for MXL . The largest non-singleton correction is 1 . 3 for doubletons in CLM . Because negative ascertainment removes a significant proportion of the variants that were present at the Native American split from other populations , we hypothesized that this effect could be well-approximated by a severe bottleneck at the time of split between non-Native and Native American ancestry . Figure 9 provides a simulated example , wherein a marginal spectrum ( top ) is compared to a spectrum negatively ascertained using 100 diploid individuals from the ‘outgroup’ population ( middle ) and to a bottleneck approximation equivalent ( bottom ) . More quantitatively , we simulated a two-populations sample diverged 12 . 1kya , and negatively ascertained using a population diverged at 16 . 5 kya , and attempted to model this as a two-population model with an early bottleneck . The inferred bottleneck timing was within of the split time with the outgroup , and the three population sizes and split time between populations 1 and 2 were within of the correct value . These biases are well within the acceptable range given other biases and uncertainties . We wish to estimate the allele frequencies at each site among segments of Native American origin , but we have to contend with a finite sample and inaccurate phasing . We therefore choose to model the underlying population frequency across all populations using Bayes rule ( 1 ) where is the observed genotype data , , and is the diploid local ancestry calls ( e . g . , for populations A and B ) . From this distribution we can calculate expected frequency and confidence intervals . We report inferred frequencies and confidence intervals at non-monomorphic sites . To estimate , we write as the frequencies of the non reference allele in populations and . We have , for ancestry and genotype heterozygous segments , , and so forth . To estimate , we first observe that because we are considering population frequencies , rather than sample frequencies , is independent of : . This suggests the use of a self-consistent , expectation-maximization procedure . We estimate the underlying frequency distribution as ( 2 ) the sum over the estimated probabilities at each site . We can thus iterate Equations ( 1 ) and ( 2 ) until self-consistency is reached to estimate both allele frequency distributions and single-site allele frequencies in each population . A final caveat is that the sum runs over all sites , including monomorphic ones . If we only observe the subset of sites that are polymorphic , an additional step is needed . If is the number of monomorphic ( unobserved ) sites ( denoted as ) , and represents the sum over polymorphic sites , we have ( 3 ) and , therefore , Intuitively , we are correcting for the proportions of sites at every frequency that might have gone undetected . Results are reported using 20 EM iterations , for sites where all individuals had both ancestry and genotype calls , and data can be downloaded at http://genomes . uprm . edu/Taino/ . To test this method , we considered 84 diploid individuals , each formed by drawing two chromosomes ( without replacement ) from 84 CEU and 84 YRI individuals , resulting in a simulated 50–50 admixture proportion . We considered 100 , 000 sites on chromosome 22 , and performed the EM inference as described . Among the 85677 sites that were found to be polymorphic , only 13 had a sample allele frequency departing from the confidence interval for the European ancestry , and 51 among the African ancestry . Confidence intervals encompass much more than of sample allele frequencies , emphasizing that the width of the confidence interval largely reflects the uncertainty about the population frequency given a fixed sample frequency , rather than the phasing uncertainty . Because the demographic model considered here does not involve migrations between Native groups , we considered the composite likelihood of three pairwise two-population allele frequency distributions , rather than the full three-population spectrum . This allows for much faster inference and better convergence of the numerical optimization . In principle , it also enables the joint inference of more than three populations . We showed through simulations that the use of a composite likelihood had an effect on inferred parameters that was much smaller than other sources of uncertainty . We used grids of 20 , 40 , and 60 grid points per population , and projected Native American allele frequencies to sample sizes of 10 in PUR , 20 in CLM , and 40 in MXL . | Populations of the Americas have a rich and heterogeneous genetic and cultural heritage that draws from a diversity of pre-Columbian Native American , European , and African populations . Characterizing this diversity facilitates the development of medical genetics research in diverse populations and the transfer of medical knowledge across populations . It also represents an opportunity to better understand the peopling of the Americas , from the crossing of Beringia to the post-Columbian era . Here , we take advantage sequencing of individuals of Colombian ( CLM ) , Mexican ( MXL ) , and Puerto Rican ( PUR ) origin by the 1000 Genomes project to improve our demographic models for the peopling of the Americas . The divergence among African , European , and Native American ancestors to these populations enables us to infer the continent of origin at each locus in the sampled genomes . The resulting patterns of ancestry suggest complex post-Columbian migration histories , starting later in CLM than in MXL and PUR . Whereas European ancestral segments show evidence of relatedness , a demographic model of synonymous variation suggests that the Native American Ancestors to MXL , PUR , and CLM panels split within a few hundred years over 12 thousand years ago . Together with early archeological sites in South America , these results support rapid divergence during the initial peopling of the Americas . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2013 | Reconstructing Native American Migrations from Whole-Genome and Whole-Exome Data |
Post-transcriptional regulatory mechanisms are widely used to influence cell fate decisions in germ cells , early embryos , and neurons . Many conserved cytoplasmic RNA regulatory proteins associate with each other and assemble on target mRNAs , forming ribonucleoprotein ( RNP ) complexes , to control the mRNAs translational output . How these RNA regulatory networks are orchestrated during development to regulate cell fate decisions remains elusive . We addressed this problem by focusing on Caenorhabditis elegans germline development , an exemplar of post-transcriptional control mechanisms . Here , we report the discovery of GLS-1 , a new factor required for many aspects of germline development , including the oocyte cell fate in hermaphrodites and germline survival . We find that GLS-1 is a cytoplasmic protein that localizes in germ cells dynamically to germplasm ( P ) granules . Furthermore , its functions depend on its ability to form a protein complex with the RNA-binding Bicaudal-C ortholog GLD-3 , a translational activator and P granule component important for similar germ cell fate decisions . Based on genetic epistasis experiments and in vitro competition experiments , we suggest that GLS-1 releases FBF/Pumilio from GLD-3 repression . This facilitates the sperm-to-oocyte switch , as liberated FBF represses the translation of mRNAs encoding spermatogenesis-promoting factors . Our proposed molecular mechanism is based on the GLS-1 protein acting as a molecular mimic of FBF/Pumilio . Furthermore , we suggest that a maternal GLS-1/GLD-3 complex in early embryos promotes the expression of mRNAs encoding germline survival factors . Our work identifies GLS-1 as a fundamental regulator of germline development . GLS-1 directs germ cell fate decisions by modulating the availability and activity of a single translational network component , GLD-3 . Hence , the elucidation of the mechanisms underlying GLS-1 functions provides a new example of how conserved machinery can be developmentally manipulated to influence cell fate decisions and tissue development .
Germ line and early embryonic gene expression rely largely on cytoplasmic mRNA control mechanisms , allowing for maximum flexibility of control [1] . A striking example is the unique ability of germ cells to transiently differentiate into gametes before forming a totipotent zygote upon fertilization . Many conserved cytoplasmic RNA-binding and RNA-modifying proteins have been found to support germline development , by associating with mRNA molecules in RNP complexes . In higher eukaryotes , these trans-acting factors can form larger RNP aggregates , termed germplasm granules [2]–[4] . Although these RNPs are anticipated to confer germ cell identity and are important for germline development their developmental regulation is largely unknown . Furthermore , it remains to be determined how these RNP complexes are utilized in an organism-specific fashion to control protein synthesis , i . e . the mRNA's translational output . In nematodes , components of germplasm granules ( P granules ) are deposited maternally to the embryo and segregate to germ cell precursors , which suggests their early requirement for germline function . One such conserved maternal component is the Bicaudal-C ( Bic-C ) protein family member GLD-3 [5] . Bic-C proteins are involved in poly ( A ) tail metabolism of mRNAs [6] , [7] . The gld-3 locus encodes two major protein isoforms , GLD-3L and GLD-3S , of which both form a cytoplasmic poly ( A ) polymerase complex with GLD-2 [8] . Similar to Drosophila Bic-C , which is required for oogenesis and patterning of the embryo , GLD-3 is required for many aspects of germline development and embryogenesis , including a role in germline sex determination and germline survival [5] , [9] , [10] . The C . elegans sperm-to-oocyte switch serves as a paradigm for the analysis of post-transcriptional mRNA regulation [11] . A sex determination pathway determines the sperm and oocyte fate . Although hermaphrodites develop somatically as females , they produce a limited number of sperm during their fourth larval stage , before switching to continuous oocyte production in the adult . Therefore , the female sex determination pathway has to be temporarily suppressed to facilitate spermatogenesis . The underlying molecular mechanism is based on multiple interconnected RNA regulators , e . g . Bic-C , PUF , and Nanos proteins , that together comprise a molecular switch to regulate the timely accumulation of first sperm and then oocyte promoting factors . Interestingly , members of these RNA regulatory protein families are broadly conserved and seem to be utilized in other , yet less well understood , cell fate decisions [11] . Two counteracting forces balance the translational output of the key male fate promoting factor , fem-3 [12]; GLD-3L acts as a translational activator whereas two very similar PUF ( Pumilio and FBF ) proteins , FBF-1 and FBF-2 , collectively referred to as FBF , are translational repressors of fem-3 mRNA . FBF-mediated repression of FEM-3 protein synthesis promotes oogenesis indirectly and is aided by a physical interaction with NOS-3 , a worm Nanos ortholog [13] , [14] . Yet to allow sperm production , in males and temporarily in the L4 hermaphrodite larvae , FBF's oogenesis-promoting activity has to be blocked . This is achieved by zygotic GLD-3L , which reduces FBF's affinity for its cognate regulatory element in the fem-3 mRNA by binding to FBF's RNA-binding domain [5] . However , in order to switch to oogenesis , FBF must then be activated by a currently unknown mechanism . These conserved RNA regulators are also involved in the less understood cell fate decision of germ cell survival [11] . In zygotes where GLD-3 is not supplied by the mother , germ cells are correctly specified during embryogenesis but degenerate during postembryonic development . Thus importantly , maternal gld-3 activity is required to prevent germ cell degeneration [5] . Consistent with a role in germline development is that maternal GLD-3 is associated with P granules in the early embryonic P lineage ( P1–P4 ) . This also suggests a role for other P granule components in germline maintenance [5] . Additionally , little is known about the roles and interactions of various Nanos and PUF proteins that are required for germline maintenance in C . elegans , Drosophila and mice [13] , [15]–[17] . Therefore , we reasoned that further insights into the controls of this cell fate decision might be more easily gained by focusing on the interactions of the single most important player conferring germ cell survival , GLD-3 . In this paper , we report the identification and characterization of gls-1 ( germline survival defective-1 ) . The gls-1 gene activity is required for multiple aspects of germline development , including the sperm-to-oocyte switch and germline survival . The GLS-1 protein was identified in a yeast two-hybrid screen for GLD-3 interactors , and is a novel P granule component . We demonstrate that during germ cell fate decisions , GLS-1 exerts its roles by modulating GLD-3 activities . We provide evidence that GLS-1 interferes with GLD-3L inhibition of FBF/Pumilio , to assist the translational repression of spermatogenic factors . Furthermore , we find that the maternal GLS-1/GLD-3 complex prevents adult germ cell degeneration . We propose the GLS-1/GLD-3 complex accomplishes this by translationally activating mRNAs encoding survival factors required in the early embryonic germ cell lineage .
Using the yeast two-hybrid system we aimed to identify new interaction partners of GLD-3 . Several partial cDNAs of the gene C36B1 . 8 , which we have named gls-1 , were repeatedly found in two independent screens ( Text S1 ) . In our analysis of the gls-1 genomic locus we found full-length ( fl ) gls-1 cDNAs trans-spliced to SL2 , suggesting a transcriptional co-regulation with the upstream gene C36B1 . 7 ( Figure 1A , B; Text S1 ) . We termed C36B1 . 7 , dhfr-1 , as its predicted ORF encodes a gene similar to dihydrofolate reductase . The gls-1 locus encodes a novel protein with no predictable functional motifs . The amino ( N ) -terminus of GLS-1 contains two consecutive regions , one rich in serines/threonines/asparagine and one rich in serines/prolines ( Figure 1C ) . The GLD-3 interaction domain is located at the very carboxy ( C ) -terminus . Although no proteins similar to GLS-1 could be identified in vertebrates we identified the gls-1 locus in C . briggsae and C . remanei where it is also in synteny with the upstream gene dhfr-1 ( Text S1 ) . All GLS-1 proteins of the Caenorhabditis clade share approximately 55% amino acid sequence identity when compared to each other . The two shaded regions in the N-terminus in Figure 1C are conserved to almost 80% identity . We conclude that gls-1 is a conserved nematode gene , co-transcribed with dhfr-1 . To study the GLS-1 protein , we raised several anti-GLS-1 sera . Two rabbit polyclonal antisera were generated using the central and C-terminal part of GLS-1 , respectively ( Figure 1C ) . Both rabbit antisera were affinity purified and recognized a recombinant His-epitope tagged GLS-1 ( FL ) of ∼150 kDa ( Figure 1D ) . A band of similar size was observed in protein extracts prepared from wild-type L4 animals of either sex but not from gls-1 ( ef8 ) deletion mutants , which delete the GLS-1 epitope ( Figure 1D ) . Furthermore , GLS-1 expression is reduced in animals that essentially lack a germ line at 25°C , glp-1 ( q224ts ) and glp-4 ( bn2ts ) ( Figure 1D ) . We conclude that GLS-1 is expressed in the germ line and soma . To address GLS-1 expression in germ cells we performed immunocytochemistry on extruded germ lines . We either used the central rabbit antibody , pre-blocked with gls-1 ( ef8 ) protein extract , or a monoclonal anti-GLS-1 antibody , raised against the C-terminus of GLS-1 . Germ cells and P granules were visualized with antibodies to the P granule component PGL-1 [18] or GLH-2 [19] . GLS-1 is expressed in all germ cells , with the exception of spermatocytes ( not shown ) . In the adult hermaphrodite , GLS-1 is present in the mitotic region , accumulates during early stages of meiotic prophase I and is slightly less abundant in maturing proximal oocytes ( Figure 2A ) . In addition to a diffused cytoplasmic localization , we observe a granular GLS-1 staining in the most distal mitotic region ( Figure 2B ) and in germ cells entering meiotic diplotene in the proximal germ line ( not shown ) . Almost all GLS-1 granules overlap with P granules , although GLS-1 intensities varied with respect to PGL-1 and GLH-2 intensities among P granules . This may reflect a selective enrichment of GLS-1 in P granules ( Figure 2B ) . The ubiquitous GLS-1 expression overlaps the ubiquitous expression of GLD-3 , yet GLS-1 and GLD-3 differ in their expression pattern during early and late meiotic prophase I ( Figure 2A ) . As a specificity control , we stained germ lines extruded from gls-1 mutant animals ( Figure 2C ) . No immunoreactivity was observed with the monoclonal GLS-1 antibody , although GLD-3 ( Figure 2C ) and PGL-1 ( not shown ) were readily detected . We conclude that GLS-1 expression and subcellular localization in the germ line is dynamic and largely overlaps with GLD-3 . GLS-1 is present in oocytes , which suggests a possible maternal role for gls-1 . In the early embryo , cytoplasmic GLS-1 is present in all cells , yet becomes gradually restricted to the germ cell lineage , and enriches in P granules ( Figure 2D , data not shown ) . In early embryos we observed many GLS-1 positive particles that failed to stain strongly with P granule markers , yet stained readily for GLD-3 ( Figure 2D , arrow ) . Granular GLS-1 and GLD-3 staining progressively overlapped with P granules during embryonic development and reached its maximum in the P4 germ cell precursor ( data not shown ) . GLS-1 expression persisted throughout embryogenesis on P granules ( data not shown ) . No GLS-1 was detected in embryos derived from gls-1 ( ef8 ) mothers ( Figure 2D , bottom row ) . Interestingly , GLD-3 localization to P granules was still observed in these embryos ( Figure 2D , compare GLD-3 and GLH-2 images ) , thus GLS-1 is not required for GLD-3 localization to P granules . Taken together , GLS-1 is dynamically expressed in the embryo and is a component of P granules in germ cell precursors . GLS-1 co-localizes with GLD-3 in the early embryo and both are enriched in germplasm granules that contain very little or no PGL-1 or GLH-2 . To investigate the specificity of the GLS-1/GLD-3 interaction we performed further tests . GLS-1 interacted positively in yeast with both GLD-3 isoforms but not with other Bicaudal-C orthologs from C . elegans or Drosophila , e . g . ceBCC-1 or dmBic-C . No interaction was observed with the GLD-3 interactors FBF-1 or FBF-2 ( Figure 3A ) . Next , we demonstrated a direct physical interaction between GLS-1 and GLD-3L with proteins expressed in insect cells ( Figure 3B ) . All proteins carried a C-terminal His-tag and GLD-3L carried in addition an N-terminal Maltose Binding affinity tag ( MBP ) . GLS-1 or GFP were co-expressed with GLD-3L by co-infection with baculoviruses encoding the individual fusion proteins . GLD-3L and associated proteins were captured on amylose resin and bead-bound proteins were analyzed by immunoblotting . GLS-1 , but not GFP , was specifically pulled down by GLD-3L and not by MBP-coated beads alone ( Figure 3B , lanes 4–6 ) . To assess if the interactions were RNA mediated , we treated the extracts with RNAse A prior to the co-purification . The GLS-1 interaction with MBP::GLD-3L was still observed ( Figure 3B ) . In addition , we confirmed the existence of an in vivo GLS-1/GLD-3 complex by co-purifying each protein from wild-type worm extracts using protein-specific antibodies coupled to agarose beads ( Figure 3C ) . Regardless of RNAse A treatment , GLD-3 and GLS-1 were strongly enriched in the anti-GLS-1 or anti-GLD-3 immunoprecipitate , respectively , but not in the IgG controls ( Figure 3C ) . Finally , we used protein truncations to map the interaction domain between GLD-3 and GLS-1 in yeast 2-hybrid tests . We detected two interaction surfaces within GLD-3L; an N-terminal region covering all KH domains and a C-terminal region containing the minimal FBF-binding region ( Figure 3D ) . We mapped the GLD-3 interaction surface of GLS-1 to a single region of 160 amino acids at the very C-terminus ( Figure 3E ) . Taken together , our results demonstrate a physical interaction between GLS-1 and GLD-3 , mediated by specific regions within the proteins . To investigate the in vivo roles of gls-1 we first reduced its gene function by RNA-mediated interference ( RNAi ) and observed 23% sterile progeny ( n = 761 ) ( Table 1 ) . Nomarski analysis revealed animals with empty gonads ( 17% ) , and germ lines without oocytes ( 4% ) or oogenesis defects ( 2% ) . Occasionally ( <2% each ) , body shape defects , vulva defects , or embryos that failed to hatch were observed . In addition , less than 2% of the progeny contained a smaller but wild-type patterned germ line , indicative of proliferation defects . The missing germ cell phenotype reminded us of the germline survival ( Gls ) defect of gld-3 ( RNAi ) progeny and suggested a shared function between GLS-1 and GLD-3 . To characterize gls-1 functions in greater detail we isolated two chromosomal deletion mutants , gls-1 ( ef4 ) , a central in-frame deletion , and gls-1 ( ef8 ) , a central out-of-frame deletion ( Figure 1B , see Materials and methods ) . Consistent with the truncated mRNA coding potentials , we detect robust expression of GLS-1ef4 protein by immunoblotting with a C-terminal rabbit anti-GLS-1 antibody and gls-1 ( ef8 ) protein extract serves as a specificity control ( Figure 1E ) . Genetic evidence presented later suggests that both gls-1 mutations behave as reduction-of-function alleles at lower temperatures and as genetic null alleles at elevated temperatures . For our overall phenotypic analysis we separated zygotic ( Z ) and maternal ( M ) functions of gls-1 by scoring adult mutants that were either the F1 progeny ( gls-1 M+Z- ) of heterozygote gls-1/+ mothers , or their F2 progeny ( gls-1 M−Z- ) ( Table 1 ) . We discovered that gls-1 M+Z- animals appeared to contain excess sperm ( ∼94% , n = 107 ) and occasionally produced sperm exclusively ( ∼2% , n = 107 ) ; a detailed sperm count is given in Table 1 . Furthermore , we observed germ lines that switched to sperm production in the distal region adjacent to more proximal germ cells undergoing oogenesis ( ∼4% , n = 107 ) . A lack of both maternal and zygotic gls-1 activity ( M−Z- ) , caused animals to display the germline survival defect at a high frequency , hence its name . Somatic defects that appeared phenotypically similar to the RNAi defects were observed at a low frequency , but were not further pursued . In summary , we conclude that gls-1 has a zygotic role in promoting the switch to oogenesis and a maternal role in ensuring germline survival . To determine the phenotypes of strong loss-of-function gls-1 mutants we generated animals hemizygote for gls-1 M+Z- by placing each gls-1 mutation in trans to the genomic deficiency nDf23 ( Figure 1A ) . In contrast to +/nDf23 animals , all collected adult hemizygotes were fully sterile and produced no living progeny ( Table 1 , Figure 4A ) ; occasionally they contained a dead embryo in their uterus . The sterility was due to defects in oogenesis , as sperm were present in all animals and homozygous males sire normal progeny; on average sperm were present in excess similarly to the homozygous gls-1 mutants ( Table 1 ) . In rare cases , gls-1/nDf23 hemizygotes contained either fully masculinized germ lines with no signs of oogenesis , or their distal germ cells reverted from oocyte to sperm production suggesting that the switch to oogenesis is not maintained in adult worms ( Figure 4C ) . In young adult hermaphrodites these germ cells are first immunoreactive with the sperm specific differentiation marker SP56 and they subsequently mature in older animals into sperm ( not shown ) . However , the number of proximal sperm produced is very similar between gls-1 homozygotes and gls-1/nDf23 hemizygotes ( Table 1 ) , suggesting that both gls-1 alleles , while perhaps not nulls , are at least strong loss-of-functions with respect to the sperm-to-oocyte switch . The oogenesis defects in animals hemizygous for gls-1 are complex . Most germ lines maintained the oogenic fate , yet lack the linear array of large oocytes arrested in diakinesis that is found in wild-type ( compare Figure 4A and 4D ) . Rather , oogenic cells remained small and were either arrested in pachytene or underwent abnormal meiotic prophase progression and failed to arrest in diakinesis , often resulting in endoreduplicating oocyte nucei ( Figure 4A , B ) . Furthermore , we observed in the most proximal germ line aberrant nuclei positioned between mature sperm and oocytes that showed no signs of clear gamete differentiation markers ( Figure 4A , B and F–I ) . In addition , we noticed an abnormal oocyte membrane organization ( Figure 4G ) , which was also seen in anti-actin and anti-anillin ( ANI-2 ) antibody stainings ( not shown ) . We conclude that gls-1 activity is required for proper oocyte differentiation and oogenic meiotic arrest . Interestingly , we can induce these oogenesis defects also in our single gls-1 homozygous mutants by shifting mid-L4 larvae to 25°C . The displayed germline defects are very similar to the phenotypes of gls-1/nDf23 hemizygotes at 20°C and all analyzed animals were sterile ( n>100 ) ( compare Figure 4A and 4E ) . We also examined the phenotype of gls-1 ( ef4 ) /nDf23 animals at 25°C . No further enhancement of the germ line phenotypes were seen ( 25/25 ) and +/nDf23 animals were fertile and appeared wild-type ( 39/40 ) . We conclude that , for oogenesis defects , both gls-1 mutations behave as genetic null alleles at elevated temperatures . To investigate the relationships between gls-1 and gld-3 and to understand their molecular interactions we first focused on their seemingly opposing roles in germline sex determination . Zygotic gld-3 is required for the sperm fate; gld-3 ( 0 ) mutants display a feminized germ line in both sexes [5] , [20] . In contrast , gls-1 mutations masculinized the hermaphrodite germ line ( Table 1 ) . Other known regulators of the sperm-to-oocyte switch include three PUF proteins ( FBF-1 , FBF-2 and PUF-8 ) and NOS-3 . The former are required for suppressing the sperm fate by translationally repressing several sperm promoting genes of the sex determination cascade and the latter reinforces FBF activity at the level of fem-3 mRNA regulation [21] , [22] . Interestingly , each single mutant produces sperm and oocytes , similar to gls-1 . However , PUF double mutants produce only sperm , exemplifying redundant controls in promoting oogenesis . To understand the role of gls-1 in sex determination and to test for a likely redundancy with fbf , we combined gls-1 with other sperm-to-oocyte switch defective genes and generated double mutants . We analyzed the germ lines with Nomarski and immunofluorescence microscopy to unambiguously identify the gamete fate in young adult hermaphrodites . We discovered an essential role for GLS-1 in the switch to oogenesis when FBF-1 is not expressed ( Table 2 ) ; gls-1; fbf-1 double mutants are fully sterile and produced only sperm and no oocytes ( Table 2; Figure 5B ) . In contrast , compromising gls-1 activity in a fbf-2 or puf-8 mutant background does not cause a sperm only ( Mog ) phenotype; sperm and oocytes were always present ( Table 2 ) . The synthetic Mog phenotype in double mutants was not enhanced or changed at elevated temperatures , which is consistent with gls-1 ( ef8 ) being a strong loss-of-function with respect to the sperm-to-oocyte switch . When we simultaneously eliminated nos-3 and gls-1 function , we observed a rather complex phenotype; germ cells either failed to adopt the oocyte fate entirely ( ∼20% ) or were not able to maintain the oogenic fate and produced sperm cells in the distal arm at a much higher frequency ( ∼20% ) than gls-1 ( ef8 ) by itself ( 1% ) ( Table 2 ) . However , the remaining 60% of gls-1; nos-3 germ lines switched to and maintained oogenesis . In summary , we find that gls-1 works genetically in parallel , or together , with fbf-1 and nos-3 to promote the sperm-to-oocyte switch in hermaphrodites . To place the action of gls-1 and fbf-1 into the sex determination pathway we performed genetic epistasis experiments with key spermatogenesis promoting genes . We either generated true triple mutants or relied on feeding RNAi experiments to generate “triple mutant” phenotypes ( Table 2 ) . Interestingly , the synthetic Mog phenotype depended on the activity of each known fem and fog gene . All “triple mutant” germ lines produced either oocytes only or oocytes in addition to sperm . The mixture of sexual fates might be due to an incomplete penetrance caused by RNAi; the wild-type animals treated in parallel were fully feminized to a similar percentage as the gls-1; fbf-1 mutants ( not shown ) . The gls-1; fbf-1; fog-2 triple mutant also displayed a mixture of cell fates , yet we observed mostly feminized germ lines ( 82% , Table 2; Figure 5C ) . Taken together , we provide evidence that gls-1 is upstream of all known spermatogenesis-promoting genes , similar to the activity of fbf-1 . To determine if this masculinization defect is due to the activity of gld-3 , we generated the gls-1 ( ef8 ) ; fbf-1 ( ok91 ) gld-3 ( q730 ) triple mutant . As expected , we were able to restore the oocyte fate in gls-1; fbf-1 germ lines by eliminating gld-3; almost all triple mutant germ lines displayed a sperm and oocyte pattern ( 97% , Table 2; Figure 5D ) . Therefore , we conclude that the synthetic Mog phenotype of gls-1; fbf-1 germ lines depends on excess gld-3 activity ( summarized in Figure 5E ) . Given the known molecular interactions of GLS-1 and GLD-3 ( Figure 3 ) , the simplest interpretation is that GLS-1 is a modulator of the sperm-to-oocyte switch by acting antagonistically to GLD-3 and in parallel to FBF . In this scenario , the switch to oogenesis is promoted by relieving FBF from GLD-3L inhibition through GLS-1 binding . Support for this model comes from our in vitro observations that the C-terminal half of GLD-3L binds consistently with higher affinity to GLS-1 than the N-terminal half ( Figure 5F ) . To test the proposed antagonistic model further we employed a competition experiment . FBF-1 was immobilized on beads and subsequently loaded with GLD-3L . Afterwards , the FBF-1/GLD-3L complex was challenged with increasing amounts of free GLS-1 . GLS-1 and dissociated GLD-3L was washed away . We observed that the GLD-3 amount bound to FBF decreased with higher GLS-1 concentrations present , while FBF-1 remained stably bound to the beads ( Figure 5G ) . Together , this data indicates that GLS-1 protein can recruit FBF-1 from the interaction with GLD-3 . Therefore , GLS-1 may liberate FBF-1 for translational repression of spermatogenic factors . Next , we addressed how GLS-1 influences GLD-3 activity to promote germline survival . The adult wild-type hermaphrodite contains two U-shaped gonadal arms filled with ∼1000 germ cells [23] . By contrast , gls-1 ( RNAi ) hermaphrodites and homozygote gls-1 M−Z- adult animals often contain an empty somatic gonad with no apparent germ cells ( Table 1 ) , resembling the maternal gld-3 mutant Gls phenotype . Hence , we characterized the genesis of the gls-1 M−Z- adult sterility during postembryonic development by Nomarski microscopy ( not shown ) and visualized germ cells by DAPI and anti-P granule staining ( Figure 6A–C ) . During the first two larval stages germ cell development of gls-1 ( ef8 ) M−Z- hermaphrodites is similar to wild-type; L1 larvae hatch with two progenitor germ cells and contain at early L2 an approximately wild-type number of germ cells . After the formation of an anterior and posterior gonadal arm in late L2/early L3 , both germ lines were clearly present in gls-1 ( ef8 ) M−Z- animals . A subtle , but apparent , germline size difference to the wild-type was visible at the L3 stage ( Figure 6A , D ) and became more pronounced as development progressed . Roughly one third of gls-1 ( ef8 ) M−Z- L4 animals displayed evident germ line loss and contained very few or no PGL-1 positive germ cells ( Figure 6B , D ) . The phenotype was exacerbated in the adult; 40% gls-1 ( ef8 ) M−Z- animals had either lost the entire germ line or it was strongly degenerated and reduced to a few unhealthy looking germ cells ( Figure 6C , D ) . Interestingly , the germ cell loss phenotype was still present in gls-1 ( ef8 ) ; ced-4 ( n1162 ) animals , where the canonical apoptotic pathway is blocked ( not shown ) . We conclude that in embryos lacking maternal gls-1 function , germ cells are correctly specified , initiate proliferation and are subsequently lost as a result of progressive degeneration , which begins to be seen as early as the L3 larval stage ( Figure 6D ) . This phenotype mimics the germline survival defect of maternally depleted gld-3 animals and is consistent with a shared and common function for maternal gls-1 and gld-3 . We recently identified the novel poly ( A ) polymerase GLD-4 as an interactor of GLS-1 [24] . Interestingly , RNAi knockdown of zygotic and maternal gld-4 by injection results in progeny with a high penetrance Gls phenotype at elevated temperatures ( Table 1 ) , suggesting that all three proteins may form a maternal complex to promote germline survival . To test the hypothesis that the common biological role of maternal GLS-1 and GLD-3 in germline survival is linked to their physical interaction , we utilized a specific temperature-sensitive gld-3 mutation . The gld-3 ( ax562 ) allele contains a missense mutation that leads to a glycine-to-arginine ( G/R ) substitution in the fourth KH domain of GLD-3S and GLD-3L ( Figure 7A ) . Homozygote gld-3 ( ax562 ) animals are fertile at permissive temperature and contain a superficially normal germ line . Strikingly , when shifted to the restrictive temperature as early embryos , these animals display the germline survival defect ( 25% , n = 130 , Figure 7C , D ) , even though both GLD-3 isoforms are expressed to wild-type levels ( Figure 7B ) . As this phenotype is similar to gls-1 removal , we asked if the G/R change would affect the GLS-1/GLD-3 interaction . Hence , we co-expressed both proteins in insect cells and consistently found that wild-type GLD-3 protein , but not GLD-3ax562 , was able to associate with GLS-1 in co-immunoprecipitation experiments ( Figure 7E ) . A C-terminal truncation of GLS-1 missing the GLD-3-binding region served as a negative control . These results were similar to our directed yeast 2-hybrid binding-tests ( not shown ) , and do not depend on the temperature that cells were grown at ( see Text S1 ) . Interestingly , GLD-3ax562 bound efficiently to the positive control , GLD-2 , in insect cells and yeast ( not shown ) . We conclude that the gld-3 ( ax562 ) point mutation specifically abrogates the interaction with GLS-1 . Taken together , our analysis suggests that the maternal GLS-1/GLD-3ax562 complex is in vivo temperature sensitive and that upon dissociation of the complex , germ cells fail to survive into adulthood .
The sperm-to-oocyte decision is remarkably complex and serves as a paradigm for post-transcriptional control mechanisms . Translational repression of fem-3 mRNA is essential for female germ cells to acquire the oocyte fate . FEM-3 is a novel protein and forces male fate development [25] , [26] . Translationally inactive fem-3 mRNA is repressed by two conserved PUF proteins ( FBF-1 and FBF-2 ) and NOS-3 , which are thought to form an RNP complex [13] , [14] . To allow transient spermatogenesis in the hermaphrodite and continuous sperm production in the male , fbf activity is antagonized by gld-3 . GLD-3 regulates the timing of the hermaphroditic sperm-to-oocyte switch , i . e . the numbers of sperm produced , and maintains the sperm fate in the male [5] . Molecularly , GLD-3L promotes the sperm fate by directly contacting the RNA-binding domain of FBF . As a consequence , FBF's affinity to its cognate target sequence in fem-3 mRNA is weakened; this promotes fem-3 mRNA translation and delays the onset of oogenesis [5] . How is FBF activated to overcome GLD-3L inhibition to execute the sperm-to-oocyte switch at the correct time ? Furthermore , how is this repression maintained in the aging hermaphrodite for continuous oocyte production ? Our work on GLS-1 provides support for a model in which FBF is permanently liberated from GLD-3L repression by the formation of a GLS-1/GLD-3L complex ( Figure 8A ) . This interaction requires a region within GLD-3L that includes the entire minimal FBF-binding site . As the GLD-3 interaction sites in FBF and GLS-1 differ in their primary sequence , we regard GLS-1 as a molecular mimic , and not necessarily a structural mimic , of FBF . Our molecular model is based on the following findings: GLS-1 is expressed in the L4 gonad ( data not shown ) when oocytes are specified . gls-1 hermaphrodite mutants produce almost twice as many sperm than wild-type , which is similar to fbf-1 single mutants [27] . A synergistic loss of gls-1 and fbf-1 prevents the switch to oogenesis entirely and these masculinized germ lines depend on gld-3 activity . Furthermore , our in vitro protein binding competition results are consistent with the conclusion that GLS-1 performs its function by directly interfering with the GLD-3L/FBF interaction . FBF-1 is the only RNA-binding protein that is known to be involved in all well characterized translational regulations that affect the sperm-to-oocyte switch ( see Figure 5E ) . For example , fbf-1 , but not fbf-2 , activity is required to repress fog-2 , a gene more upstream in the sex determination pathway than fem-3 . However , FBF-1 requires the help of PUF-8 to repress fog-2 mRNA [22] . Similarly , FBF-1 depends on FBF-2 , and presumably NOS-3 , to fully repress fem-3 mRNA [13] , [14] . Our data are also in good agreement with a more central role attributed to fbf-1 , rather than fbf-2 , nos-3 and puf-8 , in the sperm-to-oocyte switch ( see Figure 5E ) . gls-1; fbf-1 double mutants fail to produce oocytes and produce sperm only , whereas gls-1; fbf-2 and gls-1; puf-8 double mutants produce sperm and oocytes . Consistent with our model of FBF liberation from GLD-3L repression by GLS-1 , we find that gls-1 also acts upstream of fog-2 and requires gld-3 activity . These findings suggest that FBF-1 regulation is tightly linked to GLS-1/GLD-3 complex activity and that all FBF-1 spermatogenetic target mRNAs may require the assistance of GLS-1 for translational repression . In summary , it seems likely that FBF-1 and GLD-3 provide the core of the switch machinery and that GLS-1 , NOS-3/Nanos , and the other PUF proteins are accessory modulators for the sperm-to-oocyte switch . Yet , it remains an open question , how GLD-3L handover from FBF-1 to GLS-1 is regulated and executed in a developmental fashion . GLS-1 indirectly promotes the switch by releasing FBF to inhibit sperm production . Yet , it remains to be determined if GLD-3 sequestration from the GLD-3L/FBF complex may also be a way to modulate the roles of GLD-3 activity , to directly promote oogenesis through the translational regulation of yet unknown oocyte-promoting mRNAs . This scenario seems likely given their common maternal activities . We find that a reduction of maternal gls-1 or gld-3 activity in the early embryo , results in a common phenotype , i . e . the germline survival defect . A hallmark of the Gls defect is that germ cells are correctly specified in the early larvae , proliferate until the L3 stage and degenerate rather than differentiate in later development . This form of germ cell death appears to a large extent independent of CED-3 or CED-4-mediated apoptosis [5 , this work] . Although a more detailed pathological analysis of this phenomenon is missing , a molecular framework for how germline survival may be achieved becomes apparent . We propose that the formation of a larger GLS-1/GLD-3 RNP complex positively influences the translational activity of distinct mRNA ( s ) , which encodes a germline survival factor ( Figure 8B ) . Alternatively , it may repress a germline death factor . The recent discovery of the cytoplasmic poly ( A ) polymerase GLD-4 , which physically binds to GLS-1 , suggests the involvement of poly ( A ) tail length control [24] . Our model is strongly supported by our analysis of the GLS-1 interaction-deficient gld-3 ( ax562ts ) mutation . At restrictive temperature gld-3 ( ax562 ) animals produce both predominant GLD-3 isoforms to wild-type levels but carry a single glycine-to-arginine ( G/R ) replacement in their fourth KH domain . The bulky , charged side chain of arginine is expected to have severe structural consequences on KH domain folds [28] . Consistent with this , we find that GLD-3ax562 does not form a complex with GLS-1 in vitro . Importantly , other known protein interactors of GLD-3 , e . g . GLD-2 , are not compromised in interacting with GLD-3ax562 and GLS-1 remains expressed in gld-3 ( ax562 ) embryos at restrictive temperature ( Rybarska and Eckmann , unpublished results ) . Together this suggests that the G/R substitution disturbs GLD-3 folding locally to specifically inhibit GLS-1/GLD-3 complex in contrast to distorting global GLD-3 structure and compromising all GLD-3 functions . We have considered the possibility that the mutation in the KH domain of the gld-3 ( ax562 ) allele might affect the RNA-binding capacities of GLD-3 , as KH domains are known to serve as RNA-binding surfaces as well as protein interaction platforms [29] . However , we found the general RNA-binding affinity of GLD-3Lax562 in RNA homopolymer-binding assays unaffected when compared to GLD-3LWT ( Jedamzik and Eckmann , unpublished results ) . Therefore , the G/R substitution seems more likely to compromise the formation of a GLS-1/GLD-3 complex rather than interfering with interactions with mRNA targets . Unfortunately , no target mRNAs required for germline survival have been identified to date . Nevertheless , our working model invokes the formation of a larger GLS-1/GLD-3 RNP complex that positively regulates a distinct mRNA target ( s ) ( Figure 8B ) . We speculate that this type of regulation might involve poly ( A ) tail metabolism as GLS-1 is able to bind and stimulate the poly ( A ) polymerase activity of GLD-4 [24] . In the current work we find that gld-4 ( RNAi ) induces Gls animals at elevated temperatures . Hence , GLS-1 might recruit GLD-3 into a trimeric complex with GLD-4 , and together they may be critical for germline survival . Additional unknown components may influence the structure/stability and function of maternal GLS-1/GLD-3 RNP complexes in the early embryo . Analysis of the gld-3 ( ax562 ) temperature-sensitive Gls phenotype would support this . The GLS-1/GLD-3ax562 complex is only severely compromised in vivo at higher temperatures . In contrast , in vitro or in yeast the GLS-1/GLD-3ax562 complex does not form and we infer that other factors might stabilize the complex in vivo at permissive temperature . Considering the expression and co-localization of GLS-1 and GLD-3 in early embryos , we propose that germline survival is initiated , as a function of the maternal GLS-1/GLD-3 complex , during early embryogenesis rather than early postembryonic development . This is also consistent with the temperature sensitive period of gld-3 ( ax562 ) . Homozygote gld-3 ( ax562 ) animals produce adult animals without germ lines when shifted during early embryogenesis . Later shifts have a less detrimental effect on germ cell loss even when maintained at high temperatures ( Rybarska and Eckmann , unpublished results ) . In summary , GLS-1 is a master modulator of multiple GLD-3 functions throughout germline development . GLS-1 limits and extends GLD-3 availability by providing an interactive platform to bring in additional modulators to form new RNP complexes with varied activities . Proteins functionally analogous to GLS-1 are highly likely to exist in other organisms . Modulators of conserved RNPs are undoubtedly required to implement cell fate decisions during development across animals . As metazoan germ cells depend strongly on post-transcriptional regulatory mechanisms , these functional analogues may also be found in their germplasm granules .
Worms were handled according to standard procedures and grown at 20°C unless otherwise stated [30] . Strains used in this study: LGI: gls-1 ( ef4 ) , gls-1 ( ef8 ) , nDf23/unc-13 ( e1091 ) lin-11 ( n566 ) , glp-4 ( bn4ts ) ; LGII: gld-3 ( q730 ) , gld-3 ( ax562ts ) , fbf-1 ( ok91 ) ; LGIII: glp-1 ( q224ts ) , ced-4 ( n1162 ) ; LGV unc-51 ( e1189 ) , fog-2 ( q71 ) ; the wild-type strain was bristol N2 . The gls-1 ( ef4 ) and gls-1 ( ef8 ) deletion mutants were generated in an EMS based deletion screen [13] and are described further in Text S1 . Adult germ line phenotypes were scored 24 hrs past midL4 . Antibodies against the following proteins were used as described: anti-GLD-1 [31] , anti-FOG-2 [32] , SP56 [33] , anti-RME-2 [34] , anti-GLD-3 [20]; anti-PGL-1 [35] , anti-GLH-2 [19] . A rabbit polyclonal antibody serum ( C5C0 ) was generated against a GST::GLS-1 fusion comprising aa 249–576 . Affinity purification was carried out using a maltose binding protein fusion of the identical GLS-1 piece immobilized on a HiTrap ( Amersham ) column and eluted at low pH . A monoclonal antibody was generated immunizing mice with a peptide corresponding to the very C-terminus of GLS-1 ( aa 989–1011 ) . The mouse anti-GLS-1 antibody ( mo184C16 ) recognized in vitro produced GLS-1 protein ( not shown ) and is very specific for GLS-1 in staining experiments . However , we found that it is also more sensitive to paraformaldehyde ( PFA ) fixation and thus stains less prominently granules . For some P granule double labelling experiments we used an anti-PGL-1 peptide antiserum of guinea pigs that stained P granules very similar to published anti-PGL-1 antibodies [35]; the affinity purified antibody is specific as no signal was observed in pgl-1 ( bn101 ) animals . Immunofluorescence on extruded and 1% PFA fixed germ lines was carried out in solution as described [5] . Embryo ( Figure 2D ) and whole worm immunocytochemistry ( Figure 6 ) with methanol/acetone fixation was described elsewhere [36] . Images were taken on a Zeiss Imager M1 equipped with an Axiocam MRm ( Zeiss ) and processed with AxioVision ( Zeiss ) and Photoshop CS3 ( Adobe ) . For optical sections we either generated images on an Imager Z1 with an Apotome ( Zeiss ) or on a confocal microscope ( LSM Meta510 , Zeiss ) . Secondary antibodies were coupled to fluorochromes FITC , CY3 and CY5 ( Jackson Laboratories ) . Recombinant GST fusions of GLD-3L fragments were produced in BL21 ( pRIL ) E . coli . Recombinant C-terminally His-tagged protein fusions of GLS-1 , GFP and GLD-3L ( wt and ax562 ) were produced in SF+ insect cells with the help of baculoviruses , which were generated and tested for their protein expression levels in SF+ insect cells according to the manufacturers protocols ( Invitrogen ) . Co-expression was performed by viral co-infection . MBP::GLD-3L and GST::GLD-3 fragments were purified on amylose resin ( NEB ) and glutathione beads ( Sigma ) , respectively . GLS-16His and derivatives were purified in SF+ lysis buffer [50 mM HEPES pH 7 . 5 , 100 mM NaCl , 5% glycerol , 0 . 1% Triton X-100 , 1 mM DTT , Protease inhibitor cocktail ( Roche ) and E64 ( BioMol ) ] on Ni-NTA agarose beads ( Qiagen ) , eluted with 250 mM Imidazole/50 mM HEPESpH 7 . 5/500 mM NaCl and dialysed against DB200 [20 mM Tris-Cl pH 7 . 5 , 200 mM NaCl , 0 . 1% Triton X-100] . For the competition assay MBP-GLD-36His and MBP-FBF-16His were purified on amylose beads ( NEB ) in SF+ lysis buffer . The N-terminal MBP fusion part of GLD-3L6His was cleaved off by PreScission protease treatment ( GE Healthcare ) . Western Blot bands were quantified in Adobe Photoshop CS2 by sliding an equally narrow sized box over all bands and extracting the pixel intensities . For Protein co-IPs see Text S1 . GeneBank Accession Number of GLS-1: FJ610055 . | Germ cells differ from somatic cells in their unique potential to reproduce a multicellular organism . The immortal germ line links the successive generations in all metazoans , but its development is remarkably diverse . How germline development and survival are regulated in different organisms is far from understood . One fundamental similarity is the widespread use of post-transcriptional mRNA regulation to control the expression of germ cell fate determinants . The development of the C . elegans germ line is a paradigm in the study of translational regulatory networks , composed of conserved RNA-binding or modifying proteins that act as mRNA regulators . Here , we report the discovery of GLS-1 , a novel cytoplasmic protein , which we find to form a protein complex with the translational activator GLD-3/Bicaudal-C . This complex promotes and maintains the sperm-to-oocyte switch in hermaphrodites , whereby GLS-1 acts as a molecular mimic of FBF/Pumilio , a translational repressor of sperm promoting mRNAs . Furthermore , a GLS-1/GLD-3 complex may also positively regulate mRNAs important for germline survival . Therefore , GLS-1 serves as a new example of how cell fate decisions and tissue development are achieved by modulating the activities of broadly operating translational control networks . | [
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"biology/... | 2009 | GLS-1, a Novel P Granule Component, Modulates a Network of Conserved RNA Regulators to Influence Germ Cell Fate Decisions |
Feeding preference is critical for insect adaptation and survival . However , little is known regarding the determination of insect feeding preference , and the genetic basis is poorly understood . As a model lepidopteran insect with economic importance , the domesticated silkworm , Bombyx mori , is a well-known monophagous insect that predominantly feeds on fresh mulberry leaves . This species-specific feeding preference provides an excellent model for investigation of host-plant selection of insects , although the molecular mechanism underlying this phenomenon remains unknown . Here , we describe the gene GR66 , which encodes a putative bitter gustatory receptor ( GR ) that is responsible for the mulberry-specific feeding preference of B . mori . With the aid of a transposon-based , clustered regularly interspaced short palindromic repeats ( CRISPR ) /CRISPR-associated protein-9 nuclease ( Cas9 ) system , the GR66 locus was genetically mutated , and homozygous mutant silkworm strains with truncated gustatory receptor 66 ( GR66 ) proteins were established . GR66 mutant larvae acquired new feeding activity , exhibiting the ability to feed on a number of plant species in addition to mulberry leaves , including fresh fruits and grain seeds that are not normally consumed by wild-type ( WT ) silkworms . Furthermore , a feeding choice assay revealed that the mutant larvae lost their specificity for mulberry . Overall , our findings provide the first genetic and phenotypic evidences that a single bitter GR is a major factor affecting the insect feeding preference .
Chemosensory processes , including olfaction and gustation , are critical for host-plant selection in phytophagous insects [1 , 2] . Olfaction is responsible for host orientation , and gustation plays a central role in host selection [3 , 4] . Insect gustatory receptors ( GRs ) , as well as olfactory receptors ( ORs ) , therefore play critical roles in determining insect feeding preference . Most insect GRs are expressed exclusively in gustatory receptor neurons ( GRNs ) and transmit signals through GRNs to regulate insect feeding behaviors [5 , 6] . Insect GRs are known to recognize sugars , bitter compounds , and nonvolatile pheromones [7 , 8] . In Drosophila melanogaster , GR5a and GR66a are found in different populations of GRNs [5] . GR5a-positive GRNs respond to various sugars , and GR66a-positive GRNs respond to many bitter compounds [9 , 10] . In the butterfly , Papilio xuthus , a GR was reported to be involved in host-plant recognition for oviposition [11] . In addition , GRs are also required for the detection of CO2 , nutrients , light , and temperature [12–14] . Large numbers of insect GRs have been identified in many insect species [15–24] . However , most GRs have not been functionally characterized , and the roles played by these GRs in insect feeding preferences remain unclear . Based on the host-plant selection range , the feeding preferences of phytophagous insects are classified as monophagous , oligophagous , and polyphagous . Lepidoptera , the largest lineage of phytophagous insects , includes many important agricultural and forest pests that exhibit high diversity in terms of feeding preference . The domesticated silkworm , Bombyx mori , is a beneficial lepidopteran insect that has been a major contributor to silk production for thousands of years . One of the main characteristics of B . mori is its monophagous feeding preference , and silkworm larvae predominantly feed on fresh mulberry leaves ( Morus alba L . ) . Several polyphagous silkworm mutant strains that feed on the leaves of various plants that are rejected by normal silkworms have been reported [25 , 26] . Genetic analysis of one representative strain , Sawa-J , revealed that a major recessive gene on the polyphagous ( pph ) locus was potentially responsible for this change in feeding preference [27] . However , the molecular mechanism underlying the monophagous feeding preference of B . mori is unknown , and whether GR genes are involved the feeding preference of silkworm remains to be determined . Recently , a complete set of 76 GR genes was identified in B . mori [28] . Among these genes , only three sugar GRs were functionally characterized [29–31] , whereas most of the GRs remained functionally identified , including 66 putative bitter GRs [28] . The biological functions of most insect GRs are poorly understood , especially those of nondrosophilid insects , due to the lack of reverse genetic approaches for the study of these insect species . This is especially true for lepidopteran species , because RNA interference functions with variable efficiency in many species [32] . Recent advances in the development of targeted genomic manipulation tools provide great benefits for functional genomic research of lepidopteran insects . These genomic manipulation tools—including zinc-finger nucleases ( ZFNs ) , transcription activator-like effector nucleases ( TALENs ) , and the clustered regularly interspaced short palindromic repeats ( CRISPR ) /CRISPR-associated protein-9 nuclease ( Cas9 ) system—have been extensively used to generate targeted mutations at single or multiple sites in many organisms in vitro and in vivo [33–35] . Among these tools , the CRISPR/Cas9 system is the most extensively used mutagenesis system due to its high mutagenic efficiency and simple procedure . Among lepidopteran insects , the CRISPR/Cas9 system has been successfully established in B . mori [36–38] , Spodoptera litura [39] , Plutella xylostella [40] , and Helicoverpa armigera [41] . In the current study , we investigated the genetic basis for the feeding preference towards mulberry exhibited by silkworm . We mutated the GR66 gene , which encodes a putative bitter GR , in B . mori using the Cas9/small guide RNA ( sgRNA ) system . Homozygous GR66 mutant larvae exhibited expanded diets , indicating that the GR66 gene is responsible for mulberry-specific feeding behavior in the silkworm . Acquiring new feeding activity in the silkworm will contribute to modern sericulture as well as to the understanding of the molecular mechanisms of insect–host interactions .
It was reported that there are 76 putative GRs distributed on 16 of the 28 chromosomes of B . mori [28] . Among these genes , only one putative bitter GR gene , GR66 , was identified as being located on the third chromosome . The genomic locus of this gene is within the putative pph locus of the polyphagous Sawa-J silkworm strain [27] . This finding indicates that GR66 might be the candidate gene for the pph locus and could be involved in the feeding preference of silkworm . We first investigated the relative mRNA levels of GR66 in different larval tissues using quantitative real-time PCR ( qRT-PCR ) . It has been reported that most insect GRs are localized in the taste sensilla of the larval mouthparts [28 , 42] ( Fig 1A ) . As expected , GR66 was predominantly expressed in larval maxillae ( Fig 1B ) . The open reading frame ( ORF ) of the GR66 gene contains 1 , 140 base pairs and encodes a 380-amino-acid polypeptide . Bioinformatic analysis revealed that the GR66 protein consists of seven transmembrane domains with an intracellular N terminus , which is distinct from the structures of members of the G-protein-coupled receptor ( GPCR ) family ( Fig 2B ) . We further investigated the cellular localization of this protein via transfection of an enhanced green fluorescent protein ( EGFP ) -fused GR66 expression plasmid into mammalian 293T cells . The results showed that the protein is localized on the cell membrane ( Fig 1C ) . To investigate the potential involvement of GR66 in the feeding preference of silkworm , we genetically ablated GR66 using a transposon-based , Cas9/sgRNA-mediated mutagenesis system [37] . Two independent transgenic lines were established by transposon-mediated germline transformation . One transgenic line expressed Cas9 under the control of the germ-cell–specific promoter Bmnos [37] , and the other line expressed two sequence-specific sgRNAs targeting GR66 ( Fig 2A ) under the control of the BmU6 promoter [38] . Each line also expressed an IE1 promoter-derived fluorescent marker ( EGFP in the Cas9-expressing line or DsRed2 in the sgRNA-expressing line ) to facilitate the screening of positive individuals from the embryonic stage [37] . In the F1 hybrids between the Cas9 and sgRNA lines , somatic mutagenesis was identified by PCR-based analysis and subsequent sequencing . Mutants were generated at a single site or both sites ( S1A Fig ) , indicating that successful mutagenesis was induced by the transgenic CRISPR/Cas9 system . Somatic mutants of GR66 showed no deleterious phenotype compared with the wild-type ( WT ) animals , indicating that knocking out GR66 did not interfere with silkworm development and fertility . To obtain heritable , nontransgenic , homologous mutants to assess feeding preference , a series of crossing strategies and PCR-based screening experiments were performed ( S1B Fig ) as described previously [43] . Finally , two independent homozygous lines with truncated GR66 proteins were established ( S2 Fig ) . One mutant line ( ΔGR66-1 ) had a 929-bp genomic DNA deletion at the GR66 locus , resulting in a 180-bp deletion in the ORF and to a truncated 319-aa protein , which was 60 aa shorter than WT GR66 protein ( S2 Fig ) . The other mutant line ( ΔGR66-2 ) had a 931-bp genomic deletion at the GR66 locus , resulting in a 182-bp deletion in the ORF and to a 312-aa protein , which was 67 aa shorter than the WT GR66 protein ( S2 Fig ) . The truncated GR66 proteins of the ΔGR66-1 and ΔGR66-2 mutants contained only six or five transmembrane domains , respectively ( Fig 2B ) . Because the truncated proteins did not have all seven transmembrane domains that are essential for the function of the membrane proteins [44 , 45] , we presumed that both mutants lacked GR66 functions . Consistent with the transgenic somatic mutants , homologous GR66 mutant silkworms were fully viable and fertile . We first used homozygous ΔGR66-2 newly moulted fifth-instar larvae to assess feeding behavior . After 24 h of starvation treatment to facilitate feeding sensitivity , both WT and homozygous GR66-2 mutant larvae were provided various food sources for 24 h ( Fig 3A–3E ) , and then , the increase in weight and number of droppings were recorded ( Fig 3G and 3H ) . The leaves of Mongolian oak ( Quercus mongolica Fisch . ex Ledeb . ) , fruits of apple ( Malus domestica ) and pear ( Pyrus spp . ) , and seeds of soybean ( Glycine max ) and corn ( Zea mays ) were subjected to analysis . Mulberry leaves were also used as a control . Both WT and mutant larvae ate the mulberry leaves and exhibited normal development ( Fig 3A and S1 Movie ) . Leaves of Mongolian oak are known food sources of Chinese oak silkworm , Antherea pernyi , but are not consumed by B . mori . The ΔGR66-2 larvae ate the oak leaves ( Fig 3B and S2 Movie ) , and droppings were observed ( Fig 3H ) , but the body weights did not increase significantly ( Fig 3G ) . The ΔGR66-2 larvae exhibited a 15 . 96% weight increase with approximately seven droppings per larva after feeding on apple , whereas the WT animals did not attempt to consume apple , and no droppings were observed ( Fig 3C , 3G and 3H and S3 Movie ) . Furthermore , we found that the ΔGR66-2 larvae could also feed on pear ( Fig 3D and S4 Movie ) , which belongs to the same family as apple , namely , Rosaceae . A 25 . 47% weight increase was observed for ΔGR66-2 larvae , whereas no significant increase was observed for WT animals ( Fig 3G and 3H ) . The ΔGR66-2 larvae could feed on both fresh soybean and corn , with a 10 . 56% and 14 . 08% increase in weight , respectively , whereas no significant weight increase was observed for WT animals ( Fig 3E–3H , S5 Movie and S6 Movie ) . After feeding , the larvae were dissected to confirm food digestion , and the results showed that the midguts were filled with the residues of the indicated foods ( Fig 3A’–3F’ ) . Additionally , the Mongolian oak leaf residue diffused into the anterior part of the midguts ( Fig 3B’ ) , indicating that Mongolian oak leaves could not be digested well . This finding also explained why the body weight did not increase significantly ( Fig 3G ) . A similar result was obtained when the ΔGR66-1 mutant line was subjected to analysis ( S3 Fig ) . Notably , none of the larvae could survive the entire fifth-instar stage when reared on food other than mulberry ( S4 Fig ) , indicating that B . mori mostly adapted to mulberry leaves during long-term cultivation . To further investigate the feeding preference of GR66 mutants , we performed a two-choice assay in prestarved fifth-instar larvae . Given a choice between mulberry leaves and Mongolian oak leaves , the WT larvae exhibited a strong preference for mulberry leaves and did not attempt to eat Mongolian oak leaves ( Fig 4A and 4A’ ) . In contrast , the ΔGR66 larvae exhibited similar feeding preferences for both mulberry leaves and Mongolian oak leaves ( Fig 4B , 4B’ , 4C and 4C’ ) . In addition , a commercial artificial diet containing mulberry leaf powder and another artificial diet that lacked mulberry leaf ( 1:1 ratio of soybean powder to corn powder ) were also used for a two-choice assay . Similar to the previous result , the WT larvae exhibited a strong preference for the artificial diet containing mulberry ( Fig 4D and 4D’ ) , whereas the ΔGR66 larvae exhibited similar feeding preferences for both artificial diets ( Fig 4E , 4E’ , 4F and 4F’ ) . These results revealed that the GR66 mutant larvae had lost their specificity for mulberry , suggesting that GR66 is required for the mulberry-specific feeding preference of B . mori . In addition , we performed two-choice feeding assays with neonate larvae . Both the WT and GR66 mutant neonate larvae exhibited a strong preference for the artificial diet containing mulberry ( S5 Fig ) . Although this phenotypic consequence remained to be elucidated , we speculated that food choice of neonate larvae are also strongly affected by ORs , because olfaction is responsible for host orientation [46] . Most insect GRs are located in the taste sensilla of the larval mouthparts , and it has been reported that the medial sensilla are responsible for sweet taste perception and lateral sensilla are responsible for bitter taste perception in Lepidoptera [28] . To investigate whether GR66 mutants exhibit altered responses to different tastes , electrophysiological recording analysis on contact chemosensilla was performed on taste sensilla , including the medial and lateral styloconic sensilla of fifth-instar larvae in the ΔGR66-2 line . We first investigated two sweet stimulants , namely , sucrose and myo-inositol , in the lateral sensilla . No difference was detected between WT and GR66 mutants at a concentration of 10 mM , indicating that GR66 depletion was irrelevant for the perception of these two sweet stimuli ( S6A and S6B Fig ) . We subsequently investigated two bitter substances , namely , caffeine and salicin , in the medial sensilla at a concentration of 10 mM . The results showed that the electrophysiological response to these two substances was not affected by GR66 depletion ( S6C and S6D Fig ) . We further tested the response to caffeine and salicin at different concentrations , and similar results were obtained ( S6E and S6F Fig ) . These results indicated that the GR66 mutants did not exhibit altered responses to these typical sweet or bitter substances . Other compounds in mulberry leaves , especially the potential ligands of GR66 , remain to be identified .
Molecular mechanisms of host-plant selection in phytophagous insects remain to be elucidated , and how GRs are involved in their feeding behaviors is poorly understood . To reveal the molecular mechanism underlying mulberry-specific herbivory in B . mori , we genetically ablated a putative bitter GR , GR66 , via Cas9/sgRNA-mediated targeted mutagenesis . Homologous mutant larvae exhibited loss of mulberry specificity and the ability to feed on a wide range of food sources , indicating that GR66 is a determinant of the monophagous feeding preference of B . mori . Increasing numbers of insect GRs have been identified , and their critical roles in detection of environmental stimulations have been reported [7–14] . In phytophagous insects , most reported GRs belong to putative bitter GR subfamily and they are necessary in the recognition of many plant secondary metabolites , which are normally bitter compounds [47] . In B . mori , the subfamily of the bitter GRs contains up to 66 genes and is the largest subfamily among the total 76 identified GRs in B . mori [28] . None of these putative bitter GRs had been functionally elucidated until the current study on GR66 . Our data strongly suggest that GR66 is a major factor affecting the feeding preference of silkworm , because mutation of this gene could change the mulberry-specific herbivory of silkworm . We speculate that GR66 may serve as a feeding inhibitor in B . mori . This finding explains why GR66 mutagenesis could result in the acceptance of an expanded range of host-plant materials by the larvae . In WT animals , GR66 is active and inhibits the feeding behavior on nonhost materials , whereas certain compounds in mulberry leaves directly or indirectly repress GR66 activity , leading to initiation of such feeding behavior . Future validation of potential ligands of GR66 in mulberry leaves and identification of food components that dictate host specificity will be critical for elucidation of this species-specific feeding preference . In the current study , the ΔGR66 strains did not exhibit significant electrophysiological differences in the selection of sweet or bitter substances , including salicin . Our results were different from previously reported results for the polyphagous silkworm strain Sawa-J , which exhibited reduced sensitivity to the bitter compound salicin [26] . Because the pph locus in the Sawa-J strain has not been mapped to a single gene [26] , the different electrophysiological phenotypes between the Sawa-J and ΔGR66-2 strains indicated that the putative involvement of different or additional genes , such as the many other GR genes in B . mori , should be taken into account to explain the monophagous feeding preference for mulberry . We presumed that the effects of these genes led to the Sawa-J strains and GR66 mutants exhibiting different responses to salicin . Additionally , it is possible that GR66 mutagenesis did not create completely null mutants ( Fig 2 ) , and truncated GR66 may still respond to salicin . Mulberry leaves have been used as the only food source for mass rearing of silkworm for thousands of years . Due to limitations associated with labor and land consumption and seasonal cycles in the harvesting of fresh mulberry leaves , the development of silkworm strains that can feed on cost-effective diets instead of mulberry leaves has been pursued . Conversion of the monophagous silkworm to a polyphagous species by GR mutagenesis therefore provides a promising approach for the development of alternative food sources for mass rearing of silkworm . Furthermore , lepidopteran insects include a large number of agricultural and forest pests that exhibit high diversity in terms of feeding habits . Orthologous genes of GR66 or other GRs in lepidopteran insects may play key roles in the species-specific feeding preferences of these insects . Insect feeding preference is a very complicated biological process and is probably more complex than determined by a single gene . Large numbers of insect GRs remains to be functional elucidated , and they should also be considered to play important roles in feeding preference . Elucidation of the critical role of GRs in insect feeding preference will provide insights into the mechanisms underlying insect feeding behavior and insect–plant interactions , facilitating the development of novel strategies for pest management .
A multivoltine and monophagous silkworm strain , Nistari , was used in all the experiments . Larvae were fed fresh mulberry leaves at 25°C under standard conditions [48] . Heads were excised from newly hatched first-instar larvae of B . mori . The excised heads were washed in PBS and fixed with FAA solution ( 1:1:18 ratio of 37% to 40% formaldehyde to acetic acid anhydride to 50% ethanol ) . The fixed samples were dehydrated via exposure to gradually increasing concentrations of ethyl alcohol ( 50% , 60% , 70% , 80% , 90% , 95% , 100% ) using a rotary machine . The heads were dried in a critical-point dryer and then coated with platinum prior to observation under a scanning electron microscope ( JEOL ) . Total RNA was isolated from the antennae , labra , mandibles , maxillae , labia , thoracic legs , and midguts of third-day fifth-instar ( L5D3 ) larvae using TRIzol reagent ( Invitrogen ) . The RNA was treated with DNase I ( Invitrogen ) to remove genomic DNA . One microgram of total RNA was used to synthesize cDNA using the ReverAid First Strand cDNA Synthesis Kit ( Fermentas ) . Relative mRNA levels were determined by qRT-PCR using SYBR Green real-time PCR master mix ( TOYOBO ) . The PCR conditions used were as follows: initial incubation at 95°C for 1 min , followed by 40 cycles of 95°C for 15 s and 60°C for 1 min . The primers used for qRT-PCR are listed in S1 Table . Another primer pair—namely , RP49-F and RP49-R ( S1 Table ) —was used as an internal control [48] . The ORF of BmGR66 was PCR-amplified using cDNA synthesized from the total RNA isolated from the maxillae at L5D3 as a template . The PCR products obtained were directly cloned into the pcDNA-3 . 0 vector to generate GR66-pcDNA3 . 0 . To detect the expression of BmGR66 in human embryonic kidney 293T ( HEK293T ) cells , the ORF of GFP was cloned and incorporated in-frame upstream of BmGR66 with a flexible linker modifying the amino acids GGGGS . To construct the transgenic CRISPR/Cas9 system , we used the activator line pBac[IE1-DsRed2-Nos-Cas9] ( Nos-Cas9 ) , in which Cas9 was driven by a germ-cell–specific promoter , as described previously [37] . The plasmid pBac[IE1-EGFP-U6-BmGR66-sgRNA] ( U6-sgRNA ) , used to express the sgRNA , was constructed as described previously [38] . The sgRNA targeting sites were designed as GN19NGG . The primers used for plasmid construction are listed in S1 Table . HEK293T cells were cultured in Dulbecco's modified Eagle’s medium ( DMEM , Thermo Fisher Scientific ) supplemented with 10% fetal bovine serum ( FBS ) at 37°C and 5% CO2 . For receptor localization analysis , HEK293T cells were seeded in 35-mm sterilized glass-bottom dishes and incubated for 24 h . EGFP-GR66-pcDNA3 . 0 was transfected into HEK293T cells using Lipofectamine 2000 ( Invitrogen ) . After 24 h , the cells were fixed with 4% paraformaldehyde for 15 min and finally incubated with DAPI for 10 minutes . The cells were visualized by fluorescence microscopy on a Zeiss LSM 510 confocal laser scanning microscope attached to a Zeiss Axiovert 200 microscope using a Zeiss Plan-Apochromat 63×/1 . 40 NA oil immersion lens . Germline transformation of silkworm was performed as described previously [48] . For the transgenic CRISPR/Cas9 system , the Nos-Cas9 line was crossed with the U6-sgRNA line , and genomic DNA was extracted from the Nos-Cas9:U6-sgRNA as previously described [38] . Subsequently , genomic PCR followed by sequencing was carried out to identify GR66 mutant alleles . To establish a stable homozygous mutant line , the Nos-Cas9:U6-sgRNA ( F1 ) were crossed with the WT . For the F2 progeny that lacked fluorescence , PCR-based genotyping was performed using genomic DNA extracted from adult legs as templates . Removal of legs did not interfere with moth survival and fertility . Details regarding the crossing procedure are shown in S1 Fig . Briefly , we backcrossed F1 somatic mutants with WT moths and used PCR to identify heterozygous F2 mutant animals . The selected F2 mutants were backcrossed with WT moths again . The progeny of this cross were approximately 50% heterozygotes and 50% WT animals . The F3 heterozygous animals were then sib-mated . The progeny of this cross were approximately 25% homozygous mutants , 50% heterozygous mutants , and 25% WT animals . The F4 homozygous mutants were then sib-mated to obtain 100% homozygous animals , which were used in subsequent experiments . Newly moulted fifth-instar larvae were starved for 24 h prior to conducting the behavioural assay . After starvation , each larva was placed in a sterile culture dish separately . Different plant-derived food materials , such as mulberry ( M . alba ) , Mongolian oak ( Q . mongolica Fisch . ex Ledeb . ) , apple ( M . domestica ) , pear ( Pyrus spp . ) , soybean ( G . max ) , and corn ( Z . mays ) , were placed in the culture dishes . After 24 h , the weights of larvae were recorded , and the number of droppings was counted . Two-choice feeding preference tests were performed using plant leaves or artificial diets . Leaves of mulberry and Mongolian oak were placed on separate sides of the container 2 cm away from the middle . A two-choice feeding assay with an artificial diet containing mulberry leaf powder and an artificial diet that was 1:1 ratio of soybean powder to corn powder was performed as described above . Twenty newly moulted fifth-instar larvae after starvation for 24 h or a brood of neonate larvae were placed in the center . Photographs were taken at 0 and 60 min after release . Tip recordings for insect contact chemosensilla were performed on the medial and lateral styloconic sensilla of fifth-instar B . mori larvae as described previously with some modification [49 , 50] . Heads with the first thoracic segments were cut from newly hatched fifth-instar larvae that were starved for 24 h . An AgCl-coated silver loop was inserted into each head until pressure caused the mouthparts to open , and then the loop was connected to a copper miniconnector , which served as the recording electrode . A recording glass electrode filled with the stimulus solution was brought in contact with the tip of the styloconic sensillum under a dissecting microscope . Responses were recorded from both the medial and lateral styloconic sensilla on both sides of the head . Stimuli lasted 1 s and were separated by an interval of 3 min to allow for recovery and to minimize adaptation . The tip diameter size of the stimulating electrode was approximately 50 μm , which is suitable for stimulation of single styloconic sensilla . Action potentials ( spikes ) generated during the first second after stimulus onset were amplified by the amplifier ( Syntech Taste Probe DTP-1; Hilversum , the Netherlands ) and filtered ( A/D-interface , Syntech IDAC-4; Hilversum , the Netherlands ) . The electrophysiological signals were recorded and analyzed with the aid of spike analysis programs for insect data ( SAPID ) Tools software , version 16 . 0 [51] , as well as Autospike version 3 . 7 software ( Syntech , Hilversum , the Netherlands ) . Solutions of sucrose , myo-inositol , caffeine , and salicin dissolved in 2 mM KCl were used as stimulants in the electrophysiological experiments . For each stimulant and corresponding sensillum responsive to the stimulant , 15 WT and mutant larvae that hatched from 3 to 5 different rearing batches were tested . A solution of 2 mM KCl served as a control . Data are presented as the means ± standard error of the means ( SEMs ) . All the experiments in this study were performed with at least three replicates . All the data are expressed as the mean ± SEM . The differences between groups were examined by either two-tailed Student t-test or two-way ANOVA . Statistically significant differences are indicated by asterisks . | The molecular mechanism underlying species-specific feeding preference in insects is poorly understood . The silkworm , Bombyx mori , is a typical monophagous plant-eating insect , but the genetic basis for its famous mulberry-specific feeding preference is unknown . Here , we identify gustatory receptor 66 ( GR66 ) as a determinant of the silkworm’s mulberry-specific monophagy . GR66-mutant larvae generated by clustered regularly interspaced short palindromic repeats ( CRISPR ) /CRISPR-associated protein-9 nuclease ( Cas9 ) acquired new feeding activity and showed the ability to feed on various plant species that are not normally consumed by the wild-type ( WT ) animals; a two-choice assay demonstrated that the mutant larvae had lost their feeding preference for mulberry . Our genetic and phenotypic evidence therefore demonstrates that GR66 is a major factor affecting the feeding preference of the silkworm . | [
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"plants... | 2019 | A determining factor for insect feeding preference in the silkworm, Bombyx mori |
Dengue fever ( DF ) in Guangzhou , Guangdong province in China is an important public health issue . The problem was highlighted in 2014 by a large , unprecedented outbreak . In order to respond in a more timely manner and hence better control such potential outbreaks in the future , this study develops an early warning model that integrates internet-based query data into traditional surveillance data . A Dengue Baidu Search Index ( DBSI ) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors . Generalized additive models ( GAM ) with or without DBSI were established . The generalized cross validation ( GCV ) score and deviance explained indexes , intraclass correlation coefficient ( ICC ) and root mean squared error ( RMSE ) , were respectively applied to measure the fitness and the prediction capability of the models . Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence , and the model with DBSI ( ICC:0 . 94 and RMSE:59 . 86 ) has a better prediction capability than the model without DBSI ( ICC:0 . 72 and RMSE:203 . 29 ) . Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou .
Dengue fever ( DF ) is currently endemic in more than 100 countries , mainly in southeast Asia , the western Pacific islands and the Americas , with approximately 3 . 9 billion individuals at risk [1] . The annual number of infections is estimated at 390 million globally [2] , making it one of the most significant vector-borne viral diseases . In China , the first outbreak of DF was reported in Guangdong province in 1978 [3] . Since then DF cases have been reported in 26 provinces of China [4] . Guangdong province is the most affected areas in mainland China . In 2014 , this province experienced a large outbreak resulting in 45 , 224 DF cases [5 , 6] . Since there is no specific treatment for DF and vector control remains the most effective way to prevent and control it [2] . Early warning systems are considered as one of the prerequisites for adequate preparedness and response to DF epidemics [7] . Several previous studies have reported meteorological factors that were associated with DF outbreaks through early warning models [8–12] . Among various meteorological factors , temperature and rainfall contribute the most to dengue epidemics [13] . In Singapore , Yien et al . [12] developed a weather-based dengue-forecasting model that allows warning 16 weeks in advance of dengue epidemics with high sensitivity and specificity . However , the dengue epidemics in Guangzhou are generally characterized by low level epidemic caused by imported cases , followed by a sudden and rapid transmission [14] . They have varied greatly in size from year to year [4] , which poses a different challenge for prediction than in the more stable and endemic regions . Although a study conducted by Sang et al . [15] attempted to develop a model based on imported cases , minimum temperature and precipitation to predict the dengue incidence in Guangzhou , DF forecasting systems still face many difficulties due to the complexity of factors influencing DF outbreaks [16] . Over the past decade the increasing number of internet users around the world has provided new sources of data potentially useful for disease surveillance . This is increasingly being recognized as an opportunity to improve traditional disease surveillance systems [17] . For example , a study reported that using Google Flu Trends ( GFT ) could improve the prediction of influenza trends two weeks ahead of Centers for Disease Control and Prevention ( CDC ) reports in the US between 2003 and 2007 [18] . Several other studies using Google , Yahoo and other search data have been conducted worldwide to predict disease trends [19–24] . However , use of GFT data is not without its problems . For example , studies found that the surveillance data did not correspond with estimates provided by the GFT model in the US during the 2009 pandemic and the 2012/2013 epidemic season [25–27] . The reasons may be related to the proportion of the population who used the internet to obtain health-related information [17] , algorithm dynamics affecting Google’s search algorithm [28] and media bias [29] . Therefore , researchers believe that internet search data is a good supplement to , rather than a substitute for , traditional disease surveillance data [28] . In China , Baidu is the most popular search engine , and approximately 86 . 7% of internet users prefer it [30] . Some recent studies have explored the potential of using Baidu search queries to predict diseases such as influenza [31] and erythromelalgia [32] . However , there has been no similar study in utilizing such data for DF prediction in China . Therefore , the aim of this study is to examine whether an early warning model utilizing internet-based dengue query data can improve DF prediction .
Guangzhou , the capital city of Guangdong province , is the third most populous city in China . At the end of 2014 the population in Guangzhou was 13 . 1 million [33] . This city is the center of transportation , finance , industry and trade in southern China and has a large exchange in business and tourism with southeast Asia , Africa and the Indian subcontinent . It has 12 districts with an area of 7473 km2 and a typical subtropical monsoon climate , with an annual mean temperature of 22°C . DF has been a legally notifiable communicable disease in China since 1989 . Weekly DF cases in Guangzhou during the period from January 1st , 2011 to December 31st , 2014 were retrieved from China Notifiable Infectious Disease Report System ( NNIDRIS ) . DF cases before October 2014 were diagnosed according to the China National Diagnostic Criteria for dengue fever ( WS216-2008 ) [34] , and cases after October 2014 were diagnosed according to the new version of the China National Diagnostic Criteria for dengue fever ( 2014 version ) enacted by the National Health and Family Planning Commission ( http://www . nhfpc . gov . cn ) . A climate dataset was obtained from the China Meteorological Data Sharing Service System ( http://cdc . nmic . cn/home . do ) . It included weekly average minimum temperature ( °C ) and cumulative rainfall ( mm ) from 2011–2014 . The population data was collected from the Guangzhou Statistical Yearbook . The Baidu index database ( http://index . baidu . com ) contains search volumes for numerous terms entered by Baidu users since January 2011 . The Baidu search query data are available as daily counts at the city , province and country level . We transformed the data to weekly counts for the analysis for consistency with other time series data . As different terms have different search volumes and can therefore produce diverse models , term selection is the critical issue in internet search data-based surveillance . However , there are no criteria in practice [32 , 35 , 36] . Previous studies generally chose the nomenclature , clinical signs and symptoms of target diseases as the main terms [23 , 24 , 32] . Related terms were obtained from a Chinese website ( http://tool . chinaz . com/baidu/words . aspx ) . Terms suggested by the website not only include recommendations from Baidu , but also from blogs , portal websites and online reports using semantic correlation analysis [31] . Upon typing in six primary terms , we obtained a total of 32 related search terms . More terms do not necessarily lead to a better result since some recommended terms are not closely related to DF occurrence , which could reduce the detective ability of the surveillance system [32] . Hence , we filtered terms following two steps . First , we eliminated the terms irrelevant to DF and those with a search volume of zero during the study period , and after these 26 keywords remained ( S1 Table ) . Second , Spearman’s rank correlation coefficients ( ρ ) were then calculated between weekly DF and search volumes . We excluded the words with correlation coefficients smaller than 0 . 4 ( S2 Table ) . Weights of terms were defined by the value of the correlation coefficient . The weights calculation and Dengue Baidu Search Index ( DBSI ) composition formulae are as follows: weighti=ρi∑1nρi DengueBaiduSearchIndex=∑1nweightitermi Where n is the number of terms , termi and weighti represent the ith term and the weight of it . First , cross-correlation analysis was carried out to identify the correlation between DF occurrence with imported cases , minimum temperature , cumulative rainfall and DBSI with 1 to 16 weeks’ lag . Second , generalized additive models ( GAM ) were applied to fit the relationships between the variables and local DF cases . Because the variables with different time lags are highly correlated with each other , only those with maximal correlation coefficient were used to construct the model [15] . We used a cubic spline function for these variables to consider the non-linear association between factors and DF occurrence . In this study , a quasi-Poisson model was applied to allow for over-dispersion of the data . Model selection was based on the lowest generalized cross validation ( GCV ) scores . In order to examine whether internet-based dengue query data could improve the prediction , we fit two models ( with and without DBSI ) and compared the relative parameters . Model ( 1 ) ( without DBSI ) and model ( 2 ) ( with DBSI ) are as follows: Log ( ut ) =β0+s ( Tmint−e , df ) +s ( Rt−b , df ) +s ( Impt−c , df ) +s ( Localt−1 , df ) +year+s ( week , df ) +offset ( pop ) ( 1 ) Log ( ut ) =β0+s ( DBSIt−d , df ) +s ( Tmint−e , df ) +s ( Rt−b , df ) +s ( Impt−c , df ) +s ( Localt−1 , df ) +year+s ( week , df ) +offset ( pop ) ( 2 ) where ut represents the predicted mean DF cases during week t; s ( Tmint–e , df ) denotes the cubic spline of minimum temperature in the previous e weeks with corresponding df; s ( Rt–b , df ) represents the cubic spline of cumulative rainfall in the previous b weeks with corresponding df; s ( Impt–c , df ) represents the cubic spline of imported cases in the previous c weeks with corresponding df; s ( Localt–1 , df ) is the autoregressive term for local DF cases in the previous week with corresponding df; s ( DBSIt–d , df ) denotes the cubic spline of DBSI in the previous d weeks with corresponding df; year is used to control long-term trend , and s ( week , df ) denotes the cubic spline of week with corresponding df that is used to control the seasonality; and offset ( pop ) accounts for population in Guangzhou during this period [12] . The df for each variable was determined according to the GCV principles and deviance explained ( % ) [15] . Lower GCV and higher deviance explained value indicate a better fit of the model . Finally , we chose df for week variable were 4 , and other included variables were 3[37]; moreover , the sensitivity of the trend was tested by setting df to be 2 , 3 or 4 . The disease dataset was also divided into two subsets: the first , from the 1st week of 2011 to the 44thweek of 2014 was used for model construction , and the other , from the 45th to the 52nd week of 2014 for external validity assessment . We used the F test to compare the fit of models ( with or without DBSI ) . Moreover , intraclass correlation coefficient ( ICC ) and root mean squared error ( RMSE ) were applied to verify the consistency between the actual and predicted data [38 , 39] . Finally , we employed a Leave-One-Out Cross-Validation ( LOOCV ) method to validate the results of model ( 1 ) and model ( 2 ) . LOOCV is a k-fold cross-validation method [40] , and here the total dataset was divided into ( n-1 ) subsets , where n is the number of weeks from the1st week of 2011 to the 44th week of 2014 . In each subset , a single week’s data was removed , and the weekly number of dengue cases was predicted . Then we employed the ICC as a metric to test the correlations between predicted and observed cases . All the analyses were performed using the “mgcv” library in R 3 . 2 . 2 [41] with a significance level of P<0 . 05 .
During 2011–2014 , a total of 38 , 860 DF cases were reported in Guangzhou city , with 116 ( 0 . 3% ) imported DF cases and 38 , 744 ( 99 . 7% ) local DF cases . A summary of meteorological variables , DBSI and DF cases are presented in Table 1 . There was an average of 186 . 3 local DF cases and 0 . 6 imported DF cases every week over the study period . The mean values of the weekly DBSI , minimum temperature and cumulative rainfall were 80 . 8 , 19 . 0°Cand 34 . 3mm , respectively . Fig 1 shows the time series of weekly meteorological variables , DBSI , and local and imported DF cases . Both a large DF outbreak and the highest weekly DBSI during the study period occurred in 2014 . Weekly minimum temperature and cumulative rainfall showed an obvious seasonal pattern , peaking from June to August . The results of the cross-correlation of weekly local DF case numbers and prediction variables are shown in S3 Table . We found that minimum temperature in the previous 9 weeks , cumulative rainfall in the previous 12 weeks and imported cases in the previous 5 weeks have the highest correlation with local DF . Hence these variables were included in our model . Fig 2 shows the dose-response relationship between local DF cases and imported cases in the previous 5 weeks , minimum temperature in the previous 9 weeks , cumulative rainfall in the previous 12 weeks and DBSI in the previous week . Minimum temperature , cumulative rainfall and imported DF cases were non-linearly associated with the local DF cases . For cumulative rainfall , the risk of DF incidence increases with the increment of rainfall at first , peaking at 149mm , followed by a significant decrease . DBSI in the previous week had a positively linear relationship with the local DF . Fig 3 shows that both model ( 1 ) and model ( 2 ) fit the DF cases reasonably well during the training process . Our results indicate that the fit of model ( 1 ) and model ( 2 ) were both found to be significant ( F = 10 . 46 , P<0 . 001 ) . The value of model with the DBSI ( GCV:7 . 62 and Deviance explained: 99 . 23% ) fit better than the model without DBSI ( GCV: 18 . 41 and Deviance explained: 94 . 53% ) . Moreover , the effects of climate , imported cases and DBSI were found to be significant at the 0 . 05 level ( S4 Table ) . The one-week ahead predictions of dengue outbreaks that occurred from the 45th week to the 52nd week of 2014 for both models are shown in Fig 4 . Model ( 2 ) gives a better prediction of DF cases ( ICC:0 . 94 and RMSE:59 . 86 ) than model ( 1 ) ( ICC:0 . 72 and RMSE:203 . 29 ) . The results of sensitivity analyses show that the GCVs were respectively the lowest when the dfs of weekly minimum temperature , and cumulative rainfall in model ( 1 ) and DBSI in model ( 2 ) were set to 3 , which justified the df selection in our models ( S5 Table and S6 Table ) . In addition , the results of LOOCV also showed that the performance of model ( 2 ) was better than model ( 1 ) ( S7 Table ) .
DF has become an increasingly important public health concern in Guangzhou , China in recent years , and in 2014 the number of DF cases represented the highest peak in the past 25 years [4] . A recent study suggested that urbanization , climate change , international trade and population movement were important factors that influenced this re-emergence of dengue in Guangzhou [5] . In order to improve early and rapid response to dengue outbreaks in Guangzhou , we combined dengue internet-based data ( DBSI ) with imported cases , temperature and rainfall to develop an early warning model . We found that inclusion of DBSI can improve the prediction of the base model reliant on traditional disease surveillance data . The results provide a new approach to developing a dengue early warning system in Guangzhou . Many previous studies reported that climatic factors influenced DF transmission by directly or indirectly affecting each stage in the life cycle of the mosquito and the disease transmission [42 , 43] . In this study , we found that DF was positively correlated with average weekly minimum temperature at a lag of 9 weeks . This finding is generally in agreement with previous studies that indicate the crucial role of temperature in dengue transmission [44 , 45] . Possible reasons for this association with temperature are that higher temperature can reduce both mosquito maturity and reproduction time in favor of producing more mosquitoes in a shorter time [46] . We also found that rainfall has a nonlinear relationship with DF with a threshold of 149mm . This is consistent with several other studies that found that rainfall influenced vector abundance in subsequent weeks by creating more breeding habitats for mosquitoes [47] . On the other hand , it is also likely that heavy rain can destroy existing mosquito breeding sites and affect the maturation of mosquito eggs or larvae [48] . DF is not regarded as endemic in Guangzhou , and previous outbreaks were caused by imported cases [14] . Our study indicated that imported DF cases in the previous 5 weeks had a large impact on the local DF case numbers . The time delay could be the period of the life cycle of dengue transmission . To the best of our knowledge , this study is the first one to investigate the relationship between DBSI and DF cases in China . We found that DBSI in the previous week had a positive linear relationship with reported DF cases , implying that internet-based search behavior may be a useful predictor of DF incidence . This is consistent with previous studies that investigated the relationship between Google Dengue Trends ( GDT ) and DF cases [23 , 24 , 49] . In one study in Singapore and Bangkok , Althouse et al . demonstrated that the internet search terms could successfully predict incidence and periods of large incidence of dengue with high accuracy . Their model using Google search data had an r2 = 0 . 948 and 0 . 943 for Singapore and Bangkok [23] . Chan et al . also observed in five countries that the models built on the fraction of Google search volume for dengue-related queries were able to adequately estimate true dengue activity , and the correlation between values predicted by models and the surveillance data was generally quite high , ranging from 0 . 82 to 0 . 99 [24] . As we mentioned in the introduction , the GFT firstly provided us an excellent example in 2003–3007 to illustrate the contribution of internet search data on the prediction of infectious diseases [18] . However , the GFT failed to successfully predict the seasonal and pandemic influenza in the USA during the 2012/2013 season [25] . It has been debated that the internet-based query might misrepresent the epidemic curve in practice [25 , 29] . Some researchers analyzed the reasons for this failure in the GFT model suggesting that the internet-based query system can be used as a supplement to , but not a substitute for , the traditional data collection and analysis [17 , 50] . Moreover , Gluskinet al . also demonstrated in Mexico that the model using GDT data in combination with relevant covariates ( maximum temperature , logged precipitation ) can significantly improve dengue prediction [49] . In our study , similar result was also found that the model including DBSI variable had a better performance than model without it . Collectively , these results indicate that integrating internet-based dengue query data into traditional disease surveillance can improve dengue prediction , providing us with a new approach for establishing an almost real-time early warning system . In this big data era when internet-based data are easily available and collected in almost real-time [51] , its use as a supplement to traditional disease surveillance provides important progress towards establishing reliable early warning models allowing for more efficient and rapid control of infectious diseases . We validated our model by comparing the predictive results with the surveillance dengue data in the last 8 weeks of study periods , and the results show good performance of the model . However , it has been suggested that the results of models using internet search queries need to be further validated by more advanced studies to control the relevant covariates ( such as media basis , socio-economic and demographic factors ) [50] . Some limitations of our study should be mentioned . First , the guidelines of dengue diagnosis and treatment were different before and after October 11th , 2014 in China . For example , a dengue virus NS1 antigen test was added to the new version as an important criterion , which might lead to some bias to our results . However , the influences of changing diagnosis guidelines on our results are limited , because only dengue cases in the last one and half months in 2014 were diagnosed by the new guidelines . Second , the study developed the prediction model using only a 4-year period of time-series data based on weekly data , and could only be validated for an 8-week period . It is advisable to use long-term time series data in model fitting in the future . Third , this study does not examine other potential confounding factors that may be associated with dengue incidence , such as environmental , socio-economic and demographic factors [52] . In addition , it has been suggested that internet searching behavior is susceptible to the impact of media reports [23 , 53] , and we did not implement any measures to control for this . Studies could be conducted in the future to investigate how users interact with internet search sources for providing valuable information on potential biases and suggest mechanisms for improving the robustness of surveillance systems based on internet search queries .
The present study suggests that the Dengue Baidu Search Index provides useful data for early prediction of a dengue outbreak . Such improvements in prediction and hence early warning are very important for improving prevention and control of dengue epidemics in the future . | Dengue fever is an important public health problem in China , and its importance was highlighted by an unprecedented outbreak in Guangdong province in 2014 . Several previous studies have found that prediction models based on internet-based data have advantages in the timely detection of dengue epidemics . In this study , we employed the Dengue Baidu Search Index ( DBSI ) to explore whether internet-based query data can help improve disease prediction . We found that the dengue early warning system combining DBSI with traditional surveillance and meteorological data improved the prediction capability in Guangzhou , which suggests that utilizing big data from internet search engines can provide valuable supplementary data to traditional surveillance systems particularly for developing dengue early warning systems . | [
"Abstract",
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"control",... | 2017 | Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China |
Populations of cells often switch states as a group to cope with environmental changes such as nutrient availability and cell density . Although the gene circuits that underlie the switches are well understood at the level of single cells , the ways in which such circuits work in concert among many cells to support group-level switches are not fully explored . Experimental studies of microbial quorum sensing show that group-level changes in cellular states occur in either a graded or an all-or-none fashion . Here , we show through numerical simulations and mathematical analysis that these behaviors generally originate from two distinct forms of bistability . The choice of bistability is uniquely determined by a dimensionless parameter that compares the synthesis and the transport of the inducing molecules . The role of the parameter is universal , such that it not only applies to the autoinducing circuits typically found in bacteria but also to the more complex gene circuits involved in transmembrane receptor signaling . Furthermore , in gene circuits with negative feedback , the same dimensionless parameter determines the coherence of group-level transitions from quiescence to a rhythmic state . The set of biochemical parameters in bacterial quorum-sensing circuits appear to be tuned so that the cells can use either type of transition . The design principle identified here serves as the basis for the analysis and control of cellular collective decision making .
Cells often switch their state autonomously , either individually or as a group [1]–[3] . The cell-autonomous switch is exemplified by the classical molecular switch in Bacteriophage Lambda: the cI and cro genes mutually repress one another and thus operate as a genetic toggle switch between the lytic and lysogenic cycles [4] . A common network topology [5] , [6] that realizes either the positive autoregulation of inducing signals [7] or the mutual repression of inhibitory signals [8] is generally responsible for the all-or-none responses of individual cells . Bistable behavior at the single-cell level does not , however , necessarily translate into an all-or-none response at the group-level . Because of stochasticity in gene expression [9] and variability among cells in their sensitivity to environmental change [10] , [11] , the switch is graded at the population level [12] , [13]; i . e . , cells in the ON state coexist with cells in the OFF state [1]–[3] , [7] , [8] ( Fig . 1A ) . There are many cases; e . g . , bacterial quorum sensing ( QS ) [14] , [15]; however , where the transition is abrupt and occurs in an all-or-none fashion even at the group level ( Fig . 1B ) . In QS , cells secrete inducing molecules that signal neighboring cells to synthesize and secrete more of the same inducing molecules; thus , global positive feedback is realized ( Fig . 1C ) . The autoinducer Acyl-homoserine lactone ( AHL ) is an inducing molecule [16]–[19] in populations of the luminescent symbiotic bacterium Vibrio fischeri and of other bacteria species [14] , [15] . In animal development , a collective state change within a differentiating tissue is referred to as ‘community effect’ [20] , [21] . Generally , a group-level transition between cellular states manifests itself via a combination of cell-autonomous and group-level mechanisms; these two modes of transition , however , have not been clearly distinguished from one another thus far . In QS , both the graded and the all-or-none types of transitions are observed at the group level [22]–[27] . In a graded transition , cells in the ON and OFF states coexist within a population; thus , the state of the cells follows a bimodal distribution . Such a behavior is observed in populations of the free-living bacterium V . harveyi , the virulent pathogen Salmonella typhimurium , and Listeria monocytogenes; in these populations , the percentage of cells in the ON state increases gradually as cell density increases or other environmental factors change [25] , [26] , [28] . Similar behavior occurs in engineered E . coli that harbors synthetic luxI and luxR genes encoding AHL synthetase and a transcriptional activator [22] , [23] . When the regulation of the lux genes is synthetically rewired , however , the entire population synchronously switches its pattern of gene expression when cell density reaches a certain threshold [24] . Such sharp population-level transitions underlie important biological phenomena such as bioluminescence and virulence in a wide range of species from V . fischeri to the opportunistic pathogen Pseudomonas aeruginosa ( see Fig . 1 in [29]; Fig . 2 in [30]; Fig . 2 in [31] ) . Interestingly , when V . fischeri cells are isolated in a chamber while continuously being cleared of AHL by dilution , their response to exogenously applied AHL is heterogeneous [10] . Thus when making the all-or-none switch as a population , cell-cell variability must be somehow suppressed by cell-cell communication . Because most existing mathematical models of QS are formulated either entirely at the single-cell [10] , [32] , [33] or the population level [16]–[19] , the relationship between the graded and the all-or-none transitions and the underlying bistability of cellular states have not received a full theoretical treatment . To clarify the mechanisms of group-level transitions , we numerically and analytically studied general classes of mathematical models that describe QS across two levels of organization; i . e . single-cell and cellular-ensemble . We show that graded transitions occur when the intracellular positive feedback , mediated by the synthesis and accumulation of autoinducer molecules within the cells , alone can support bistability . Conversely , we show that all-or-none transitions occur when the secreted signal within the population serves predominantly to realize bistability at the group-level . We identify a unique dimensionless parameter , representing the respective relative contributions to the regulatory feedback of the intracellular and extracellular autoinducer molecules , that determine the type of transition and the underlying bistability . We find that in many bacterial species , this parameter is near the optimal value for allowing the bacteria to select between the two transition types depending on environmental conditions . We explored this common design principle in a basic circuit with negative feedback . The types of cells harboring such circuits range from particle-based chemical reactions [34] to engineered E . coli [35] , [36] , yeasts [37] , and the social amoeba Dictyostelium discoideum [38] . These systems are known to exhibit density-dependent transitions from quiescence to an oscillatory state [39] . We show that the same unique parameter determines whether the transition from quiescence to oscillation occurs gradually or synchronously .
To analyze group-level transitions at both the single-cell level and the group level , we studied three basic circuit topologies ( Fig . 1C–E ) . For simple autoinduction ( Fig . 1C ) and a dual positive-feedback circuit ( Fig . 1D ) , we employed a previously described quantitative model [27] . First , for the simple autoinduction circuit ( Fig . 1C ) , when the extracellular and intracellular synthesis and degradation of the autoinducer are rapid compared with changes in the synthase concentration , the autoinducer concentration can be approximated by the steady-state . Accordingly , the equations can be simplified to ( 1 ) ( See Supporting Information Text S1 1 . 1 for a detailed derivation ) , where xi , , si , and ki are the normalized intracellular concentration of the synthetase , the population mean concentration of the synthetase , the normalized intracellular concentration of the autoinducer and the normalized the threshold concentration for the induction ( ; See Table 1 for representative examples in bacterial QS ) . ρV represents the volume fraction ρV = NcellVcell/Vtot , where Ncell , Vcell , and Vtot denote the number of cells , volume of a single cell and the total volume including both intracellular and extracellular space , respectively . Based on experimental data ( Table S1 ) , Hill coefficient is set to 2 which supports bistability . When the Hill coefficient is equal to 1 , bistability does not exist ( Text S1 2 . 1 ) . The amplification factor λ determines the ratio of basal to maximal rate of QS molecule synthesis ( Eq . S1-4 in Text S1 ) when si is above a certain concentration ki ( Eq . S1-3 in Text S1 ) [14] , [15] , [18] . The dimensionless parameter ε is given by ( 2 ) ( See Eq . S1-13 in Text S1 for derivation ) , where , and γex and csec denote the degradation and secretion rates , respectively . ε essentially compares synthesized autoinducer concentration with the threshold ( Eq . S1-14 in Text S1 ) . In the present study , intracellular degradation of the autoinducer was not taken into account . This approximation holds as long as the intracellular degradation rate is much smaller than csec . The overall results are not affected by this assumption , because ε is independent of the ratio between γin and γex ( see Text S1 1 . 5 for a detailed calculation ) . The advantage of this simplification is that , besides the volume fraction of cell density , the model is left with only two parameters , ε and λ , both of which can be experimentally measured ( Tables S1 and S6 ) and manipulated [27] , [40] ( Table S2 ) . In this model and those described below , the value of ki is randomly distributed [11] around the mean to account for cell-cell variability in the response to exogenously applied autoinducer [10] ( Fig . S1A–B ) . In addition , to reflect heterogeneous gene expression within the population [9] , [25] , the model assumes intrinsic stochasticity in the rate of synthetase production , which follows Gaussian white noise ηi [41] , [42] . The molecules are passively transported into and out of the cells at the rate csec , and degraded extracellularly at the rate γex ( Eqs . S1-1 and S1-2 in Text S1 ) [43] . Here , we assumed that the autoinducer molecules diffuse rapidly so that they are well mixed in the extracellular space . The extracellular concentration of the autoinducer is proportional to the cell density ρV ( Eq . S1-1 in Text S1 ) [17] and can be considered almost uniform in space for systems smaller than 1 mm ( Text S1 1 . 6 ) . The second model that we shall study here describes a circuit with an additional intracellular positive feedback ( Fig . 1D ) : ( 3 ) where zi is the normalized intracellular concentration of a transcriptional activator ( Fig . 1D; Table 1 ) . In addition to the three parameters already described; i . e . ρV , λ , and ε ( Eq . 2; Eq . S1-18 in Text S1 ) ; m and n denote the Hill coefficients of binding between the signal molecule and the transcriptional activator and between the transcriptional activator and its target promoter , respectively ( see Text S1 1 . 2 for a derivation ) . Such dual positive-feedback loops are common in bacterial QS [15] , [27] ( Table 1 ) . In the lux operon of V . fischeri , the autoinducer AHL binds to the transcriptional regulator LuxR , forming a complex that binds to a promoter of both the luxR and the luxI genes , which encode the LuxR and the synthetase , respectively [14] , [15] , [18] , [27] . Because LuxR cannot be exported outside the cell , the LuxR feedback mechanism only works intracellularly; the autoinduction mediated by the LuxI feedback , however , works both intracellularly and extracellularly . Our third model describes a basic circuit with positive and negative feedback loops ( Fig . 1E ) . The model is given by ( 4 ) where yi denotes the normalized concentration of an inhibitor , and ρ = ρV/ ( ρV+γex/csec ) ( see Text S1 1 . 3 for a derivation and Table 2 for representative examples ) . Here , the mean threshold corresponds to the inverse of the order parameter ε ( Eq . 2; Eq . S1-24 in Text S1 ) . This type of circuit has been previously modeled and implemented in a synthetic circuit where AHL activates the production of its own synthetase , LuxI ( X ) , and of lactonase AiiA ( Y ) ; and AiiA degrades AHL [35] . Numerical integration of Eqs . 1 , 3 , and 4 was performed using the fourth-order Runge-Kutta algorithm . All programs were written using the C programming language . The cell-density dependence was examined by decreasing the volume of extracellular space exponentially while keeping the number of cells at 1 , 000 ( Fig . 2 ) . Accordingly , cell density ρV and extracellular autoinducer concentration increase exponentially thereby effectively implementing the growth phase of a population . The rate of volume decrease is set to 1/40 of the degradation rate of the synthetase ( Eqs . 1 and S1-1 in Text S1 ) for the phase diagrams ( Figs . 3A and S3 . ) . As long as this ratio is small , the present results do not depend on the exact rate of volume reduction , as will be described in the Results section . Except where we study the phase diagrams and the time course of the negative-and-positive-feedback circuit ( Fig . 4A–B ) , plots were obtained at the steady state . The initial concentrations of xi , yi , zi , and si were set randomly between 0 and 0 . 01 . To examine cell density dependence , we first defined the threshold cell density for each cell . For the autoinduction and dual positive-feedback circuit ( Eqs . 1 and 3 ) , the threshold density for the i-th cell ρi was determined by the volume fraction of density ρV at which the normalized concentration xi ( ) took the half maximum xi = 0 . 5 . In case of the positive-and-negative-feedback circuit ( Eq . 4 ) , the threshold density ρi was defined by the density at which the temporal evolution of xi switched from quiescence to oscillations . As a measure of cell-cell variability at the onset of the transition , the standard deviation of ρi normalized by its population mean was denoted CVρ ( coefficient of variation of ρi ) . Likewise , CVk was defined by the standard deviation of ki normalized by the mean . ki follows lognormal distribution with CVk = 0 . 5 . Following the formulation of a chemical Langevin equation [44] , the variance in noise |ηi|2 at the steady states of Eqs . 1 , 3 , and 4 is given by |ηi|2 = 2 xi/γX NON ( Eq . S1-11 in Text S1 ) , where NON and γX are the number of synthetase molecules within a cell that is in the ON state and the degradation rate of the synthetase , respectively ( Eqs . S1-12 and S1-3 in Text S1 ) . We set γXNon to 140 . γX = 1 corresponds to Non = 140 molecules , or a 60 nM synthetase concentration with cell volume of approximately 3 . 6×10−15 L [45] .
First , we will numerically study the cell-state transitions that depend on the cell density ρV in the simple autoinduction circuit ( Eq . 1 ) . The key parameter that distinguishes the transitions at the group level is the concentration of intracellular autoinducing signal ( si = γex xi/csec+ in Eqs . 1 and S1-6 in Text S1 ) : γex xi/csec represents the intracellular feedback on signal synthesis caused by the cell itself , and measures the strength of the feedback mediated by the secreted signal . When the threshold concentration ki and the secretion rate of autoinducer molecules csec are low , the secretion-mediated feedback is relatively weak , so the induction depends mainly on the feedback from intracellular synthesis . In this case , the cells turn themselves on individually , provided that the concentration of the autoinducer accumulated inside the cell ( si∼γex xi/csec ) is higher than the threshold ki . The switch gives rise to two stable states that are each self-enforcing . The OFF state at xi∼λ−1 keeps cells in a state of low autoinducer synthesis ( upper panel of Fig . 2A ) . Likewise , once the cells are in the ON state ( xi∼1 ) , the high rate of autoinducer synthesis will keep them in the ON state ( upper panel of Fig . 2A ) . Because the two stable states do not require secreted signal from other cells ( see coexistence of ON and OFF cells in Fig . S1A ) , we shall refer to this as “cell-autonomous bistability” . The word “group-level” is a relative term; and more accurately , it is the volume fraction of cell density , rather than the absolute number of cells , that essentially controls the level of extracellular autoinducer molecules . Indeed , a single cell confined to a small chamber has been shown to turn on its QS genes [46] , [47] . The signature of group-level transitions that are driven by the single-cell-level switch is the coexistence of cells in the ON state and cells in the OFF state within the population ( low density in the upper panel of Fig . 2A ) . Here , the percentage of cells in the ON state gradually increases as a function of the cell density ( lower panel of Fig . 2A ) . This is clearly demonstrated by the bimodal distribution of cellular states xi when the cell density ρV is in the intermediate range ( Fig . 2C ) . Individual cells switch in an all-or-none manner , however , at different densities ( upper panel of Fig . 2A; see also Fig . S1C for the coexistence in the nullclines ) . Thus , at the population level the transition becomes graded . Recent experimental observations of bimodal distributions of cell states and graded group-level transitions [22] , [23] , [25] , [26] suggest that , in many bacterial QS systems , the contribution of cell-cell communication is rather weak and the switch is caused by cell-autonomous bistability . In contrast , at high ki and csec the amount of inducing molecules secreted extracellularly ( in Eq . 1 ) becomes profound . When the secretion-mediated feedback becomes negligible in the isolated condition , because of the continuous clearance of extracellular signals via degradation or dilution [10] , [38] , the cells cannot exhibit bistability ( Fig . S1B ) . Fig . 2B shows that above a cell-density threshold , all cells change their state simultaneously . Although the ON state ( xi∼1 ) and the OFF state ( xi∼λ−1 ) are identical to the states that appear in the case of cell-autonomous bistability , the entire population must now either be ON or OFF ( Fig . 2B and 2D ) . The two states cannot coexist within the population ( see also Fig . S1D for a nullcline analysis ) . This group-level all-or-none transition is mediated by the feedback from the secreted autoinducer molecules in the extracellular space ( in Eq . 1 ) . Because the concentration of the synthesized signal within the ON-state cells ( γex/csec ) is below the threshold ki , the ON state cannot be self-sustaining unless a sufficient amount of signaling molecules are synthesized and secreted by other cells . To distinguish this form of bistability from the cell-autonomous bistability described above , we shall hereafter refer to it as “group-level bistability” . Frequent experimental observations of such all-or-none transitions in many bacterial systems [14] , [15] , [30] , [31] suggest that the occurrences of group-level bistability are widespread . Graded transitions and all-or-none transitions both involve a combined action by the cells . Although cell-autonomous bistability underlies the graded transition , the switch is nonetheless density dependent , and there is a cooperative effect within the group of cells . Whether a cell can switch its state depends on its position in the state space relative to the basin of attraction ( Fig . S1C , low density ) . By plotting the synthetase production rate dxi/dt as a function of the synthetase concentration xi , we see that the range of initial concentrations that converge to the ON state expands as the density is increased ( Fig . S1E ) . Because the concentration of the autoinducer in the OFF state ( si∼γexλ−1/csec+ρV λ−1; xi∼λ−1 ) is close to the threshold ki , the probability that a cell switches from the OFF state to the ON state increases with the density ρV . On the other hand , cell-cell communication is absolutely essential for group-level bistability . At intermediate cell densities , the concentration of secreted autoinducer ( ) exceeds the average threshold ( i . e . , , Fig . 2B ) , so cells that have not yet switched are forced to do so ( Fig . S1F ) . Likewise , when the synthesized concentration is insufficient to sustain the cells in the ON state , the whole population converges to the OFF state at the steady state . Thus , although there is a difference of degree , both types of bistability depend on the interactions among the cells within the population . To help identify the design principle underlying graded and all-or-none transitions , we analytically derived a unique dimensionless parameter ε ( Eq . 2 ) that determines the nature of the bistability ( Text S1 2 . 1 and 2 . 2 ) . Essentially , ε compares the magnitude of the inducing signal that is synthesized intracellularly ( γex/csec ) with the response threshold ( Eq . S1-13 in Text S1 ) . A solid line in Fig . 3A indicates the analytically obtained boundary ( ε∼2 ) in the parameter space ( ε , λ ) that separates autonomous bistability from group-level bistability ( Text S1 2 . 2 ) . The border matches well with the results of numerical simulations ( Fig . S2A ) . For ε>2 , even isolated cells can take two stable fixed points ( Eq . S2-15 in Text S1 ) , which indicates cell-autonomous bistability ( closed circles in Fig . S1C ) . For ε<2 , the autonomous bistability disappears; instead , the whole population can only be at one of the two stable fixed points ( red lines in Fig . 2B upper panel and closed circles in Fig . S1D ) . When the group average of the synthetase concentration is greater than the value of the unstable fixed point ( yellow line in Fig . 2B and open circle Fig . S1D ) , the entire population immediately jumps to the ON state . Otherwise , all of the cells converge to the OFF state . Thus , the value of ε determines the origin of the bistability and the form of the resulting group-level transition . When ε is increased above ( Eq . S2-15 in Text S1 ) , the intracellular signal concentration always exceeds the threshold regardless of the extracellular autoinducer concentration , so the cells are constitutively in the ON state at all cell densities ( right of dashed line in Fig . 3A ) . Thus , autonomous bistability appears when is satisfied ( Eq . S2-15 in Text S1 ) . The condition indicates that the region of the parameter ε that supports autonomous bistability ( between the solid and dashed lines in Fig . 3A ) broadens as λ is elevated , meaning that the bistability becomes less sensitive to variation in ε . In addition , when λ is decreased below λ = 9 , the two stable states disappear and the system undergoes a pitchfork bifurcation . The cells thus become monostable at all cell densities ( dotted line in Fig . 3A; Text S1 2 . 1 ) . In summary , the analytical calculations indicate that autonomous bistability requires , whereas group-level bistability requires both λ>9 and ε<2 . To clarify whether the above conditions for the two types of bistability directly translate into the conditions for group-level transitions , we examined whether group-level bistability always results in an all-or-none response and , similarly , whether autonomous bistability always gives rise to a graded response . This can be verified by checking whether or not the variability of the response is reduced by the secreted signal . To this end , we numerically measured the ratio between the coefficient of variation ( CV ) of the threshold cell density ρ ( CVρ; Fig . 2A–B; see Models ) and the CV of the intrinsic heterogeneity of ki ( CVk; Fig . S1A–B ) . Consistent with the above analysis , we see that CVρ/CVk>1 for cell-autonomous bistability , indicating graded transitions ( red and pink region in Fig . 3A ) . In contrast , for group-level bistability , CVρ/CVk is almost always lower than unity , indicating a reduction in the variation ( blue and cyan region in Fig . 3A ) . The condition CVρ/CVk = 1 marks the borderline between the cell-autonomous and group-level switch for a wide range of growth rate ( Model; Fig . S4 ) . In addition , CVρ/CVk decreases further as ε decreases and λ increases ( Figs . 3A and S2B–C ) . At high λ , a state change within a small fraction of the population can elicit a sufficient increase in the extracellular signal concentration to override cell-cell variability in response sensitivity . Thus , the simulations show that while the effect of cell-cell variability is deleterious to simultaneous switch at low λ ( Fig . S2D for λ = 10 ) , the switch becomes more abrupt when λ is elevated ( Fig . 1B for λ = 100 ) . In group-level bistability , a large λ promotes all-or-none transitions by reducing the intrinsic heterogeneity ( CVρ/CVk<1 ) . Thus , the conditions for autonomous ( ) bistability and group-level ( λ>9 and ε>2 ) bistability directly translate into the necessary conditions for the all-or-none and graded transitions , respectively . The parameter region of autonomous bistability ( between the solid and dashed lines in Fig . 3A ) indicates robustness to variation in ε , while CVρ/CVk<1 indicates robustness of group-level bistability to intrinsic variation of threshold ki ( Figs . 3A , 2B and S2C–D ) . The robustness is further enhanced when we include an additional positive feedback in the model circuit ( Eq . 3 ) . First , in experimental observations of the synthetic lux gene circuits [23] , the region of ε that supports autonomous bistability for the dual positive-feedback circuit ( Eq . 3 ) is wider than that for the autoinduction circuit ( Eq . 1 ) . The bistable region further expands when the Hill coefficients , m and n , of AHL-LuxR and LuxR-promoter binding are increased ( Figs . 3B and S3 ) . We derived analytically that the boundary between autonomous bistability and the constitutively monostable state ( dashed lines in Fig . 3B ) is given by , which monotonically increases with m and n ( Eq . S2-23 in Text S1 2 . 3 ) . In contrast , the boundary between autonomous bistability and group-level bistability is almost independent of m and n ( ε = 2∼3; solid line in Fig . 3B; Eq . S2-23 in Text S1 ) . Second , the value of CVρ/CVk for the group-level bistability decreases further ( Fig . S3 ) than that for the simple autoinduction circuit ( Fig . 3A; e . g . , at λ = 10∼100 ) . Thus , the dual positive-feedback is highly effective in reducing the intrinsic variation . Such strengthening of group-level bistability explains the observation that a group-level switch of the rewired lux operon occurs much more abruptly in a dual positive-feedback circuit than in a simple autoinduction circuit [24] . In summary , in both the simple autoinduction and the dual positive-feedback circuits , a large amplification factor λ increases the robustness of both the graded and the all-or-none transitions . Although microbial populations exhibit either graded [22] , [23] or all-or-none [24] transitions , little is known about their benefit . Depending on the nature of environmental fluctuations , the coherence of cell-state transitions could significantly affect the chance of survival . When the environment varies more rapidly than the cellular response , the autonomous switch of individual cells could be more beneficial , because survival strategies can be diversified due to the heterogeneous response [48]: e . g . , bistability in the expression of the lac gene in E . coli under certain growth conditions [49] and in the lysis/lysogeny decision of Lambda phage . In other words , the autonomous bistability is a bet-hedging or risk-spreading strategy in the population [50] . On the other hand , when the cells are able to respond as quickly as the environment changes , an all-or-none switch of the whole population allows more cells to survive and therefore could be a better strategy . Thus , depending on the time-scale of environmental fluctuations , being able to choose between autonomous and group-level switches provides an added advantage over a fixed survival strategy . The selection is more feasible when the order parameter ε of the population is close to the borderline; i . e . ε = 2 . There , cells can choose between the two types of bistability by only slightly adjusting either the signal threshold , the maximum signal synthesis rate , or the transport rate ( Eqs . 2 and S1-18 in Text S1 ) . To examine the survival strategy of bacterial species , we estimated the values of the parameter ε for four gene circuits in three bacterial species: the rhl and las operons in P . aeruginosa , the car operon in the plant pathogen Erwinia carotovora , and the lux operon in V . fischeri ( Text S1 3 ) . Each system has a dual positive-feedback network topology with cooperative gene regulation ( Eq . 3; Table 1 ) [15] . We estimated the csec and γex in Eq . 2 from the export and hydrolysis rates of AHL , respectively . We estimated the normalized threshold from the threshold signal concentration for gene expression within the operon with the extracellular signal concentration above a threshold density ( Eq . S3-3 and Table S6 ) . We found that not only do all QS systems analyzed fall within the appropriate range of ε that supports group-level or autonomous bistability ( Fig . 3B ) , they also appear to converge on the boundary between the two types of bistability; i . e . ε∼2 . The results suggest that bacteria could be adjusting the coherence of their state transitions in response to environmental conditions . Several lines of evidence suggest that the parameters that determine ε are in fact being exploited in microbial populations . According to our estimate of the lux system ( ε = 20∼30 ) , the system should have a preference for a graded transition ( Fig . 3B ) . Although this is true in E . coli harboring the synthetic lux system [22] , [23] , [27] , all-or-none transition is observed in V . fischeri [29] , [30] . This discrepancy could be caused by the fact that our estimate of the threshold concentration of an AHL 3-oxo-C6-HSL ( corresponding to ki in Eq . 1 ) was based on a synthetic lux system in E . coli . In the real lux system of V . fischeri , an antagonist C8-HSL ( HomoSerine Lactone ) is endogenously synthesized and competitively binds to LuxR [51] . A microfluidic study of single V . fischeri cells showed that the presence of 100 nM C8-HSL increases the threshold concentration for 3-oxo-C6-HSL by as much as 10-fold [52] . Based on this evidence , we predict that , the addition of C8-HSL to synthetic lux systems should decrease ε by at least 10-fold and , as a consequence , would result in an all-or-none type transition . Likewise , the real V . fischeri lux system should exhibit a graded transition by eliminating C8-HSL or suppressing its synthesis . Similarly , in the las system of P . aeruginosa , addition of an antagonist furanone , which eukaryotic cells produces to interfere with the bacterial QS [53] , [54] , suppresses the las gene expression [55] so that the concentration of the autoinducer 3-oxo-C12-HSL decreases . Conversely , the concentration of 3-oxo-C12-HSL is increased four-fold by the addition of a nutrient amino acid [31] which leads to inhibition of RNA synthesis [56] – bacterial survival strategy to avoid exhausting nutrients . Between P . aeruginosa stains that were clinically isolated from patients with severe polytrauma or congestive heart failure , there were large variations in the synthesized concentrations of 3-oxo-C12-HSL [57] . In addition , there was nine-fold decrease in threshold concentration of the rhl system in the absence of an antiactivator QslA [58] . The increase in autoinducer synthesis and the decrease in the threshold act to increase ε ( Eqs . 2 , S1-14 and S1-18 in Text S1 ) so that the graded transition is likely to emerge in the las and rhl systems . The heterogeneous response is in line with the fact that , in P . aeruginosa biofilms , the las and rhl systems are utilized for cell differentiation [59] , [60] . Unlike laboratory conditions , nutrient conditions in natural habitats such as those surrounding biofilms inside animal hosts tend to fluctuate at various time scales [61] . Thus , by maintaining ε∼2 , many bacterial populations may have the option of choosing between the two modes of transition by slightly changing their kinetic parameters . To further explore the applicability of the design principle ( Fig . 3A ) of group-level decision making , we introduced a negative feedback loop into the simple autoinducing circuit ( Fig . 1E; Eq . 4 ) . When the negative feedback takes place at a much slower time scale than the positive feedback does , qualitatively different dynamics may appear; the cells become oscillatory or excitable – ability to respond transiently to changes in the signal concentrations [62] , [63] . Excitatory responses appear during the differentiation of Bacillus subtilis into the state of competence [40] , the stress response of bacterial and mammalian cells [64] , [65] , the relay response of chemoattractant cyclic-AMP ( cAMP ) of Dictyostelium discoideum [38] , the Ca2+ concentration response of pancreatic β cells [66] , and the decision of the fate of embryonic stem cells [67] . When the cells are confined to a small chamber , the secreted signal becomes large that cells switch from a quiescent state to a rhythmic state as a group ( Fig . 4 ) . The oscillatory transition is referred to as dynamical quorum sensing ( DQS ) [34]–[39] . The presence of quiescent cells at low density in DQS is a marked contrast to the Kuramoto-type transition [68]–[71] , where all cells are independently oscillatory and the transition to a collective state is realized by phase synchronization . While such a transition is believed to take place in populations of fireflies [72] and in the neurons of the mammalian suprachiasmatic nucleus [73] , other examples have shown a state of quiescence at low cell density [38] , [74] , [75] . Individual Dictyostelium cells do not exhibit cAMP oscillations at low density , and they only become oscillatory above a certain density [38] . A slightly different case is found in the NADH oscillations of Saccharomyces cerevisiae , where the fraction of oscillatory cells gradually increases when the dilution rate of secreted factors is decreased [75] . The parameter ε ( Models; Eqs . 2 and S1-24 in Text S1 ) in DQS also determines whether the transition is graded or all-or-none . As shown by the numerical simulations , when ε is high the transition is graded ( Fig . 4A ) ; a fraction of cells oscillate individually , whereas the others remain quiescent . As in the bistable circuits , cells become autonomously oscillatory when the intracellular autoinducer concentration ( γex xi/csec ) exceeds the threshold ki ( Eq . 4 ) . Because of intrinsic cell-cell heterogeneity in the sensitivity threshold ki ( Fig . S5A ) , a small fraction of the population is already oscillatory even at low cell densities ( Figs . 4A and S5C ) . As we have seen in the bistable system ( Fig . 2A ) , the proportion of oscillatory cells gradually increases with increasing cell density ( Fig . 4A ) . Accordingly , while the amplitude of a single cell is kept constant ( local maximum of the blue line in Fig . 4A ) , the amplitude of the cellular ensemble gradually increases ( red line in Fig . 4C ) . Such gradual increases in the mean amplitude have been observed in engineered E . coli [35] and in the glycolytic oscillations of yeasts [37] . The oscillatory transition is all-or-none when ε is low: all cells simultaneously switch to the oscillatory state above a threshold cell density ( Fig . 4B; see also Fig . S5D for the density dependence of the nullclines ) . Moreover , at the onset of oscillations , the pulse is highly synchronized among the cells ( black dots in Fig . 4B ) . Note that this occurs despite the presence of cell-cell heterogeneity in the response threshold ( Fig . S5B ) . An all-or-none transition is observed as both an abrupt increase in the oscillation amplitude averaged over the population ( red line in Fig . 4B ) as well as an increase in the fraction of oscillatory cells ( Fig . 4D ) . A group-level excitatory response to a common level of signaling molecule is responsible for the all-or-none transition in DQS . There are almost no cells that oscillate below the threshold density , because the synthesized concentration γex xi/csec is below the threshold ki regardless of xi ( Eq . 4 ) . When a certain fraction of the population is excited because of cell-cell variability in ki , a subsequent increase in the secreted signal invokes the excitation of the remaining population . Thus , the positive feedback supports a chain reaction of excitatory responses , because the secreted signals mutually enhance the excitation of other cells ( time ∼3600 in Fig . 4B ) . Such group-level excitation captures the essence of what has been observed in the abrupt transition from quiescence to highly synchronized oscillations in particle-based Belouzov-Zhabotinsky reactions [76] and in the cAMP signaling of Dictyostelium [38] . Following the argument for the coupled bistable circuits described above ( Fig . 3A ) , the nature of oscillatory transitions in the coupled excitable circuits could also be numerically classified by CVρ/CVk ( Fig . 5A ) ; i . e . , whether or not the intrinsic variability of threshold ki is reduced: all-or-none when CVρ/CVk<1 ( blue and cyan in Fig . 5A ) and graded when CVρ/CVk>1 ( red and pink in Fig . 5A ) . The boundary between the transition types ( CVρ/CVk = 1; green line in Fig . 5A ) is located between ε = 2 and ε = 10 . ε>10 roughly corresponds to the necessary condition for cell-autonomous oscillations ( black dashed line in Fig . 5C ) , while ε>2 is shown analytically to be the necessary condition for cell-autonomous excitation in isolated cells ( black solid line in Fig . 5C; Fig . S6A; see Text S1 2 . 4 for a derivation ) . To examine the role of autonomous excitability , intrinsic noise is introduced into the kinetics of the synthetase ( ηi in Eq . 4 ) , as was done for the bistable circuit . At ε>2 , the cells are repetitively excited by the intrinsic signal noise rather than by the secreted signal , so there are cell-autonomous stochastic pulses frequently observed in excitable systems [38] , [77] . Thus the transition becomes graded ( right of the yellow line indicating CVρ/CVk = 1 in Fig . 5B–C ) . The convergence of the boundary to ε = 2 in the presence of noise occurs irrespective of the remaining free parameter g ( yellow line in Fig . S6B–E ) . Thus , the autonomous excitation and oscillation mediated by the intracellular feedback lead to graded transitions; whereas the group-level excitation mediated by the secreted autoinducer invokes all-or-none transitions to highly synchronized oscillations . Moreover , the position of the boundary ( ε∼2 , Figs . 5B–C and S6B–E; Eq . S2-27 in Text S1 ) agrees well with that obtained for the bistable circuits ( Fig . 3A; Eq . S2-15 in Text S1 ) , indicating that the relative contributions to the feedback from the autoinducer that is synthesized and accumulated within the cell and that which is secreted and shared with other cells are the key determinants of the group-level transition . In the engineered E . coli with a positive-and-negative-feedback ( Fig . 1E; Eq . 4 ) mediated by the lux system [35] , it is reasonable to expect ε>2 ( SI Text 3 . 2 . 4 ) , since the expression of ε is identical with that of the autoinduction circuit ( Eq . 2; Eq . S1-24 ) . As a result , the mean amplitude increases gradually with cell density ( Fig . 4A ) . This suggests that the oscillatory transition in the engineered E . coli . [35] is graded . Future works should clarify the limit and applicability of the common design principle elucidated in this study by exploring more complex circuit topologies in a wide variety of biological contexts . Our models did consider spatial heterogeneity of the extracellular autoinducer concentration that could potentially form a spatial gradients [78] or propagating waves [35] , [79] ( Models ) . The spatial heterogeneity becomes important , for example when we consider spatial structure of microbial colonies , aggregates or biofilms with a diameter of more than 1 mm ( Text S1 1 . 6 ) . The autonomous bistability presented here faithfully reproduces microbial group-level dynamics such as the bimodal distribution ( Fig . 2C; [22] , [23] ) and the continuous increase in the fraction of ON cells as cell density increase ( Fig . 2A upper panel; [25] , [28] ) . We should note , however , that there may also be other types of bistability . In V . harveyi , the maximum fraction of the ON-state cells never reaches 100% even at high densities [25] . It also appears that not all V . fischeri cells can exhibit state transition when isolated in a chamber and perfused with high dosages of autoinducer [10] . Such a property could be due to either a large variability in the threshold value ki , presence of an antagonist [52] that suppresses autoinducer synthesis , or another negative feedback that adds a repressive cell-cell interaction [80]–[82] so as to render coexistence of ON and OFF cells ( Fig . 1E ) more likely in a wide range of model parameters . Delineating these possibilities will be an important avenue for future studies . To further test applicability of the common design principle , we expanded the simple transport system for the autoinducer ( Fig . 1C–E ) to describe transmembrane signal recognition and transduction [15] , [32] , [83] . For transmembrane recognition systems , in addition to the extracellular feedback of the autocrine signaling , an intracellular positive feedback is required for a graded transition ( Text S1 1 . 4 ) , as in the simple autoinduction ( Eq . 1 ) and the dual positive-feedback circuits ( Eq . 3 ) . Consistently , the parameter ε tunes the graded and all-or-none transitions in QS ( Fig . S7 and Eq . S1-33 in Text S1 ) as well as in DQS ( Fig . S8 ) . Hence , the design principle should be widely applicable to cell density-dependent fate decisions [84] in a broad spectrum of cell populations; e . g . , in animal embryogenesis [20] , [21] , stem-cell differentiation in tissue engineering [85] , [86] , influenza virus infection [87] , and cancer metastasis [88] . We have seen that when individual cells alone can harbor dynamic stabilities , the transition at the group level becomes graded ( Figs . 2A and 4A ) . These dynamic stabilities are cell-autonomous bistability , in the case of autoinducing circuits , and cell-autonomous excitability , in case of negative-feedback circuits ( Figs . 2B and 4B ) . In contrast , group-level all-or-none transitions between cellular states are supported when these stabilities require a sufficient number of cells . In both bistable circuits and excitable circuits ( Fig . 1C–E ) , the two parameters ε and λ determine the transition type ( Figs . 3 and 5 ) . For the cells to switch their states , inducing molecules need to accumulate to a certain level within the group . ε compares the contribution of intracellular local feedback with that of secretion-mediated global feedback . For ε>2 , bistability or excitability can be reduced to a single-cell property . For ε<2 , the switch requires group-level cooperation mediated by secreted signaling molecules . The necessary conditions for autonomous and group-level stabilities are directly translated into those for graded and all-or-none transitions , respectively ( Figs . 3A and 5C ) . The greater the amplification factor λ is , the more robust the transitions are to cell-cell variability ( Figs . 3A and S2C ) and parameter variations ( Fig . 3B ) . Future studies should be able to experimentally verify this design principle by tuning λ and ε with inducible promoters [27] , [40] or by applying agonists and antagonists to the system [52] , [54] , [55] . | Although the genetic circuits underlying state switching at the single-cell level are well understood , how such circuits work in concert among many cells to support the population-level switching of cellular behaviors is not fully explored . Experiments using microbial signaling systems show that group-level changes in cellular state occur in either a graded or an all-or-none fashion . We show that the type of group-level decision making used by populations is uniquely determined by a single dimensionless parameter that compares the quorum-signaling molecules accumulated within the cells with those secreted by the population . Bacterial quorum-sensing circuits appear to be tuned so that the cells can convert between the two types of decision-making in response to slight biochemical variations . Furthermore , the role of the parameter is universal such that it not only applies to the autoinducing circuits typically found in bacteria but also to the more complex gene circuits involved in transmembrane receptor signaling and negative feedback . The design principle that we describe thus serves as the basis for the analysis and control of collective cellular decision making in general . | [
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"c... | 2013 | A Design Principle of Group-level Decision Making in Cell Populations |
Although many theoretical models of sympatric speciation propose that genes responsible for assortative mating amongst incipient species should be associated with genomic regions protected from recombination , there are few data to support this theory . The malaria mosquito , Anopheles gambiae , is known for its sympatric cryptic species maintained by pre-mating reproductive isolation and its putative genomic islands of speciation , and is therefore an ideal model system for studying the genomic signature associated with incipient sympatric speciation . Here we selectively introgressed the island of divergence located in the pericentric region of the X chromosome of An . gambiae s . s . into its sister taxon An . coluzzii through 5 generations of backcrossing followed by two generations of crosses within the introgressed strains that resulted in An . coluzzii-like recombinant strains fixed for the M and S marker in the X chromosome island . The mating preference of recombinant strains was then tested by giving virgin recombinant individuals a choice of mates with X-islands matching and non-matching their own island type . We show through genetic analyses of transferred sperm that recombinant females consistently mated with matching island-type males thereby associating assortative mating genes with the X-island of divergence . Furthermore , full-genome sequencing confirmed that protein-coding differences between recombinant strains were limited to the experimentally swapped pericentromeric region . Finally , targeted-genome comparisons showed that a number of these unique differences were conserved in sympatric field populations , thereby revealing candidate speciation genes . The functional demonstration of a close association between speciation genes and the X-island of differentiation lends unprecedented support to island-of-speciation models of sympatric speciation facilitated by pericentric recombination suppression .
Unravelling the genomic processes underlying sympatric speciation , the evolution of new species from a single ancestral species within the same geographical region , is fundamental to our understanding of biodiversity . At the core of this interest is the search for distinct genomic signatures that can help us understand what is thought to be a relatively narrow and unlikely set of genetic and ecological conditions facilitating the emergence and divergence of two gene pools from an originally panmictic population . For sympatric speciation with gene flow to occur , divergent selection acting on locally adapted genes has to overcome the homogenizing effects of migration and recombination [1 , 2] . Theoretical models of sympatric speciation have long recognised that this can only occur under a restricted set of conditions in which the genomic architecture often plays a major role [1 , 3 , 4] . Features of the genome such as chromosomal inversions and peri-centromeric regions that suppress recombination and link together genes of pre-mating isolation and ecological adaptation genes are predicted to facilitate sympatric speciation [1 , 5 , 6 , 7] . In addition , hemizygosity and lower recombination rates are thought to predispose sex chromosomes to the more rapid accumulation of genes of pre and post-mating isolation [8] . These general predictions have found empirical support from a limited number of studies designed to map genes involved in speciation [9 , 10] and/or to detect loci under divergent selection across genomes [11 , 12 , 13 , 14] . Currently this evidence concerns almost exclusively species already separated by both pre-zygotic and intrinsic post-zygotic reproductive isolation in which teasing out the genomic signature of the onset of speciation from the genomic processes that follow post-mating reproductive isolation constitutes a major challenge [1 , 3 , 4] . The Anopheles gambiae complex comprises the main vector species responsible for malaria transmission . It is also a species complex rich in cryptic taxa separated by various degrees of reproductive isolation [15 , 16] and could provide the ideal system for studying the genomic signature of pre-mating isolation independent of intrinsic isolation processes . The sibling species An . coluzzii and An . gambiae s . s . were until recently known as the 'M and S' molecular forms of An . gambiae in reference to diagnostic genetic differences in the ribosomal DNA regions [17] . These incipient species co-occur over large areas of West Africa and do not exhibit intrinsic post-mating barriers to reproduction [18 , 19] . Across much of their sympatric range their integrity is maintained by strong assortative mating [20 , 21] resulting in rare hybrids and low levels of genetic introgression between the two taxa [22 , 23] . However , in the Western coastal countries of Guinea Bissau and Senegal hybrids can occur locally at much higher frequencies [24] , resulting in a large hybrid zone between the incipient sibling species and high levels of genetic introgression [22 , 23] . Divergence between these species is thought to be driven by larval adaptation to different types of bodies of water [25 , 26] . Transplant experiments have shown that An . coluzzii larvae survive better than those of An . gambiae s . s . in habitats that are more permanent and rich in aquatic predators while the reverse is true in predator-free temporary water bodies [25 , 27] . Adults of both species have similar feeding and resting habits and mate in swarms at dusk in villages . Swarm site segregation is thought to contribute to assortative mating [28] , but the occurrence of mixed swarms at various frequencies [21 , 28] points towards additional conspecific recognition mechanisms , possibly based on flight tones [29 , 30] . Several groundbreaking studies have shown that sympatric speciation in these two incipient species probably involved the divergence of a few ‘islands of divergence' that possibly contain clusters of speciation genes and located in areas of low recombination [31 , 32] . These putative 'islands of speciation' include 3 pericentromeric islands of divergence located on the X , 2L and 3L chromosomes as well as smaller islands located in the vicinity of inversion breakpoints [31 , 32] . Perfect linkage disequilibrium between the X , 2L and 3L islands was found in samples from sympatric populations of An . coluzzii and An . gambiae s . s . from central West Africa [32] . This pattern suggested very low gene flow between the sibling species and the possibility that pericentromeric islands of divergence were merely 'incidental rather than instrumental' to the speciation process [32] . Subsequent genomic studies reinforced this view , suggesting divergence at many other loci across the genome and a more advanced stage of sympatric speciation [33] . However , recent studies have shown that the linkage disequilibrium between the pericentromeric islands breaks down to various degrees in areas with higher introgression between the sibling species [23 , 24] . Taken together the comparative genomics data would therefore support a model of genomic divergence in which pericentromeric divergence islands could play a major role in speciation in the face of varying levels of gene flow [22 , 23] . Since this view is currently subject to debate and because there are limits to the inferences that can be drawn from comparative genomics studies , we set out to demonstrate the role of divergence islands in the sympatric speciation process using an experimental functional genomics approach . We hypothesized that the largest putative 'speciation island' of the X chromosome would be a prime candidate for protecting assortative mating and ecological adaptation genes in the face of ongoing gene flow because it combines pericentromeric recombination suppression with the hemizygosity and decreased recombination typically associated with the X chromosome . Next , we selectively introgressed the S-form X-linked island of divergence of An . gambiae s . s . into an . coluzzii genetic background to create recombinant strains that shared an An . coluzzii genetic background but differed at their X-chromosome islands of speciation . Standardised assortative mating experiments were then used to test the potential association of the X-island molecular type with the mating preferences of recombinant and parental strains . These were combined with full-genome strain comparisons as well as targeted genome sequencing from sympatric populations . This strategy enabled us to identify the size of the pericentromeric region introgressed from An . gambiae s . s . into the An . coluzzii background , fixed protein-coding differences distinguishing recombinant strains , and conserved differences putatively relevant to speciation . The results demonstrate the close association of assortative-mating genes with the X-island of speciation and thus lend support to models of speciation involving pericentric recombination suppression in these sympatric incipient species . In addition , the development of a laboratory-based model-system for studying assortative mating is an important step towards the description of key reproductive isolation genes and mechanisms responsible for pre-mating isolation among these cryptic taxa .
Following their creation , the RbMM and RbSS recombinant strains and Mopti and Kisumu parental strains were further characterized by genotyping of their 2L and 3L pericentromeric divergence islands [32] and karyotyping of inversion polymorphisms on their 2L and 2R chromosome ( Table 1 ) . Predictably , the M-form Mopti strain exhibited M-type 2L and 3L islands in addition to its M-type X-island . It was also fixed for inversion a on chromosome 2L and polymorphic for u on 2R . Unexpectedly , the long-established S-form Kisumu strain was polymorphic at the 2L and 3L islands suggesting historical contamination with an M-form strain . The Kisumu was also polymorphic for a on 2L and standard on 2R . Amongst the recombinant strains , RbMM was M-like across all 3 islands . The RbSS was polymorphic at the 2L locus but fixed for the M-type allele at the 3L . Both recombinant strains were also polymorphic for a on 2L and u on the 2R chromosome ( Table 1 ) . Thus their genotypes and karyotypes were those expected from successful backcrossing and crossin steps . Next , the mating choice preferences of females and males were tested using a standardized assortative mating assay ( see methods ) . Reciprocal experiments were conducted in which virgin females or males from the same RbMM and RbSS cohorts were given a choice between mates with matching or non-matching X-island type ( Table 2 ) . Females from the RbMM were found to mate almost exclusively assortatively ( Fig 1A ) ( P< 0 . 001 ) . Females RbSS had their mating preferences effectively swapped and mated entirely with recombinant males with matching S-type islands ( Fig 1A ) ( P< 0 . 001 ) . In contrast , males from the RbMM and RbSS recombinant strains did not significantly prefer females with matching X-islands ( P = 0 . 284 and 0 . 611 respectively ) . Since assortative mating amongst laboratory strains from An . coluzzii and An . gambiae s . s has never been reported , female and male choosiness were also assessed in the Mopti and Kisumu parental strains . Here again , females mated significantly assortatively ( P< 0 . 001 in both strains ) but not males ( P = 0 . 073 and 0 . 163 ) ( Fig 1B and S1 Table ) . The size of the X-island of divergence and flanking regions differing between the RbMM and RbSS strains was determined through genome-wide genetic differentiation ( FST ) scans and the occurrence of fixed coding differences between the recombinant strains . Estimates of genetic differentiation between the RbMM , RbSS and Mopti strains were calculated for 3 , 743 , 318 SNPs across the X , 2nd and 3rd chromosomes . The genome-wide FST scans showed that the RbSS differs from the RbMM and Mopti strains on chromosome X from the centromere to the reference position ~14 . 8Mb ( Fig 2 ) . This region covered the entire island of speciation plus a large flanking region . In addition , the RbSS and RbMM strain were genetically differentiated at a ~2Mb S-form fragment extending roughly from positions 11 . 5–13 . 5Mb ( Fig 2 ) . There were no other sizeable S-like regions detected through comparisons of the RbMM , RbSS and Mopti genomes , indicating that the selective introgression design worked as hoped for . Importantly , amongst all fixed differences observed between the RbMM and RbSS strains , non-synonymous differences inducing protein-coding changes ( n = 160 ) were found only in the selectively-introgressed pericentromeric region ( Fig 2 ) . Fixed differences located elsewhere in the genome were either coding synonymous changes or non-coding . Given that the Kisumu and Mopti strains were colonized from allopatric populations over 25 and 7 years ago , some of the differences observed between the RbMM and RbSS strains could be due to genetic divergence of the original populations or result from genetic drift and inbreeding [34] . Consequently , we compared the protein-coding differences identified between RbMM and RbSS with those observed between 2 sympatric An . gambiae s . s . and An . coluzzii populations from Southern Ghana . Deep-pooled-targeted exon re-sequencing of the region extending from 17Mb to the centromere showed that , in 114 of the 117 coding differences distinguishing RbSS from RbMM , the M-form allele was fixed or nearly fixed ( freq >0 . 95 ) in the field population of An . coluzzii . In the sympatric An . gambiae s . s . , the alternate S-type allele was found at frequencies >0 . 8 in 61 of the 114 differences; and 20 of those were fixed or nearly fixed ( freq >0 . 95 ) and thus conserved differences between sibling species . Conserved differences started from position ~18 . 1Mb ( Fig 2 ) , increased in frequency with proximity to the centromere and affected a total of 12 genes ( Fig 3 , Table 3 ) .
This is the first functional genomic study aiming to test the role of the putative X-chromosome island of speciation in pre-mating reproductive isolation between An . coluzzii and gambiae s . s . and demonstrating its potential importance in their process of incipient sympatric speciation . Albeit demonstrated here using laboratory recombinant strains , the close association observed between pre-mating isolation genes and the X-island supports the hypothesis that pericentric regions can create linkage disequilibrium ( LD ) and thus protect associations between genes of pre-mating isolation and ecological adaptation [7] and facilitate the onset of sympatric speciation [1 , 6] . The results are consistent with the notion that hemizygosity and the lower recombination rates of sex chromosomes predispose them to accumulating genes of pre and post-mating isolation [8] . In the case of the X-island of speciation , the pericentric and X-chromosome recombination suppression effects synergise , thereby further reducing recombination and promoting high LD . Although previous studies have linked loci responsible for reproductive isolation to inversions [12 , 13] , pericentromeric regions [35 , 36] and sex chromosomes [9 , 10 , 35] , this is one of the few studies focusing on incipient sympatric species in which intrinsic post-reproductive barriers to reproduction are not yet established and therefore investigating the genomic architecture of pre-mating isolation independent of intrinsic post-mating processes . In sulfur butterflies Colias eurytheme and C . philodice , pre-mating isolation was associated with an inversion located on the X-chromosome [37] , but these species co-occur only over a limited hybridization zone . In the hawthorn and apple-infesting races of Rhagoletis pomonella flies genomic divergence was found accentuated around 'continents of differentiation' containing inversions on autosomes [38] . However , pre-mating isolation in this model system is ecological and incidental to specialization to different host plants , hence patterns of genetic divergence , migration and gene flow are more akin to those of micro-allopatric speciation . Paradoxically , elucidating the processes underpinning the genomic structure of speciation of An . coluzzii and An . gambiae s . s . whose sympatric range spans large areas of Western Africa has proved challenging because of the contrasted patterns of introgression and selection observed within that range [17 , 22 , 23] . The experimental functional genomics approach taken here clarifies the role of so-called speciation islands in the speciation process . The results suggest a simple genetic mechanism whereby the low-recombining pericentromeric X-island enables these incipient species to maintain their genetic integrity in Central and Eastern areas of Africa where introgression is uncommon or temporally limited [20 , 23] and in the hybrid zones of coastal Western Africa where gene flow is extensive [23 , 24] . The results are compatible with model of sympatric speciation in which the X-chromosome island played an active role in speciation that involved 'divergence hitchhiking' around key speciation loci [1 , 3 , 4] . Whether the 2L and 3L islands have played similar important roles remains to be demonstrated . In this study we found no evidence of a direct association of the 2L and 3L islands with asssortative mating since the recombinant where either homozygous or nearly homozygous M-type for these regions . Therefore it is unlikely that these islands of differentiation play a major role in conspecific mate recognition . In terms of physical size and the number of genes it contain , the X-island of speciation was the largest of the three pericentromeric islands described between An . coluzzii and An . gambiae s . s . through genome-wide studies [31 , 39] . Depending on the markers and populations considered , previous studies reported it as spanning 3–5Mb and as many as 75–200 genes [39 , 40] . Two studies showed that recombination was reduced by as much as 16 and 35-fold near the centromere compared to elsewhere on the X chromosome [40 , 41] . Based on our comparisons of sympatric populations from Ghana and the recombinant strains , we estimated that the island is over 6Mb-long and extends from positions ~18 . 1 to 24 . 2Mb . Within the island , 20 unique protein-coding changes affecting 12 genes were identified between the two sibling species . Amongst these genes , 6 have putative biological functions: AGAP001002 ( Toll protein ) is involved in development and immunity and AGAP001033 ( mab-21 like protein ) in neural and sensory organ development; AGAP001050 ( chondroitin polymerizing factor ) and AGAP001052 ( ubiquitin carboxyl-terminal hydrolase ) are involved in protein secretion and proteolysis; AGAP001022 ( gastrin/cholecystokinin receptor ) is a receptor for peptides in the brain and gastrointestinal tract; and AGAP001025 ( protein msta ) is involved in negative regulation of gene expression . Some of these genes might directly be involved with mating or interact with mating genes located elsewhere in the genome . However , others could also affect genes contributing to the ecological speciation of the sibling species , such as those responsible for form-specific larval habitat use [26 , 42] and larval predator avoidance behaviour [25 , 43] . Although we did not target intergenic regions when re-sequencing the X-island , these would warrant further investigation as they might contain regulatory elements with cis and trans effects on genes within the X , 2L and 3L island and possibly elsewhere in the genome . Trans-acting effects could explain the generally poor correspondence between differentially-expressed genes and islands of divergence observed in some An . coluzzii and An . gambiae s . s . populations [44 , 45] . In this study , the parental Mopti and Kisumu strains did not quite mate as perfectly assortatively as the RbMM and RbSS strains with homogenized genome . This would suggest that , in the parental strains , genetic interactions between the X-island and other parts of the parental genomes might have been responsible for a more variable mating phenotype . It should be note that although fixed non-synonymous coding differences were only identified within the selectively-introgressed X-island region , a number of non-coding differences were identified elsewhere in the genome . These might either be chance fixations due to the introgression process or be genuine differences between the parental Kisumu and Mopti strains . Therefore , and albeit we consider it highly unlikely , we cannot strictly rule out the possibility that a non-coding difference may have accidentally resulted in an assortative mating mechanism unique to the Kisumu and Mopti laboratory pairing and the resulting recombinant strains model system . Despite the methodological complexities inherent in the creation of assortatively-mating recombinant strains and their behavioural and genomic characterization , the resulting laboratory-based model holds much promise for further characterization of assortative mating in An . gambiae . The occurrence of strict female mate-choice in behavioural assays implies the perfect phenotypic expression of species-specific recognition mechanisms in females as well as perfect cues in males . Although no male-driven mate choice was detected in the laboratory , this does not mean that males do not contribute to assortative mating in nature . In the field , males are known to contribute to assortative mating via swarm spatial segregation [21 , 28] . Furthermore , in large outdoor enclosure mate-choice experiments in which either virgin females or males of An . coluzzii were presented with an equal number of conspecific and interspecific mates , both females and males were found to mate significantly assortatively [46] . We are currently conducting finer phenotypic characterization of the RbMM and RbSS strains in an attempt to identify con-specific mate recognition mechanisms that result in assortative mating . Importantly , the availability of the RbMM and RbSS strains and the development of a standardized laboratory-based mating assay offers the possibility of direct validation of candidate reproductive isolation genes via knockdown and/or knockout experiments combined with accurate phenotyping . Unravelling the genetic basis of mate choice and assortative mating is not only relevant to our understanding of processes of speciation , it can also play a crucial role in improving the mating behaviour of anopheline strains that are mass-reared for sterile-male release programmes and the control of malaria .
The Kisumu strain of an An . gambiae s . s . ( S molecular form ) was used for selective introgression of its X-island of speciation into the Mopti strain of An . coluzzii ( M molecular form ) genetic background . The Kisumu strain was colonised over 25-years ago from the area of Kisumu , Kenya where An . gambiae s . s . populations are from the Savanna chromosomal form characterized by the presence of the b inversion on chromosome 2R [15 , 47 , 48] . The Mopti strain was colonized in 2003 by the Lanzaro lab ( UC Davis ) from the village of N’Gabacoro droit near Bamako , Mali , where An . coluzzii populations are characterized by the bc and u inversion polymorphisms on 2R typical of the Mopti chromosomal form [15 , 47 , 48] . Both strains are well adapted to the laboratory and lay eggs reliably , which was the most important consideration given the complexity of the envisaged genetic crosses . The two strains were kept at 25°C±1°C and 70–80% relative humidity and reared under standard conditions in order to achieve homogeneity in phenotypic quality [49] . Adult females were fed on horse blood using an artificial feeder ( Hemotek membrane feeding system , Discovery workshops , UK ) and newly emerged first instars were reared in plastic trays ( 34x24cm ) at a density of 200 larvae per tray in 1L of water . They were fed daily on ground fish food ( Tetra werk , Melle , Germany ) . Pupae were placed in standard 5L rearing cages for emergence and newly emerged male and female mosquitoes were kept together and with access to 5% glucose solution at all time . The genetic differences distinguishing the ribosomal DNA from the M molecular form An . coluzzii and S molecular form An . gambiae s . s . were originally described within the large X-chromosome island at a locus very near the centromere [50 , 51] . Using this marker , the island of speciation from the S molecular form Kisumu strain was introgressed into the M molecular Mopti strain background through 4 generations of selective introgression . The parental strains were first checked for possible contaminations by genotyping of the diagnostic rDNA IGS locus located in the X-chromosome island of speciation using the PCR-RFLP method developed by Fanello et al . [52] . Here and elsewhere 'M' and 'S' refer to the male genotype at the marker rDNA locus ( males have one copy of the X-chromosome ) and 'MM' , 'SS' and 'MS' to possible homozygous and heterozygous female genotypes ( females have two copies of the X-chromosome ) at the same locus . Hybrids between the two strains were created by crossing 100 M Mopti males with 100 SS Kisumu females ( Fig 4 ) . In order to obtain the 1st backcross progeny , 100 virgin MS hybrid females were mated with 100 virgin M Mopti males , resulting in male progeny of genotype M or S at the r-DNA locus and female of genotypes MM or MS ( Fig 4 ) . From generation 2 to 4 , MS progeny females were backcrossed with M Mopti virgin males resulting in 4 generations of backcrossing ( Fig 4 ) . At each generation MS hybrid and MM families were obtained by bloodfeeding and setting up 80 females for individual oviposition . MS hybrid families were then distinguished from MM families by genotyping ten 2nd instar larvae reared in individual trays . The progenies of trays identified as containing MS hybrid larvae was pooled and reared under standard rearing conditions to obtain the next generation MS backcross females . Families identified as MM were discarded . In order to obtain two strains with M and S X-island types but with high genetic similarity elsewhere in the genome , 2 generations of crosses within the introgressed strains were conducted . MS and MM females and M and S males from the 4th backcross were randomly mated with one another in a mixed cage ( 100 males and 100 females ) resulting in MM or MS 6th generation families ( Fig 4 ) . Those progenies of families identified as being mixed M and S by larval genotyping which featured all possible male and female genotypes were randomly mated to one another in a mixed cage ( 100 males and 100 females ) for a 7th cross in order to generate MM , MS and SS families . The resulting MM and SS families were pooled together in order to obtain a pair of MM and SS strains , sharing a very large proportion of the Mopti genetic background but differing at their rDNA locus and linked X-chromosome island of speciation . The whole process was done twice simultaneously to generate two MM and two SS recombinant strains . Of those four strains , two were found to be heterozygous at the rDNA locus during their behavioural characterization suggesting possible contaminations . Therefore all analyses focus on the remaining MM and SS recombinant strains referred to as RbMM and RbSS throughout the text . Following the first backcross generation , the ratio of MS to MM females ( 36/3 = 92 . 8% ) significantly deviated from the expected 1:1 ratio ( Chi-square Likelihood ratio: χ2 = 39 . 2 , df = 1 , P< 0 . 001 ) suggesting a strong hybrid advantage ( S1 Fig ) . Thereafter no differences in the ratio of MS and MM females were found , with the percentage of MS females fluctuating from 46 . 2 in the 2nd ( P = 0 . 579 ) , to 64 . 8 in the 3rd ( P = 0 . 0683 ) , and 51 . 9 ( P = 0 . 782 ) in the 4th backcross generations ( S1 Fig ) . The proportion of MS to MM females in the fifth generation ( 1st cross within introgressed strain ) did not significantly differ from the expected 3:1 MS/MM ratio ( Chi-square Likelihood ratio: χ 2 = 0 . 19 , df = 1 , P = 0 . 664 ) . Following the publication of evidence showing linkage disequilibrium between the X , 2L and 3L islands in field M and S molecular form populations from West-Africa [32] , the parental strains and recombinant strains were also characterized as ‘M-like or S-like’ at the 2L and 3L islands of speciation using archived DNA and the PCR-RFLP diagnostics developed by the same authors [32] . Primers and PCR conditions were as described by the authors except for the use of more sensitive AmpliTaq Gold DNA Polymerase ( Life Technologies ) . Polytene chromosome preparations were made from the ovaries of 10–20 semi-gravid females per strain using established protocols [53 , 54] . Inversions present on chromosomes 2L and 2R were scored from chromosome spread under the light microscope . An assortative mating assay was developed in order to create conditions in which the mating choice preferences of females could be tested within standard laboratory breeding cages . Virgin males and females from each recombinant strain ( RbMM and RbSS ) were produced using our standardized rearing procedure ( see first section ) and kept in separate cages with access to 5% glucose solution at all times . In preliminary assortative mating experiments , 2-5-day-old virgin females from a given recombinant strain and X-chromosome island type were given a choice of 2-5-day-old recombinant males matching and non-matching their own X-island type . For each of 4 replicates , 30 females , 30 M-type and 30 S-type island males were placed in a standard 5L rearing cages with access to 5% glucose solution and given 24 hours to mate ( 6pm start ) . Infrared video recordings show that under these conditions , males initiated swarms soon after the insectary lights went off . At the end of these preliminary experiments all females were collected and stored in 70% ethanol until dissected for sperm genotyping . Across both mating combinations , the genetic analyses of the sperm extracted from the spermathecae of mated females showed that females mated preferentially with recombinant males with an X-island matching their own ( S2 Table ) . Recombinant females with M-type X-islands mated with matching M-type X-island males on average 77% of the time ( Chi-square = 15 . 9 , n = 52 , P< 0 . 001 ) ( S2 Fig ) . Females with S-type X-island mated with recombinant males with matching S-type X-island 81% of the times ( Chi-square = 19 . 2 , n = 47 , P< 0 . 001 ) ( S2 Fig ) . However , the mating assays were further improved in terms of percentage of assortative mating by using exactly 5-day-old virgin female and male mosquitoes ( see results ) . The RbMM and RbSS strains and the parental Mopti and Kisumu strains were then characterized in assortative mating experiments designed to test both female and male mate preferences . For recombinants strains , recombinant females from the RbMM or RbSS strains were given a choice of virgin males with X-islands matching their own type or not . Male choice experiments were the exact reciprocal of female choice experiments . Experiments were repeated twice and the female and the male choice for both strains ( 4 mating combinations ) tested each time with mosquitoes of the exact same cohort and age from both strains so as to avoid potential confounding factors due to variation in body size or phenotypic quality . The mating preferences of females and males from the parental Mopti and Kisumu strains were tested using the exact same methodology and experimental design with females and males choosing between individuals of the opposite sex from their own strain or not . Females were dissected in order to determine their mating status and to determine the rDNA type of the male they mated with . Their spermatheca was isolated , broken open , and the sperm bundle transferred to a 1 . 5ml centrifuge tube as described in previous studies [18 , 20] . DNA extractions were done using the ChargeSwitch gDNA Micro Tissue Kit ( Life Technologies , USA ) following the manufacturer's instructions . The sperm DNA was genotyped using the PCR-RFLP diagnostic as described elsewhere [52] . Archived DNA from 13 Mopti , 24 RbMM and 23 RbSS individuals was amplified by multiple-displacement amplification using the Illustra GenomiPhiV2 DNA Amplification kit ( GE Healthcare Bio-sciences , Piscataway , NJ ) , purified using a MinElute Reaction Cleanup Kit ( Qiagen , Hilden , Germany ) and DNA pools were sent to the Liverpool Centre for Genomic Research ( CGR ) for sequencing . DNA libraries were prepared according to the Illumina TruSeq DNA protocol ( Illumina , San Diego , CA ) , multiplexed and sequenced on two lanes of an Illumina HiSeq 2000 sequencer . Base-calling of indexed reads was performed with the program CASAVA 1 . 8 . 2 ( Illumina ) . The reads were trimmed using the software Cutadapt 1 . 2 . 1 [55] and Sickle 1 . 200 [56] and mapped to the An . gambiae ( PEST ) reference sequence ( assembly AgamP3 ) using Bowtie 2 . 1 . 0 [57] . Alignments were filtered to remove low mapping quality reads and redundant duplicate reads were filtered out using the Picard MarkDuplicates Tool 1 . 85 ( http://picard . sourceforge . net ) . Mapped reads were locally re‐aligned around indels using the Genome Analysis Tool Kit ( GATK ) version 2 . 1 . 13 [58 , 59] . The mean coverage depth after local re‐alignment and duplicate removal of reads was equal to 25 . 3x for RbSS , 30 . 9x for RbMM and 34 . 1x for the Mopti parental strain . Variant detection was performed using the GATK 'UnifiedGenotyper' package [58 , 59] with an expected SNP heterozygosity of 0 . 01 . An expected ploidy of 20 was used ( i . e . allele frequencies calculated in increments of 5% ) in order to best balance accurate sample representation and computational efficiency . Variants were further filtered using the GATK 'VariantFiltration' package [58 , 59] . This resulted in the characterization of ~6 million SNPs ( ~4 . 8 million passing all filters ) and 900 , 000 indels in each of the sequenced strains . All variants were annotated using snpEff 3 . 1 [60] . Visual alignment inspections were performed using the Integrative Genomics Viewer ( IGV ) [61] . Estimates of genetic differentiation FST between two populations a and b were calculated based on SNPs satisfying GATK's most stringent 'pass' criteria and using the formula: FST = 1- Hs/Ht where Hs is the mean heterozygosity across populations a and b and Ht , the total heterozygosity across all populations [62] . Hs = 1- Σpi2 where pi are the mean frequencies of the major and minor alleles calculated from the a or b populations and: Ht = 1- Σpi2 with pi being SNP frequencies calculated across all five populations . Pair-wise FST estimates of genetic differentiation were used for generating scans of genetic differentiation across chromosomes with the software JMP 10 ( SAS Institute , Inc ) . In order to best outline the genomic region ( s ) introgressed from An . gambiae s . s . into An . coluzzii in the RbSS and RbMM strains , the 'spline' function was fitted over every high-confidence SNPs with δ = 2 . 7216 . Separate data analyses identified all unique protein-coding differences between the RbSS , RbMM and Mopti strains . These differences were checked by visual inspection and comparison of their genomes using the software IGV . Anopheles gambiae s . l . larvae were collected in Akoti-Chirano ( Lat . 6° 6’ 17 . 08” N; Long . 2° 19’ 0 . 45” W ) in the Bibiani-Anhwiaso-Bekwai district of the Western Region of Ghana , West Africa . Populations of An . coluzzii and An . gambiae s . s . co-occur in this deciduous forested area and are both of the 'Forest chromosomal form' characterized by standard karyotypic arrangements ( no paracentric inversions ) [63] . Larvae were reared to adulthood at the Department of Animal Biology and Conservation Science , University of Ghana , Legon , West Africa , stored in ethanol , and shipped to Keele University . The samples were then individually characterized as An . coluzzii ( M molecular form ) and An . gambiae s . s . ( S molecular form ) as described above . The DNA from 30 individuals of each sibling species was pooled and purified using a MinElute Reaction Cleanup Kit ( Qiagen , Hilden , Germany ) and DNA pools were sent to the Liverpool Centre for Genomic Research ( CGR ) for sequence capture and sequencing . SureSelect RNA oligomer baits ( Agilent , Santa Clara , CA ) were designed to cover coding regions from position 17Mb to the centromere of the X-chromosome based on the ( PEST ) reference sequence ( assembly AgamP3 ) . Prior to the amplification of pre-capture libraries , DNA fragments larger than ∼700bp were removed from DNA pools using Agencourt AMPure XP beads ( Beckman Coulter , Brea , CA ) . Following amplification and adapter-ligation , 750ng of pre‐capture libraries were hybridised to 2μl of RNA oligomer baits for ∼24 hours at 65°C . Captured libraries were amplified , indexed , pooled and sequenced on 1 lane of an Illumina HiSeq 2000 . All other procedures were as described above . Within the targeted pericentric region of the X chromosome , the mean coverage depth after local re‐alignment and duplicate removal of low mapping quality and redundant reads was 200x for An . gambiae s . s . , leading to the identification of 26 , 974 SNPS and 2 , 593 indels . In An . coluzzii , coverage depth was 255x and 18 , 772 SNPS and 1 , 738 indels were identified . All statistical analyses were performed using the software JMP 10 ( SAS Institute , Inc ) . Pearson Chi-square tests of randomness and goodness of fit ( likelihood ratios ) were used for detecting deviations from random mating in assortative mating experiments and to compare observed M and S X-chromosome frequencies to expected Mendelian and Hardy-Weinberg Equilibrium ( HWE ) proportions at different generations of the genetic crosses . | Anopheles gambiae is the most important vector of malaria in Africa . This species is undergoing speciation and a number of subpopulations have been identified which can produce viable hybrid offspring but are reproductively isolated through assortative mating and ecological adaptation . This complex structure provides an ideal system for studying the unique genetic and behavioural processes required for speciation . Anopheles gambiae’s subpopulations differ genetically in limited regions of their genomes called islands of speciation . Theoretical studies predict that these islands , characterized by restricted genetic rearrangements , may protect genes of assortative mating between emerging species , and are fundamental to the speciation process . We set out to test this prediction by performing complex genetic crosses between the sister species Anopheles coluzzii and Anopheles gambiae s . s . and creating recombinant strains differing only at their X-chromosome island of speciation . We show through behavioural studies that recombinant females consistently mated with matching island-type males thereby associating assortative mating genes with the X-island of divergence . By sequencing the genetic code of the recombinant strains and natural populations , we could confirm these findings and identify candidate assortative mating genes . These findings suggest an important role of divergence islands for the genetic and behavioural processes associated with speciation . | [
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] | [] | 2015 | Experimental Swap of Anopheles gambiae's Assortative Mating Preferences Demonstrates Key Role of X-Chromosome Divergence Island in Incipient Sympatric Speciation |
Plant microRNAs ( miRNAs ) are critical regulators of gene expression , however little attention has been given to the principles governing miRNA silencing efficacy . Here , we utilize the highly conserved Arabidopsis miR159-MYB33/MYB65 regulatory module to explore these principles . Firstly , we show that perfect central complementarity is not required for strong silencing . Artificial miR159 variants with two cleavage site mismatches can potently silence MYB33/MYB65 , fully complementing a loss-of-function mir159 mutant . Moreover , these miR159 variants can cleave MYB33/MYB65 mRNA , however cleavage appears attenuated , as the ratio of cleavage products to full length transcripts decreases with increasing central mismatches . Nevertheless , high levels of un-cleaved MYB33/MYB65 transcripts are strongly silenced by a non-cleavage mechanism . Contrary to MIR159a variants that strongly silenced endogenous MYB33/MYB65 , artificial MYB33 variants with central mismatches to miR159 are not efficiently silenced . We demonstrate that differences in the miRNA:target mRNA stoichiometry underlie this paradox . Increasing miR159 abundance in the MYB33 variants results in a strong silencing outcome , whereas increasing MYB33 transcript levels in the MIR159a variants results in a poor silencing outcome . Finally , we identify highly conserved nucleotides that flank the miR159 binding site in MYB33 , and demonstrate that they are critical for efficient silencing , as mutation of these flanking nucleotides attenuates silencing at a level similar to that of central mismatches . This implies that the context in which the miRNA binding site resides is a key determinant in controlling the degree of silencing and that a miRNA “target site” encompasses sequences that extend beyond the miRNA binding site . In conclusion , our findings dismiss the notion that miRNA:target complementarity , underpinned by central matches , is the sole dictator of the silencing outcome .
Ubiquitously found in plants and animals , microRNAs ( miRNAs ) are a group of 20–24 nucleotide ( nt ) small RNAs ( sRNAs ) that have been demonstrated to be critical regulators of gene expression . In plants , there are hundreds of known miRNAs [1] , many of which have been shown to play critical roles in many different developmental and physiological processes [2] . They silence gene expression by guiding the RNA induced silencing complex ( RISC ) to target mRNAs via base pairing [2] . In plants , the subsequent repression of the target transcript occurs mainly through mRNA cleavage and/or translational inhibition [3] . Plant miRNAs recognise highly complementary binding sites , which enabled accurate bioinformatics prediction of their targets [4] . Moreover , experimentally determined targets were defined by the landmark study of Schwab et al . ( 2005 ) , formulating the empirical parameters governing miRNA target recognition in plants , which are based purely on complementarity between the miRNA-target mRNA pairs [5] . It was found that there should be no more than one mismatch in the important “seed region” which corresponds to the 5′ end of the miRNA from positions 2 to 12 , including no mismatches at the cleavage site ( positions 10 and 11 ) , and no more than two contiguous mismatches in the 3′ of the miRNA ( positions 13–21 ) [5] , [6] . Although there are examples of miRNA targets that do not fully conform to these sequence parameters [4] , [7] , generally most experimentally validated miRNA targets satisfy these parameters , and have been verified through degradome sequencing [8] , [9] . Consequently , these parameters have been widely accepted and incorporated into many bioinformatic programs that predict miRNA targets [10] . Despite this extensive analysis on the complementary requirements for miRNA target recognition , and the subsequent mechanism of miRNA mediated repression [11] , there has been very little analysis on the factors that impact the strength or efficacy of miRNA-mediated gene silencing in plants . It has been clearly shown that alterations in complementarity between the miRNA and its target can impact on the strength of the silencing outcome [12] . Additionally , the abundance of the miRNA can also functionally impact the silencing outcome [13] , but generally no other factors have been considered in plants . As most plant miRNA-mRNA duplexes have high complementarity with perfect central pairing , transcript cleavage is generally considered a key silencing mechanism which results in the irreversible destruction of the transcript . This has been hypothesised to clear target transcript , leading to an absolute silencing outcome [14] . However , some plant miRNA targets with near perfect complementarity appear regulated primarily at the translational level [15]–[17] , and this mechanism is likely widespread in plants [18] . Unlike cleavage , translational repression is thought not to result in mRNA destruction and thus is potentially reversible [3] . In animals , rather than an absolute silencing or “switch” mechanism , it acts as a tuning mechanism [19] , however whether this is the case in plants is unclear . Due to this combinatorial mechanistic complexity , it is generally very difficult to appraise the strength of any miRNA-target interaction . Contributing to this in plants has been the lack of easily scorable systems that can gauge the extent of silencing . Consequently , the efficacy of plant miRNA-mediated regulation has remained largely ignored . In this paper , we have used the highly conserved Arabidopsis miR159-GAMYB regulatory module to address fundamental questions regarding plant miRNA-mediated gene silencing . The Arabidopsis miR159 family is composed of three genes , MIR159a , MIR159b and MIR159c , although the MIR159c gene appears to be obsolete [20] , [21] . Both miR159a and miR159b are predicted to regulate over 20 target genes in Arabidopsis , however regulation of only two targets appears physiologically important; namely MYB33 and MYB65 , two redundant genes encoding R2R3 MYB transcription factors [20] , [21] . MiR159a and miR159b redundantly silences these two genes in rosettes , and perturbation of their activity enables MYB33/MYB65 expression and results in a decreased rosette size and upwardly curled leaves [20] , [22] . This easily scorable phenotype , along with molecular indicators , enables accurate appraisal of miR159 silencing efficiency . Here , we complement a strong loss-of-function mir159ab double mutant with different artificial miR159 variants to demonstrate that perfect central complementarity is not required for the potent silencing of MYB33 and MYB65 . Instead , a strong non-cleavage repression mechanism was evident in transgenic plants accumulating high levels of MYB33 transcripts , and appears to be a major contributing factor behind the strong efficacy of miR159 . Finally , we show that factors beyond complementarity are critical for the silencing outcome . Firstly , the stoichiometric ratio of the miRNA:target mRNA strongly impacts the silencing outcome , especially when target cleavage is compromised . Moreover , mutation of sequences flanking the miR159 binding site of MYB33 leads to an attenuation of silencing that is comparable to cleavage site mutations . This reveals an important role for binding site context in plant miRNA-mediated gene regulation , suggesting , in essence , that a miRNA “target site” encompasses sequences that extend beyond the complementary motif to which a miRNA directly binds .
The mir159ab double mutant presents itself as an ideal tool to examine miRNA-mediated gene silencing , as all mir159ab defects are specifically caused by the deregulation of the redundant GAMYB-like gene pair MYB33/MYB65 , and these mutant phenotypes , including leaf curliness and dwarfed stature , are easily scored [20] . Therefore , by complementing mir159ab with different miR159 variants with modified central complementarity to MYB33/MYB65 , the impact of central pairing on the silencing outcome of miR159 or their variants can be evaluated . Since miR159a has the highest complementarity to MYB33/MYB65 and is the most abundant miR159 family member [23] , five MIR159a variant constructs were generated by performing site-directed mutagenesis on a MIR159a genomic clone ( Figure 1A ) . The two original mismatches of miR159a to MYB33/MYB65 at positions 7 and 20 were corrected , to keep the miRNA-target pairing free energy of all variants as high as possible ( Figure 1B ) . All five variants were identical except for their number of central mismatches to MYB33/MYB65 , which ranged from zero to four . MiR159a0 was included as a positive control to ensure that changes at nucleotide positions 7 and 20 would not abolish target silencing , since these two mismatches are highly conserved between miR159 and the binding site of potential GAMYB target genes from different monocotyledonous and dicotyledonous species ( Figure S1 ) . For all constructs , compensatory mutations were made to the miR159a* sequence to maintain the original predicted MIR159a stem-loop secondary structure ( Figure S2 ) . The five MIR159a variant constructs were individually transformed into mir159ab and the phenotypes of multiple primary transformants were scored . Correction of the mismatches at positions 7 and 20 had no influence on the silencing outcome , as all 30 MIR159a0 T1 plants were fully complemented ( Figure 1C ) . Surprisingly , all 44 MIR159a1 and all 46 MIR159a2 primary transformants obtained appeared fully wild type ( Figure 1C ) , suggesting that mismatches at the cleavage site does not prevent silencing . In contrast , all 90 MIR159a3 and 71 MIR159a4 primary transformants appeared indistinguishable from mir159ab ( data not shown ) , indicating that the introduction of more than two central mismatches between miR159 and MYB33/MYB65 prevents complementation of the mir159ab phenotype . To confirm that these mir159ab plants were fully complemented by the MIR159a1 and MIR159a2 variants , the expression of the GAMYB-like downstream gene CYSTEINE PROTEINASE1 ( CP1 , AT4G36880 ) was examined by qRT-PCR , as its mRNA is highly expressed in mir159ab due to the deregulation of MYB33/MYB65 [22] . Consistent with the morphological phenotypes , the mRNA level of CP1 in mir159ab plants complemented by MIR159a0 , MIR159a1 or MIR159a2 was close to that of wild-type , indicating that MYB33/MYB65 expression was being suppressed to approximately wild type levels by these variants ( Figure 1D ) . Next , we measured the abundance of the mature miR159a variants in complemented mir159ab plants using customized TaqMan sRNA assays . The levels of mature miR159a0 , miR159a1 and miR159a2 in multiple independent transgenic lines were within the same order of magnitude as miR159a in wild type ( Figure 1E ) . This suggests that gross overexpression of the miR159 variants was not an explanation for silencing targets with two central mismatches . Hence , their silencing potency in this regard would not be considered a transgenic artefact . In both animal and plants , it is a popular notion that miRNA-guided cleavage of target genes is significantly attenuated by central mismatches [24] , [25] . Hence , to investigate the mechanism by which the miR159a1 and miR159a2 variants are silencing MYB33 and MYB65 , we firstly measured their un-cleaved mRNA levels by using qRT-PCR primers that span the miR159 binding site . Interestingly , MYB33/MYB65 levels were much lower in both MIR159a1 and MIR159a2 lines compared to mir159ab ¸ but were not reduced to wild-type levels . Instead these intermediate MYB mRNA levels appeared dependent on the number of central mismatches , with more mismatches correlating with higher MYB mRNA levels ( Figure 2A and B , Figure S4 ) . This demonstrates that miR159a1 and miR159a2 can still reduce MYB33/MYB65 transcript levels , suggesting that the miR159a variants are still mediating cleavage despite the existence of central mismatches . To investigate this possibility , we performed a modified 5′-RACE miRNA cleavage assay [26]–[28] . Instead of gel purifying and cloning PCR products with sizes similar to the expected cleavage product followed by sequencing individual clones , we directly sequenced the entire PCR reaction and analysed the chromatograph peaks to approximate the proportion of degraded MYB33 transcripts that correspond to miR159-guided cleavage products ( Figure 3A and S3 ) . For the wild-type control sample , sequencing of the 5′-RACE reaction using a MYB33-specific primer resulted in clean single peaks with very little baseline noise for each MYB33 nucleotide up to the miR159-guided cleavage site , and then into the RNA adaptor sequences ( Figure 3B ) . Such a clean signal indicates that the vast majority of degraded MYB33 transcripts amplified by this assay were miR159-guided 3′-end cleavage products , which is consistent with previous strong degradome signatures of MYB33 [8] , [9] . By contrast , the mir159ab sample exhibited overlapping peaks in the portion of the sequence immediately upstream of the miR159 cleavage site ( Figure 3C ) . This demonstrates that the degraded MYB33 transcripts recovered by 5′-RACE in mir159ab were no longer predominantly miR159-guided cleavage products , but mixed populations arising independently of miR159-guided cleavage . By contrast , the chromatographic traces of reactions from MIR159a1 and MIR159a2 plants were indistinguishable from that of wild type ( Figure 3D and 3E ) . This clearly demonstrates that miR159a1 and miR159a2 are capable of mediating cleavage despite the presence of central mismatches . Additionally , as this cleavage is occurring at the canonical MYB33 cleavage site , this suggests that that the intended miR159a1 and miR159a2 variants are being processed from the modified pri-MIR159a transcripts , rather than other possible products arising from imprecise DCL1 processing . Although these 5′-RACE assays demonstrate that miR159a1 and miR159a2 can guide cleavage of MYB33 , these assays are not quantitative . Therefore , a qRT-PCR assay was developed to quantitate the amount of miR159-guided 3′-end cleavage products of MYB33 mRNA . An equal amount of total RNA from each sample was ligated to a 5′-end RNA adaptor followed by retro-transcription with poly T primers . To specifically amplify miR159-guided cleavage products , a hybrid forward primer was designed that included the last 19 nucleotides of the adaptor followed by five nucleotides of MYB33 sequence immediately downstream of the miR159 cleavage site ( Figure S5 ) . The specificity of the assay was confirmed by testing Col-0 and mir159ab samples with two different forward primers ( Figure S5 ) . Both un-cleaved MYB33 mRNA and 3′ end cleavage products were measured and the ratio was calculated to obtain an indication of miR159 cleavage efficiency , where the higher this ratio is the more inefficient cleavage is . For wild-type , the steady levels of the miR159-guided 3′end MYB33 cleavage products were detectable , but were approximately 9-fold lower than the un-cleaved MYB33 mRNA levels ( Figure 4 ) . Such a low amount could be considered consistent with rapid degradation of these products by exoribonuclease XRN4 [9] , [29] . For mir159ab , the levels of 3′ end cleavage products were negligible ( Figure 4 ) . Supporting the notion that the miR159a variants are able to guide cleavage , independent transgenic MIR159a1 plants had comparable ratios to wild type , however the ratio values approximately doubled in MIR159a2 plants . Together , these data suggest whilst miR159a2 can still direct cleavage , but it has been attenuated by the introduction of two central mismatches ( Figure 4 ) . Despite the higher levels of un-cleaved MYB33 and MYB65 transcripts , MIR159a1 and MIR159a2 plants appeared morphologically indistinguishable from wild type and the CP1 mRNA remained essentially at wild type levels ( Figure 1D ) . This leads to the question of whether miR159 also recruits a non-cleavage mechanism to achieve complete silencing of MYB33 and MYB65 , especially when cleavage is attenuated . To investigate this , we attempted to generate transgenic plants that strongly transcribe MYB33 . To do this , MYB33 or mMYB33 ( miR159-resistant ) constructs were generated ( Figure 5B ) using a genomic MYB33 clone [30] which contains the endogenous MYB33 promoter . These constructs were transformed into the loss-of-function myb33 mutant , a T-DNA knock-out allele of the MYB33 gene [30] . Myb33 is phenotypically indistinguishable from wild type and the absence of endogenous MYB33 transcript enables accurate quantitation of the MYB33 and mMYB33 transgene mRNA levels . Consistent with previous experiments [30] , [31] , expression of mMYB33 caused pleiotropic developmental defects in myb33 , as all 20 primary transformants had upwardly curled leaves and dwarfed stature resembling mir159ab ( Figure 5A ) . In stark contrast , none of the 30 MYB33 primary transformants recovered had any conspicuous defects ( Figure 5A ) . QRT-PCR was then performed on rosettes to quantitate the steady state level of un-cleaved MYB33 transcripts . As would be predicted , the MYB33 transcript level was elevated in mMYB33 lines ( 1 and 2 ) , being approximately ten fold higher compared to wild type ( Figure 5D ) . Surprisingly , all six MYB33 transgenic lines examined also accumulated un-cleaved MYB33 transcripts to a much greater level than wild type , where 5–15 fold increases were observed ( Figure 5D; lines 1–6 ) . This suggests that miR159 is unable to reduce MYB33 mRNA to a wild type level through a transcript cleavage mechanism . Moreover , in MYB33 lines 5 and 6 , the un-cleaved MYB33 mRNA levels were even higher than those in the mMYB33 lines . The expression of these transcripts appeared to be strongly repressed by miR159 , as the corresponding transgenic plants exhibited no curly leaves or reduction in rosette size ( Figure 5A and 5C ) . In addition , whereas the CP1 mRNA level was dramatically elevated in mMYB33 plants ( ∼120 times higher than wild type ) , such increases were not seen in any MYB33 plants ( Figure 5E ) . These data together support the previous notion that miR159 suppresses MYB33 not only by transcript cleavage , but also by a non-cleavage mechanism ( s ) [22] . Therefore , in this case , the steady state mRNA levels are a poor indicator of silencing . To investigate the reason why miR159 fails to repress MYB33 mRNA to wild type levels in MYB33 plants , we quantified 3′-end MYB33 cleavage products and un-cleaved MYB33 transcript levels as previously described . Interestingly , in the MYB33 samples ( lines 1 and 2 ) , the 3′ end cleavage product appeared to increase proportionally with the levels of uncleaved MYB33 mRNA , while no 3′ end cleavage product was apparent in the mMYB33 sample ( Figure 6 ) . This indicates that cleavage is unlikely to be attenuated by the high MYB33 transcript level , and the amount of cleavage products present appears dependent on the amount of MYB33 transcripts present . The miR159a variants result could lead to a surprising conclusion that central matches are not important for a strong silencing outcome . To further investigate this , three MYB33 variants were made , all of which were identical except that the number of mismatches to miR159 at positions 10 and 11 ranged from zero to two ( Figure 7B ) . To keep the overall complementarity and free energy close to the original value when pairing to miR159a , the original mismatches at positions 7 and 20 were corrected ( Figure 7B ) . Therefore , the positions of mismatches of MYB33-0 cm , MYB33-1 cm and MYB33-2 cm to miR159a mirror those of miR159a0 , miR159a1 and miR159a2 to the endogenous MYB33/MYB65 genes respectively . Although these sequence alterations result in one to two amino acid changes in the MYB33 protein , all substitutions made were conservative changes to minimize possible changes to the biochemical properties of the protein . Primary transformants for each construct were generated in the myb33 background . Consistent with the result that mir159ab can be fully complemented by miR159a0 , all 52 MYB33-0 cm transgenic plants generated were indistinguishable from wild type ( Figure 5A ) . However , counter to miR159a1 and miR159a2 , all 46 MYB33-1 cm and 41 out of 42 MYB33-2 cm T1 plants recovered had developmental defects , exhibiting phenotypes characteristic of mir159ab , including curled leaves and a smaller rosette ( Figure 7A ) . This demonstrates that the MYB33-1 cm and MYB33-2 cm transgenes are not efficiently silenced by endogenous miR159 . Molecular and phenotypic analyses were carried out to investigate this . Firstly , to dismiss that MYB33-1 cm and MYB33-2 cm are acting as decoys to suppress endogenous miR159 activity [32] , we measured MYB65 transcript levels in the MYB33 variants and control plants . As the mRNA levels were found to be similar in all plants ( Figure 7C ) , this demonstrates that MYB65 has not been deregulated in MYB33 variants , meaning that miR159 activity has not been perturbed . Therefore , the abnormal rosette phenotypes were resulting from failure of miR159 to silence the MYB33 variants . To phenotypically assess the severity of the developmental phenotypes , multiple MYB33-1 cm and MYB33-2 cm T2 transgenic lines were grown side by side with Col-0 , mir159ab and mMYB33 plants . Although most transgenic MYB33-1 cm and MYB33-2 cm lines displayed traits characteristic of mir159ab , the extent of leaf curl and the reduction in rosette size of most plants were less severe compared to either mir159ab or mMYB33 plants ( Figure 7A ) . Therefore , the repression of MYB33-1 cm or MYB33-2 cm by miR159 does not appear completely abolished . To examine this , total RNA was extracted from individual transgenic lines using rosettes of multiple plants exhibiting similar phenotypes ( Figure 7A ) . QRT-PCR analysis found that the un-cleaved MYB33 mRNA levels were 10–15 fold higher in MYB33-1 cm and MYB33-2 cm lines compared to wild type , while only a six-fold increase was observed in mir159ab ( Figure 7D ) . Even considering the fact that MYB65 is also de-regulated in mir159ab , these very high MYB33-1 cm/2 cm mRNA levels would have been expected to result in phenotypic severities more akin to that of mir159ab , if they were completely devoid of miR159 regulation . Moreover , transcript levels of CP1 , which reflects the abundance of MYB protein , were found to be much lower in all MYB33-1 cm/2 cm lines examined than in mir159ab ( Figure 7E ) . Together , these data suggest that the high levels of MYB33-1 cm and MYB33-2 cm transcripts are still being partially repressed by miR159 , which again suggests a non-cleavage mechanism in operation . 5′-RACE was also performed to assay the degraded 3′-end MYB33 mRNA population as described before ( Figure S3 ) . A MYB33 transgenic line with a very high MYB33 mRNA level was included as control ( line 5 from Figure 7 ) , and its sequencing chromatograph was identical to that of wild type ( Figure 8C ) . This demonstrates that the vast majority of 3′ degraded MYB33 transcripts amplified by this assay are miR159-guided 3′ end cleavage products , regardless of how high the transcript level is . In contrast , no signals from the adaptor sequence but those from the transgene were recovered for mMYB33 , which is completely resistant to cleavage ( Figure 8D ) . For MYB33-1 cm , past the cleavage site , the signal became a mixture of adaptor and MYB33-1 cm sequence , indicating that although some miR159-guided cleavage products were present , it was a mixed population ( Figure 8A ) . For MYB33-2 cm , the signal from the adapter sequence corresponding to the canonical miR159 cleavage site was negligible , where the signal was dominated by MYB33-2 cm sequences ( Figure 8B ) . This demonstrates that 3′ end miR159-guided cleavage products are poorly represented in the reactions from the MYB33-1 cm and MYB33-2 cm samples when compared to the MYB33 sample , arguing that miR159-guided cleavage of MYB33-1 cm and MYB33-2 cm is not as efficient as MYB33 , which is likely due to the introduction of central mismatches . It appears that central mismatches attenuate cleavage in both the miR159a variant-MYB33/MYB65 and miR159-MYB33 variant relationships , therefore additional factors must be impacting the differential silencing outcomes observed . Firstly , although the duplex pairs have identical mismatch positions , the nucleotides changes are not identical and hence will result in duplexes with different thermodynamics stabilities ( Table S1 ) , possibly impacting the silencing outcome . Secondly , although MYB33 and MYB65 were being transcribed at wild type levels in MIR159a1 and MIR159a2 plants , it is clear that mRNA from MYB33 , MYB33-1 cm or MYB33-2 cm transgenes accumulated to higher levels than endogenous MYB33 ( Figure 5D , 7D ) . This would be predicted to alter the stoichiometric ratio of MYB33 transcripts to miR159 . Since target mRNA: miRNA stoichiometry is important for the silencing outcome in animal miRNA-target genes relationships [33]–[35] , an altered miR159: MYB33 stoichiometry may explain the observed differential silencing outcomes . As it is impossible to introduce identical central mismatch nucleotides in both the miR159a variant-MYB and miR159-MYB33 variant duplexes , we aimed to alter the stoichiometric ratio between MYB33 and miR159 in the MIR159a and MYB33 variant plants instead . This will determine whether stoichiometry can alter the silencing outcome , or whether the different thermodynamic stabilities of two different duplexes override any stoichiometric alteration . Firstly , the wild type MYB33 transgene was transformed into individual MIR159a1 and MIR159a2 T3 transgenic lines ( mir159ab genotype , wild-type phenotype ) to see whether higher levels of MYB33 transcript could override the complementation by the miR159a variants . In contrast to the introduction of MYB33 into wild-type plants that resulted in no phenotypic abnormalities ( Figure 5 ) , the introduction of the MYB33 transgene into multiple lines of MIR159a1 and MIR159a2 plants resulted in a large proportion of transgenic plants displaying the characteristic phenotypic abnormalities of MYB33 expression , including leaf curl and smaller rosette size ( Figure 9A ) . QRT-PCR analysis confirmed strong increases of MYB33 transcript levels in these MYB33 [MIR159a1 ( mir159ab ) ] and MYB33 [MIR159a2 ( mir159ab ) ] plants , whilst the mature miR159a1 and miR159a2 levels remained similar ( Figure 9C ) . Consistent with the morphological defects , CP1 mRNA levels were elevated , especially in the MYB33 [MIR159a2 ( mir159ab ) ] plants . This clearly demonstrates that the MIR159a variants are poor silencers of MYB33 when MYB33 is highly transcribed , resulting in an unfavourable stoichiometric miR159:MYB ratio for silencing . Secondly , the wild-type MIR159a construct was transformed into MYB33-1 cm and MYB33-2 cm T3 transgenic lines . The severe leaf curl and small rosette size observed in MYB33-1 cm and MYB33-2 cm plants were largely suppressed in many MIR159a ( MYB33-1 cm ) and MIR159a ( MYB33-2 cm ) transformants ( Figure 9A ) . The introduction of MIR159a resulted in elevated levels of mature miR159a by approximately five-fold and a simultaneous reduction of the MYB33 transcript levels to approximately 50% of that found in parental MYB33-1 cm and MYB33-2 cm lines ( Figure 9C ) . Consistent with these observations , the levels of CP1 were suppressed in these MIR159a ( MYB33-1 cm ) and MIR159a ( MYB33-2 cm ) transformants ( Figure 9B ) . A ratio between the relative abundance of MYB33 and miR159a was calculated ( levels of both MYB33 and miR159a in Col-0 were normalized to 1 , Figure 9C ) . The higher this ratio was , the poorer the silencing outcome became; and vice versa . These data clearly demonstrate that the miRNA: target mRNA stoichiometric ratio has a critical role in determining the silencing outcome when central complementarity is compromised . Although we are unable to address whether thermodynamic differences between the duplexes may in part contribute to the differential silencing outcome , it , or any other factor , is clearly not strong enough to override stoichiometry . Interestingly , the stoichiometric ratio of endogenous miR159 to MYB33 appears less important , as even in transgenic plants transcribing MYB33 many fold higher than in wild-type , MYB33 appears to be strongly silenced ( Figure 5 , 6 ) . This argues that the sensitivity of MYB33 to miR159 regulation is strong enough to override an unfavourable stoichiometric ratio . In order to understand this sensitivity , we initiated a characterization of the MYB33 region containing the miR159 binding site by aligning genes encoding MYB33 homologs from different monocotyledonous and dicotyledonous plant species . Alignment of the nucleotide sequences of 15 of these genes revealed multiple conserved nucleotides flanking the miR159 binding site , many of which are located in the third codon position ( Figure 10A ) , suggesting that conservation is not at the protein but RNA level . Moreover , these nucleotides are among the strongest stretches of nucleotide conservation outside of the R2R3 MYB domain or the miR159 binding site ( Figure S6 ) , suggesting the conservation has functional significance . To test their role in miR159 regulation of MYB33 , eleven nucleotides flanking the miR159 binding site were mutated in the genomic MYB33 construct to generate the MYB33-Flanking site Mutant construct ( MYB33-FM , Figure 10B ) . These alterations result in sequence changes in the MYB33 protein , however all three resulting amino acid substitutions were conservative , minimizing possible changes to the biochemical properties of the protein . Moreover , these amino acids do not appear to be critical for MYB33 function , as a previous study has shown that a 33 bp deletion in the MYB33 coding region , including the miR159 binding site and these flanking nucleotides , still results in the production of a functional protein [30] . MYB33-FM and the positive control , MYB33 , were individually transformed into myb33 plants , and multiple primary transformants were selected and analyzed . Consistent with previous results , none of the MYB33 plants showed any phenotypic abnormalities , indicating that MYB33 is fully repressed by miR159 ( Figure 10C ) . In contrast , 116 out of 126 MYB33-FM plants showed strong developmental defects characterized by curly leaves and stunted growth ( Figure 10C ) . As these are phenotypic characteristics of mir159ab , it appears that miR159 regulation of MYB33 has been compromised . Molecular analysis was carried out on RNA extracted from primary transformants that had been categorized according to phenotypic severities . As before , MYB33 transcript levels were elevated in MYB33 plants ( Figure 10D ) , but remained strongly silenced as these plants were indistinguishable from wild-type ( Figure 10D ) . Elevated MYB33 levels were also observed in all MYB33-FM plants examined , however , in contrast to MYB33 plants , the levels positively correlated with the phenotypic severity observed , as well as the CP1 mRNA levels ( Figure 10D ) . By contrast , MYB65 mRNA levels were unchanged in all transgenic plants compared to wild type , indicating that miR159 activity has not been perturbed in any of the transgenic lines , therefore MYB33-FM is not acting as a decoy ( Figure 10D ) . In conclusion , this data clearly demonstrates that in transgenic plants where MYB33-FM is highly transcribed , the silencing efficacy of miR159 against MYB33-FM is strongly perturbed . Our data has shown that under a poor stoichiometric ratio , both the central and flanking nucleotides are critical for strong miR159 efficacy against endogenous MYB33 . In an attempt to determine the degree to which MYB33 silencing is perturbed by these different mutations , a GUS-reporter system was used to quantitatively measure silencing at both the mRNA and protein level . The MYB33 , MYB33-1 cm , MYB33-2 cm and MYB33-FM transgenes were each translationally fused with a GUS reporter and corresponding transgenic lines were generated in myb33 plants [30] . For each sample , more than 50 primary transformants were bulked together from which both RNA and protein samples were prepared , so that both transcript ( qRT-PCR ) and GUS activity ( MUG assays ) quantification could be then performed , enabling direct comparison of measurements . Un-cleaved MYB33 transcript levels in MYB33-1 cm:GUS and MYB33-FM:GUS were similar to the level in MYB33:GUS plants , whereas the MYB33-2 cm:GUS mRNA level was approximately three-fold higher ( Figure 11F ) , again supporting that central mismatches attenuate cleavage . Despite MYB33-1 cm:GUS and MYB33-FM:GUS mRNA levels being similar to MYB33:GUS , these MYB33-1 cm:GUS and MYB33-FM:GUS mRNA must be translated more efficiently , as GUS activity in these lines are approximately three fold higher than in MYB33:GUS lines ( Figure 11G ) . By contrast , the much higher MYB33-2 cm:GUS mRNA levels did not translate into dramatically higher GUS activity when compared to GUS activity in the MYB33-1 cm:GUS and MYB33-FM:GUS lines ( Figure 11G ) , which again supports the existence of a strong non-cleavage silencing mechanism ( Figure 2A ) . Separately , in situ GUS staining was performed on over a hundred of primary transformants for each construct . The results were consistent with the MUG assays , with MYB33-1 cm:GUS MYB33-2 cm:GUS and MYB33-FM-GUS all having stronger staining than that of MYB33:GUS ( Figure 11A–E ) . Therefore , the nucleotides that flank the miR159 binding site appear just as necessary for strong silencing of MYB33 as the central nucleotides of its miR159 binding site . Together , these data argues that a miRNA “target” site encompasses not only the miRNA binding site , but the sequence context with which it is in .
It is generally assumed that central matches are required for cleavage , and this is a prerequisite for a strong silencing outcome [36] . However , we functionally demonstrate in planta that miRNAs can potently silence target genes with two central mismatches . This directly challenges the empirical parameters of sequence requirements for miRNA-mediated gene silencing , which state there should be no mismatches at positions 10 and 11 of the miRNA-target gene pair [5] . There have been reports of single central mismatches at these positions in naturally occurring miRNA-target pairs . Firstly , degradome signatures have found such miRNAs that can cleave target mRNAs; however the strength of the silencing imposed or the functional outcome of these central mismatches is unknown [37] . Secondly , miR390 that can trigger tasiRNA production has a binding site which includes a single central mismatch , but it is unknown whether the miRNA can cleave its target in vivo [38] . However , it is clear that for strong regulation to occur under the scenario of central mismatches , the miRNA:target stoichiometric ratio must strongly favour the miRNA . Although we show that perfect central complementarity is not mandatory for miR159-guided cleavage , the introduction of central mismatches does attenuate cleavage . Firstly , the average steady state mRNA levels of MYB33 and MYB65 were elevated in MIR159a1 and MIR159a2 plants ( Figure 3 ) , where the ratio of un-cleaved MYB33 transcripts to 3′ end cleavage products increased with the number of central mismatches ( Figure 4 ) . Secondly , the sequencing profiles of the 5′-RACE products from MYB33-1 cm and MYB33-2 cm plants imply that the cleavage of these centrally mismatched MYB33 transgenes is attenuated ( Figure 8 ) . This is consistent with a large body of data that perfect central complementary promotes cleavage , whereas central mismatches compromise cleavage [3] . In fact , the Arabidopsis transcriptome has many mRNAs with potential miRNA binding sites with mismatches at positions 10 and 11 , most of which have not been annotated as miRNA targets [32] . As one of the best-studied canonical miRNA targets being cleaved by miR159 and characterised by a strong degradome signature [8] , [9] , here we show that MYB33 is also efficiently silenced by a non-cleavage mechanism . In the case of MYB33 ( Figure 5 ) or MIR159a2 ( Figure 1–2 ) transgenic plants where MYB33 mRNA levels are high , this mechanism becomes more apparent , where it suppresses MYB33 expression to a phenotypically inconsequential level . This argues that cleavage is limiting to some degree , and this non-cleavage mechanism acts in combination to ensure silencing of un-cleaved transcripts . It is probable that this non-cleavage mechanism corresponds to mechanism commonly referred to as translational repression , the phenomenon where target protein accumulation is inconsistent with its transcript level due to miRNA regulation . The repression of MYB33 at both the transcript and translational level is consistent with the proposal that most known plant miRNAs regulate their targets using both mechanisms [3] , [18] . Indeed , most targets which are claimed to be regulated primarily at the translational level also undergo cleavage , where the compositions of the degradome signatures for these “translationally repressed” targets appear indistinguishable from miRNA targets that appear to be predominantly regulated by cleavage [8] , [9] It has been hypothesised that cleavage could be a mechanism that results in the fast targeted and irreversible clearing of regulatory mRNAs [14] , which in terms of expression would result in a switch outcome [19] . Although miR159 totally silences MYB33/MYB65 , it is obvious that miR159 does not clear MYB33 transcripts , especially in the MYB33 transgenic plants . However , miR159 repression of MYB33 via this non-cleavage “translational repression” mechanism appears potent , where it seems to be having a “switch” effect . This is in contrast to “translational repression” in animals , which has been proposed to dampen or tune translation to obtain the desired level of protein synthesis [19] . Although sequence complementarity is generally the sole factor considered when predicting plant miRNA-mediated silencing , here we show there are other strong contributing factors for the silencing outcome . Firstly , we demonstrate that the silencing outcome can be strongly influenced by the stoichiometric ratio of miRNA to target , a factor that has been largely ignored in plants to date . By transgenically altering the relative abundance of either MYB33 target or miR159 in the variant plants , we could change the silencing outcome independent of complementarity . It appears that the more inefficient a miRNA–target interaction is , the more important stoichiometry becomes . For instance , a combination of high target concentration together with mismatches at the cleavage site resulted in a poor silencing efficacy . This is consistent with previous findings in animals , where a target with central mismatches can saturate the miRNA at a lower concentration than a perfect matched target [33] , [34] . This is because the miRISC catalytic rate is slowed down by central mismatches , therefore sequestering the miRNA from further rounds of silencing [35] . As miRISC recycling would make the silencing process more energy efficient , this could be a strong selective driver for perfect central matches in miRNA-target duplexes , which the vast majority of bona fide miRNA-target pairs contain [5] . We have clearly demonstrated that nucleotides flanking a miRNA binding site can impact the efficacy of miRNA-mediated silencing . Based on the facts that the vast majority of MYB33-FM plants had developmental defects and the GUS expression level of MYB33-FM:GUS was similar to MYB33-2 cm:GUS , these flanking nucleotides clearly have a large impact on the efficacy of miR159-mediated silencing . This provides the first evidence in plants that the sequence in which a miRNA binding site is embedded plays a critical role in miRNA-target interactions , possibly affecting the silencing outcome comparably to nucleotides within the binding site itself . We speculate that these flanking nucleotides provide a favourable context for recognition of the MYB33 mRNA by miR159 , with important consequences for the efficacy and specificity of their interaction . We have previously shown that miR159 is functionally specific for MYB33 and MYB65 despite bioinformatics predicting approximately 20 genes to be miR159 regulated in Arabidopsis [21] . It is possible that the context of the miR159 binding site in MYB33/MYB65 denotes them as sensitive targets to miR159 regulation , and thereby is a strong contributing factor to this narrow miR159 functional specificity . This could have broad implications for miRNA target predictions and artificial miRNA design , both of which are solely based on sequence complementarity , but in which contextual features appear to have a strong impact on the silencing outcome [39] , [40] . Furthermore , we have recently shown that the efficacy of miR159 in silencing MYB33 is tissue specific , where it has much weaker efficacy in the seed relative to the rosette [41] . As the miR159-MYB33 relationship has been conserved for many millions of years , it is conceivable that higher orders of regulation have arisen during this time . Possible factors , such as RNA secondary structures and RNA binding proteins have proven to play a critical role in both specificity and regulation of the silencing outcome in animals [42]–[44] . However , to date these features have been virtually ignored in plants . We believe our findings warrant attention to these possibilities . A molecular model for plant miRNA-mediated target recognition and subsequent silencing is proposed to explain our results ( Figure 12 ) . The nascent target transcripts are recognized and bound by the miRNA-loaded RISC ( miRISCs ) . The ability/efficiency of the miRISCs to recognize target mRNA is influenced by at least three factors: the miRNA binding site complementarity , the target mRNA structure/accessibility ( which together make the miRNA target site ) , and the relative concentration of target mRNA to the miRNA , which becomes increasingly important when cleavage , or recognition is attenuated . Inefficient target recognition can be caused by negative changes to any of the above factors solely , or combinatorially , and lead to a prolonged duration before the target gets bound by the miRISCs enabling translation . If the target mRNA gets recognized and bound by the miRISC , it becomes immediately non-translatable and hence silenced . Cleavage occurs from this pool of miRISC-mRNA complexes , the half-life of which is determined by the cleavage efficiency , which is strongly affected by the complementarity of the miRNA–target duplex , especially at positions 10 and 11 . As it has been shown that miRISCs can direct multiple rounds of mRNA cleavage in vitro [45] , [46] , a major outcome of this event is not only to irreversibly destroy the target transcript but to recycle the miRISC so that it may participate in further target silencing . Of course additional experiments will be needed to determine how accurate this model is .
Arabidopsis thaliana ecotype Columbia-0 ( Col-0 ) was used in all experiments and is referred to as wild type . The myb33 mutant used is in ecotype Col-6 with a glaborous1 background mutation which has no trichomes [30] and the mir159ab mutant is as previously described [20] . Plants were grown on soil ( Debco Plugger soil mixed with Osmocote Extra Mini fertilizer at 3 . 5 g/L ) either under long day conditions ( 16 hr light/8 hr dark , 150 µmol/m2/sec at 22°C ) , or under short-day conditions ( 12 hr light/12 hr dark at 150 µmol/m2/sec at 22°C ) . For complementation of mir159ab , a 3642 bp genomic MIR159a ( AT1G73687 ) fragment including the miR159 stem loop region and its extensive 5′ and 3′ flanking sequences , was PCR amplified from Arabidopsis genomic DNA with primers containing attB sites , and sub-cloned into the Gateway donor vector pDONOR/ZEO ( Invitrogen ) by performing BP reaction using BP Clonase II enzyme mix ( Invitrogen ) . A mutagenesis approach based on Liu and Naismith [47] was then used to generate entry vectors for miR159a0 , miR159a1 , miR159a2 , miR159a3 and miR159a4 variants . Two pairs of primers were designed for each variant to mutate the corresponding miR159a* and miR159a sequences , by performing PCR on 100 ng of MIR159a entry vector , following the setting of 1 cycle of 98°C for 2 min , 20 cycles of 98°C/10 sec , 55°C/30 sec and 72°C for 30 sec/kb extension time , finished with 1 cycle of 55°C/5 min , 72°C/10 min . Each pair of forward and reverse primers contained non-overlapping sequence at 3′ end and primer-primer complementary sequences at the 5′ end , to minimize primer dimerization and enable primers to use the PCR product as template . The non-overlapping sequences were larger than the complementary sequence and had a 5–10°C higher melting temperature . The mutations were placed in both the complementary region and non-overlapping region . The subsequent PCR product was digested with 2 µl DpnI enzyme at 37°C for five hours and purified using Wizard SV Gel and PCR Clean-Up System ( Promega ) , and transformed into E . coli Alpha-Select Gold Efficiency competent cells ( Bioline ) . All entry vectors were confirmed by diagnostic restriction enzyme digestion and sequencing , and were subjected to LR reaction with pMDC100 vector [48] to generate the corresponding binary vectors . For MYB33 variants , a 4356 bp genomic fragment of MYB33 ( AT5G06100 ) , containing identical genomic elements to MYB33:GUS [30] , was amplified by PCR from genomic DNA and sub-cloned into pDONOR/ZEO ( Invitrogen ) by BP reaction . This contained 1991 bp of genomic sequence upstream of the MYB33 start codon , the whole MYB33 coding region , and 585 bp of sequences 3′ of the MYB33 stop codon . The subsequent MYB33 entry vector was mutated using the same strategy described above , to generate the MYB33-0 cm , MYB33-1 cm , MYB33-2 cm , and the MYB33-FM entry vectors respectively . For the mMYB33 construct , the mutagenesis was performed on a MYB33 entry vector with a 24 bp Strep-II tag coding sequence inserted in front of the stop codon . After confirmation by diagnostic restriction enzyme digestion and sequencing , all these entry vectors were subjected to LR reaction with pMDC123 vector [48] to generate the corresponding binary vectors . The MYB33:GUS and mMYB33:GUS constructs were generated previously [30] . For the generation of MYB33-1 cm:GUS , MYB33-2 cm:GUS and MYB33-FM:GUS translational fusions , the GUS gene was cleaved out of the MYB33:GUS construct with NcoI and ligated into the NcoI site of MYB33-1 cm , MYB33-2 cm and MYB33-FM , respectively . This resulted in the GUS gene being fused in frame to the coding region of MYB33-1 cm , MYB33-2 cm and MYB33-FM , respectively , 55 amino acids from the end of the gene . A clone containing the GUS gene in the right orientation and with the correct sequence was identified for each construct . All expression vectors were transformed into Agrobacterium tumefaciens strain GV3101 by electroporation [49] . Using the floral dip method [50] , MIR159a and its five variant constructs were transformed into the mir159ab mutant; while all MYB33-related constructs were transformed into myb33 . Phusion High Fidelity DNA Polymerase ( Finnzymes ) was used in all PCR reactions following the standard protocol provided by the manufacturer unless stated elsewhere . All primers used were listed in supplementary file , Table S2 . TRIzol ( Invitrogen ) was used for RNA extraction of tissues from plants at different growth stages . The extraction procedure was carried out as per manufacturer's instructions except the following modifications: ( 1 ) Approximately 500 mg of plant material was used with 1 mL of Trizol reagent for each extraction; ( 2 ) Homogenization of tissues was carried out using a mortar and pestle; ( 3 ) The chloroform extraction step was repeated once; ( 4 ) Precipitation of RNA was carried out overnight at −20°C to maximize the recovery of small RNAs . RQ1 RNase-Free DNase ( Promega ) was used to treat RNA samples for qRT-PCR , except those for Taqman sRNA assays . 30–50 µg of total RNA was treated for each sample in a 100 µL reaction volume following the protocol provided , with the addition of RNaseOutRecobinant RNase Inhibitor ( Invitrogen ) at a concentration of 1 µL/10 µg RNA . Treated RNA was then purified using Spectrum Plant Total RNA Kit ( Sigma Aldrich ) following instructions from the manufacturer's manual . cDNA synthesis was carried out using SuperScript III Reverse Transcriptase ( Invitrogen ) and an oligo dT primer according to manufacturer's protocol . For each sample , 250 ng - 5 µg of total RNA was used . The 20 µL reaction was then diluted 50 times in nuclease free distilled water and used for subsequent qRT-PCR . For qRT-PCR , Platinum Taq DNA Polymerase ( Invitrogen ) with SYB Green ( Sigma ) and dNTPs ( Fisher Biotec ) added was used as a master mix . 10 µL of each cDNA sample was added to 9 . 6 µL of SYB/Taq master mix with 0 . 4 µL of forward and reverse primers at 10 µmol each . All qRT-PCR reactions ( for both reference and genes of interests ) were carried out on a Rotor-Gene Q real time PCR machine ( QIAGEN ) in triplicate , under the following cycling conditions: 1 cycle of 95°C/5 min , 45 cycles of 95°C/15 sec , 60°C/15 sec , 72°C/20 sec . Fluorescence was acquired at the 72°C step . A 55°C to 99°C melting cycle was then carried out . CYCLOPHILIN 5 ( At2g29960 ) was used to normalise mRNA levels using the comparative quantitation program in the Rotor-Gene Q software package provided by QIAGEN . The value for each gene represents the average of triplicate assays . Customized Taqman sRNA assays ( Applied Biosystem ) were used to quantitate the mature miR159a variants following protocols described by Allen et al . , ( 2010 ) . Each cDNA sample was assayed in triplicate using a Rotor-Gene Q real time PCR machine ( QIAGEN ) under the following cycling conditions: 1 cycle of 95°C/5 min , 45 cycles of 95°C/15 sec , 60°C/15 sec , 72°C/20 sec . Fluorescence was acquired at the 72°C step . Expression of all miR159a variants were normalized with sno101 using the comparative concentration analysis program from Rotor-Gene Q software ( QIAGEN ) . The specificity of the assay was tested by performing miR159a2 assays on RNA from mir159ab , MIR159a0 and MIR159a1 plants , and only background signals were detected ( data not shown ) . In situ GUS staining was performed on 8-day-old seedlings using the method previously described [30] with the following modifications: seedlings were collected and fixed with 90% acetone for 20 minutes at room temperature , followed by a wash with GUS staining buffer containing 50 mM Na phosphate buffer , pH 7 . 2 , 0 . 2% Triton X-100 , 2 mM potassium ferricyanide and 2 mM potassium ferrocyanide . Histochemical reactions were performed with 2 mM X-Gluc ( 5-bromo-4-chloro-3-indolyl-β-D-glucuronide ) in GUS staining buffer at 37°C overnight . All fixative and substrate solutions were introduced into the plants with a 10–15 min vacuum infiltration . Plants were cleared with 70% ethanol for easy GUS observation . MUG assays were performed using 12-day-old seedlings . 50–100 seedlings were ground into a fine powder in liquid N2 using a mortar and pestle and homogenized with 450 µl of GUS extraction buffer ( 0 . 5 M NAPO4 , 0 . 5 M EDTA , 10% SDS , 10% Triton X-100 , 1 M DTT ) followed by centrifugation at 4°C for 10 min at 12 , 000 rpm . Subsequent supernatant containing total protein was transferred into a new microfuge tube and the protein concentration was determined by a standard Bradford assay . A GUS fluorometric assay was prepared in a microtitre plate using 20 µg of protein , 90 µl of GUS Assay buffer ( 1 mM 4-methylumbelliferyl-β-d-glucuronic acid ( MUG ) in GUS Extraction buffer ) and GUS Extraction buffer to a final volume of 150 µl . GUS activity was determined by measuring fluorescence of 4-methylumbelliferone ( 4-MU ) using a Fluostar Fluorometer . Successive fluorescence readings were determined at a wavelength of 355/460 nm over a two hours interval . GUS activity was determined from a set of 4-MU standards and expressed in nmols 4-MU/min/mg protein . RNA was extracted from either inflorescences or rosettes , and treated with DNaseI and purified as described above . A GeneRacer Kit ( Invitrogen ) was used to ligate 5 µg of the total purified RNA directly to 1 µg of the RNA oligo adapter provided , without carrying out the de-capping procedure described in the manual . After a one hour incubation at 37°C , the ligation mixture was diluted with 90 µL nuclease free distilled water , and 100 µL of phenol∶chloroform was added and was then vortexed vigorously . The aqueous phase was recovered by centrifugation at 14 , 680 rpm at room temperature for 5 min , and precipitated with 2 µL 10 mg/mL mussel glycogen , 10 µl 3 M sodium acetate pH 5 . 2 and 220 µL 95% ethanol overnight at −20°C . The RNA was pelleted by centrifugation at 14 , 680 rpm for 20 min at 4°C . After one wash with 70% ethanol , the pellet was dried and resuspended in 11 µL water . 1 µL of ligated RNA was analysed on a 1% agarose gel by electrophoresis . The remaining 10 µL was retro-transcribed in a 20 µL reaction . The cDNA synthesized ( 20 µL ) was diluted 25 times , and 25 µL of this diluted cDNA was used in a 50 µL PCR reaction with nested GeneRacer oligo-specific and MYB33-specific primer . PCR was carried out using Platinum Taq DNA Polymerase ( Invitrogen ) using the setting of 1 cycle of 94°C/2 min; 30 cycles of 95°C/30 sec , 60°C/30 sec , 72°C/1–2 min; 1 cycle of 72°C for 5 min . The PCR products obtained were purified using Wizard SV Gel and PCR Clean-Up System ( Promega ) and sequenced using a MYB33 specific primer downstream the miR159 cleavage site . | In plants , microRNAs ( miRNAs ) are critical regulators of gene expression . As most validated targets are of high complementarity , whose transcripts are cleaved by the miRNA , both complementarity and cleavage are thought to be the major factors determining the degree to which a target gene is silenced . Here , we explore this principle utilizing the highly conserved miR159-MYB33/MYB65 regulatory module in the model flowering plant Arabidopsis . Firstly , we demonstrate that perfect central complementarity facilitates efficient transcript cleavage but is not required for a strong silencing outcome , as miR159 variants with two central mismatches can recognize and silence MYB33/MYB65 effectively in planta . Driving this silencing is a potent miR159-mediated non-cleavage mechanism that ensures total silencing even when MYB33 transcript levels are very high . Secondly , we demonstrate that the stoichiometric ratio of miRNA to target mRNA is a critical determinant of a silencing outcome , and that ratio becomes increasingly important for inefficient miRNA-target interactions . Finally , we show that nucleotides flanking the miR159 binding site of MYB33 are essential for efficient silencing , demonstrating that the sequence context in which the miRNA target site resides in has a major impact on the silencing outcome . Together , we have shown that although high complementarity underpinned by efficient transcript cleavage may be a prerequisite for a strong silencing outcome , many additional factors that modulate the strength of the miRNA-target interaction are at play . These findings will have ramifications for bioinformatics prediction of miRNA targets and design of artificial miRNAs . | [
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] | 2014 | Determinants beyond Both Complementarity and Cleavage Govern MicroR159 Efficacy in Arabidopsis |
The vast burden of cryptococcal meningitis occurs in immunosuppressed patients , driven by HIV , and is caused by Cryptococcus neoformans var . grubii . We previously reported cryptococcal meningitis in Vietnam arising atypically in HIV uninfected , apparently immunocompetent patients , caused by a single amplified fragment length polymorphism ( AFLP ) cluster of C . neoformans var . grubii ( VNIγ ) . This variant was less common in HIV infected individuals; it remains unclear why this lineage is associated with apparently immunocompetent patients . To study this host tropism we aimed to further our understanding of clinical phenotype and genomic variation within Vietnamese C . neoformans var . grubii . After performing MLST on C . neoformans clinical isolates we identified 14 sequence types ( STs ) ; ST5 correlated with the VNIγ cluster . We next compared clinical phenotype by lineage and found HIV infected patients with cryptococcal meningitis caused by ST5 organisms were significantly more likely to have lymphadenopathy ( 11% vs . 4% , p = 0 . 05 Fisher’s exact test ) and higher blood lymphocyte count ( median 0 . 76 versus 0 . 55 X109 cells/L , p = 0 . 001 , Kruskal-Wallis test ) . Furthermore , survivors of ST5 infections had evidence of worse disability outcomes at 70 days ( 72 . 7% ( 40/55 ) in ST5 infections versus 57 . 1% ( 52/91 ) non-ST5 infections ( OR 2 . 11 , 95%CI 1 . 01 to 4 . 41 ) , p = 0 . 046 ) . To further investigate the relationship between strain and disease phenotype we performed genome sequencing on eight Vietnamese C . neoformans var . grubii . Eight genome assemblies exhibited >99% nucleotide sequence identity and we identified 165 kbp of lineage specific to Vietnamese isolates . ST5 genomes harbored several strain specific regions , incorporating 19 annotated coding sequences and eight hypothetical proteins . These regions included a phenolic acid decarboxylase , a DEAD-box ATP-dependent RNA helicase 26 , oxoprolinases , a taurine catabolism dioxygenase , a zinc finger protein , membrane transport proteins and various drug transporters . Our work outlines the complexity of genomic pathogenicity in cryptococcal infections and identifies a number of gene candidates that may aid the disaggregation of the pathways associated with the pathogenesis of Cryptococcus neoformans var . grubii .
Cryptococcosis is a range of disseminated infections caused by yeasts belonging to the genus Cryptococcus . Cryptococcosis generally occurs in individuals with cell-mediated immune defects , particularly those infected with Human Immunodeficiency Virus ( HIV ) . As a result of this tropism for the immune-compromised , cryptococcal diseases are commonly fatal . Meningitis is the commonest and most severe disease manifestation , leading to an estimated 600 , 000 deaths from approximately a million cases per year , globally [1] . Human cryptococcal infections are caused almost exclusively by two species: Cryptococcus neoformans ( subdivided into 2 varietal forms , Cryptococcus neoformans var . grubii and C . neoformans var . neoformans ) , and Cryptococcus gattii [2] . The mechanisms that determine the disease prevalence of the cryptococcal species are unknown but associated with host and geographical factors [3] . C . gattii can be readily isolated from the environment in the tropics and subtropics , but despite comparatively high rates of HIV infection in these regions , human C . gattii disease is uncommon . C . neoformans var . neoformans is largely restricted to Western Europe , where it accounts for an estimated 25% of cases of cryptococcal meningitis in HIV infected patients [2 , 4] . In contrast to C . gattii and C . neoformans var . neoformans , C . neoformans var . grubii has a global distribution and a devastating impact on the immune-suppressed population [1 , 2] . Driven by the high prevalence of HIV infection , the overwhelming majority of cryptococcal meningitis cases occurs in the tropics and sub-tropics and is caused by C . neoformans . We previously reported a case series of HIV uninfected patients from Vietnam where the majority of patients did not have a recognized underlying immune suppressive disease [5] . Despite the apparent immune competence of the patients , we found that the majority of infections were caused by C . neoformans var . grubii . We subsequently demonstrated that most cases of disease in HIV uninfected patients in Vietnam were caused by a single amplified fragment length polymorphism ( AFLP ) defined cluster of C . neoformans var . grubii that we named VNIγ [6] . VNIγ was found to be responsible for 84% of the cases of C . neoformans var . grubii meningitis in HIV uninfected patients and 93% of cases of meningitis in apparently immunocompetent patients , but only 38% of disease in HIV seropositive patients [6] . Furthermore , in the HIV uninfected group , additional underlying diseases were more common in those with non-VNIγ ( VNIδ ) infections than in those with VNIγ infections . It is unclear why some C . neoformans lineages are associated with apparently immunocompetent patients . We hypothesize that the ability to cause disease in these individuals is dependent either on the exploitation of an unidentified immune deficit , or an increased pathogenic potential of the specific lineages . This study has two main aims . First , to determine whether infection with VNIγ strains results in a different clinical phenotype in HIV infected patients , and second to refine our genomic understanding of the variation between Vietnamese C . neoformans var . grubii lineages . We combine MLST profiling of C . neoformans var . grubii with clinical data to describe the phenotypic differences caused by this genotype in HIV infected patients [7] . We then perform genome sequencing and comparative genomics on eight C . neoformans var . grubii to determine the genetic loci that may facilitate an enhanced pathogenic phenotype .
All clinical studies were approved by the Hospital for Tropical Diseases Ethical Review Board , and either the Oxford Tropical Ethics Committee , or the ethics committee of the Liverpool School of Tropical Medicine . All patients , or their responsible next of kin , gave written informed consent to enter the study . All strains were clinical isolates from HIV infected and uninfected patients from the Hospital for Tropical Diseases , Ho Chi Minh City , Viet Nam enrolled into either a randomised controlled trial of antifungal therapy or a prospective descriptive study [5 , 8] . All patients , or their responsible next of kin , gave written informed consent to enter the study . All studies were approved by the Hospital for Tropical Diseases Ethical Review Board , and either the Oxford Tropical Ethics Committee , or the ethics committee of the Liverpool School of Tropical Medicine . All strains were identified using classical mycological methods , sugar assimilation tests ( API 32C , BioMerieux , France ) and were confirmed as C . neoformans var . grubii molecular group VNI using URA5 RFLP as previously described [9] . Fifty-one strains from HIV uninfected patients were collected between 1996 and 2009 . 37 of these were C . neoformans var . grubii , all molecular group VNI . 14 strains were C . gattii , 13 molecular group VG1 and 1 VG II . The clinical characteristics of the HIV uninfected patients have been published previously—underlying potentially immunosuppressive disease was present in 11 patients [5] . The results of AFLP typing of these strains have been reported previously [6] . The strains from HIV infected patients were the same as those for which we have previously reported AFLP typing . These 100 strains had been randomly selected from the baseline isolates of 238 HIV infected patients who were enrolled into a randomized controlled trial of antifungal therapy in cryptococcal meningitis by 2009 [8] . All 299 strains isolated from patients enrolled in the trial underwent pyrosequencing of 3 MLST loci to divide them into the VNIγ ( ST5 ) versus non-VNIγ lineage . Control strains ( C . neoformans var . grubii URA5 RFLP types VNI and VNII ) were kindly provided by Associate Professor Wieland Meyer , Westmead Millennium Institute for Medical Research , Sydney , Australia . Colonies were revived on Sabouraud’s agar at 30°C for 72 h . Single colonies were spread for confluent growth and incubated at 30C for 24 h . Chromosomal DNA was extracted from approximately 0 . 5 g ( wet weight ) of yeast cells according to the method described by Wen et al . [10] . The DNA pellet was resuspended in 100 uL of Tris-EDTA ( TE ) buffer containing 100 ug of RNase . RFLP analysis of the URA5 gene was carried out according to the methods of Meyer et al . [9] . The final product was separated by electrophoresis on a 3% agarose gel at 100 V for 3 h . RFLP patterns were assigned visually by comparison with known standards . Multi-locus sequence typing was performed according to the ISHAM consensus MLST scheme for the C . neoformans/C . gattii species complex [7] . The seven loci sequenced were: capsule polysaccharide ( CAP59 ) , glycerol 3-phosphate dehydrogenase , ( GPD1 ) , laccase ( LAC1 ) , phospholipase B1 ( PLB1 ) , superoxide dismutase ( SOD1 ) , orotidine monophosphate pyrophosphorylase ( URA5 ) genes and the intergenic spacer ( IGS1 ) region . PCR primers and thermocycling conditions can be found on the ISHAM website ( http://mlst . mycologylab . org ) . Sequencing was carried out using a 3130xL Genetic Analyzer ( ABI ) . The seven individual loci sequences from the 136 C . neoformans var . grubii strains were concatenated ( 4 , 407bp ) and aligned to identify 24 polymorphic sites . Allele ( AT ) and sequence types ( ST ) were determined using pairwise alignment through the ISHAM Cryptococcus neoformans MLST database ( mlst . mycologylab . org ) . VectorNTI ( Thermo Fisher , MA , USA ) was used for multiple alignments , Bionumerics v7 ( Applied Maths , Belgium ) with MLST add-in was used for MLST phylogenetic analysis , Bioedit [11] and FigTree ( http://tree . bio . ed . ac . uk/software/figtree/ ) were used for building phylogenetic trees . Phyloviz with the geoBurst algorithm was used to interrogate sequence type structure [12] . TAR1 internal PCR was carried out using primer pairs 5’-CACGAATTGGGACAGGAAGT-3’ and 5’-GAAGAGAAGGAGGCGGAACT-3’ ( for further details see Supporting Information S1 Methods ) . In order to determine the integrity of the VNIγ genotype genomic DNA , amplification of VNIγ-specific DNA ( s_1_scaffold4325 ) was carried out using primer pairs 5’-ATATCAATCGTCGCCTGCTC-3’ and 5’-TTTCTTGGGTTGAGGGTCAG-3’ . DNA from the MLST alleles IGS1 , GPD1 and URA5 were PCR amplified using biotinylated primer pairs targeting the region containing the SNP distinguishing the ST5 genotype . PCR amplifications were performed in 60 μl reactions containing 1 × Hotstart PCR buffer , 1 . 5 mM of MgCl2 , 200 μM of dNTP , 10 pM of each primer , 1 . 25 Units of Hotstart DNA polymerase ( Qiagen , USA ) and 3 μl of template DNA . Reactions were cycled once at 95°C for 15 min , followed by 30 cycles of 94°C for 1 min , 60°C for 1 min and 72°C for 1 min , with a final elongation of 72°C for 10 min . All PCR amplifications were visualized on 2% agarose gels prior to pyrosequencing . A pyromark Q96 ID DNA pyrosequencer ( Biotage , Sweden ) was used to detect the SNPs of interest in each allele as per the manufacturer’s recommendations . PCR amplicons were combined with 56 μl of binding buffer and 4 μl of streptavidin sepharose beads . The resulting mixture was agitated for 5 min before denaturation in denaturation buffer and washing with the Vacuum Prep Tool ( Biotage , Sweden ) . DNA fragments were transferred into a 96-well plate containing 3 . 5 pmol of sequencing primer in 40 μl of annealing buffer and the DNA sequencing reaction was performed using the Pyro Gold Kit ( Biotage , Sweden ) . The SNP detection mode in the software PyromarkID v1 . 0 ( Biotage , Sweden ) was used to analyze sequence data . All data were from a published randomized controlled trial of combination antifungal therapy for cryptococcal meningitis , Controlled-Trials . com number , ISRCTN95123928 [8] . All patients from the trial with available infecting isolate genotype information were included . Baseline variables ( present at study entry ) as described in the trial paper were summarized by genotype and compared between the two groups using the Mann-Whitney U test and Kruskal-Wallis test for continuous data and Fisher’s exact test for categorical data . Early Fungal Activity ( EFA ) , i . e . the rate of clearance of yeast colony forming unit counts from CSF during the first 14 days of antifungal therapy , was estimated in each arm with a mixed effects model of log10-transformed longitudinal fungal count measurements . Comparisons of EFA between ST5 versus non-ST5 were based on a mixed effects model with the fixed intercept term depending on the genotype , the fixed slope term depending on genotype and treatment assignment , and random intercept and slope terms . A Cox regression model was used to determine the impact of sequence type ( ST5 versus non-ST5 ) on survival until 10 weeks ( primary endpoint ) and six months following study randomisation after adjusting for treatment group , and after adjusting for treatment group , Glasgow coma score at baseline and log10 transformed CSF fungal burden ( log10 colony forming units/mL ) at baseline . The proportion of patients with disability ( defined using the Rankin score and two simple questions as described previously ) was compared amongst survivors using logistic regression adjusted for the treatment assignment [8] . Statistical analyses were performed with the use of R software , version 3 . 1 . 2 [13] . 1–3 μg genomic DNA from each clinical isolate was used to prepare Illumina paired-end sequencing libraries with Illumina Nextera , sequenced on a HiSeq2000 to deliver approximately 260X coverage The sequencing reads were then assembled by SOAPdenovo [14] and Opera [15] . Genotype-specific DNA sequences , which were found in all the genome assemblies of only one genotype , were identified by alignment using NUCmer [16] . Blastx analysis of the genotype-specific DNA sequences using the NCBI non-redundant protein sequences ( nr ) database or H99 protein database ( Cryptococcus neoformans var . grubii H99 Sequencing Project , Broad Institute of Harvard and MIT ( http://www . broadinstitute . org/ ) was carried out with a cut-off e-value of 1e-10 [17] . Protein homologs in the H99 genome were identified as proteins with BLASTx hits that had e-value > 1e-10 . BlastN analysis of the genotype-specific DNA scaffolds identified the location of these DNA sequences in the H99 genome assembly ( Broad Institute ) . Repeat elements were identified by RepeatMasker ( Smit , AFA , Hubley , R & Green , P . RepeatMasker Open-3 . 0 . ) using fungi retrotransposon database ( Repbase 17 . 01 ) [18] . SNPs were identified using a combination of Stampy and SOAP analysis using C . neoformans var grubii H99 reference genome ( 22 SNPs identified by this strategy were verified to be 100% accurate by Sanger sequencing ) . Indels were identified by Stampy and quality filtered . The location of the SNPs and indels in the genome were predicted using snpEff [19 , 20] . For phylogenetic analysis data were mapped to the C . neoformans H99 reference genome ( NCBI accession: NC_026745 ) using BWA mem v 0 . 7 . 13 [21] , more than 95% of the genome had high quality mapping for each strain . SNPs were called with GATK v3 . 3 [22] and filtered with the following thresholds: minimum depth ≥ 5x , ≥ 90% consensus , GQ ≥ 30 . Genome positions that had a SNP passing quality thresholds in at least one strain were then extracted using snp-sites and RAxML v8 . 2 . 8 ( GTR GAMMA model and Lewis ascertainment correction ) used to derive a maximum likelihood tree [23] . Data are available at the European Nucleotide Archive ( http://www . ebi . ac . uk/ena ) under the study code PRJEB17690 .
Aiming to better define the apparent Vietnamese C . neoformans var . grubii VNIγ tropism for non-immune compromised individuals we performed MLST on seven loci ( CAP59 , LAC1 , PLB1 , GPD1 , SOD1 , URA5 and IGS1 ) on all available ( n = 38 ) C . neoformans var . grubii isolated from the cerebrospinal fluid ( CSF ) of HIV uninfected patients with cryptococcal meningitis at our hospital in Ho Chi Minh City ( HCMC ) in Vietnam [7] . We additionally performed MLST on 96 randomly selected C . neoformans var . grubii strains isolated from HIV infected patients attending the same hospital over the same time period . Two additional reference strains ( WM148 ( VN1 ) and WM626 ( VN2 ) ) were included; all 136 isolates are described in S1 Table . The seven loci ( 4 , 407 bp in total ) from the C . neoformans var . grubii contained 24 polymorphic sites ( five singletons ) . We next determined the allele type ( AT ) and sequence type ( ST ) , identifying 19 independent ATs , which resulted in 14 STs ( S1 Table ) . ST4 ( n = 32 ) , ST5 ( n = 65 ) and ST6 ( n = 12 ) were the most commonly identified . We found two previously unreported ATs at the CAP59 and PLB1 loci in strains BMD1367 and BK55 , respectively . Of the 14 STs , five were novel ( ST306 , ST337 , ST338 , ST339 , and ST340 ) . Each novel ST was comprised of a single strain , four originating from HIV infected patients and one from an HIV uninfected patient . Fig 1 shows a minimum spanning tree of the 14 detected STs and their relative distribution between HIV infected and HIV uninfected patients . We found that all ST5 strains precisely correlated with our previously identified AFLP cluster , VNIγ . However , one previously unreported ST ( ST337 ) , and one of the eight ST93 strains , was formerly in the VNIγ AFLP cluster . After stratification by HIV status , we found that organisms belonging to ST5 ( previously VN1γ ) were responsible for 82% ( 31/38 ) of the cryptococcal meningitis cases in HIV uninfected patients and 35% ( 34/98 ) of the cryptococcal meningitis cases in HIV infected patients ( OR 8 . 2 , 95% CI 3 . 1 to 24 . 5 , p<0 . 001 , Fisher’s exact test ) . C . neoformans ST5 were significantly associated with the immune status of the patient suggesting they may have a different pathogenic potential . Therefore , we aimed to determine whether this ability to infect apparently immune competent patients was associated with any additional ability to affect disease presentation or outcome . As clinical disease phenotype in patients with cryptococcal meningitis is confounded by HIV status we compared the clinical phenotypes of ST5 C . neoformans var . grubii infections against non-ST5 C . neoformans var . grubii in HIV infected individuals only . Using four Single Nucleotide Polymorphisms ( SNPs ) and two insertion/deletion ( indel ) sequences within the IGS1 , URA5 and SOD1 loci we further screened 290 C . neoformans var . grubii from HIV infected patients enrolled into an anti-fungal RCT in Vietnam [8] . One hundred and three ( 35 . 6% ) of the C . neoformans var . grubii from this cohort were inferred to be ST5 , or within the ST5 complex . The remaining 187 ( 64 . 4% ) isolates were categorized as non-ST5 strains . The clinical characteristics of patients stratified by ST5 and non-ST5 infecting strains are shown in Table 1 . The baseline characteristics between HIV patients infected with ST5 versus non-ST5 organisms were largely similar . However , HIV infected patients with cryptococcal meningitis caused by ST5 organisms were significantly more likely to have lymphadenopathy ( 11% vs . 4% , p = 0 . 05 Fisher’s exact test ) and a higher blood lymphocyte count ( median 0 . 76 versus 0 . 55 X109 cells/L , p = 0 . 001 , Kruskal-Wallis test ) . CD4 counts tended to be higher in ST5 infected patients , but this was not of statistically significance ( median 22 vs . 14 . 5 cells/uL , p = 0 . 053 Kruskal-Wallis test ) . Conversely , ST5 infected individuals were less likely to have fungi isolated from blood ( 61 . 9% vs . 79 . 8% , p = 0 . 02 Kruskal-Wallis test ) and had lower yeast burdens in CSF at baseline ( median 5 . 4 log10 CFU/ml vs . 5 . 9 log10 CFU/ml , p<0 . 01 Kruskal-Wallis test ) . There was no difference in the rate with which yeast was cleared from CSF , defined as early Fungicidal Activity ( EFA ) , by genotype ( Table 1 ) . We next investigated the role of ST on disease outcome; the survival curves for cryptococcal infection were similar between the infecting STs ( Fig 2 ) . Formal Cox regression analysis ( adjusted for treatment assignment ) further demonstrated no significant differences in survival up to 70 days or six months post-randomization between patients infected with ST5 or non-ST5 organisms ( Hazard Ratio ( HR ) 0 . 94 ( 95%CI 0 . 62 to 1 . 44 ) , p = 0 . 72 , and HR 0 . 90 ( 95%CI 0 . 62 to 1 . 31 ) , p = 0 . 57 , respectively ) . These findings were unchanged after further adjustments for baseline fungal burden and Glasgow Coma Score ( GCS ) . However , survivors of ST5 C . neoformans infections had a borderline significant increased rate of disability at 70 days ( 72 . 7% ( 40/55 ) in ST5 infections versus 57 . 1% ( 52/91 ) with non-ST5 infections , ( OR 2 . 11 ( 95%CI 1 . 01 , 4 . 41 ) , p = 0 . 046 ) . Our results demonstrated that Vietnamese ST5 C . neoformans organisms are preferentially isolated from HIV non-infected individuals and may induce differing meningitis phenotypes in HIV infected patients . These data suggest that lineage specific genetic loci may be associated with host immune status and/or pathogenicity . To test this hypothesis we selected eight Vietnamese ( VN ) C . neoformans var . grubii for whole genome sequencing ( WGS ) . The characteristics of the eight strains selected for WGS and the sequencing statistics are shown in Table 2 . The selected organisms comprised five ST5 organisms , two ST4 organisms and one ST306 organism and were isolated from both HIV infected and uninfected individuals . The sequencing reads from each of the eight isolates were individually assembled into ~18 . 3 Mb draft genome assemblies comprising the 14 chromosomes of C . neoformans var . grubii . The assembled sequences had a median scaffold length of 119 kbp with a maximum scaffold length of 464 kbp . The eight draft genome assemblies exhibited approximately 99 . 4% nucleotide sequence identity with the C . neoformans var . grubii H99 reference sequence . We additionally identified 165 kbp of sequence ( 0 . 9% of genome assembly , in fragments >500 bp ) specific to either H99 or the VN isolates ( Table 3 ) , signifying that the VN organisms were more closely related to each another than to H99 . Chromosomal divergence between the eight Vietnamese isolates and the H99 reference strain is illustrated in S1 Fig . The absence of genetic rearrangements on chromosome 3 and chromosome 11 in VN isolates , in contrast to H99 [24] , further suggests that the VN isolates share a more recent common ancestor than strain H99 . We next analyzed the mating-type locus in the VN isolates and found that all strains belonged to the most prevalent cryptococcus mating type , MATα . To infer the phylogenetic relationship between the Vietnamese ( VN ) isolates , using H99 as a reference , we identified SNPs and indels in the VN strains . We found a mean of 41 , 290 SNPs and 6 , 487 indels in each of the VN C . neoformans var . grubii genome assemblies in comparison to H99 , and 4 , 364 indels distinguishing ST5 from non-ST5 . SNPs were not evenly distributed throughout the genomes but varied with areas of distinct high frequencies , some shared between lineages and others uniquely associated with ST5 ( Fig 3 ) . A neighbor-joining tree confirmed that the ST5 and non-ST5 VN isolates were phylogenetically distinguishable and distantly related to the reference genome ( Fig 4 ) . We next performed comparative genomics to contrast the genome content of the VN C . neoformans var . grubii against H99 . The eight VN genomes exhibited approximately 99 . 7% nucleotide sequence identity , with between 45 kbp ( non-ST5 ) and 67 kbp ( ST5 ) of genotype-specific DNA sequence variation between these genotypes . This was approximately three times less than the extent of the sequence variation between H99 and the VN isolates . A blastn comparison demonstrated that VN lineage specific DNA was located on multiple chromosomes . Rearrangements and gene loss in Cryptococcus are associated with transposable elements and play an integral role in genomic architecture [25 , 26]; centromeres are “hotspots” for retrotransposons TCN1 and/or TCN6 [27] . We identified several retrotransposons and repeat elements within the sequences from the VN strains ( Table 4 ) . These elements were enriched in chromosomal regions proximal to the telomeres and centromeres . [26] . We found that repeat elements such as the retrotransposons Ty1/Copia and Ty3/Gypsy and the Harbinger interspersed repeat subfamily constituted almost 2% of the VN C . neoformans genome sequences . The majority of the repeat elements identified in the VN strains belonged to the Ty3/gypsy retrotransposons group ( Table 4 ) . Notably , with respect to lineage specific genetic composition , the percentage of repeat elements between ST5/non-ST5 was comparable , with the exception of MuDR DNA transposon sequences , which were found to be more than twice as common in the ST5 isolates ( average MuDR repeats; 170 vs . 69 in non-ST5 ) . To test our hypothesis of lineage-specific genetic variation accounting for phenotypic distinction in HIV/non-HIV infected patients we performed comparative genomics between the ST5 and the non-ST5 sequences . The ST5 and non-ST5 specific DNA sequences were de novo assembled; forming 45 contiguous sequences ( 25 in ST5 , 20 in non-ST5 ) ; these are described in Table 5 . These genotype specific sequences ranged from 0 . 5 to 20 kbp and contained between one and six predicted coding sequences . The 45 genotype specific regions were subjected to blastx/p to identify homologous and orthologous DNA and protein sequences . The majority of these genotype-specific sequences encoded cryptococcal proteins with multiple homologues across the C . neoformans var . grubii genome ( S2 Table ) . As many of these homologues were likely to share functional redundancy we focused on non-redundant lineage specific coding sequences for further investigation . The specific sequences found in the ST5 strains encoded 19 predicted proteins with a previously annotated function and eight hypothetical proteins . Notably , with the exception of a fungal-specific phenolic acid decarboxylase ( PAD ) previously identified in Meyerozyma guilliermondii , all ST5 genotype-specific sequences could be found in C . neoformans H99 [28] . ST5-specific genes encoding proteins potentially associated with virulence included a DEAD-box ATP-dependent RNA helicase 26 ( CNAG_07651 ) , oxoprolinases ( implicated in host colonization ) , a taurine catabolism dioxygenase ( stress resistance ) , a zinc finger protein , membrane transport proteins and drug transporters [29] . We additionally found that the ST5 specific phenolic acid decarboxylase specific gene ( PAD ) was located on a DNA scaffold that could be aligned to the telomeric region of chromosome 11 . This region was adjacent to a non-LTR retrotransposon Cnl1 ( C . neoformans LINE-1 ) encoding region; we conclude this was a likely insertion into the ST5 genome . The DNA sequence of the PAD gene was found to be sufficiently divergent ( blastx E-value = 3e-16 ) from that of other fungi PAD genes ( Meyerozyma guilliermondii PAD , sequence homology for 30 of the 168 aa ) and overall GC content of the scaffold did not differ substantially from that of the overall genome sequence . Taken together these data suggest that this insertion event was not recent [28] . We confirmed the presence of PAD in a publically available ST5 strain from South Africa ( SRA sequence accession ERR1810411 ) . In the context of the Vietnamese cohort in which they were isolated non-ST5 specific sequences potentially represent ‘anti-virulence’ encoding regions , since they are not present in ST5 organisms [30] . The non-ST5 specific DNA sequences ( n = 20 ) included regions encoding three hypothetical proteins ( CNAG_07666 , CNAG_00127 and CNAG_07313 ) , a sugar transporter ( CNAG_06527 ) , a heat shock protein and TAR1 ( temperature associated repressor gene; CNAG_04934 ) [31 , 32] . CNAG_07666 was found to contain a domain related to the CAP ( cysteine-rich secretory proteins , antigen 5 , and pathogenesis-related 1 proteins ) family and exhibited homology to the pathogenesis-related protein , Pr-1 . However , atypically for proteins within the CAP family , CNAG_07666 was found to have only a single cysteine residue , and at 16 kDa was predicted to be only half the size of other previously described crypotococcal Pr-1 type proteins [33] . CNAG_00127 was predicted to encode a hypothetical protein with no known homologues and no previously described protein domains or motifs . The region encoding the CNAG_00127 protein was partially deleted in all of the sequenced ST5 isolates , and the proportion of gene deleted across the different ST5 isolates was variable [31] . TAR1 inhibits the expression of melanin at 37°C , and has previously been shown to be a potential virulence factor for Cryptococcus [32] . We confirmed the presence of TAR1 encoding sequences in the genome assemblies of non-ST5 isolates by blastn and blastx ( sequences exhibited 100% DNA sequence identity with strain H99 ) . Temperature dependent gene expression may represent an advantageous host adaptation and therefore such genes are potentially associated with virulence . Two further regions encoding enzymes with temperature dependent expression ( allantoate permease CNAG_06875 ( ST5-specific ) and aldo-keto reductase CNAG_01257 ( non-ST5 specific ) ) were also found to be genotype specific [34] . We additionally found that a region encoding a sodium/bile acid cotransporter ( CNAG_01461 ) protein was partially truncated in the non-ST5 strains ( 381 aa instead of 515 aa ) in comparison to the H99 reference . The majority of proteins encoded by genotype-specific DNA were predicted to have enzymatic function , but given their high sequence homology to other homologues and orthologues in the genome it was difficult to infer their significance and function in genetic variation . In addition to genotype-specific sequence , we identified a number of genotype-specific SNPs ( in relation to the H99 reference genome ) likely to significantly impact protein function because they resulted in premature truncation of translation ( Table 6 ) . In the ST5 strains these SNPs were found to be located in three previously identified virulence associated proteins; a cytosine-purine permease ( CNAG_04982 ) expressed during macrophage infection , a temperature dependent hypothetical protein ( CNAG_06731 ) and an Ire1protein kinase ( CNAG_03670 ) [35–37] . SNPs resulting in premature gene truncation occurred in a further 16 ST5-specific genes included those encoding a calcineurin-like phosphoesterase and nine hypothetical proteins . Similarly , a number of such SNPs were identified in the non-VNIg strains . Twenty-five genes were affected , four which have previously been identified as virulence determinants , including two expressed during macrophage infection ( CNAG_01464 and CNAG_01445 ) and two with temperature dependent ( ( 37°C ) expression ( CNAG_01257 and CNAG_03754 ) [35 , 36] . Thirteen of the 25 affected genes were conserved hypothetical proteins; a further three were predicted proteins .
Although cryptococcal meningitis due to infection with C . neoformans var . grubii is predominantly a disease of immunocompromised patients , disease in the apparently immunocompetent is increasingly recognized in Asia [5 , 38 , 39] . Our analyses demonstrate that our previously defined VNIγ cluster , responsible for the vast majority of disease in HIV uninfected patients in Vietnam , consists of a single MLST type ( ST5 ) , and that the divergence of this genotype from other Vietnamese strains is not recent [6] . In contrast , clinical isolates from HIV infected patients in Vietnam appear to be more diverse , with at least 14 different STs found to cause disease in this group . However , while we have demonstrated clear segregation of strains according to host immune phenotype , ST5 strains are also the single most frequent cause of disease in HIV infected patients , accounting for >30% of cases . The dominance of ST5 strains in HIV infected patients could be explained by increased abundance of this ST in the environment , leading to more exposure and opportunity for infection . Alternatively , this ST may have enhanced pathogenic potential compared with other STs , predicting it has a greater inherent capacity to cause infections in humans . In the latter case , its prevalence in the HIV infected population would be expected to be more common than its prevalence in the environment . The dominance of ST5 strains in immunocompetent patients , the relative low incidence of this disease , together with the low HIV prevalence in Vietnam ( 1% ) is consistent with a hypothesis of increased pathogenicity and low environmental prevalence . Systematic and sensitive randomized environmental sampling is needed to test this hypothesis . Despite the differential segregation of strains by host immune status , the clinical data from HIV patients does not suggest that ST5 strains are more virulent in this patient group . Although patients with ST5 infections tended to have lower levels of consciousness , which have previously been associated with worse outcomes , we did not find significant differences in rates of death , and only marginal differences in disability in survivors , by infecting genotype [8] . This finding is similar to a study from South Africa , which also did not identify statistically significant differences in outcome in HIV infected patients according to infecting sequence type [40] . Surprisingly , the burden of fungus in the CSF , which has previously been identified as an important prognostic factor , was significantly lower in patients infected with ST5 strains [41] . We found no difference in duration of symptoms between the ST5 induced infections and the non-ST5 infections . Therefore , the lower fungal burdens associated with ST5 infections are unlikely to be a consequence of earlier presentation of a more severe illness . An alternative interpretation is that ST5 strains are more ‘potent’ on a cell-by-cell basis , leading to similar clinical outcomes despite lower yeast burdens . Consistent with the previously identified immune segregation of strains , there was a trend towards higher CD4 counts in HIV patients infected with ST5 . Consequently , while it seems that , in HIV patients at least , the infecting genotype does not have a major impact on disease course , ST5 strains may have an advantage in either the colonization or invasion of hosts . Pathogenicity factors that confer these abilities are not well defined although we did identify differences in genes between strains that have previously been associated with macrophage infection . The genetic differences between ST5 and the other strains in genes encoding hypothetical and predicted proteins are intriguing prospects for further study in experimental models with respect to these qualities . Cryptococcal disease is not transmitted person to person , and humans are a dead-end host . Therefore , the drivers for genetic divergence must be related to C . neoformans’ ( as yet unidentified ) ecological niche in Vietnam . The ability to cause disease is thought to be a by-product of such adaptations—so called ‘bystander pathogenicity’ [6] . Recognized ecological niches for Cryptococcus species in other geographic locations include bird guano , soil , rotting wood and various tree species [2] . The presence of a novel phenolic acid decarboxylase ( PAD ) gene associated exclusively with the ST5 strains may provide evidence for the adaptation of these strains to a particular niche in Vietnam . Phenolic acids are important lignin-related constituents of plant cell walls , and therefore prevalent in the environment of C . neoformans . Cell wall-bound phenolic acids interfere with cell wall degrading enzymes and mycelia growth of fungi; the acquisition of this PAD may have been positively selected to combat plant defenses [42] . Of note , depending on the fungal growth medium , phenolic acid can also be incorporated into melanin , which is known to be an important Cryptococcus virulence factor [43] . The acquisition of the PAD gene presumably represents a horizontal gene transfer event , possibly from a closely related member of the Cryptococcus species or an alternative fungal species inhabiting the same niche . Notably , we found Cnl1 retrotransposon elements adjacent to this gene , likely indicating their role in the insertion of this gene [44] . However , the Cnl1 element was incomplete , suggesting this gene is defunct and the gene is now fused into the genome following transposition . This scenario resembles a previous report of interspecies gene transfer between fungi [45] . More studies are necessary to determine whether PAD is functional in ST5 isolates and whether it plays a significant role in virulence . However , in our isolates , the vast majority of genotype-specific DNA encoded previously identified known cryptococcal associated proteins , suggesting that the loss of cryptococcal genes is of greater evolutionary significance for disease than the acquisition of genes supporting novel functions [46] . Evidently , the loss and acquisition of genetic material we observed here is limited to those that do not affect survival outside the host . Gene loss can be tolerated because of functional redundancy between similar proteins . Further , where there may be only a single copy of a gene , there may be pleiotropic effects such that genes have both an essential housekeeping function as well as playing a key role in virulence [47] . We found differences in the presence and absence of numerous temperature dependent genes between the ST5 and non-ST5 isolates . Such genes enable some limited adaptation towards the environment in the susceptible human and are important virulence candidates . Of particular interest was the deletion of the TAR1 gene in the ST5 isolates . TAR1 was initially reported to inhibit laccase expression in a temperature-associated manner , resulting in reduced production of melanin at 37°C [32] . Therefore , this gene could be considered to have an antivirulence function in humans . TAR1 has been reported to have a small but significant attenuating effect in a cryptococcal mouse infection model [48] . Such disabled antivirulence mechanisms are not novel—they have been reported as a mechanism of increased virulence in several bacterial pathogens , where they may have been acquired for adaptation to the environment [30] . However , the deletion of TAR1 is unlikely to be sufficient to explain the extent of the clinical differences between ST5 and other non-ST5 isolates . Moreover , the regulatory effects of TAR1 seem to vary by strain , indicating that melanin production is controlled through multiple pathways [48] . Further potential cause of differences in pathogenicity in Cryptococcus are genomic rearrangements with consequent gene disruptions or altered expression of adjacent genes due to transposon insertions , excision events , gene deletions , duplications , inversions and translocation events due to ectopic recombination [26] . We were unable to explicitly test gene duplication using our data given the limitations of short read Illumina sequence data . These limitations also mean large-scale rearrangements would not have been apparent in the draft genome assemblies . There is a higher probability of DNA rearrangement occurring in fungi exposed to environmental stresses , the acquisition of which presumably offers an evolutionary advantage in a specific ill-defined niche [49] . In our isolates , the location of novel genotype-specific DNA showed a telomeric bias , possibly due to alternative selective constraints at the telomeres or because of neighboring gene co-expression [50] . DNA rearrangements may also influence recombination between the various genotypes , which can contribute to the “speciation” of new genotypes [51 , 52] . A potential weakness of our study is that we sequenced and typed only single isolates from our patients . Thus , we may have missed mixed lineage infections . However given the association between ST5 strains and HIV uninfected patients is so statistically robust it is unlikely that the distribution we see is an artefact [6] . In conclusion , we have performed comparative genomics and a clinical comparison of Cryptococcus neoformans var . grubii isolates and shown that ST5 and non-ST5 strains have a comparable genetic content , despite significantly different ability to induce disease in non-HIV infected individuals . Our analysis identified a number of gene candidates for further study to disaggregate the pathways associated with the pathogenesis of Cryptococcus neoformans . While we found little difference in the outcome of disease in HIV infected patients according to infecting sequence type , this is in contrast with the asymmetrical distribution of sequence types seen in clinical practice according to host immune phenotype . Therefore we postulate that the genetic differences identified between strains in this study in some way result in different abilities in effecting either host colonization , invasion , or latency . Currently we lack robust models of disease in immunocompetent patients for these important phases of infection , but ex vivo gene expression studies , particularly from patients with different immune phenotypes , are likely to be more revealing and offer the prospect of identifying novel drug targets . | Cryptococcal meningitis is a brain infection caused by a yeast , Cryptococcus neoformans , and results in an estimated 600 000 deaths each year . Disease usually only occurs in patients who have some problem with their immune systems—most commonly Human Immunodeficiency Virus ( HIV ) infection . However , it is increasingly recognized that disease can occur , particularly in southeast and east Asia , in patients with apparently normal immune systems ( ‘immunocompetent’ ) . We previously showed that almost all infections in immunocompetent patients in Vietnam are due to just one small ‘family’ ( or lineage ) of Cryptococcus neoformans var . grubii , which we called VNIγ . This is in contrast to disease in HIV infected patients , which can be caused by a number of different families . This suggests that VNIγ strains have an increased ability to cause disease . Here , we define the pattern of disease caused by VNIγ infections compared with other strains in HIV infected patients , and use whole genome sequencing—comparing the entire genetic codes from different strains—to try and understand which genes give the VNIγ family this special ability to cause disease in immunocompetent patients . | [
"Abstract",
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... | 2017 | Comparative genomics of Cryptococcus neoformans var. grubii associated with meningitis in HIV infected and uninfected patients in Vietnam |
Macroautophagy ( autophagy ) is crucial for cell survival during starvation and plays important roles in animal development and human diseases . Molecular understanding of autophagy has mainly come from the budding yeast Saccharomyces cerevisiae , and it remains unclear to what extent the mechanisms are the same in other organisms . Here , through screening the mating phenotype of a genome-wide deletion collection of the fission yeast Schizosaccharomyces pombe , we obtained a comprehensive catalog of autophagy genes in this highly tractable organism , including genes encoding three heretofore unidentified core Atg proteins , Atg10 , Atg14 , and Atg16 , and two novel factors , Ctl1 and Fsc1 . We systematically examined the subcellular localization of fission yeast autophagy factors for the first time and characterized the phenotypes of their mutants , thereby uncovering both similarities and differences between the two yeasts . Unlike budding yeast , all three Atg18/WIPI proteins in fission yeast are essential for autophagy , and we found that they play different roles , with Atg18a uniquely required for the targeting of the Atg12–Atg5·Atg16 complex . Our investigation of the two novel factors revealed unforeseen autophagy mechanisms . The choline transporter-like protein Ctl1 interacts with Atg9 and is required for autophagosome formation . The fasciclin domain protein Fsc1 localizes to the vacuole membrane and is required for autophagosome-vacuole fusion but not other vacuolar fusion events . Our study sheds new light on the evolutionary diversity of the autophagy machinery and establishes the fission yeast as a useful model for dissecting the mechanisms of autophagy .
Macroautophagy ( hereafter autophagy ) is a catabolic pathway that transports cytoplasmic materials into a degradative organelle , the vacuole or lysosome . This self-digestion process is upregulated during starvation , when cells have to rely on the turnover of intracellular substances to provide the building blocks for synthesizing new macromolecules [1] . Autophagy is critically important for the survival of unicellular organisms such as yeasts , whose cells are directly exposed to a fluctuating environment [2] , [3] . In recent years , diverse roles of autophagy in the development and health of multicellular organisms have also been uncovered [4] , [5] . Molecular understanding of autophagy began with the identification of autophagy-related ( ATG ) genes in S . cerevisiae , which remains the organism where the autophagy machinery has been best characterized [6]–[8] . The Atg proteins required for all autophagy-related pathways are referred to as the core Atg proteins , and most of them are involved in the generation of a double membrane-enclosed transport vehicle called autophagosome . Two protein complexes are important for initiating the autophagosome formation process . One complex consists of Atg1 kinase and its associated proteins . The other is the phosphatidylinositol 3-kinase ( PI3K ) complex composed of Vps34 , Vps15 , Atg6 , and Atg14 , which generates phosphatidylinositol 3-phosphate ( PI3P ) at the sites where autophagosomes are assembled . PI3P is recognized by Atg18 ( homolog of mammalian WIPI proteins ) , which together with Atg2 , regulates the retrograde trafficking of Atg9 , the only core Atg protein with transmembrane domains . The expansion of the autophagosome precursor , called isolation membrane or phagophore , requires the conjugation of a ubiquitin-like protein Atg8 to phosphatidylethanolamine . Factors involved in this conjugation include the Atg8 processing enzyme Atg4 , the E1 enzyme Atg7 , the E2 enzyme Atg3 , and the E3-like complex Atg12–Atg5·Atg16 . Atg12 is another ubiquitin-like protein whose conjugation to Atg5 requires the E2 enzyme Atg10 . Many ATG genes in S . cerevisiae have readily recognizable homologs in other eukaryotes , indicating that autophagy is an ancient and conserved pathway . On the other hand , differences in the autophagy machinery between S . cerevisiae and other organisms have also been documented and it has been argued that studying additional model organisms will help us better understand the evolution and mechanisms of autophagy [9] , [10] . The fission yeast S . pombe is evolutionarily very distant from S . cerevisiae . Molecular clock studies estimated that these two species diverged more than 500 million years ago [11] . The existence of a homolog of mammalian Atg101 in S . pombe but not S . cerevisiae underscores the potential value of S . pombe for studying the divergences of autophagy mechanisms [12] . However , no screen for autophagy genes has been conducted in S . pombe , and published works on fission yeast autophagy have been limited to a subset of close homologs of budding yeast ATG genes [3] , [13] . In this study , through unbiased genome-wide screening , we discovered new autophagy factors in S . pombe . Characterization of these and other autophagy factors in fission yeast demonstrated the utility of S . pombe in uncovering novel autophagy mechanisms .
Unlike S . cerevisiae , haploid S . pombe cells of opposite mating types ( h− or h+ ) do not mate on the standard rich medium . Instead , mating is most efficiently triggered by nitrogen starvation , and immediately followed by meiosis and sporulation [14] , [15] . From classical genetic screens , about 20 mating genes have been identified in S . pombe , which are called ste ( for sterile ) or ral ( for ras-like ) genes [16 and references therein] . According to PomBase [17] , 15 ste and ral genes have been cloned thus far . The classical screens are far from saturating , as a few dozen additional genes , uncovered through other means , have been implicated in mating [15] . The mating defect of autophagy mutants , which is attributed to an inability to supply enough nitrogen intracellularly , was only discovered during a focused study on these mutants [3] . With an aim of identifying new autophagy genes , we screened the mating phenotype of a fission yeast deletion collection [18] , using a barcode sequencing technology we have developed [19] . In our screening procedure ( Figure 1A ) , we mixed a pool of haploid deletion strains , which are h+ , with an equal amount of wild-type ( WT ) h− cells on solid mating media . After 4 days of incubation , we isolated the spores from the mating mixtures . Genomic DNA was extracted from both the input mutant pool and the spores . Barcodes associated with the deletions were amplified by PCR and sequenced . For each mutant/gene , we calculated a mating defect ( MD ) score , which is a normalized log2 ratio of barcode sequencing counts in input vs . spores . For mating genes , we expected MD scores higher than 0 because their barcodes should be depleted among the spores . On the other hand , genes involved in meiosis and/or sporulation but not mating should have MD scores close to 0 , as their deletions are unlikely to manifest a phenotype in the heterozygous diploid state . Under the standard mating conditions ( pregrowth in liquid YES medium , and mating on the SPA solid medium supplemented with 45 mg/l of leucine , uracil , and adenine ) [20] , we obtained MD scores for more than 2800 mutants , representing about 80% of the non-essential S . pombe genes . The distribution of MD scores largely conforms to a normal distribution centered at 0 , except for a long right tail , which represents mating-defective mutants with higher than usual MD scores ( Figure 1B and 1C ) . We repeated the screen twice , and identified the mating-defective mutants as the ones passing a false discovery rate ( FDR ) cutoff of 0 . 1 in all three screens ( Figure S1 ) . Using this stringent criterion , a total of 206 deletion mutants were found to be mating-defective , to different extents , under the standard mating conditions ( Table S1 ) . The mutants of 9 ste and ral genes ( byr1/ste1 , ste4 , ras1/ste5 , ste6 , ste7 , byr2/ste8 , ste20 , ral2 , scd2/ral3 ) were among the deletion strains screened . It was satisfying to see all of them scored as mating-defective by our analysis . Moreover , seven of them were among the top 10 hits ranked by the average MD scores ( Table S1 ) . It is probably no coincidence that all of these ste and ral genes are involved in the nutrient sensing or pheromone response pathways , as mutants blocking these signaling pathways are known to have the most severe mating defect [14] , [15] . As expected , Gene Ontology ( GO ) term analysis showed that among the 206 screen hits , genes involved in starvation response , sexual reproduction , and macroautophagy are significantly enriched ( Figure 1D ) . Surprisingly , genes encoding mitochondrial proteins are also heavily enriched , suggesting that mitochondria may play a previously under-appreciated role in mating . Using barcode sequencing-based analysis , we could recapitulate the finding that the severity of the mating defect of autophagy mutants is influenced by the mating conditions [3] . Raising the concentrations of supplements in the mating medium from 45 mg/l to 200 mg/l led to a significant reduction of the MD scores of autophagy mutants , but did not change those of the top-ranked signaling mutants ( compare Figure 1C and 1E ) . This observation suggests that extending our analysis to alternative mating conditions may help classifying mating genes into different functional categories . Thus , in addition to the three screens performed under the standard mating conditions , we conducted 19 screens under 18 non-standard conditions ( Table S2 ) , resulting in a total of 63146 MD score measurements for 2915 mutants ( Table S3 ) . As predicted , hierarchical clustering of the data from the 22 screens revealed conspicuous patterns of phenotypic variations among the mating genes , with many falling into tight clusters ( Figure 1F and Figure S2 ) . Two of the most distinct clusters are enriched for mitochondrial protein-coding genes and autophagy genes , respectively . In this study , we focused on the genes in the autophagy cluster , but our extensive phenotyping data should be a useful resource for future investigation on other genes and cellular processes . There are 21 genes in the autophagy cluster ( Figure 1F ) , including 10 genes known to be required for starvation-induced autophagy ( atg2 , atg4 , atg5 , atg9 , atg12 , atg13 , atg15 , atg18a , atg18b , atg18c ) [3] , [13] . These 10 genes represent all known fission yeast autophagy factors detectable by our analysis , as the other 4 characterized autophagy genes did not have MD scores , two due to lack of deletion strains ( atg3 and atg8 ) , one due to lack of decoded barcodes ( atg1 ) , and one due to low barcode read counts ( atg7 ) . Among the remaining 11 genes in the autophagy cluster , atg6 , atg11 , atg17 , and atg101 have reported homology to autophagy genes in other organisms [21] , but no experimental data on these 4 genes have been published . We hypothesized that these 4 genes , as well as the other 7 genes , which are unnamed and have no reported connections to autophagy , may also function in starvation-induced autophagy . To test this hypothesis , we monitored nitrogen starvation-induced autophagy using the Atg8 fusion protein processing assay [13] , [22] , [23] ( Figure 2A ) . We constructed a strain expressing from the endogenous promoter an Atg8 protein N-terminally tagged with cyan fluorescent protein ( CFP ) , and then introduced the deletions of the autophagy cluster genes individually into this strain by PCR-based gene targeting . When wild-type ( WT ) cells expressing CFP-Atg8 were shifted from a growth medium ( EMM ) to a nitrogen-free medium ( EMM-N ) , immunoblotting analysis showed the appearance of a free CFP band , due to the autophagic delivery of CFP-Atg8 into vacuoles and the subsequent proteolysis that releases the protease-resistant CFP . The processing of CFP-Atg8 was not observed in the 14 mutants known to be defective in autophagy or vacuolar proteolysis ( atg1 , atg2 , atg3 , atg4 , atg5 , atg7 , atg9 , atg12 , atg13 , atg15 , atg18a , atg18b , atg18c , and isp6 ) . In addition , we found that atg6 , atg11 , atg17 , and atg101 are also required for CFP-Atg8 processing , thus providing for the first time evidence that they are required for autophagy . Among the 7 unnamed genes , five are also required for CFP-Atg8 processing . For reasons that will be explained below , we give these five genes the names of atg10 , atg14 , atg16 , ctl1 , and fsc1 , respectively . The other two genes ( SPCC757 . 04 and SPCC417 . 09c ) , when deleted , had no effect on CFP-Atg8 processing . We suspect that the deletion library strains for these two genes may harbor background mutations that interfere with autophagy . These two genes were not pursued further . Three core components of the autophagy machinery , Atg10 , Atg16 , and Atg14 , have not been identified in S . pombe [21] , [24] . We found that three new autophagy genes uncovered in our screens share distant homology with genes encoding these three Atg proteins in other species . SPAC227 . 04 contains a Pfam domain ( PF07238 ) associated with Atg3 and Atg10 proteins . Our sequence homology analysis suggested that SPAC227 . 04 is more closely related to Atg10 proteins in metazoa and plants than to any Atg3 proteins ( Figure S3 ) . If SPAC227 . 04 is indeed Atg10 in S . pombe , removing it should abolish the conjugation of Atg12 to Atg5 . In crude extracts made from wild-type cells expressing TAP-tagged Atg5 ( Atg5-TAP ) , we detected by immunoblotting one major band of Atg5-TAP , presumably in the form of Atg12–Atg5 conjugate , as this band disappeared in atg12Δ extracts , as well as in atg7Δ extracts , which is defective in the E1 enzyme ( Figure 2B ) . A faster-migrating band , likely representing the free form of Atg5 , appeared in atg12Δ and atg7Δ extracts . When SPAC227 . 04/atg10 was deleted , only the free form of Atg5 was detected , thus confirming our prediction . SPBC405 . 05 is currently annotated as a sequence orphan . We found that it shares homology with S . cerevisiae Atg16 in both the N-terminal Atg5-binding domain and the C-terminal coiled-coil domain ( Figure S4 ) . Similar to what has been reported for S . cerevisiae [25] , we found in a co-immunoprecipitation ( co-IP ) experiment that , S . pombe SPBC405 . 05/Atg16 protein interacts with Atg5 both in the presence and in the absence of Atg12 ( Figure 2C ) . SPAC25A8 . 02 , also annotated as a sequence orphan , was shown by our PSI-BLAST analysis to be related to metazoan Atg14 proteins . The most conserved sequence feature in Atg14 proteins is a pair of CXXC motifs termed “cysteine repeats” [26] . SPAC25A8 . 02 contains such a sequence feature , as well as a coiled-coil domain following the cysteine repeats , thus sharing the same domain arrangement with Atg14 proteins in other organisms ( Figure 2D ) . Consistent with our homology analysis , we could co-immunoprecipitate SPAC25A8 . 02/Atg14 with Atg6 , the expected binding partner of Atg14 in a PI3K complex ( Figure 2E ) . In PomBase , another gene , SPBC18H10 . 19 , is currently annotated as atg14 because of its match to a Pfam domain ( PF10186 ) associated with budding yeast Atg14 . This domain is also found in metazoan UVRAG and Atg14 proteins , which are mutually exclusive subunits of Beclin 1-containing PI3K complexes [27] . In budding yeast , the likely counterpart of UVRAG , Vps38 , resides in a PI3K complex distinct from the Atg14-containing complex and is dispensable for autophagy [28] . SPBC18H10 . 19 lacks the N-terminal cysteine repeats typical for the Atg14 proteins . Furthermore , the deletion library strain of SPBC18H10 . 19 showed no mating defect in our screens and an independent deletion made in the CFP-Atg8 strain did not block starvation-induced CFP-Atg8 processing ( Figure 2A ) . Thus , we conclude that SPAC25A8 . 02 is the S . pombe Atg14 , and SPBC18H10 . 19 may be the fission yeast equivalent of metazoan UVRAG and budding yeast Vps38 . Our analysis of the autophagy cluster genes increased the number of experimentally defined fission yeast autophagy factors from 14 to 23 , and the identification of Atg10 , Atg16 , and Atg14 completed the roster of expected core autophagy components . We were , therefore , afforded an opportunity to comprehensively characterize the autophagy machinery in this organism for the first time . To survey the properties of fission yeast autophagy factors , we expressed them as fluorescent protein-tagged forms under the control of their endogenous promoters , and examined their subcellular localization by live cell imaging . Atg8 is the only fission yeast Atg protein whose localization has been investigated [3] , [13] . As reported by previous studies , we found that nitrogen starvation triggered the formation of bright CFP-Atg8 puncta in the cytoplasm . Co-expressing other autophagy proteins tagged with YFP in the CFP-Atg8 strain showed that 14 Atg proteins and Ctl1 colocalized with Atg8 on the punctate structure ( Figure 3A ) . In S . cerevisiae , the same set of Atg proteins also colocalize at a punctuate structure , which has been termed the pre-autophagosomal structure or phagophore assembly site ( PAS ) [29]–[31] . Because of the similarity in the way Atg proteins assemble together , we propose that the structure where fission yeast Atg proteins colocalize during starvation is the counterpart of PAS in budding yeast , and will refer to it as PAS hereafter . The majority of the PAS-localizing fission yeast Atg proteins do not accumulate on distinct subcellular structures under non-starvation conditions . The exceptions are Atg1 , Atg11 , Atg6 , Atg18a , Atg9 , and Ctl1 . In vegetatively growing cells , Atg1 , Atg11 and Atg18a were observed on the vacuole membrane ( Figure S5A ) . Atg18a also formed puncta co-localizing with an endosomal marker ( Figure S5B ) . Atg6 was observed on punctate structures labeled by an endosomal marker as well , presumably reflecting its role in the vacuolar protein sorting pathway [28] ( Figure S5C ) . The localization patterns of Atg9 and Ctl1 will be described below . In S . cerevisiae , the localization of Atg8 at PAS is influenced by many other autophagy factors [29] , [31] . To assess how fission yeast autophagy factors act , we analyzed the localization of CFP-Atg8 in atg mutants during starvation ( Figure 3B ) . Mutants of the Atg8 conjugation system , atg3Δ , atg4Δ , atg5Δ , atg7Δ , atg10Δ , atg12Δ , and atg16Δ , completely abolished Atg8 puncta formation , so did the PI3K mutants atg6Δ and atg14Δ . In contrast , Atg8 puncta were readily detected in atg1Δ , atg11Δ , atg13Δ , atg17Δ , atg101Δ , atg2Δ , atg18bΔ , and atg18cΔ . These eight mutants can be classified into three groups based on the number , intensity , and emergence timing of the Atg8 puncta . Group 1 consists of atg1Δ and atg11Δ , in which Atg8 puncta appeared relatively normal in the first hour after starvation , but their numbers did not decline afterwards as happened in the wild type . Group 2 consists of atg13Δ , atg17Δ , and atg101Δ , which lacked obvious Atg8 puncta during the first hour after starvation , and the puncta emerged later appeared dimmer than those found in the wild type . Group 3 consists of atg2Δ , atg18bΔ , and atg18cΔ , in which the Atg8 puncta were much more numerous than in the wild type at all time points , and some of the puncta were notably brighter than those in the wild type . Despite the superficial resemblance of the snapshot images of the Atg8 puncta in atg1Δ cells and wild type cells , time-lapse imaging analysis showed that unlike wild type cells , in which Atg8 puncta were dynamic structures with durations mostly in the range of 100 to 200 seconds , Atg8 puncta persisted much longer in atg1Δ cells ( Figure 3C ) . This is similar to the observations in S . cerevisiae [32] , [33] . In addition , we found that Atg8 puncta in atg2Δ cells were also long-lasting structures ( Figure 3C ) . One particularly intriguing observation was the lack of Atg8 puncta in atg18aΔ cells ( Figure 3B ) , suggesting that Atg18a plays a role different from that of the other two Atg18/WIPI paralogs , Atg18b and Atg18c . To assess how atg18aΔ may affect the PAS organization , we examined the localization of several representative Atg proteins in this mutant ( Figure 4A ) . Atg1 , Atg13 , Atg14 , and Atg2 still formed puncta in starved atg18aΔ cells . In contrast , neither Atg5 nor Atg16 formed detectable puncta . Thus , atg18aΔ blocked the recruitment of Atg5 and Atg16 to PAS , and probably as a consequence , indirectly abolished the PAS localization of Atg8 . We have shown that in S . pombe , Atg5 mainly exists in the form of Atg12–Atg5 conjugate , and physically interacts with Atg16 ( Figure 2C ) . Neither Atg12–Atg5 conjugate formation ( Figure 4B ) , nor the interaction between Atg5 and Atg16 ( Figure 4C ) , was affected by atg18aΔ . Thus , the Atg12–Atg5·Atg16 complex remains intact in atg18aΔ cells , and the localization defect is probably due to a failure to recruit this complex as a whole to PAS . We hypothesized that Atg18a may physically interact with the Atg12–Atg5·Atg16 complex . To test this idea , we performed co-IP experiments and found that , indeed , Atg5 was co-precipitated with Atg18a , and in a reciprocal IP , Atg18a was co-precipitated with Atg5 ( Figure 4D ) . Thus , Atg18a may serve as a binding platform for the recruitment of the Atg12–Atg5·Atg16 complex to PAS . The Atg18 family proteins bind PI3P in a manner dependent on a conserved FRRG motif [34] , [35] . When the FRRG motif in Atg18a was mutated to FTTG , the protein became diffusely distributed and could no longer support the puncta formation by Atg8 ( Figure 4E ) . Together , our data support a sequential recruitment model in which Atg18a is targeted to PAS by PI3P binding , and then in turn recruits the Atg12–Atg5·Atg16 complex . We gave the previously uncharacterized gene SPCC1682 . 11c the name ctl1 because it encodes the sole member of the choline transporter-like ( CTL ) protein family ( Pfam PF04515 ) in S . pombe . This protein family is ubiquitous in eukaryotes , with one member in S . cerevisiae ( Pns1 ) , one member in C . elegans ( CHTL-1 ) , two members in D . melanogaster , and five members in humans [36] ( Figure S6 ) . One vertebrate CTL protein , CTL1/SLC44A1 , was shown to be a choline transporter on the plasma membrane and in mitochondria [37] , [38] . However , S . cerevisiae Pns1 and C . elegans CHTL-1 do not act as choline transporters [39] , [40] . Thus , choline transport does not appear to be a universal function of CTL proteins . Like other members of the CTL family , S . pombe Ctl1 is predicted to be a multi-transmembrane protein , with several methods agreeing on the same prediction that Ctl1 contains 10 transmembrane helices with both its N terminus and C terminus facing the cytoplasm ( Figure 5A ) . To corroborate the result of our CFP-Atg8 processing assay , we used a fluorescence loss in photobleaching ( FLIP ) assay to monitor the non-specific autophagy of an abundant cytosolic protein Tdh1 , which is the major form of glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) in fission yeast [41] . In this assay , the fluorescence signal of the diffusible pool of Tdh1-YFP is depleted by repetitive photobleaching of a small region near one tip of the cell . If Tdh1-YFP is trapped or immobilized in certain cellular compartments and cannot freely diffuse to the site of photobleaching , such cellular compartments should stand out in post-FLIP images due to the remaining YFP signal . In non-starved cells , the only compartment with visible YFP signal in post-FLIP images is the nucleus ( Figure 5B ) , perhaps due to the reported association of Tdh1 with RNA polymerase II [42] . Upon starvation , in post-FLIP images of wild-type cells ( Figure 5C ) , YFP signal became detectable in vacuoles , which were labeled by mCherry-tagged Cpy1 ( carboxypeptidase Y , or CPY ) , a vacuolar lumenal protein [43] . The starvation-induced vacuolar YFP signal is due to the autophagic delivery of Tdh1-YFP , because deletion of atg5 abolished such signal ( Figure 5C ) . ctl1Δ also blocked the starvation-induced vacuolar targeting of Tdh1-YFP ( Figure 5C ) . This result confirmed that Ctl1 is required for non-specific autophagy . To understand how Ctl1 contributes to autophagy , we examined the localization of Atg8 in ctl1Δ cells . In wild type cells , the level of starvation-induced CFP-Atg8 puncta peaked at around 1 h after starvation . However , in ctl1Δ cells , no Atg8 puncta were observed in the first few hours after starvation ( Figure 5D ) . After prolonged starvation ( >4 h ) , Atg8 became concentrated on cytoplasmic structures in ctl1Δ cells . Remarkably , some of these CFP-Atg8 labeled structures are not dot-like as seen in wild type , but rather C-shaped or ring-shaped ( Figure 5D ) . Time-lapse analysis showed that these distinctly shaped structures represent different stages of a dynamic process , in which a CFP-Atg8 labeled structure first emerges as a dot , then elongates and bends into a C-shape , and subsequently grows to form a ring before its eventual disappearance , in a total time frame of several minutes ( Figure 5E ) . The C shape may correspond to a cup-like structure in three-dimensional space , and the ring shape may correspond to a hollow sphere-like structure . Interestingly , YFP-tagged Atg proteins localized to different regions of the Atg8-labeled structure in ctl1Δ cells: Atg5 perfectly co-localized with Atg8 , whereas Atg1 , Atg17 , Atg6 , Atg14 , Atg2 , and Atg18b localized to sub-regions of the Atg8-labeled structures , with Atg2 and Atg18b concentrating at the tips of the expanding structures ( Figure 5F and G ) . Thus , even though this structure is a pathological outcome of the loss of Ctl1 , it resembles the PAS in that many Atg proteins localize on it . We suspect that the distinct localization patterns of the Atg proteins on this structure may reflect their intrinsic properties . Consistent with this idea , we found that unlike Atg2 and Atg18b , Atg18a perfectly colocalized with Atg8 on this structure ( Figure 5F ) , echoing its role in promoting the PAS localization of Atg5 and Atg8 . To determine whether Ctl1 is associated with other autophagy factors , we performed affinity purification coupled with mass spectrometry analysis and found that Atg9 co-purified with Ctl1 ( unpublished data ) . Reciprocal co-IP experiments demonstrated that Ctl1 and Atg9 indeed interact with each other ( Figure 6A ) . In S . cerevisiae , Atg9 cycles between PAS and non-PAS compartments , and the retrograde trafficking of Atg9 from PAS requires Atg1 and Atg2 [44]–[46] . Similarly , we found that in S . pombe , Atg9-YFP localized to punctate cytoplasmic structures , and partially co-localized with Atg8 puncta during starvation ( Figure 6B ) . In starved atg1Δ or atg2Δ cells , Atg9 puncta almost completely overlapped with Atg8 puncta ( Figure 6B ) , suggesting that recycling of Atg9 from PAS requires Atg1 and Atg2 , like in S . cerevisiae . The Ctl1-Atg9 interaction prompted us to examine whether Ctl1 influences the distribution of Atg9 . In non-starved wild-type cells , besides the punctate structures , we also found weak Atg9-YFP signal on the vacuole membrane ( Figure 6C ) . In non-starved ctl1Δ cells , Atg9 puncta no longer stood out , whereas the vacuole membrane signal was more noticeable , suggesting that Atg9 may be partially mislocalized ( Figure 6C ) . In starved ctl1Δ cells , Atg9 puncta became more prominent , and some of them co-localized with mCherry-tagged Atg17 ( Figure 6C ) , suggesting that Atg9 can still traffic to the PAS in the absence of Ctl1 . Similar to Atg9 , Ctl1 tagged with YFP localized to cytoplasmic punctate structures , and upon starvation , a fraction of Ctl1-YFP puncta colocalized with CFP-Atg8 puncta ( Figure 6D ) . In addition , Ctl1 became largely restricted to PAS in starved atg1Δ or atg2Δ cells ( Figure 6D ) , suggesting that like Atg9 , Ctl1 is recycled from PAS in an Atg1 and Atg2-dependent manner . In S . cerevisiae , it has been shown that Atg9 traffics from Golgi apparatus to PAS via small vesicular carriers [46] . As Ctl1 and Atg9 may share similar cycling routes , we monitored the spatial relationship between Ctl1 and a Golgi marker , Anp1 [47] . In starved wild-type cells , Ctl1 puncta partially colocalized with Anp1 , whereas in starved atg9Δ cells , nearly 100% of Ctl1 puncta colocalized with Anp1 ( Figure 6D ) , indicating that Ctl1 travels from Golgi to PAS in an Atg9-dependent manner . We gave the previously uncharacterized gene SPAC22H12 . 05c the name fsc1 because it encodes a protein containing five fasciclin domains ( Pfam PF02469 ) ( Figure 7A and Figure S7 ) . Fasciclin domain-containing proteins exist in animals , fungi , plants , bacteria , and cyanobacteria [48] . In animals and plants , this type of protein is usually found at the cell surface and mediate cell adhesion [49] , [50] . However , one mammalian fasciclin domain protein , stabilin-1 , was reported to have intracellular trafficking roles [51] . Fsc1 is predicted to be a type I transmembrane protein , with the bulk of its amino acids exposed in the lumenal/extracellular space ( Figure 7A ) . In vegetatively growing cells , Fsc1 tagged at its C-terminus with YFP localized to the vacuole membrane ( Figure 7B ) . Interestingly , upon starvation , bright puncta of Fsc1-YFP were observed on the vacuolar rim of a small number of vacuoles ( Figure 7C ) , indicating a dramatic concentration of Fsc1 at special sites on the vacuole membrane . The starvation-induced Fsc1 puncta were dynamic structures with durations of less than a minute ( Figure 7D ) , and they were abolished in atg1Δ and atg11Δ cells , and diminished in atg13Δ cells ( Figure 7E ) . No overlap between Fsc1 puncta and Atg8 puncta was observed ( Figure S8 ) , indicating that these two types of starvation-induced structures are spatially distinct entities . The fact that Fsc1 localizes to the destination compartment of autophagic trafficking led us to hypothesize that Fsc1 may be required for a late step of autophagy , the fusion between autophagosomes and vacuoles . To test this idea , we applied the Tdh1-YFP FLIP assay to fsc1Δ cells . Upon starvation , YFP signal in round cytoplasmic structures became visible in post-FLIP images of fsc1Δ cells ( Figure 8A ) . However , unlike wild-type cells ( Figure 5C ) , the YFP signal in fsc1Δ cells did not overlap with Cpy1-mCherry . Thus , Tdh1 entered a closed membrane compartment but did not reach the vacuole . Such YFP-labeled structures were absent in the post-FLIP images of fsc1Δ atg5Δ cells , suggesting that the Tdh1-containing membrane structures accumulated in fsc1Δ cells are autophagosomes . To verify the results obtained by the FLIP assay , we performed transmission electron microscopy ( TEM ) analysis . In starved fsc1Δ cells , we observed the accumulation of spherical membrane structures whose lumenal contents had the same electron opacity as the cytosol , suggesting that they are autophagosomes ( Figure 8B ) . Moreover , TEM images showed that autophagosomes in these cells were often in extensive contact with vacuoles , suggesting that docking of autophagosomes onto vacuoles had occurred but membrane fusion was blocked . To our knowledge , all known autophagosome-vacuole fusion factors in S . cerevisiae , such as Mon1/Aut12 and Vam3 , are required not only for autophagosome-vacuole fusion , but also for other vacuolar fusion events , such as those occurring in the CPY trafficking [52] , [53] , and homotypic vacuole fusion [54] . In contrast , Fsc1 appears to be dispensable for these processes , as unlike aut12Δ cells , Cpy1 was not missorted to cell surface in fsc1Δ cells ( Figure 8C ) , and homotypic vacuole fusion induced by hypo-osmotic stress occurred normally in fsc1Δ cells ( Figure 8D ) [55] . Thus , Fsc1 is specifically required for autophagosome-vacuole fusion .
In S . cerevisiae , PAS is the site where the Atg proteins involved in autophagosome formation assemble together , as observed by fluorescence microscopy . Here , we identified a similar entity in S . pombe by live cell imaging of fluorescent protein-tagged Atg proteins , and named it PAS due to its resemblance to the PAS in S . cerevisiae . The similarities include: ( 1 ) In both species , PAS is a dot-like structure whose finer details cannot be resolved by conventional light microscopy; ( 2 ) At any given time during starvation , in the majority of PAS-containing cells , only one PAS punctum can be observed; ( 3 ) The same set of Atg proteins can be colocalized at PAS , with the exception of Atg29 and Atg31 , which are absent in S . pombe , and Atg101 , which is absent in S . cerevisiae; ( 4 ) PAS is a dynamic structure with a duration in the range of minutes , as revealed by the time-lapse analysis of Atg8 puncta; ( 5 ) The assembly of Atg proteins at PAS is controlled in a hierarchical manner , with Atg8 being one of the most downstream factors , whose recruitment to PAS or dynamics at PAS is altered in the mutants defective in any other PAS-localizing Atg proteins . There are also notable differences between these two organisms in terms of PAS organization and the roles of PAS-localizing Atg proteins: ( 1 ) In S . cerevisiae , a constitutive biosynthetic route termed cytoplasm-to-vacuole targeting ( Cvt ) pathway utilizes the Atg proteins to transport cytosolic hydrolases into the vacuole [56] , and thus the assembly of Atg proteins at PAS occurs under nutrient-rich conditions; in contrast , PAS cannot be detected in S . pombe under nutrient-rich conditions , presumably due to the lack of the Cvt pathway , whose key factors Ape1 and Atg19 do not have apparent homologs in S . pombe; ( 2 ) Atg11 in S . cerevisiae is dispensable for starvation-induced autophagy , whereas Atg11 in S . pombe is essential for starvation-induced autophagy and appears to have a closer relationship with Atg1 than the other putative Atg1 regulators , consistent with a proposition that S . pombe Atg11 may be more similar to mammalian FIP200 than to budding yeast Atg11 [12]; ( 3 ) There are three Atg18/WIPI proteins in each species , but only one of the three paralogs in S . cerevisiae ( Atg18/Svp1 ) is essential for starvation-induced autophagy , whereas all three paralogs in S . pombe are needed for starvation-induced autophagy . We found that the mutants of fission yeast Atg18 paralogs exhibited different phenotypes , with atg18aΔ abolishing the Atg8 puncta , and atg18bΔ or atg18cΔ elevating the levels of Atg8 puncta . This is analogous to the situation in mammalian cells , where LC3 ( a mammalian homolog of Atg8 ) puncta increased upon the depletion of either WIPI1 or WIPI4 but decreased upon the depletion of WIPI2 [57]–[59] . Such functional distinctions cannot be readily explained by the phylogenetic relationships among Atg18/WIPI proteins ( Figure S9 and S10 ) . In mammals , WIPI1 and WIPI2 are much more similar to each other than to WIPI4 , and in S . pombe , Atg18b and Atg18c do not show significantly higher sequence homology to each other than to Atg18a . Our analysis suggests that the lack of Atg8 puncta in atg18aΔ cells is due to a defect in the PAS targeting of the Atg12–Atg5·Atg16 complex , which physically interacts with Atg18a . To our knowledge , this is the first time a physical interaction between a WIPI/Atg18 protein and the Atg12–Atg5·Atg16 complex has been observed . Similar interactions may underlie the roles of S . cerevisiae Atg18 and its paralog Atg21 in promoting the PAS localization of Atg5 and Atg16 [35] , [60] , and the role of mammalian WIPI2 in the recruitment of LC3 to the omegasome , which may be the equivalent of PAS in mammalian cells [57] . As Atg18a accumulates on subcellular structures other than PAS , it probably cooperates with additional factors for the specific targeting of Atg12–Atg5·Atg16 to PAS . Atg2 is unlikely to be such a factor , as its mutant behaved like atg18bΔ and atg18cΔ . Ctl1 is a novel autophagy factor uncovered by our screens . Our phylogenetic analysis showed that fungal CTL proteins fall into two clades , with S . pombe Ctl1 in one clade , and S . cerevisiae Pns1 in the other ( Figure S6 ) . Pns1 , whose function is unknown , has been localized at the plasma membrane [61] , and it does not appear to be required for starvation-induced autophagy ( unpublished data ) . Thus , among the fungal CTL proteins , perhaps only the ones falling into the same clade as Ctl1 are autophagy factors . As fungal species in many lineages have both Ctl1-like and Pns1-like proteins ( Figure S6 ) , these two types of proteins might have co-existed in the common ancestor of fungi , but one of them was lost in the lineage leading to S . cerevisiae , while the other was lost in the lineage leading to S . pombe . In ctl1Δ cells , autophagosome formation appears to be defective , as we did not observe any cytoplasmic signal of Tdh1-YFP in post-FLIP images . The late emerging Atg8-labeled structures in ctl1Δ cells may be aberrant isolation membranes that cannot mature into completely sealed autophagosomes . Ctl1 may regulate the distribution of Atg proteins on the expanding isolation membrane , and in its absence , Atg proteins occupy different regions of the isolation membrane , instead of concentrating at one subregion . It is interesting to note that the distinct localization patterns of Atg proteins we observed in ctl1Δ cells bear remarkable resemblance to the three types of Atg protein distribution patterns observed in S . cerevisiae when Ape1 is overexpressed [62] . In both ctl1Δ S . pombe cells and Ape1-overexpressing S . cerevisiae cells , Atg8 and Atg5 are distributed all over a cup-shaped structure , whereas Atg2 and an Atg18 family protein ( Atg18 in S . cerevisiae and Atg18b in S . pombe ) concentrate at the edge of this structure . In Ape1-overexpressing S . cerevisiae cells , Atg17 , Atg6 , and Atg14 localize to a subregion of the cup-shaped structure , termed vacuole-isolation membrane contact site ( VICS ) ; in ctl1Δ cells , these three proteins also localize to subregions of the cup-shaped structure . Thus , the spatial separation of Atg proteins under these two circumstances probably reflects evolutionarily conserved functional distinctions among the Atg proteins . In S . cerevisiae , all known mutants blocking autophagosome-vacuole fusion are also defective for vacuolar fusion in the CPY and ALP pathways , as well as vacuole–vacuole homotypic fusion [63] . Thus , it is unclear whether there are mechanisms specifically regulating autophagosome-vacuole fusion in budding yeast . Here , we showed that , in S . pombe , a vacuole membrane protein Fsc1 is uniquely required for autophagosome-vacuole fusion , thus revealing a specific control of autophagic traffic at the vacuolar fusion step , and providing a molecular entry point for dissecting the mechanism of such a control . Fsc1 formed puncta on the vacuole membrane during starvation . We speculate that these structures may be in some way connected to autophagosome-vacuole fusion . For example , they may correspond to fusion-ready zones on the vacuole membrane , or the actual fusion sites , or special post-fusion structures . Many fungal species have at least one protein sharing the exact same domain organization as Fsc1 . The S . cerevisiae homolog of Fsc1 is Ylr001c ( Figure S7 ) , which like Fsc1 , also localizes to the vacuole membrane [61] . However , Ylr001c seems to be dispensable for starvation-induced autophagy ( unpublished data ) , perhaps due to functional redundancy in S . cerevisiae , or differences in vacuole physiology between the two organisms . One obvious difference is that an S . cerevisiae cell has one or a few large vacuoles , whereas an S . pombe cell has about 80 small vacuoles [55] . Thus , there may be a need for more elaborate fusion target selection in S . pombe to avoid overwhelming the degradative capacities of some vacuoles while leaving other vacuoles idle . Mammalian cells , where a large number of lysosomes are present in each cell , may share this need . Several lines of recent evidence suggest that autophagosome-lysosome fusion in mammalian cells utilizes mechanisms distinct from other lysosomal fusion events [64]–[66] . We expect that further analysis of Fsc1 may provide mechanistic insights relevant to autophagosome-lysosome fusion in mammalian cells .
The fission yeast strains used in this study are listed in Table S4 . Genetic methods for strain construction and composition of media are as described [20] . To construct a strain expressing CFP-Atg8 under the control of the endogenous promoter , we amplified by overlap-extension PCR the atg8 promoter and the N-terminal region of the atg8 ORF using primers 5′-GATCTAGAGAAGCGCTTATTTGTTTAC-3′ , 5′-CGagatctTTGAGAACGCATGAGAACTCTCAAACTTCTTGC-3′ , 5′-CTCATGCGTTCTCAAagatctCGTTCTCAATTCAAGG-3′ , and 5′-GCGTCGACACCAACTGTAAGGTCAGATGG-3′ . The final PCR product contained a BglII site ( lowercase letters in the primer sequences ) inserted near the start codon . The PCR product was digested with XbaI and SalI , and inserted into an integrating vector pJK148 [67] . DNA encoding the CFP tag was inserted into the BglII site to obtain the pJK148-CFP-Atg8 plasmid . The plasmid was linearized with SpeI , which cuts in the middle of the N-terminal region of the atg8 ORF , and transformed into fission yeast . Most of the deletion strains used in this study were constructed by PCR amplifying the deletion cassettes in the Bioneer deletion strains and transforming the PCR product into strains from our lab strain collection . The exceptions are atg1 , atg3 , atg6 , and atg12 , whose deletion strains were made without the aid of Bioneer strains , by standard PCR-based gene targeting [68] . Strains expressing Atg proteins fused with the YFP-FLAG-His6 ( YFH ) tag under native promoters were constructed by an overlap-extension PCR approach [69] , using the ORFeome plasmids as templates [70] . Strains expressing proteins fused with other tags were made by PCR-based tagging [68] . Tdh1-YFH was expressed from an ORFeome plasmid under the control of the nmt1 promoter . Deletion strain pools of Bioneer version 1 . 0 haploid library ( catalog number M-1030H ) and Bioneer version 1 . 0 upgrade package ( catalog number M-1030H-U ) were constructed as described [19] . Frozen aliquots of the two mutant pools were thawed at room temperature , mixed together , washed once with YES medium , and pre-grown in YES or EMM medium for 3 generations at 30°C . An equal amount of log-phase wild-type h− strain ( DY3984 ) grown in YES medium was mixed with the deletion mutants , and washed twice with water . The cell suspension was diluted to 100 OD600 units/ml in water , spotted on the solid mating medium , and incubated for 4 days . The mating mixtures were treated with 0 . 5% ( v/v ) glusulase overnight at room temperature , and the spores were purified with a Percoll step gradient [71] . The spore preparations were more than 99% pure as judged by microscopy . Genomic DNA extraction , barcode PCR , and Illumina sequencing were performed as described [19] . The sequencing data are publicly available at NCBI Sequence Read Archive ( http://www . ncbi . nlm . nih . gov/sra/ ) under the accession number SRA068523 . The data are split into 26 runs , which correspond to 4 input samples and 22 spore samples . Descriptions of the 26 runs are in Table S5 . Mating phenotype screen data were processed as described [19] , with a few modifications . For read count normalization , we used the upper-quartile normalization method [72] . To avoid noises associated with very small read counts , for a MD score to be computed for a gene , we required the read count of at least one of its barcodes to be no smaller than 1/40 of the upper-quartile read count , and also no smaller than 12 , in either the input sample or the spore sample . For a gene with a single barcode decoded , its MD score is the normalized log2 fold change ( input versus spore ) of that barcode . For a gene with both uptag and dntag decoded , its MD score is a weighted average of the normalized log2 fold change of the two barcodes , where the weight for a barcode is the ratio of the sum of the read counts of that barcode in input and spore samples to the sum of the read counts of both barcodes . To select mating defective mutants , we calculated for each gene a robust Z-score , which is the deviation of its MD score from the median MD score expressed in the number of the normalized interquartile range ( NIQR ) . Tail area-based FDR values were calculated from the robust Z-scores using the software fdrtool version 1 . 2 . 8 [73] . Genes with FDR values <0 . 1 in all three screens performed under standard conditions were deemed the screen hits . GO term enrichment analysis was conducted with AmiGO version 1 . 8 using GO database release 2013-02-02 [74] . Hierarchical clustering analysis was performed using the correlation ( uncentered ) similarity metric and the complete linkage clustering method . Cell lysates were prepared using a post-alkaline extraction method [75] . Samples were separated by 12% SDS-PAGE and immunoblotted with an anti-GFP antibody ( Roche ) . The Peroxidase Anti-Peroxidase ( PAP ) soluble complex ( Sigma ) was used in immunoblotting to recognize the TAP tag fused to Atg5 . For immunoprecipitation , cell lysates were made by glass bead beating . GFP-trap and RFP-trap agarose beads ( ChromoTek ) were used for immunoprecipitating YFP- and mCherry-tagged proteins , respectively . Except for the FLIP assay , light microscopy was performed using a DeltaVision PersonalDV system ( Applied Precision ) equipped with a CFP/YFP/mCherry filter set ( Chroma 89006 set ) and a Photometrics CoolSNAP HQ2 camera . Images were acquired with a 100× , 1 . 4-NA objective , and analyzed with the SoftWoRx software . Photobleaching of the Tdh1-YFP signal and image acquisition were carried out with a PerkinElmer Ultraview VoX spinning disk system , using a 100× objective . Image analysis was performed with the Volocity software . Cells were prepared for electron microscopy by fixation with glutaraldehyde and KMnO4 [76] . Samples were dehydrated with graded ethanol series , and embedded in Spurr's resin . Thin sections were stained with uranyl acetate and Sato's lead , and visualized on a transmission electron microscope . | Autophagy is a eukaryotic cellular process that transports cytoplasmic contents into lysosomes/vacuoles for degradation . It has been linked to multiple human diseases , including cancer and neurodegenerative disorders . The molecular machinery of autophagy was first identified and has been best characterized in the budding yeast Saccharomyces cerevisiae , but little is known about the autophagy machinery in another important unicellular model organism , the fission yeast Schizosaccharomyces pombe . In this study , we performed an unbiased and comprehensive screening of the fission yeast autophagy genes by profiling the mating phenotypes of nearly 3000 deletion strains . Following up on the screening results , we systematically characterized both previously known and newly identified fission yeast autophagy factors by examining their localization and the phenotype of their mutants . Our analysis increased the number of experimentally defined fission yeast autophagy factors from 14 to 23 , including two novel factors that act in ways different from all previously known autophagy proteins . Together , our data reveal unexpected evolutionary divergence of autophagy mechanisms and establish a new model system for unraveling the molecular details of the autophagy process . | [
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] | 2013 | Global Analysis of Fission Yeast Mating Genes Reveals New Autophagy Factors |
Plant development is affected by the integration of light and phytohormones , including jasmonates ( JAs ) . To address the molecular mechanisms of possible interactions between blue light and JA signaling in Arabidopsis thaliana , we used molecular and transgenic approaches to understand the regulatory relationships between FAR-RED INSENSITIVE 219 ( FIN219 ) /JASMONATE RESISTANT1 ( JAR1 ) and the blue-light photoreceptor cryptochrome1 ( CRY1 ) . FIN219 overexpression in the wild type resulted in a short-hypocotyl phenotype under blue light . However , FIN219 overexpression in cry1 , cry2 and cry1cry2 double mutant backgrounds resulted in phenotypes similar to their respective mutant backgrounds , which suggests that FIN219 function may require blue light photoreceptors . Intriguingly , FIN219 overexpression in transgenic plants harboring ectopic expression of the C terminus of CRY1 ( GUS-CCT1 ) , which exhibits a hypersensitive short-hypocotyl phenotype in all light conditions including darkness , led to a rescued phenotype under all light conditions except red light . Further expression studies showed mutual suppression between FIN219 and CRY1 under blue light . Strikingly , FIN219 overexpression in GUS-CCT1 transgenic lines ( FIN219-OE/GUS-CCT1 ) abolished GUS-CCT1 fusion protein under blue light , whereas GUS-CCT1 fusion protein was stable in the fin219-2 mutant background ( fin219-2/GUS-CCT1 ) . Moreover , FIN219 strongly interacted with COP1 under blue light , and methyl JA ( MeJA ) treatment enhanced the interaction between FIN219 and GUS-CCT1 under blue light . Furthermore , FIN219 level affected GUS-CCT1 seedling responses such as anthocyanin accumulation and bacterial resistance under various light conditions and MeJA treatment . Thus , FIN219/JAR1 and CRY1 antagonize each other to modulate photomorphogenic development of seedlings and stress responses in Arabidopsis .
Integration of light and phytohormones affects many aspects of plant growth and development , including seed germination [1 , 2] , hypocotyl elongation [3–6] and defense responses [7–9] . The molecular mechanisms underlying the interaction leading to physiological responses have been revealed recently [10–12] . Light-activated phytochromes enhance seed germination by negatively regulating PHYTOCHROME-INTERACTING FACTOR3-LIKE5 ( PIL5 ) -mediated activation of GA2ox2 , DELLA and abscisic acid biosynthetic genes [13] . Light integrates with almost all known phytohormones to modulate hypocotyl elongation of seedling development [7] . Recent evidence has revealed a vital role for a light-mediated dynamic balance between plant development and defense responses in regulating the early development of seedlings [8 , 14 , 15] . However , the molecular mechanisms underlying the interaction of monochromatic light such as blue light and jasmonates ( JAs ) remain poorly understood . FAR-RED INSENSITIVE 219/JASMONATE RESISTANT1 ( FIN219/JAR1 ) participates in far-red ( FR ) light signaling [3 , 16 , 17] and functions as a JA-conjugating enzyme responsible for the formation of a physiologically active form , JA-isolecucine ( JA-Ile ) [17] . Ectopic expression of FIN219 in wild-type Columbia ( Col-0 ) resulted in a shorter hypocotyl phenotype than in Col-0 under blue light [3 , S1 Fig] , which suggests that FIN219 may play a role in blue light . In addition , under FR light , FIN219 interacts with CONSTITUTIVE PHOTOMORPHOGENIC 1 ( COP1 ) , a repressor of photomorphogenesis in the dark [18] . Further studies indicated that FIN219 regulates the levels of COP1 negatively and HY5 positively [18] . The blue-light photoreceptors , cryptochromes cry1 and cry2 , regulate hypocotyl elongation and flowering in response to blue-light irradiation [19 , 20] . Ectopic expression of the C-terminal domain of CRY1 or CRY2 in Col-0 ( GUS-CCT1 or GUS-CCT2 ) resulted in a short hypocotyl phenotype under all light conditions , including the dark , which is similar to the cop1 mutant phenotype [21] . Further studies revealed that the cop1-like phenotype caused by GUS-CCT1 overexpression was due to the interaction of GUS-CCT1 and COP1 , which led to a release of HY5 and photomorphogenic development [22 , 23] . Thus , whether FIN219/JAR1 plays a role in blue-light signaling and has a regulatory relationship with CRY1 remains to be elucidated . Another signaling component , SUPPRESSOR OF PHYA-105 ( SPA1 ) , is a repressor of phytochrome A-mediated responses in FR light [24] . SPA1 interacts with COP1 to downregulate HY5 levels , which leads to reduced photomorphogenesis [25] . SPA1 can interact with CRY1 to suppress COP1 activity in response to blue light [26] . Moreover , FIN219 negatively regulates SPA1 transcript levels [18] . Whether FIN219 affects the relative relations among CRY1 , COP1 and SPA1 in response to blue light remains elusive . Here we investigated the regulatory relationship between FIN219 and CRY1 by introducing FIN219 overexpression in GUS-CCT1 transgenic plants with blue light and JA treatment . FIN219 and CRY1 negatively regulated each other by direct interaction in response to JA under blue light . We reveal a vital mechanism in the integration of blue light and JA signaling to control seedling development in Arabidopsis .
We crossed FIN219-overexpressing lines ( FIN219-OE ) with cry1 , cry2 and cry1cry2 mutants and examined the phenotypes of the resulting homozygous transgenic lines ( FIN219-OE/cry1 , FIN219-OE/cry2 , and FIN219-OE/cry1cry2 ) under different light conditions . FIN219-OE/cry1 , FIN219-OE/cry2 , and FIN219-OE/cry1cry2 seedlings exhibited a long-hypocotyl phenotype similar to the respective genetic backgrounds under blue , FR and white light conditions ( Fig 1A and 1B ) , which suggests that the FIN219 function under blue light may require functional CRY1 and CRY2 . FIN219 is mainly responsible for the formation of a physiologically active form of JA-Ile [27] , but how FIN219 functions with CRY1 and CRY2 remains elusive . CRY1 has been implicated in promoting R protein-mediated plant resistance to Pseudomonas syringae in Arabidopsis [28] . CRY1 was also shown to function in drought response [29] , and CRY1 and CRY2 are involved in osmotic stress response in wheat [30] . Thus , stress responses mediated by blue-light photoreceptor cryptochrome may involve JA-mediated signaling . To further elucidate the FIN219 functional relationship with CRY1 in blue-light signaling , we crossed FIN219-OE lines with GUS-CCT1 transgenic lines ectopically expressing the C terminus of CRY1 in a Col-0 background and obtained FIN219-OE/GUS-CCT1 transgenic seedlings . FIN219-OE/GUS-CCT1 transgenic seedlings showed a rescued phenotype similar to Col-0 under blue , FR , white light , and dark conditions and only a partially rescued phenotype , with intermediate hypocotyl length , as compared to Col-0 and GUS-CCT1 under red light ( Fig 1C and 1D ) , so FIN219 may need other factors to complement the GUS-CCT1 phenotype in red light . To understand the molecular mechanisms underlying the phenotypic responses of FIN219-OE/cry1 , FIN219-OE/cry2 , and FIN219-OE/cry1cry2 seedlings under blue light , we examined FIN219 protein level in these seedlings under blue and FR light . Under blue light , FIN219 level increased in the cry1 mutant and only slightly increased in the cry2 mutant ( Fig 2A and 2B ) . In contrast , FIN219 level was substantially greater in FIN219-OE/cry1 than FIN219-OE seedlings , which suggests that CRY1 negatively regulates FIN219 protein level . However , FIN219 level was significantly lower in FIN219-OE/cry2 than FIN219-OE seedlings ( Fig 2A ) , so the CRY2 effect on FIN219 levels might involve mechanisms different from those of CRY1 . FIN219 protein level was strikingly increased in FIN219-OE/cry1cry2 seedlings ( Fig 2A ) . Hence , under blue light , FIN219 overexpression in the cry1cry2 double mutant may suppress other negative regulators , both CRY1 and CRY2 may negatively regulate FIN219 levels , or the substantial increase in FIN219 levels in cry1cry2 may involve unknown mechanisms , not leading to photomorphogenic development for FIN219-OE/cry1cry2 under blue light ( Fig 1A ) . In addition , under FR light , CRY1 negatively regulated FIN219 , but CRY2 played a minor role in modulating FIN219 level ( S2 Fig ) . Moreover , FIN219 level in the cry2 mutant and other mutant backgrounds remained largely the same as in Col-0 ( S2 Fig ) . Thus , CRY1 negatively regulated FIN219 levels under both blue and FR light conditions . Since FIN219 overexpression in GUS-CCT1 seedlings resulted in a rescued phenotype under most of the light conditions examined , we further examined FIN219 protein level in FIN219-OE/GUS-CCT1 seedlings . Under blue light , FIN219 level was lower in GUS-CCT1 than wild-type Col-0 seedlings and was higher in FIN219-OE/GUS-CCT1 than GUS-CCT1 seedlings ( Fig 2B ) but was lower in FIN219-OE/GUS-CCT1 than FIN219-OE seedlings ( Fig 2B ) . The finding is consistent with CRY1 negatively regulating FIN219 under blue light ( Fig 2A ) . Surprisingly , GUS-CCT1 fusion proteins were not detected in FIN219-OE/GUS-CCT1 seedlings but were greatly expressed in GUS-CCT1 seedlings ( Fig 2B ) . Quantitative real-time PCR ( qPCR ) and RT-PCR analyses detected GUS-CCT1 transcripts in FIN219-OE/GUS-CCT1 seedlings ( Fig 2C; S3 Fig ) , which suggests that the disappearance of GUS-CCT1 fusion protein may involve posttranscriptional regulation under blue light . Moreover , the transcript levels of GUS-CCT1 in two independent transgenic lines #11–21 and #13–4 of FIN219-OE/GUS-CCT1 were much lower than that in GUS-CCT1 line . Conversely , FIN219 transcript levels in both #11–21 and #13–4 were greatly abundant compared to Col-0 and GUS-CCT1 line ( Fig 2C; S3 Fig ) . Taken together , the mutual regulation between FIN219 and CRY1 may involve transcriptional as well as posttranscriptional levels . Thus , we further crossed the GUS-CCT1 transgenic line with the fin219-2 mutant and obtained fin219-2/GUS-CCT1 lines to determine whether GUS-CCT1 fusion proteins exist in a fin219 mutant background . In addition , we generated a new line , PGR219/GUS-CCT1 , by crossing the GUS-CCT1 transgenic line with the inducible FIN219-overexpressing line PGR219 to confirm the GUS-CCT1 fusion proteins . GUS-CCT1 fusion protein levels were stably accumulated in fin219-2/GUS-CCT1 seedlings but not FIN219-OE/GUS-CCT1 or PGR219/GUS-CCT1 seedlings ( Fig 2D ) . Thus , FIN219 overexpression in GUS-CCT1 seedlings resulted in undetected levels of the GUS-CCT1 fusion proteins under blue light . COP1 interacts with GUS-CCT1 in the dark and under blue light [22 , 23] . As well , COP1 is negatively regulated by FIN219 [18] . Here , we found that FIN219 negatively regulated COP1 under blue light ( Fig 2A; S4 Fig ) . However , COP1 level in GUS-CCT1 and FIN219-OE/GUS-CCT1 was greater than that in FIN219-OE line , which suggests that COP1 may be modulated by GUS-CCT1 as shown with increased COP1 levels in the cry1 mutant ( S4 Fig ) . Level of HY5 , a positive regulator in photomorphogenesis , was significantly reduced in all samples examined under blue light as compared with Col-0 ( Fig 2B ) . Previous study indicated that FIN219 positively modulated HY5 levels under FR light . Of note , HY5 was downregulated in the fin219-2 mutant as compared to Col-0 and was greatly reduced in FIN219-OE lines under blue light ( Fig 2B ) . Therefore , FIN219-regulated HY5 levels under blue light may involve other factors to modulate seedling development likely through COP1 . We further examined whether degradation of GUS-CCT1 fusion proteins was mediated by the ubiquitin/26S proteasome system . We performed light transition studies by transferring FIN219-OE/GUS-CCT1 seedlings from darkness to blue light for various times with or without the 26S proteasome inhibitor MG132 . GUS-CCT1 fusion proteins were stably present in the dark and greatly reduced at 60 min under blue light ( Fig 3A ) and barely detected at 1 h or longer under blue light ( Fig 3B ) . In contrast , MG132 addition could efficiently stabilize GUS-CCT1 fusion protein level under 15- and 60-min blue-light exposure ( Fig 3A ) , so the degradation of GUS-CCT1 fusion proteins was mediated by 26S proteasome and occurred rapidly under blue light . FIN219 overexpression in GUS-CCT1 seedlings ( FIN219-OE/GUS-CCT1 ) led to degradation of GUS-CCT1 fusion protein under blue light ( Figs 2B , 2D and 3A ) . This observation raises two possibilities: the degradation of GUS-CCT1 protein is caused by 1 ) JA-Ile , because overexpression of FIN219 produces more JA-Ile in cells , or 2 ) protein–protein interactions triggered by FIN219 overexpression . To test the first possibility , we examined the effect of exogenous MeJA on GUS-CCT1 fusion proteins in GUS-CCT1 seedlings under blue light and dark . GUS-CCT1 protein level was stable under both light conditions without MeJA treatment . MeJA slightly reduced GUS-CCT1 level under both light conditions ( Fig 4A ) . In contrast , FIN219 level in GUS-CCT1 seedlings was greatly reduced under blue light as compared to the dark; however , MeJA could greatly enhance FIN219 level under blue light but only slightly in the dark ( Fig 4A ) . We further examined the level of GUS-CCT1 in fin219-2/GUS-CCT1 seedlings . GUS-CCT1 level was present in the dark and increased under blue light as compared to the dark; MeJA addition substantially reduced GUS-CCT1 level in fin219-2/GUS-CCT1 seedlings in the dark , with a marked decrease under blue light as compared to without MeJA ( Fig 4B ) . Thus , MeJA with the conversion to JA-Ile in cells may not be the major factor in the degradation of GUS-CCT1 protein in FIN219-OE/GUS-CCT1 seedlings under blue light . Protein–protein interaction mediated by FIN219 overexpression is likely mainly responsible for the degradation . To further test the possibility of FIN219 and CRY1 interaction , we performed in vitro pull-down assays with the recombinant proteins FIN219 full-length ( GST-FIN219 ) and the N and C terminus of FIN219 ( GST-FIN219N and GST-FIN219C , respectively ) as well as the recombinant proteins CRY1 full-length ( CBP-CRY1 ) and N terminus ( CNT1 ) and C terminus ( CCT1 ) of CRY1 ( Fig 5A ) . FIN219 could interact with CBP-CRY1 and CCT1 with higher affinity via its C than N terminus ( Fig 5B ) . CRY1 proteins from different species show a light-dependent nucleocytoplasmic shuttling pattern [21 , 30 , 31] and FIN219 is mainly a cytoplasmic protein [3] . To further confirm the interaction of FIN219 and CRY1 , bimolecular fluorescence complementation ( BiFC ) assays under the dark revealed that FIN219 interacted with CCT1 , rather than CRY1 in both the cytoplasm and the nucleus and MeJA addition can enhance their interaction in the whole cell ( Fig 5C , top panel ) . In contrast , FIN219 could interact with both CCT1 and CRY1 under blue light ( Fig 5C , bottom panel ) , which suggests that FIN219 interacts with the photoactivated CRY1 . FIN219 also interacted with COP1 under blue light , but did not under the dark , without or with MeJA ( Fig 5C ) . Further co-immunoprecipitation ( Co-IP ) studies were performed with GUS-CCT1 seedlings grown in the dark and blue light with or without MeJA treatment . Indeed , FIN219 could interact with GUS-CCT1 in the dark , but this interaction was greatly reduced under blue light , likely because of a strong interaction between FIN219 and COP1 under the same condition ( Fig 5D ) . Intriguingly , MeJA could greatly enhance the FIN219 and GUS-CCT1 interaction , especially under blue light , but largely abolished the FIN219 and COP1 interaction ( Fig 5D ) . To validate the full-length CRY1 and FIN219 interaction in wild-type Col-0 , Co-IP assays further indicated that FIN219 did interact with CRY1 in Col-0 with less intensity under the dark than blue light . MeJA addition could greatly increase their interaction under the dark and also lead to their interaction with similar intensity to the blue light alone ( Fig 5E ) . The discrepancy between FIN219 and CRY1 interacting affinity detected by BiFC and Co-IP is likely due to protein levels as well as tight regulation in response to blue light and MeJA ( Fig 5C–5E ) . Alternatively , the exposed C terminus of CRY1 ( CCT1 ) may have higher affinity with its interacting partners such as COP1 than the full-length CRY1 . MeJA-induced FIN219 likely competitively binds with GUS-CCT1 under blue light ( Fig 5D ) . It seems that the photoactivated full-length CRY1 can interact with FIN219 via its C terminus ( GUS-CCT1 ) under blue light ( Fig 5D and 5E ) . Thus , FIN219 overexpression in GUS-CCT1 seedlings , likely leading to more JA-Ile , may enhance the FIN219 and GUS-CCT1 interaction under blue light , thereby increasing COP1 association with the FIN219 and GUS-CCT1 complex to cause the degradation of GUS-CCT1 protein . To associate the regulatory relation of FIN219 and CRY1 with physiological responses , we examined the responses of CRY1-related transgenic seedlings with or without MeJA treatment . The cry1 mutant was more sensitive to MeJA-inhibited hypocotyl elongation than Col seedlings under blue light ( Fig 6B and 6C ) . However , GUS-CCT1 seedlings showed an opposite hypocotyl response to MeJA under blue light as compared to the dark ( Fig 6A to 6C ) , which suggests that blue-light irradiation reduces the sensitivity of GUS-CCT1 seedlings to MeJA . FIN219 overexpression in GUS-CCT1 seedlings ( FIN219-OE/GUS-CCT1 ) could compromise GUS-CCT1 phenotypic responses such as hypocotyl elongation and anthocyanin accumulation in the dark ( Fig 6A and 6D ) . In contrast , FIN219 level in GUS-CCT1 seedlings ( FIN219-OE/GUS-CCT1 or fin219-2/GUS-CCT1 ) affected the sensitivity of GUS-CCT1 seedlings to MeJA-inhibited hypocotyl elongation under low and high blue light ( Fig 6B and 6C ) . Moreover , fin219-2/GUS-CCT1 seedlings showing a response to MeJA largely similar to fin219-2 under dark , low and high blue light may reflect CRY1 response to MeJA with a requirement of FIN219 , especially under the dark and high blue light ( Fig 6A–6C ) . Furthermore , GUS-CCT1 seedlings showed more anthocyanin accumulation under all light conditions , especially blue and FR light ( Fig 6D ) ; however , both FIN219-OE/GUS-CCT1 and fin219-2/GUS-CCT1 seedlings showed less anthocyanin accumulation than GUS-CCT1 seedlings ( Fig 6D ) . Therefore , regulation of anthocyanin accumulation under light conditions may involve more signaling regulators in addition to FIN219 and CRY1 . Arabidopsis cryptochromes have been implicated to participate in regulating stress responses [28–30] . We further examined the effect of FIN219 levels on the responses of GUS-CCT1 leaves to the bacterium Pseudomonas syringae pv . tomato ( Pst ) DC3000 . GUS-CCT1 showed a resistant response to Pst . DC3000 infection as compared with the cry1/2 double mutant and fin219-2 , which showed a sensitive response as compared to Col-0 ( Fig 6E ) . However , FIN219-OE/GUS-CCT1 compromised the GUS-CCT1 response , thereby resulting in a phenotype similar to wild-type Col-0 . The fin219-2/GUS-CCT1 was susceptible to Pst . DC3000 , similar to the cry1/2 double mutant . Thus , GUS-CCT1 resistance to Pst . DC3000 may depend on optimal levels of FIN219 .
Our studies revealed the cross talk between blue light and JA signaling in regulating photomorphogenic responses in Arabidopsis such as hypocotyl elongation and anthocyanin accumulation as well as bacterial pathogen response . In the dark , FIN219/JAR1 interacted with the C terminus of CRY1 ( CCT1 ) and the full-length CRY1 ( Fig 5D and 5E ) . COP1 and suppressor of phytochrome A-105 1 ( SPA1 ) interacts with HY5 in the dark [25 , 32] , which leads to the degradation of HY5 and a skotomorphogenic development of Arabidopsis seedlings ( Fig 7A ) . In contrast , FIN219/JAR1 strongly interacts with COP1 and CRY1 under blue light , thereby leading to CRY1-mediated photomorphogenesis ( Fig 7B ) . Under blue light and MeJA treatment , FIN219/JAR1 greatly interacts with CCT1 and weakly with COP1 , likely enhancing COP1 access to CCT1 , which results in the suppression of hypersensitive short-hypocotyl phenotype of GUS-CCT1 ( Fig 7C ) , leading to the outcomes with a rescued phenotype of FIN219-OE/GUS-CCT1 under blue light ( Fig 1C and 1D ) . The cross talks between light and JA signaling , especially FR light and JA signaling , are being revealed [12 , 33 , 34] . Phytochrome chromophore-mediated signaling and the JA signaling pathway mutually regulate each other in an antagonistic manner [35 , 36] . Moreover , phytochrome inactivation by FR light greatly reduces plant sensitivity to jasmonates [35] . phyA was shown to be required for JA- and wound-mediated JAZ1 degradation [12] . As well , phyA in rice requires JA for photo-destruction [37] . Therefore , phyA- and JA-mediated signaling regulate each other to modulate seedling development . In addition , AtMYC2/JIN1 , a basic helix-loop-helix transcription factor , can bind to the Z- and G-box light-responsive elements of light-regulated promoters and acts as a vital regulator in light , abscisic acid ( ABA ) and JA signaling pathways in Arabidopsis [38] . LeMYC2 in tomato functions in a similar manner to regulate photomorphogenesis and tomato growth in response to the combined effects of blue light , ABA and JA signaling [39] . Recent studies also indicated that rice phyAphyC mutant seedlings greatly increased JA and JA-Ile levels as compared with the wild type in response to blue light [40] , which suggests that phyA and phyC in rice may redundantly and negatively modulate JA biosynthesis under blue light . Indeed , we found that the blue-light photoreceptor CRY1 negatively regulated the levels of FIN219 , a JA-conjugating enzyme for the formation of JA-Ile [27] , under blue light ( Fig 2A ) . How FIN219/JAR1 regulates cry1-mediated blue light signaling is intriguing . Ectopic expression of FIN219 in wild-type Col produced a hypersensitive short-hypocotyl phenotype under blue light ( Fig 1 , S1 Fig ) , which suggests functional roles of FIN219 in blue light-inhibited seedling development . This speculation is substantiated by results showing that ectopic expression of FIN219 in different cry mutants produced a long-hypocotyl phenotype as well as a FIN219-rescued GUS-CCT1 phenotype under blue light ( Fig 1 ) . Further evidence revealed greatly enhanced FIN219 and GUS-CCT1 interaction with MeJA treatment under blue light . Moreover , FIN219 in GUS-CCT1 seedlings strongly interacted with COP1 under blue light ( Fig 5D ) , which may restrict COP1 activity and release HY5 , thereby leading to photomorphogenesis . In addition , CCT1 interacting with SPA1 depends on blue light , which leads to reduced COP1 and SPA1 interaction as well as COP1 E3 ligase activity [26 , 41] . FIN219 and COP1 interaction as well as CCT1 and SPA1 interaction under blue light are likely highly involved in photomorphogenic development of seedlings . As FIN219 level increases , it becomes associated with CRY1 , which suppresses CRY1 functions likely by competing out SPA1 binding with CRY1 , thereby producing longer hypocotyls under blue light as compared with GUS-CCT1 seedlings ( Fig 1 ) . In addition , FIN219 itself is mainly localized in the cytoplasm [3] . However , it could be localized in the nucleus when associated with nuclear-localized proteins . CRY1 is more specifically localized in the nucleus in response to blue light ( Fig 5C , bottom panel ) [31 , 42] . Here , we found that FIN219 interacted with CRY1 in the nucleus under blue light ( Fig 5C , bottom panel ) . However , FIN219 interacted with COP1 under blue light likely in the cytoplasm ( Fig 5C , bottom panel ) . Our previous studies showed that FIN219 overexpression resulted in COP1 accumulation in the cytoplasm even in the dark [18] . In contrast , ethylene under light conditions can trigger COP1 accumulation in the nucleus [6 , 43] . Thus , JA and ethylene in addition to the light effect may antagonize each other to modulate the subcellular location of COP1 in regulating plant photomorphogenic development . Therefore , FIN219 levels need to be tightly regulated to modulate seedling development in response to various light conditions . This conclusion is also consistent with GUS-CCT1-mediated responses such as anthocyanin accumulation ( Fig 6D ) and resistance to Pst . DC3000 ( Fig 6E ) . In addition , FIN219 levels were greater in cry1 mutant than Col-0 seedlings and substantially greater in FIN219-OE/cry1cry2 than cry1cry2 mutant or FIN219-OE seedlings ( Fig 2A ) ; however , the hypocotyl phenotype of FIN219-OE/cry1cry2 and the cry1cry2 mutant was similar ( Fig 1A and 1B ) , which suggests that FIN219 function in blue light may require functional CRY1 and CRY2 . Moreover , the increased accumulation of FIN219 in FIN219-OE/cry1cry2 seedlings might be in an inactive form of FIN219 involving posttranslational modifications such as phosphorylation , which remains to be further elucidated . Our study revealed an antagonistic regulation between CRY1 and FIN219/JAR1 in modulating blue light-inhibited hypocotyl elongation in Arabidopsis and their protein levels under blue light ( Figs 1 and 2 ) . FIN219/JAR1 function requires functional CRY1 in regulating hypocotyl elongation . Moreover , CRY1 functions require an optimal level of FIN219/JAR1 to optimize photomorphogenic development and stress responses such as anthocyanin accumulation and pathogen resistance ( Fig 6 ) . Thus , the cross talks between blue light and JA signaling pathways are critical in regulating seedling development and biotic stress responses in Arabidopsis .
Throughout this study , the wild-type plant is Arabidopsis thaliana ecotype Columbia . Blue-light photoreceptor mutants cry1 ( cry1-304 ) [19] , cry2 ( cry2-1 ) [44] and the cry1cry2 ( cry1-304cry2-1 ) [45] double mutant were in the Columbia ecotype . The fin219 knockout mutant ( fin219-2 ) in the Columbia ecotype was the T-DNA insertion line SALK_059774 obtained from ABRC [18] . Transgenic GUS-CCT1 was established previously [21] . The overexpression of FIN219 ( FIN219-OE ) in a Columbia background was established previously [3] . FIN219-OE/cry1 , cry2 or cry1cry2 and FIN219-OE/GUS-CCT1 were generated by crossing FIN219-OE with respective mutants . The resulting progenies were selected with 100 μg/ml gentamycin ( MdBio , Taipei ) for FIN219-OE and with genotyping for cry1 and cry2 mutants by using primer sequence pairs: cry1-304 , 5'- CATGAGCTCATGTCTGGTTCTGTATCTGGTT -3' and 5'- CATGTCGACTGAAAGCGCTTCATGAA -3'; cry2-1 , 5'- CATGAGCTCATGAAGATGGACAAAAAGAC -3' and 5'- CATGGTACCAGCTTTAGCTAGTAGCTCACG -3' . Hygromycin 25 μg/ml ( MdBio , Taipei ) was also used for selecting FIN219-OE/GUS-CCT1 . We crossed a GUS-CCT1 transformant with fin219-2 and an inducible FIN219-overexpressing line PGR219 ( pGR:FIN219 ) . F3 and F4 seedlings were selected with 25 μg/ml hygromycin and 50 μg/ml kanamycin for PGR219/GUS-CCT1 , and 50 μg/ml kanamycin and genotyping by using primer sequence pairs for FIN219-LP-F: 5’-CTACATTTTTGCTGCTCCGTC-3’; FIN219-RP-R: 5’-AAAAGCAGTGCGAAACAGTTG-3’; and LBb1 . 3: ATTTTGCCGATTTCGGAAC for fin219-2/GUS-CCT1 . Seeds were surface-sterilized with 20% bleach containing 0 . 5% Tween-20 [18] and sown on GM agar plates containing 0 . 3% sucrose for phenotype analysis and MeJA ( 50 μM ) for its effects and molecular analysis . Seedlings grown in continuous blue light ( 2 μmol m-2 s-1 ) or far-red light ( 3 μmol m-2 s-1 ) for 3 days were harvested for protein extraction . For MG132 treatment , FIN219OE/GUS-CCT1 seedlings were grown in the dark for 2 days , then transferred to blue light with or without MG132 ( 50 μM ) for 15 or 60 min or 24 h . Total proteins were extracted with extraction buffer ( 50 mM Tris-HCl , pH7 . 5 , 150 mM NaCl , 10 mM MgCl2 , 0 . 1% NP-40 , 1 mM PMSF and 1X protease inhibitor ) as described [18] . Total proteins , 50 μg , were loaded in each lane and separated on 10% SDS-PAGE and transferred to a PVDF membrane ( Millipore ) . Protein gel blot analysis involved standard methods and bands were detected with GUS , COP1 , HY5 or FIN219 ( monoclonal ) antibodies [7] . Total RNA was isolated from 4-day-old seedlings grown under the dark or blue light . For high-fidelity RT-PCR , 5 μg total RNA was treated with RQ1 DNase I ( Promega , Madison , WI ) according to the manufacturer’s instructions to remove possible DNA contamination . Then 3 μg of DNase-treated total RNA underwent reverse transcription at 42°C for 1 h with Ready-To-Go RT-PCR beads ( Amersham-Pharmacia Biotech , Rome , Italy ) and was inactivated at 95°C for 10 min . GUS-CCT1 was amplified by using GUS3’-F and CRY1-R primers from 1 μl of 50 μl of cDNA by PCR for 30 cycles ( 95°C , 30 s; 55°C , 30 s; 72°C , 40 s ) with a Peltier thermal cycler ( MJ Research , Watertown , MA ) ; c-myc-FIN219 for 35 cycles ( 95°C , 30 s; 52°C , 30 s; 72°C , 150 s ) with Myc5’-F and FIN219-R-XhoI primers , and ubiquitin10 for 28 cycles ( 95°C , 30 s; 60°C , 30 s; 72°C , 40 s ) with UBQ10-F and UBQ10-R primers . pGEX-4T-1 , FIN219FL/pGEX-4T-1 , FIN219N300/pGEX-4T-1 and FIN219C274/pGEX-4T-1 were created as described [18] . Fragments of CRY1 , CNT1 , and CCT1 PCR-amplified with gene-specific primers were cloned into SalI and SacI sites of pCal-n ( Stratagene , La Jolla , CA ) . Resulting constructs ( CRY1/pCal-n , CNT1/pCal-n , and CCT1/pCal-n ) were used for expressing the recombinant fusion proteins CBP-CRY1 , CBP-CNT1 , and CBP-CCT1 , respectively . The recombinant plasmids were transformed into E . coli BL21 , then induced with 0 . 1 mM isopropylthio-β-galactoside ( IPTG ) at 25°C overnight ( pGEX-4T-1 , FIN219FL/pGEX-4T-1 and CCT1/pCal-n ) or 1 mM IPTG at 37°C for 4 h ( FIN219N300/pGEX-4T-1 , CRY1/pCal-n , and CNT1/pCal-n ) for expression in E . coli hosts BL21 ( DE3 ) codon plus ( pGEX-4T-1 , FIN219FL/pGEX-4T-1 , FIN219N300/pGEX-4T-1 , and CRY1/pCal-n ) or E . coli BL21 ( DE3 ) pLysS ( FIN219C274/pGEX-4T-1 , CNT1/pCal-n , and CCT1/pCal-n ) . Recombinant fusion proteins were purified with use of GSH sepharose ( Amersham-Pharmacia Biotech , Rome , Italy ) for GST , GST-FIN219 full-length , and GST-FIN219-C274; calmodulin affinity resin ( Stratagene , LaJolla , CA ) for CBP-CCT1; or electroelusion ( Bio-Rad , Hercules , CA ) for GST-FIN219-N300 , CBP-CRY1 , and CBP-CNT1 according to the manufacturer’s procedures . All recombinant fusion proteins were precipitated with acetone and resuspended in PBS solution ( 80 mM Na2HPO4 , 20 mM NaH2PO4 , 100 mM NaCl ) . Then recombinant fusion proteins were concentrated by using Amicon Ultra-4 ( Millipore , Billerica , MA ) for downstream analysis . An amount of 5 μg purified recombinant protein was mixed in 500 μl interaction buffer ( 50 mM Tris-HCl , pH 7 . 5 , 10 mM MgCl2 , 100 mM NaCl , 1 mM phenylmethylsulfonyl fluoride , 0 . 04% NP-40 ) with 1 x protease inhibitors ( Invitrogen , Carlsbad , CA ) , then incubated at 4°C at 30–40 rpm for 1 h . Well-equilibrated GSH sepharose was added to the mixture and incubated at 4°C for another hour . After centrifugation ( 500 g for 5 min ) , the pellet was washed with interaction buffer and analyzed by protein gel blot analysis . Seedlings grown in the dark or continuous blue light for 3 days were ground with extraction buffer as described [18] . Co-immunoprecipitation analysis followed the manual ( GE , USA ) . A total of 2 mg protein was mixed with beads and incubated at 4°C for 4 h , then washed three times with TBST washing buffer ( TBS with 0 . 05% Tween-20 , pH7 . 5 ) . Pellets were analyzed by SDS-PAGE and protein gel blot analysis . Arabidopsis mesophyll protoplast isolation and transfection were as described previously [18] . We constructed BiFC plasmids as described [18] . The full-lengths of CRY1 or COP1 , and FIN219 were cloned into 35p-YFP-N155/pRTL2 and 35p-YFP-C84/pRTL2 , respectively . The nuclei of protoplasts were marked with NLS-mCherry cloned into the pEarlyGate 100 . All fluorescence images were obtained by use of a Nikon CI-L/Nikon Ri2 Cooling fluorescence microscope and processed by use of Adobe Photoshop . For anthocyanin determination in seedlings , harvested samples were weighed and ground in liquid nitrogen , and total plant pigments were extracted overnight in 300 μl 1% HCl in methanol . After the addition of 200 μl H2O , chlorophyll was separated from anthocyanin by extraction with an equal volume of chloroform . The content of anthocyanin in the upper phase was quantified by spectrophotometry ( A530-A657 ) and normalized to the fresh weight of seedlings [11] . Bacteria ( Pseudomonas syringae pv . tomato DC3000 ) grown on King’s medium B [38] containing 50 μg/ml rifampicin for 2 days at 28°C were diluted with appropriate 10 mM of MgCl2 solution ( 1X106 cfu ml-1 , OD600 = 0 . 002 ) . For infiltration inoculation , bacterial suspension cells were injected into leaves of 5-week-old Arabidopsis plants grown under short-day conditions through stomatal pores on the leaf surface by using a needle-less syringe . After 2 days , infected leaves were collected , weighed and grounded with plastic pestles . To assay bacterial populations , samples were serially diluted with 10 mM MgCl2 and plated on KB solid medium at 28°C for 2 days to count colony units . One-way ANOVA was used to quantify hypocotyl length , root length , chlorophyll content , anthocyanin accumulation and bacteria number by using SAS 9 . 3 . | The crosstalks between light and plant hormones are critical in plant growth and development as well as stress responses . This study reveals the interaction between blue light and jasmonate ( JA ) signaling via direct interaction of the photoreceptor CRY1 and a JA-conjugating enzyme FIN219/JAR1 . FIN219 function in blue light requires the functional blue light photoreceptor CRY1 . Gene expression and genetic studies showed a mutually antagonistic relationship between FIN219 and CRY1 , which suggests that a critical balance between blue light and JA signaling is important for seedling development and stress responses . Intriguingly , FIN219-mediated rescue of the GUS-CCT1 seedlings was due to degradation of GUS-CCT1 fusion protein as a result of FIN219 interaction with GUS-CCT1 under blue light , which likely involves increased activity of COP1 and reduced levels of HY5 . Thus , our studies reveal an antagonistic link between blue light and JA signaling pathways in regulating seedling development and stress responses in Arabidopsis . | [
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"experime... | 2018 | FIN219/JAR1 and cryptochrome1 antagonize each other to modulate photomorphogenesis under blue light in Arabidopsis |
Treponema pallidum ssp . pallidum ( TPA ) , the causative agent of syphilis , and Treponema pallidum ssp . pertenue ( TPE ) , the causative agent of yaws , are closely related spirochetes causing diseases with distinct clinical manifestations . The TPA Mexico A strain was isolated in 1953 from male , with primary syphilis , living in Mexico . Attempts to cultivate TPA Mexico A strain under in vitro conditions have revealed lower growth potential compared to other tested TPA strains . The complete genome sequence of the TPA Mexico A strain was determined using the Illumina sequencing technique . The genome sequence assembly was verified using the whole genome fingerprinting technique and the final sequence was annotated . The genome size of the Mexico A strain was determined to be 1 , 140 , 038 bp with 1 , 035 predicted ORFs . The Mexico A genome sequence was compared to the whole genome sequences of three TPA ( Nichols , SS14 and Chicago ) and three TPE ( CDC-2 , Samoa D and Gauthier ) strains . No large rearrangements in the Mexico A genome were found and the identified nucleotide changes occurred most frequently in genes encoding putative virulence factors . Nevertheless , the genome of the Mexico A strain , revealed two genes ( TPAMA_0326 ( tp92 ) and TPAMA_0488 ( mcp2-1 ) ) which combine TPA- and TPE- specific nucleotide sequences . Both genes were found to be under positive selection within TPA strains and also between TPA and TPE strains . The observed mosaic character of the TPAMA_0326 and TPAMA_0488 loci is likely a result of inter-strain recombination between TPA and TPE strains during simultaneous infection of a single host suggesting horizontal gene transfer between treponemal subspecies .
Treponema pallidum ssp . pallidum ( TPA ) and Treponema pallidum ssp . pertenue ( TPE ) strains , the causative agents of syphilis [1] and yaws [2] , infect more than 12 and 2 million people annually , respectively [3] . Whereas syphilis is a sexually transmitted and congenital disease affecting adults and newborns worldwide , yaws is transmitted predominantly through direct skin contact and affects preferably children in warm , humid , rural areas . During the last several years , a number of treponemal genomes have been completely sequenced including TPA Nichols ( GenBank acc . no . AE000520 . 1 [4] ) , TPA SS14 ( CP000805 . 1 [5] ) , TPA Chicago ( CP001752 . 1 [6] ) , TPE Samoa D ( CP002374 . 1 ) , TPE CDC-2 ( CP002375 . 1 ) , TPE Gauthier ( CP002376 . 1 ) [7] and T . paraluiscuniculi strain Cuniculi A ( CP002103 . 1 [8] ) . In general , when compared to TPE strains , TPA strains differ by less than 1 , 200 nucleotide positions [7] , [9] . Phylogenetic trees constructed from whole genome binary restriction target site data [9] , from multilocus sequencing [10] and whole genome sequence alignments [10] showed a distinct clustering of TPA and TPE strains . As shown by Centurion-Lara et al . [11] and Gray et al . [12] , the unusual clustering of the Mexico A TP0131 gene with several TPE strains is the result of intra-chromosomal gene conversion events . Three different alleles of the tprD ( TP0131 ) gene ( D , D2 , and D3 ) have been identified among TPA and TPE strains [11] and the presence of individual gene alleles determines the cluster patterns [12] . The TPA Mexico A strain was isolated in 1953 from an 18-year-old male , with primary syphilis , living in Mexico [13] . Attempts to cultivate TPA Mexico A strain under in vitro conditions revealed a lower growth rate ( compared to other tested TPA strains ) and also a decreased percentage of motile treponemes compared to TPA strain Nichols [14] . The lower growth potential of Mexico A is likely to result from genetic differences between this strain and other TPA strains . Our previous study [9] revealed that the Mexico A strain contained the largest genome of all investigated TPA strains . In this study , we compared the complete genome sequence of TPA Mexico A to complete TPA and TPE genome sequences and found a mosaic character of the Mexico A TPAMA_0326 ( tp92 ) and TPAMA_0488 ( mcp2-1 ) loci , i . e . having both TPA and TPE specific nucleotide sequences .
The TPA Mexico A strain used in this study was kindly provided by David L . Cox , CDC , Atlanta , GA , USA . The DNA was amplified directly from 1 µl of cells ( 105 cells per µl ) frozen in glycerol using a QIAGEN Whole Genome Amplification REPLI-g Kit ( QIAGEN , Valencia , CA , USA ) . To separate treponemal cells from rabbit testicular cells , the samples were first centrifuged at 100×g for 5 min . Supernatant containing treponemal cells was carefully extracted and centrifuged at 14 , 100×g for 3 min . The resulting pellet containing treponemal cells was washed 2× in PBS buffer and centrifuged at 14 , 100×g for 3 min . The supernatant was removed for a final volume of 3 µl and the procedure continued according to the manufacturer's instructions . Amplified DNA was purified using a QIAEX II kit ( QIAGEN , Valencia , CA , USA ) . The resulting DNA concentration was 602 ng/µl in a 30 µl volume . The chromosomal DNA was sequenced using the Illumina ( Illumina , San Diego , CA , USA ) technique . Several chromosomal regions of the Mexico A strain , representing sequentially related and repetitive components of treponemal genome , were amplified using a GeneAmp XL PCR kit ( Applied Biosystems , Foster City , CA , USA ) using the previously described TPI amplicons [9] , [15] . These regions comprised the following TPI amplicons ( genes ) : TPI-11 ( tprC ) , TPI-12 ( tprD ) , TPI-13B ( TP0136 ) , TPI-17A ( 5S , 16S and 23S rRNA [rRNA operon 1] ) , TPI-21B ( 5S , 16S and 23S rRNA [rRNA operon 2] ) , TPI-25A ( tprE ) , TPI-25B-A ( tprF ) , TPI-25B-B ( tprG ) , TPI-26 ( TP0326 ) , TPI-32B ( arp ) , TPI-34 ( TP0470 ) , TPI-38 ( TP0488 ) , TPI-42A ( TP0548 ) , TPI-48 ( tprI , tprJ ) , TPI-66A ( TP0868 ) and TPI-67 ( tprK ) . From these XL-PCR amplicons , small insert libraries were prepared and the resulting clones were sequenced as previously described [5] . Alternatively , amplified DNA was Sanger sequenced directly using specific primers . A set of 639 Illumina contigs ( 100–69 , 908 bp in length ) and 16 Sanger contigs , resulting from sequencing of XL-PCR products , were assembled using the TPA SS14 reference genome [5] . This assembly contained 122 gaps ( 8 . 9 kb in length ) in the TPA Mexico A sequence . Altogether , 117 DNA regions ( containing all 122 gaps ) were additionally PCR amplified and sequenced using the Sanger method . The TP0326 ( tp92 ) and TP0488 ( mcp2-1 ) loci of Treponema pallidum subsp . endemicum ( TEN ) , strain Bosnia A , were amplified using GeneAmp XL PCR kit ( Applied Biosystems , Foster City , CA , USA ) and Sanger sequenced using specific primers . The resulting genome assembly was verified using the previously described fingerprinting technique [15] , [16] . The experimentally identified DNA fragments ( resulting from DNA digestion at 1774 restriction target sites; [7] ) were compared to the corresponding in silico restriction fragment lengths . The 1774 restriction target sites corresponded to a total sequence length of 10 . 6 kb . The average error rate of WGF was calculated previously [8] and corresponded to 27 . 9 bp ( 1 . 6% of the average fragment length ) with a variation range between 0 and 132 bp . Considering the close relatedness of the Mexico A and SS14 genomes ( 99 . 99% identity at the nucleotide level ) , the Mexico A genome was annotated according to the SS14 genome [5] with minimal gene length of 150 bp . Genes identified in the Mexico A genome were denoted with the prefix TPAMA followed by four numbers to indicate gene number . Putative virulence factors were defined as those previously described by Čejková et al . [7] and comprised 31 genes ( including tpr , arp , and TPAMA0136 genes ) . All of these genes are listed in Table S1 . The G+C content was calculated in 501 bp windows using CLC Bio software ( CLC Bio Katrinebjerg , Denmark ) . The whole genome sequence of TPA strain Mexico A was placed in the GenBank under accession number CP003064 . 1 . Sequences of TP0326 ( tp92 ) and TP0488 ( mcp2-1 ) of TEN strain Bosnia A were deposited in the GenBank under accession numbers JX392330 . 1 and JX392331 . 1 , respectively .
The genome of the Mexico A strain was determined to be 1 , 140 , 038 bp with 1 , 035 predicted ORFs . The final assembled genome sequence was verified using a fingerprinting technique [15] , [16] where 1774 experimentally identified DNA fragments were compared to in silico restriction fragment lengths . No differences in fragment lengths were identified indicating correct overall assembly of the Mexico A genome . The 1774 restriction target sites corresponded to a total sequence length of 10 . 6 kb . Since no discrepancies between the in silico and the experimental restriction analysis were found ( i . e . in 10 . 6 kb of the genome sequence out of 1 , 140 kb ) , the sequencing error rate was estimated to 10−4 or less . From all annotated ORFs , 161 ( 15 . 6% ) are involved in general metabolism , 125 ( 12 . 1% ) in cell structure and cell processes , 51 ( 4 . 9% ) in DNA replication , repair and recombination , 173 ( 16 . 7% ) in regulation , transcription and translation , 113 ( 10 . 9% ) in transport , and 31 ( 3% ) in virulence . 327 ORFs ( 31 . 6% ) had unknown function . In addition , 54 ( 5 . 2% ) genes encoded RNAs . Coding regions represented 93 . 5% of the Mexico A genome . As in the SS14 ( CP000805 . 1 [5] ) and Chicago ( CP001752 . 1 [6] ) genomes , the tprK gene ( TPAMA_0897 ) is represented by a number of variable sequences and the consensus sequence , therefore , contains unidentified nucleotides in these regions . Altogether , six genes ( pseudogenes ) were annotated to contain authentic frameshifts ( AF ) in the Mexico A genome ( TPAMA_0009 , TPAMA_0146 , TPAMA_0316 , TPAMA_0520 , TPAMA_0532 and TPAMA_0812 ) compared to 9 genes with AF annotated in the Nichols and SS14 genomes , where 3 additional genes with AF were described ( TP0217 , TP0575 and TP0866 ) . In an additional 21 cases , frameshift mutations identified in the Mexico A genome resulted in gene fusions ( Table S2 ) . Whole genome sequence of the TPA strain Mexico A has been compared with other sequenced genomes of TPA strains including the Nichols strain ( AE000520 . 1 [4] ) , SS14 ( CP000805 . 1 [5] ) , and Chicago ( CP001752 . 1 [6] ) using the Lasergene software package ( DNASTAR , Madison , WI , USA ) and Crossmatch ( P . Green , unpublished ) . Because of high sequence diversity , TP0131 ( tprD ) and TP0897 ( tprK ) were excluded from our calculations . The Mexico A genome differed from the SS14 genome in 175 substitutions , 85 insertions and 28 deletions , from the Chicago genome in 419 substitutions , 18 insertions and 20 deletions , and from the Nichols genome in 438 substitutions , 94 insertions and 38 deletions ( ambiguously identified bases present in the Nichols genome were not counted ) . Changes differentiating Mexico A and Nichols genomes were found in 206 ORFs listed in Table S3 . Since it is known that the Nichols and SS14 genomes contain about 200 nt errors ( [10] , Pospíšilová , unpublished results ) , we also compared the Mexico A genome with the improved version of the Nichols genome ( Pospíšilová , unpublished results ) . From 206 ORFs originally identified as sequentially different , 138 ORFs ( 67% ) also showed differences when compared to the improved Nichols genomic sequence . The originally identified nucleotide changes in the remaining 68 ORFs ( 33% ) were considered to be Nichols sequencing errors . However , in the case of 14 Nichols ORFs ( 1 . 3% of the total Nichols ORFs ) , only partial or no sequencing data were available . In general , the identified changes were more frequently found among genes encoding putative virulence factors and among genes involved in cell structure and processes and in genes coding for DNA replication , repair and recombination . In contrast , genes encoding components associated with general metabolism , transcription , translation , gene regulation and transport contained nucleotide changes less frequently ( Table S4 ) . In addition to TPA strains , the Mexico A genome sequence was also compared with whole genome sequences of three TPE strains including Samoa D ( GenBank acc . no . CP002374 . 1 ) , CDC-2 ( CP002375 . 1 ) and Gauthier ( CP002376 . 1 ) [7] . Of all the annotated genes , two ( TPAMA_0326 ( tp92 ) and TPAMA_0488 ( mcp2-1 ) ) showed a mosaic character , which combined sequences from both TPA and TPE strains ( Fig . 1 ) . The complete set of nucleotide changes found in the TPA and TPE regions for TP0326 and TP0488 loci are shown in Table 1 and Table 2 , respectively . In the TP0326 locus , there were 8 single nucleotide positions and one 15 bp deletion that differentiated TPE strains ( Samoa D , CDC-2 and Gauthier ) from TPA strains ( Nichols , SS14 , Chicago ) . Out of these 9 positions , the TPAMA_0326 locus contained 5 nucleotide positions with an identical sequence to the TPA strains and 4 regions that were identical to the TPE regions , including 3 nucleotide positions and the 15 bp deletion ( Fig . 1 , Table 1 ) . Similarly , the TP0488 locus contained 30 nucleotide positions that were found to be different for all analyzed TPA and TPE strains . In addition , two nucleotide positions ( 584 , 1655 ) differentiated the Nichols and Chicago strains from TPE strains and from the SS14 strain . In TPAMA_0488 , 12 of these 30 positions contained sequences identical to TPA strains , whereas 18 positions corresponded to sequences of TPE strains ( Fig . 1 , Table 2 ) . In the remaining part of the Mexico A genome , similarities to the TPE sequences were only found in the tprC sequence and at two additional nucleotide positions ( present in TP0314 locus and TPAMA_0319 , respectively ) . The average G+C content of the Mexico A genome was found the same as for other treponemal species , 52 . 8% . Based on an analysis of G+C content , codon and amino acid usage , and gene positions , 77 ( 8 . 32% ) of the TPA genes were predicted to be horizontally transferred [17] . To identify chromosomal regions with horizontal transfer potential , G+C content was calculated in 501 bp windows in TPA Mexico A , TPA SS14 [5] , TPE Samoa D [7] and Treponema paraluiscuniculi strain Cuniculi A ( CP002103 . 1 [8] ) ( Fig . 2 ) . The chromosomal regions showing different G+C content ( defined as G+C content higher than 63% or lower than 41% ) showed a similar pattern in all four tested genomes . We compared regions with higher/lower G+C content with 5 kb-long chromosomal regions containing 40 or more nucleotide changes differentiating TPA and TPE strains which were previously identified by Čejková et al . [7] . From 11 such regions [7] ( Fig . 2 ) , only 3 showed significant differences in G+C content . Similarly , no clear association was found in regions with different G+C content and tpr-containing DNA regions .
Complete genome sequences of the TPA Mexico A strain was revealed . The genome size , G+C content and gene order was identical with other already sequenced TPA genomes [4]–[6] . The Mexico A genome was most closely related to SS14 genome and differed in less than 300 hundred substitutions and indels . Since it has been published that the Nichols and the SS14 genomes contain about 200 nt errors [10] a lower number of nucleotide changes differentiating the Mexico A and SS14 genome can be expected . In fact , the number of nucleotide differences between Mexico A and SS14 genomes ( except of differences present in the tprD and tprK genes ) is probably lower than one hundred ( Pětrošová , unpublished results ) . In any of these comparisons , the identified differences were more frequently present in ( i ) genes encoding putative virulence factors , ( ii ) genes involved in cell structure and processes and ( iii ) genes coding for DNA replication , repair and recombination . In contrast , genes encoding components of general metabolism , transcription , translation , gene regulation and transport appear to be conserved . The observed mosaic character of the Mexico A TPAMA_0326 ( tp92 ) and TPAMA_0488 ( mcp2-1 ) loci , combining both TPA- and TPE-specific nucleotide sequences , can be , in principle , explained by six independent mechanisms including i ) an ancestral position of the Mexico A strain with respect to both TPA and TPE strains , ii ) rapid accumulation of nucleotide changes during evolution of TPA strains from TPE strains with the Mexico A as an intermediate , iii ) intra-strain recombination between paralogous sequences , iv ) artifacts during PCR amplification ( as a result of contamination with TPE genomic DNA ) and/or contamination with TPE-amplified DNA , v ) convergent evolution and vi ) inter-strain recombination between TPA and TPE strains during simultaneous infection of one host . i ) The first explanation can be ruled out because only two chromosomal loci ( TPAMA_0326 and TPAMA_0488 ) showed demonstrable similarity to TPE strains . Moreover , the number of Mexico A-specific mutations ( i . e . , mutations that are only present in the Mexico A genome and not in other sequenced TPA genomes ) is not significantly different from the number of specific mutations in other TPA genomes ( data not shown ) . In a predicted common ancestor , one would expect a considerably higher number of ancestor-specific mutations in comparison to progenies . ii ) The second hypothesis is illustrated in Fig . 3B . The hypothetical evolution scheme comprises TPA , TPE and TEN strains arranged according to their relatedness to other TP strains [18] ( see also Fig . 3A ) . We sequenced TP0326 ( tp92 ) and TP0488 ( mcp2-1 ) loci in TEN strain Bosnia A ( GenBank acc . no . JX392330 . 1 and JX392331 . 1 , respectively; our TP0326 sequence is identical to partial tp92 sequence of Bosnia A published by Harper et al . [19] ) . The sequencing data showed that TEN strain Bosnia A contains the same nucleotide mosaic in the TP0488 ( mcp2-1 ) locus as Mexico A ( with the exception of 2 single nucleotide substitutions ) and similarly , some TPA isolates belonging to the SS14-like group of TPA strains show a TEN-specific pattern in the TP0326 ( tp92 ) locus . It was impossible to propose an evolutionary model based only on accumulation or loss of nucleotide changes ( see Fig . 3B ) , and this fact supports recombination hypothesis . iii ) The third hypothesis was rejected when we failed to identify potential recombinant ( donor ) sites for the TPAMA_0326 and TPAMA_0488 genes in the Mexico A genome , despite several attempts to identify such regions using several computer programs and algorithms ( RDP3 , EditSeq ( DNASTAR ) , BLAST ) . iv ) While it is known that PCR amplification of two sequentially related templates can result in the production of chimeric DNA amplicons [20] , contamination of the Mexico A genomic DNA with TPE genomic DNA can be ruled out because recombinant genes were only found for two genes of the genome . Contamination with TPE-amplified DNA ( corresponding to TPAMA_0326 and TPAMA_0488 genes ) was excluded based on careful analysis of Illumina reads , where no TPA- or TPE-specific Illumina reads were found in any of these regions . In fact , the presence of 15 bp-deletions in the TPAMA_0326 gene was found in all 169 individual Illumina reads covering this region . Similar analysis of the TPAMA_0488 region revealed no TPA- or TPE-specific Illumina reads; and all 37 reads , covering regions with both TPA and TPE molecular signatures , revealed the Mexico A consensus sequence . Since Illumina technology sequences individual DNA molecules , contamination of Mexico A genomic DNA with TPE PCR product can be excluded . To exclude artifacts during REPLI-g kit amplification of the Mexico A genomic DNA , three different REPLI-g amplifications were used for TPAMA_0326 and TPAMA_0488 sequencing . No discrepancies were identified during analysis of Sanger reads in these regions . Moreover , Harper et al . [19] sequenced partial tp92 locus of the Mexico A strain ( obtained directly from CDC , Atlanta ) and the sequenced region ( 960 nt , GenBank acc . no . EU102088 . 1 , containing TPE-like sequence in three nucleotide positions and a 15-bp deletion ) was identical to our sequence . Sequences of TP0326 ( tp92 ) from various TPE isolates published by Harper et al . [19] contained the 15 bp TPE-like deletion and also corresponded to TPE-like changes in the South Africa treponemal isolate . All 21 South Africa partial nucleotide sequences available in the GenBank [19] were 100% identical to the corresponding sequences of Mexico A published by Harper et al . [19] . Therefore , the South Africa strain appears to be another strain that is identical , or very closely related , to the Mexico A strain . Nevertheless , we found 3 nucleotide changes differentiating South Africa and Mexico A sequences published by Harper et al . [19] from our own sequences of Mexico A . Two of these differences were found in homopolymeric stretches ( in fliG-tp0027 and tp0347 regions ) and one SNP ( C→T ) was found in the rpiA-tp0617 region . Since both Mexico A strains came from the same laboratory ( D . L . Cox , CDC Atlanta ) , the data suggest that possible sequencing errors in sequences published by Harper et al . [19] may explain these differences . To further asses the frequency of strains similar to Mexico A/South Africa , we investigated clinical samples published by Flasarová et al . [21] for Mexico A-specific mutations . No such nucleotide changes were found in 49 genotyped samples , indicating that the Mexico A/South Africa group of strains is not prevalent in central Europe . v ) Since convergent evolution assumes acquisition of the same biological trait in unrelated lineages ( operating on the level of biological function ) , it is extremely unlikely that it would result in exactly the same amino acid sequence of the relevant proteins . Due to degeneration of the genetic code , it is even more unlikely that convergent evolution would end up in two identical nucleotide sequences . vi ) In contrast to previous alternatives , inter-strain recombination cannot be ruled out despite the fact that the probability of such event is relatively low . Moreover , the mosaic character of the TPAMA_0326 and TPAMA_0488 loci , combining both TPA- and TPE-specific nucleotide sequences , is a typical result of a recombination event after horizontal gene transfer [22]–[24] . Also , patterns found in TEN strains indicate that observed mosaics in the Mexico A genome are not artifacts , but rather the results of recombination events in the common ancestor of TPA and TEN strains ( see Fig . 3C ) . There are several possible molecular mechanisms that could lead to the formation of the mosaic structure seen at the TPAMA_0326 and TPAMA_0488 loci . We propose two models ( Fig . 4 ) that are based on the incorporation of TPE double stranded DNA . In the first model , dsDNA was integrated into the chromosome of the Mexico A ancestor through homologous recombination . The resulting DNA heteroduplex was block-repaired via mismatch repair mechanisms . Similar reparation patterns have been observed after DNA transformation of Escherichia coli [25] and Helicobacter pylori [24] . In other bacteria , mismatch repair involves the cleavage of a daughter strand by MutH , which recognizes methylated cytosine in the GATC sequence . Since TPA does not contain a MutH orthologue and no methyltransferases , the mechanism of DNA cleavage remains unknown . Both mutS and mutL have been annotated to the TPA genome . The second mechanism is based on gene conversion events following internalization of dsDNA . Gene conversion is a common mechanism for producing antigenic variability in TPA [26] . Since TPA possesses only the RecF recombination pathway , gene conversion in TPA is likely to follow the successive half crossing-over model [27] , as shown in Fig . 4 . However , the mosaic structure observed at the TPAMA_0326 and TPAMA_0488 loci would require multiple successive gene conversion events in both loci , which is unlikely . One possible explanation would presume a partial mosaic structure ( Fig . 4 ) in both loci in the TPE donor DNA prior to crossing-over . Assuming this , the observed mosaic sequence at the TPAMA_0326 and TPAMA_0488 loci could result from a single gene conversion/recombination event . Alternatively , there is a possibility of active DNA uptake across the cell membrane , which is more efficient , compared to natural competence of bacteria . Although no gene orthologs involved in natural competence have been identified in the TPA genomes , one cannot exclude this activity in one or more genes with unknown function . Internalization of TPE ssDNA would follow the model of mismatch repair . TPAMA_0326 and TPAMA_0488 are mosaics resulting from interchromosomal recombination/gene conversion between TPA and TPE strains , while tprC and tprD alleles are the results of intrachromosomal recombination in tprC and tprD loci [12] . Therefore , similarities to TPE strains seen in tprC locus and TPAMA_0326 and TPAMA0488 loci arose via different mechanisms . Except for the TPAMA_0326 and TPAMA_0488 loci , two additional nucleotide positions ( 2 out of 1 , 192 single nucleotide changes differentiating TPA and TPE strains [7]; i . e . 0 . 168% ) were found in the TP0314 locus and TPAMA_0319 gene . In these cases the Mexico A sequence was identical to the TPE sequences . These two nucleotide differences appear to represent differences that occurred by chance . For a single nucleotide position , the theoretical probability is 1 , 192/1 , 140 , 038*1/3 ( i . e . 0 . 035% ) , where 1/3 is the probability that a particular nucleotide would be changed into a TPE nucleotide . Moreover , since the set of 1 , 192 single nucleotide changes that differentiate TPA and TPE strains is only based on comparisons of three TPA and three TPE strains , it is likely that the number of nucleotide positions differentiating all TPA and TPE strains will decrease with the newly reported whole genome sequences from other TPA and TPE strains . Horizontal gene transfer ( HGT ) is an important process in bacterial evolution and the most frequently transferred genes usually bring selective advantage to the host cell . The TPA genome contains no prophages or IS-elements [28] or plasmids [29] . Nevertheless , the absences of modification and restriction systems together with the presence of genes for homologous recombination in TPA strains [4] appear to allow incorporation of foreign DNA molecules with subsequent integration into the chromosomal DNA . DNA transformation is commonly used in cultivable Treponema denticola [30] and related Borrelia burgdorferi strains [31] . Moreover , natural gene transfer among Borrelia burgdorferi has been observed [32] . In fact , 77 ( 8 . 32% ) TPA genes were identified to be horizontally transferred by analysis of G+C contents , codon and amino acid usage , and gene position [17] . In our analysis , we did not find DNA regions of different G+C content to be associated with regions that differentiate TPA and TPE strains [7] , nor were such associations found in tpr regions , indicating that the genome rearrangements took place before the diversification of these strains . It is therefore likely that the diversification of TPA and TPE strains was due to an accumulation of more subtle changes . As shown by Centurion-Lara et al . [11] , recombination mechanisms are more active during treponemal infection and gene conversion events represent important mechanisms for avoiding the host immune response . Therefore , uptake of TPE DNA by TPA strain , during a simultaneous TPA and TPE infection of a single host , with subsequent integration into TPA chromosome , appears to be a plausible explanation . Simultaneous infection with TPA and TPE is certainly possible during the early stages of syphilis infection . It has been shown that experimental infection with either TPA or TPE strains did not result in complete cross-protection , which suggests differences in the pathogenesis of syphilis and yaws [33] , [34] . Although syphilis is preferentially transmitted sexually among adults , and yaws is preferentially transmitted via direct skin contact among children , simultaneous infection in a single host cannot be ruled out . The Haiti B strain , originally classified as a TPE strain due to having been isolated from “typical yaws lesions” in an 11-year-old child [13] , has been recently reclassified as a TPA strain [19] , [35] , [36] . Moreover , Mexico A strain was isolated in a geographic region where both TPA and TPE infections occurred [37]–[39] . Nevertheless , recombination could also take place outside Mexico . The mosaic TPAMA_0326 protein ( Tp92 ) belongs to a relatively small group of treponemal outer membrane proteins [40] and is an ortholog of the BamA protein involved in outer membrane biogenesis [41] . BamA protein was identified as a TPA antigen exhibiting reactivity with sera from patients with syphilis [42] , [43] , and antibodies against this protein have opsonized living treponemes [44] . The 15 bp ( TPE-like ) deletion in the TPAMA_0326 influences the polyserine tract in a predicted large extracellular loop of TPAMA_0326 protein , which serves as a potential site for attachment to the host cells [44] . TPAMA_0488 encodes the methyl-accepting chemotaxis protein ( Mcp2-1 ) [45] . Mcp2-1 is strongly expressed during experimental rabbit infections [46] and elicits a humoral response [45] . In the Mcp2-1 protein , there are 18 TPE-like changes , 8 of which are localized in the Cache domain [47] , which binds small molecules during chemotaxis . All of these TPE-like changes cause amino acid changes , 7 non-conservative and 1 conservative . Taken together , due to described changes in extracellular/sensoring protein domains , both proteins can exhibit different antigenic epitopes and/or ligand binding activities . Both TPAMA_0326 and TPAMA_0488 genes are under positive selection within TPA strains , as well as between TPA and TPE strains ( genes were tested using codon-based testing by Čejková et al . [7] ) . The recombinant TPA strain ( Mexico A ) can thus possess a selective advantage in an infected host and could provide evasion from the host's immune system . However , it was recently shown that β-barrel structures , including surface-exposed loops of TPAMA_0326 , where the TPE-like deletion is present , do not induce antibody response in humans [41] , [48] On the other hand , positive selection need not be driven solely by the production of antibodies and may also comprise T-cell mediated cellular response , similar to the case of TprK [49] . In addition , positive selection operating on the periplasmic Cache domain of TPAMA_0488 , recognizing small molecules , could reflect changed tissue tropism of TPE bacteria in comparison to TPA . Despite selective advantage in the infected host ( evasion from immune response , changed tissue tropism ) , these changes could result in the observed lower growth ability of the Mexico A strain compared to the Nichols strain under in vitro conditions [14] . Under positive selection , such a change can still have a growth advantage relative to the selective pressure on the host's immune system . In summary , the mosaic character of the TPA Mexico A genome is likely the result of interstrain recombination between TPA and TPE strains during simultaneous infection in one host and similar patterns can be observed among other TP strains . These findings suggest the importance of horizontal gene transfer in the evolution of pathogenic treponemes . | Treponema pallidum is a Gram-negative spirochete that causes diseases with distinct clinical manifestations and uses different transmission strategies . While syphilis ( caused by subspecies pallidum ) is a worldwide venereal and congenital disease , yaws ( caused by subspecies pertenue ) is a tropical disease transmitted by direct skin contact . Currently the genetic basis and evolution of these diseases remain unknown . In this study , we describe a high quality whole genome sequence of T . pallidum ssp . pallidum strain Mexico A , determined using the ? next generation ? sequencing technique ( Illumina ) . Although the genome of this strain contains no large rearrangements in comparison with other treponemal genomes , we found two genes which combined sequences from both subspecies pallidum and pertenue . The observed mosaic character of these two genes is likely a result of inter-strain recombination between pallidum and pertenue during simultaneous infection of a single host . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"biology"
] | 2012 | Whole Genome Sequence of Treponema pallidum ssp. pallidum, Strain Mexico A, Suggests Recombination between Yaws and Syphilis Strains |
Recent studies have detailed a remarkable degree of genetic and linguistic diversity in Northern Island Melanesia . Here we utilize that diversity to examine two models of genetic and linguistic coevolution . The first model predicts that genetic and linguistic correspondences formed following population splits and isolation at the time of early range expansions into the region . The second is analogous to the genetic model of isolation by distance , and it predicts that genetic and linguistic correspondences formed through continuing genetic and linguistic exchange between neighboring populations . We tested the predictions of the two models by comparing observed and simulated patterns of genetic variation , genetic and linguistic trees , and matrices of genetic , linguistic , and geographic distances . The data consist of 751 autosomal microsatellites and 108 structural linguistic features collected from 33 Northern Island Melanesian populations . The results of the tests indicate that linguistic and genetic exchange have erased any evidence of a splitting and isolation process that might have occurred early in the settlement history of the region . The correlation patterns are also inconsistent with the predictions of the isolation by distance coevolutionary process in the larger Northern Island Melanesian region , but there is strong evidence for the process in the rugged interior of the largest island in the region ( New Britain ) . There we found some of the strongest recorded correlations between genetic , linguistic , and geographic distances . We also found that , throughout the region , linguistic features have generally been less likely to diffuse across population boundaries than genes . The results from our study , based on exceptionally fine-grained data , show that local genetic and linguistic exchange are likely to obscure evidence of the early history of a region , and that language barriers do not particularly hinder genetic exchange . In contrast , global patterns may emphasize more ancient demographic events , including population splits associated with the early colonization of major world regions .
The languages of Northern Island Melanesia ( NIM ) belong to two major groups: Oceanic and Papuan . Oceanic is a major branch of the widespread Austronesian language family that appeared in the region about 3 , 300 years ago [43] , almost certainly associated with the Lapita cultural complex [42] , [44] . In NIM , Oceanic languages are found mainly on the smaller offshore islands and along the coasts of the major islands ( see Figure 1 ) , though they are spoken in some large island interiors as well . Our sample includes populations that speak 14 of the more than 150 Oceanic languages spoken in the region today . The Papuan languages are likely descendents of languages spoken by people who began arriving in the region more than 40 , 000 years ago [38] , [45] . As a result of their antiquity , they do not form a coherent language family according to conventional historical linguistic criteria , but are rather a residual category of non-Austronesian languages [37] . The Papuan languages in NIM tend to be restricted to the interior highlands of New Britain and Bougainville ( Figure 1 ) . Our sample includes populations that speak 9 of the 20 or so Papuan languages spoken in the region today . The standard method of constructing the historical relationships between languages , called the Comparative Method , is a tree-building technique that relies on recognizing sets of words in different languages that are related in meaning and form ( cognates ) and which show regular sound changes ( i . e . , shared innovations ) demonstrating that they derive from a single ancestral language . Because cognates change relatively rapidly , reconstructions using the Comparative Method cannot generally be made beyond 8 , 000 years [32] . In NIM , the Papuan languages share no clearly related cognates , possibly because they have been isolated from one another for so long , making the Comparative Method inapplicable for examining their relationships [37] , [46] , [47] . Recently , Dunn and colleagues [37] proposed the use of abstract structural linguistic features to address the time-depth constraint . These features could provide an independent phylogenetic measure , not related to the lexical evidence . Structural features include syntactic patterns such as constituent order in clauses and noun phrases , paradigmatic structures of pronouns , and the structure of verbal morphology [38] . It is an open question whether structural features are in general more resistant to exchange between different languages , but in contrast to cognate data , the Papuan languages of NIM do show some structural similarity , suggesting that , at least in this case , structural features are more stable [37] . However , structural features are not without their problems , including possible non-independence and homoplasy . To examine their utility and consistency for historical linguistic reconstruction , Dunn and colleagues [37] compared an Oceanic language classification constructed with structural data to one constructed using the Comparative Method . The topologies of the two trees were quite similar . Their structural classification of Papuan languages in NIM also captured the geography of the region fairly well , with its major branches representing the languages of different islands and its more terminal branches joining geographic neighbors within islands . These results were confirmed in subsequent analyses [48] , [49] and suggest that structural linguistic features may well produce reliable language trees and linguistic distances estimates , at least in NIM . The branching model predicts that the patterns of linguistic and genetic variation will be treelike , so that for our datasets , the Oceanic- and Papuan-speaking populations will cluster on separate branches of the language and genetic trees , and it also predicts that the topologies within the separate Oceanic and Papuan clusters will be similar in both trees . We tested these predictions by comparing simulated and observed patterns of genetic variation and the topologies of gene and language trees . The isolation by distance model predicts that genetic and linguistic distances will be correlated with one another not because of congruent tree-like evolution but because of ongoing genetic and linguistic exchange between neighboring populations . If genetic and linguistic exchange have occurred independently of one another , the genetic-linguistic distance correlation will lose statistical significance when geographic distance is held constant . If they have moved largely in concert with one another , the genetic-linguistic distance correlation will remain significant when geographic distance is held constant . These predictions were tested using computer simulations , matrix correlation and partial correlation tests , and by examining plots of genetic , linguistic and geographic distances .
The detailed genetic and linguistic datasets were recently collected from 33 populations located on the major islands of the Bismarck Archipelago and Bougainville in NIM [38] , [39] , [50] ( Figure 1 , Table 1 ) . The genetic data consist of 751 autosomal microsatellite loci drawn from Marshfield Screening sets # 16 and # 54 , and the loci were typed in 776 individuals . The linguistic data consist of 108 abstract structural features scored as present or absent in 23 Northern Island Melanesian languages . The features provide broad typological coverage of the known linguistic variation of the region and represent features typically described in a published sketch grammar . Three language groups covered in the genetic survey had not been analyzed ( see Table 1 ) , and for them , we substituted data from very closely related languages . The population names are linguistically based . Where genetic data were collected from more than one group in a language area , we added a distinguishing letter ( e . g . , Anêm-K and Anêm-P for the two Anêm-speaking groups from the Keraiai and Purailing areas ) . Table 1 lists each population name , island , language affiliation , geographic coordinates , genetic sample size and allelic identity ( by which the populations are ordered ) . Because of recent movements , three populations could not be clearly classified as coastal or interior , and they were therefore classified as “intermediate” . The linguistic and genetic data are available from the authors upon request . Our basic unit of genetic similarity is the allelic identity between individuals , defined as the probability that two alleles of the same locus drawn from two random individuals , either within the same population or from two different populations , are identical [51] . Heat plots were employed to examine the geographic and linguistic patterns of the within- and between-population allelic identities .
As mentioned , Figure 2 shows the presumed history of population splits used as the basis for the simulated branching model . Figure 3A shows the simulated heat plot derived from the simulations of this branching history . The simulated allelic identities in Figure 3A are lowest between the Oceanic and Papuan populations , higher between populations on different islands , higher still between populations within islands , and highest within populations . The level of allelic identity is also uniform between populations at different levels in the hierarchy , reflecting the isolation of branches following ancient population splits . The hierarchical organization and the uniformity of allelic identity within major clusters are fundamental properties of the branching process . Figure 3B shows the observed allelic identity heat plot , with the populations arranged in the same order as in 3A ( i . e . , clustered first by language group , then by island ) . The poor fit with the predicted properties of the branching model in 3A is obvious . The Oceanic-Papuan comparisons do not have low and uniform allelic identities . For example , the allelic identities between the Oceanic-speaking Mamusi and Nakanai-S on the one hand and the Papuan-speaking Ata on the other are high compared to the identities between same-language-speaking populations ( Figure 3B , circled squares ) . These are three neighboring groups in the interior of central New Britain . Identities are also high between the four Bougainville populations , even though two of them speak Oceanic languages ( Saposa and Teop ) and two speak Papuan languages ( Aita and Nasioi ) . Figure 3C shows the same allelic identities arranged simply by island and neighborhood ( i . e . , not by language ) . While the fit to the expected pattern is still poor , this reordering shows that allelic identities are relatively high between populations on the same island , and relatively low and uniform between populations on different islands . It also underlines the high identities between the linguistically diverse Mamusi , Nakanai-S , and Ata in the New Britain interior , and between the different language speaking populations on Bougainville . In sum , the observed pattern of allelic identity variation is not consistent with the branching model . It shows that significant genetic exchange has occurred between local populations within islands whether they belong to the same major language group or not , but that genetic exchange between islands may have been relatively restricted for some time . The language and genetic trees in Figure 4 reinforce this scenario . Neither tree completely separates the Oceanic- from the Papuan-speaking populations . Instead , the trees tend to group populations from the same island . The island grouping is particularly strong for the genetic tree , which also clusters geographic neighbors within islands better than the language tree , e . g . , it contains the Mamusi/Nakanai-S/Ata cluster from inland New Britain . The language tree does not contain this cluster , but instead groups the geographically distant Ata and Anêm together , both of which speak Papuan languages . Overall , the language tree has a stronger tendency than the genetic tree to group Papuan-speaking populations separately from Oceanic-speaking populations , suggesting that structural linguistic features are more resistant to exchange than genes between the major language groups , or that linguistic exchange has been comparatively more common within the language groups than between them . The results may also reflect relatively low information content in the linguistic data . The bootstrap values of the language tree are low , and the linguistic data contain only 108 features compared to the 6 , 437 alleles for the microsatellite loci . The results of the model-fitting procedure are shown in Tables 2 and 3 . The Λ values for the fitted baseline and language trees are reported in Table 2 . Λ for the baseline tree is very high relative to the degrees of freedom , indicating that it does not capture the genetic structure of the NIM populations very well . The lack of fit is also shown by the plot of the observed genetic distances vs . the expected genetic distances for the baseline tree shown in Figure 5A . This result is not surprising given the lack of similarity between the structure-less baseline tree and the topologically complex genetic tree . However , even though the observed and expected genetic distances are not perfectly congruent , the correlation coefficient for the plot is fairly high , indicating that even the baseline tree captures some of the genetic structure of NIM populations . The reason for the high correlation is that the model-fitting procedure estimates the individual population allelic identities fairly accurately for the baseline tree , and this identity is one of the two parameters used to estimate genetic distance . The reason the correlation is not even higher is that the other parameter used to estimate genetic distance is the between-population allelic identity , and , since the baseline tree has only one internal node , the model-fitting procedure estimates only one value for this between-population identity . In the observed data , there are many different values for the between-population identities , causing the discrepant results . Λ is much lower for the fitted language tree than it is for the fitted baseline tree ( Table 2 ) . The F-test indicates that the superior fit is statistically significant ( Table 3 ) . This superior fit may not be because of any deep congruence between the linguistic and genetic structures , but only because of a few superficial internal nodes ( tips ) shared by the language and genetic trees ( e . g . , Aita - Nasioi ) . To test this possibility , we used the model-fitting method to fit a tree that contained only these shared tips . Λ for this tips-only tree was much lower than it was for the baseline tree ( Table 2 ) , but it was still not nearly as low as it was for the complete language tree . This result suggests that the language tree captures more than just some superficial aspects of the genetic structure . Figure 5B is the plot of the observed genetic distances vs . the expected genetic distances based on the language tree . The relatively high squared correlation for the plot also confirms that the language tree captures more of the genetic structure than the baseline tree . There are , however , several clear outlier points in the plot , and Λ is still very high for the language tree relative to its degrees of freedom , meaning that its fit is far from perfect . The lower plot in Figure 5B shows that of all of the groups , the Kol contribute most to the high Λ of the language tree . Λ for the language tree reconstructed after removing the Kol is 5 , 777 compared to 8 , 593 for the full language tree ( see Table 4 ) . The plot shows that the Kol are generally closer to neighboring populations than the language tree would predict , reflecting the greater tendency of the genetic tree to group neighboring populations on the same island . For example , in the genetic tree , the Kol , who speak a Papuan language , cluster with the nearby Oceanic-speaking Mengen , whereas in the language tree , they cluster with other Papuan-speaking populations who are more distant geographically . These different tree patterns confirm the greater tendency of genes to move between Papuan- and Oceanic-speaking populations than structural linguistic features . The contributions of other populations to the lack of correspondence between the observed and expected genetic distances are shown in Table 4 . Methods described in Text S1 were used to identify four additional populations that contributed disproportionately to the lack of correspondence . Three of these four outliers also involved neighboring Oceanic- and Papuan-speaking populations that clustered together in the genetic tree but not in the language tree . Λ for the language tree lacking the Kol and these other four outlier populations is 1 , 992 ( Table 2 ) , which represents a dramatic reduction compared to the full 23 population language tree ( F-test p<0 . 0001 , Table 4 ) . The revised 18-population language tree is shown in Figure 4C , and the plot of the observed genetic distances vs . the expected genetic distances for this revised tree is shown in Figure 5C . The very high squared correlation coefficient in 5C confirms its superior fit relative to the full 23-population language tree . However , Λ is still high for this revised language tree , indicating that even it does not fully capture the genetic structure of NIM populations . The lower plot in Figure 5C shows that the Mali are the largest outlier in this comparison . The Mali are closer to other New Britain populations in the genetic tree , regardless of the language they speak , than they are in the language tree . Overall , the results show the pervasive pattern of closer genetic than linguistic proximity between populations on the same island . Figure 6 shows the heat plot for the simulated isolation by distance model allelic identities . The simulated identities are highest within populations and then fall off steadily as the geographic distance between populations increases ( indicated by the change in color moving horizontally or vertically away from the diagonal ) . There is some hint of this fall-off for some populations in the observed matrix , but , overall , the observed pattern diverges from the predicted . In the simulations , the populations are arrayed next to one another in a linear stepping stone pattern , but the 33 sampled NIM populations are not located next to one another in a simple linear fashion . However , the lack of congruence between the heat plots is not because of this difference . Isolation by distance predicts decreasing allelic identity with increasing geographic distance regardless of the actual sampling locations , and this pattern does not occur for the observed allelic identities . This conclusion is supported by additional simulations reported in the last section of Text S1 . Table 5 shows the matrix correlation results . Waypoints did not improve the correlations , so we report only the results for the direct great circle distances . The correlations listed for the full sample are suggestive of an isolation by distance coevolutionary process in the region , but several of the correlations are not statistically significant at the multiple tests-adjusted level . However , when the correlation coefficients are calculated for localized geographic and linguistic comparisons , many of them increase in magnitude and cross the threshold of statistical significance . Figure 7 shows plots of the genetic , linguistic and geographic correlations and highlights the localized geographic and linguistic comparisons . Figure 7A and 7B shows the genetic-geographic distance correlation , with different localized sets highlighted . In Figure 7A , the interior and coastal sets are highlighted in red and blue . The lack of mixing of the colors suggests that there has been limited genetic exchange between island interiors and coasts . Figure 7B highlights the Papuan and Oceanic sets . The mixing of the colors shows that Papuan and Oceanic-speaking populations have exchanged genes . This exchange has occurred primarily between the interior Oceanic-speaking Mamusi and Nakanai-S with interior Papuan-speaking populations , and between the coastal Papuan-speaking Kuot and Sulka with coastal Oceanic-speaking populations . Table 6 shows how the Oceanic and Papuan genetic-geographic distance correlations improve when these four outlier populations are removed . Plots 7C and 7D show the linguistic-geographic distance correlations , with the different sets highlighted as before . As one might expect for the linguistic correlations , the coastal and interior strata are less clearly distinguished than the Oceanic and Papuan strata . This is again consistent with the argument that there has been little linguistic exchange between Oceanic and Papuan languages where they occur in neighboring groups ( e . g . , the four outliers ) . The poorer distinction for the interior and coastal strata is caused by these outliers . Table 6 shows that the interior and coastal linguistic-geographic distance correlations improve dramatically when the four outliers are removed . Plots 7E and 7F show the genetic-linguistic distance correlations with similar highlighting . They suggest that any linguistic-genetic correlation is driven solely by the Papuan-speaking populations , but as Table 6 shows , when the four outliers are removed , the correlation for the Oceanic comparisons increases dramatically and becomes statistically significant . These results provide further support for the conclusion that linguistic exchange has been comparatively limited between Oceanic- and Papuan-speaking populations where they overlap geographically . The plots also show that for any given geographic distance , the interior/Papuan-speaking populations have higher genetic and linguistic distances among them than do the coastal/Oceanic-speaking populations . The correlation coefficients are also generally larger between interior/Papuan populations than they are between coastal/Oceanic populations . This distinction is the result of the comparatively restricted movement in the rugged highland interiors [68] , coupled with the much longer tenure of Papuan-speaking populations . The correlations are particularly high in the New Britain interior ( Table 5 , blue squares in Figure 7 ) . The genetic-geographic distance correlation is 0 . 94 ( p<0 . 0000 ) , which , to our knowledge , is the highest such correlation reported for any region worldwide . The high linguistic-geographic ( 0 . 59 ) and genetic-linguistic correlations ( 0 . 67 ) for the New Britain interior are also significant at a high level of probability , but the partial correlation , in which geographic distance is held constant , is not . As mentioned , the correlation and partial correlation patterns are consistent with an isolation by distance process where genetic and linguistic exchange have occurred largely independently of one another . The results on the New Britain coast suggest a separate isolation by distance pattern there as well . All of the correlation coefficients there are high , but only the genetic-linguistic distance correlation is statistically significant ( Table 5 ) . The p-values for the other correlations are low ( genetic-geographic = 0 . 0066; linguistic-geographic = 0 . 0099 ) , but they are above the multiple tests adjusted significance level ( p = 0 . 0024 ) . When the two Papuan-speaking populations are removed from the coastal New Britain sample , the correlations increase in magnitude and the partial correlation also crosses the threshold of statistical significance ( Table 6 ) , despite the fact that the sample contains only six populations . We suspect that a larger sample would reveal an even more robust isolation by distance pattern on the coast and on the other islands in the region .
The tests of the branching model in Northern Island Melanesia show that genetic and linguistic exchange between local populations has erased evidence that may have once existed for a branching process there . Genes have tended to move freely between nearby populations , regardless of the languages they speak . On the other hand , structural linguistic exchange has been particularly limited between neighboring Oceanic and Papuan languages . In these instances , the Oceanic-speaking populations have become very similar genetically to their Papuan-speaking neighbors ( the best example of this is the high allelic identity between the Ata , Mamusi and Nakanai-S shown in the heat plot in Figure 3B ) . Although an alternate explanation for this situation is that Oceanic languages have simply been adopted by formerly Papuan-speaking groups [c . f . , 50] , this now appears most unlikely , because the general tendency in Northern Island Melanesia is for neighboring populations , regardless of their languages , to become genetically similar ( other clear examples are the Kove/Anêm and also the Kuot and their neighbors on New Ireland ) . Previous analyses of the autosomal microsatellites [50] as well as Y-chromosome data [67] suggest that Papuan-speaking groups , who entered NIM first and expanded there long before the arrival of the early Oceanic-speakers , have contributed much more genetically to Oceanic-speaking groups than vice versa over the last three millennia . The genetic , linguistic and geographic distance correlations are consistent with an isolation by distance coevolutionary process in the interior of the largest island in the region , New Britain . For the correlations to be so strong , the patterns of ancestral residence and local migration must have persisted for a considerable period . It is remarkable that the patterns have persisted in the face of the destabilizing influence of European contact [42] , [69] and also of displacements caused by major volcanic eruptions [70] . One reason for the persistence is the continuing ties of the people to their land . Even today , most people in our sample remain in small villages and continue to farm their local gardens , or they maintain dual residences there and in larger population centers [68] . The matrix correlation results show that studies of prehistory and coevolution at the regional level must take into account the geographic and linguistic heterogeneity of a region , since ecological and sociocultural variation are likely to strongly influence biological and cultural patterning . Parallels to the heterogeneity found in NIM probably exist , in many cases unidentified , in every major world region and in various locations within each region [71]–[74] . Our results are apparently at odds with the studies of Cavalli-Sforza et al . [4] , [5] that identified a strong correspondence between global gene and language trees . One explanation is that global patterns are more likely to emphasize ancient demographic events , such as population splits associated with the colonization of major world regions , while local patterns will generally emphasize more recent demographic events . Wilkins and Marlowe [75] , for example , showed that genetic data collected from local populations are more likely to reveal recent changes in migration associated with the rise of agriculture than data collected from a global sample . However , it is also possible that the differences between the global results of Cavalli-Sforza and colleagues and ours are not so pronounced . In their studies , they identified several instances of disagreement between the language and genetic trees caused by different patterns of genetic and linguistic exchange and language shift , so the global pattern may also reflect , to a substantial degree , the types of local population interactions we identified in NIM . The structural linguistic data used in this study [48] , [76] have recently come under attack , both in terms of their quality and what they capture ( i . e . , just more recent contacts , or mainly ancient language splits ) . Our results certainly suggest that structural features may well be more resistant to dynamics of diffusion than genes , and therefore likely contain considerable information about language splits as well as language contacts . The structural features may also be more resistant to diffusion than lexical items , making them more suitable than cognate data for examining linguistic splits in NIM , and probably in other regions as well . Dunn et al . [48] , [49] have addressed the criticisms of data quality in detail , but they acknowledge that there are some problems . The linguistic features are not completely independent of one another , the data may contain substantial homoplasy [37] , [49] , and for the NIM dataset , there are 8 . 7% missing data . Despite these shortcomings , the significant correlations between the linguistic , genetic , and geographic distances certainly show that the structural linguistic data contain important information about the relationships between NIM languages . In particular , the separation of the Oceanic and Papuan groupings in the plots of linguistic vs . geographic distances ( Figure 7D ) suggests that , even if the data only reveal linguistic contacts , the contacts have been stronger between populations within each major language group than between populations in different language groups [see also 39] . Another relevant point is that the linguistic data and methods typically used in studies of coevolution have usually been of comparatively poor quality . To illustrate the higher quality of our structural linguistic dataset , we employed the commonly used method of node counting to estimate linguistic distances between NIM languages in a classification constructed using the Ethnologue ( http://www . ethnologue . com/ ) , and we then examined the correlation between these distances and the genetic and geographic distances . None of the correlations were statistically significant . If not for the structural linguistic data , we would have failed to identify any linguistic relationship to genetic or geographic patterns at all . The limitations of these sorts of data are not restricted to Northern Island Melanesia . Hunley et al . [16] tested the branching and isolation by distance models in South America , where linguistic divergence has been occurring for a considerably shorter period . They examined the fit of language and gene trees constructed from linguistic cognate data and mtDNA sequences , and identified correspondences only between the tips of the language and genetic trees , i . e . , only between very recently diverged groups . In the current study , the language and genetic structures shared more than just a few superficial similarities , clearly suggesting the results are indicative of more ancient relationships . Studies of coevolution will clearly benefit greatly from using similar structural linguistic datasets . The highly informative nature of the genetic data available to us ( i . e . , the 751 microsatellite loci with 6 , 437 different alleles ) also undoubtedly led to our finding of comparatively high correlations in our various analyses . Many recent studies have used mitochondrial d-loop data and Y-chromosome data to investigate genetic and linguistic correspondence in various world regions [15] , [16] , [20] , [77]–[81] , but these data are comparatively uninformative . The Y-chromosome data typically contain only a few loci , and the mitochondrial d-loop data are plagued by homoplasy , which confounds the construction of genetic classifications and limits the accuracy of genetic distance estimation [82] . In an earlier publication , information content issues prevented us from successfully fitting our structural language tree to mtDNA and Y-chromosome data collected from most of the same populations [66] . The mitochondrial d-loop data were able to recreate some of the same correlation patterns we found using the autosomal microsatellite data , but the correlations were always weaker than those we have reported here . The implications of our results for broader issues in Pacific prehistory are important but must be interpreted carefully . While our results provide little support for the branching model in Northern Island Melanesia , this is different from arguing that branching did not occur in very early periods there , or elsewhere in the Pacific , and it does not mean that our microsatellite data lack important information about the deeper prehistory of the entire region . For example , two contrasting scenarios for the origins of the Polynesians have persisted in recent Pacific prehistory debates , and they bear a very close relationship to the two models examined in this paper . The first has been called the phylogenetic model [83] , [84] , which is essentially identical to the branching model , and the second , called a reticulate model [85] , is essentially identical to the isolation by distance model [see also rebuttal by 86] . A number of mixed models , perhaps more realistic than either of these , have also been proposed [87] . Bellwood [83] also argued that phylogenetic differentiation should be expected to occur primarily during or shortly after the early rapid range expansions in new territories , while the reticulate model , which stresses a continuous and relatively uncoordinated shifting of linguistic , cultural , and biological boundaries through assimilation , intermarriage , borrowing , and diffusion , may become more evident in subsequent periods . The genetic data have been interpreted to support several of these Polynesian origin scenarios . Some have indicated that a clear phylogenetic signal exists between Taiwan Aborigines and Polynesians , with little intermixture taking place in Near Oceania , while other datasets have been interpreted to suggest heavy intermixture with , or major contributions from , Near Oceanic and Wallacean populations [50] , [65] , [88]–[93] . While the results of our present study are broadly inconsistent with phylogenetic models in Northern Island Melanesia , our group did identify in the same microsatellite data a small but clear genetic coancestry between certain Taiwanese populations and Oceanic-speaking groups in Island Melanesia , as well as a much stronger Taiwan Aboriginal signal in Polynesia , indicating that intermixture over the past 3 , 000 years has not completely erased genetic signals of early Oceanic origins in either NIM or Polynesia [50] . The more comprehensive nature of our genetic and linguistic coverage in this region has now allowed a more complete , if complex , picture of ancient population dynamics to emerge . | The coevolution of genes and languages has been a subject of enduring interest among geneticists and linguists . Progress has been limited by the available data and by the methods employed to compare patterns of genetic and linguistic variation . Here , we use high-quality data and novel methods to test two models of genetic and linguistic coevolution in Northern Island Melanesia , a region known for its complex history and remarkable biological and linguistic diversity . The first model predicts that congruent genetic and linguistic trees formed following serial population splits and isolation that occurred early in the settlement history of the region . The second model emphasizes the role of post-settlement exchange among neighboring groups in determining genetic and linguistic affinities . We rejected both models for the larger region , but found strong evidence for the post-settlement exchange model in the rugged interior of its largest island , where people have maintained close ties to their ancestral lands . The exchange ( particularly genetic exchange ) has obscured but not completely erased signals of early migrations into Island Melanesia , and such exchange has probably obscured early prehistory within other regions . In contrast , local exchange is less likely to have obscured evidence of population history at larger geographic scales . | [
"Abstract",
"Introduction",
"Materials",
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"Methods",
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] | [
"genetics",
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] | 2008 | Genetic and Linguistic Coevolution in Northern Island Melanesia |
Mounting evidence supports that LINE-1 ( L1 ) retrotransposition can occur postzygotically in healthy and diseased human tissues , contributing to genomic mosaicism in the brain and other somatic tissues of an individual . However , the genomic distribution of somatic human-specific LINE-1 ( L1Hs ) insertions and their potential impact on carrier cells remain unclear . Here , using a PCR-based targeted bulk sequencing approach , we profiled 9 , 181 somatic insertions from 20 postmortem tissues from five Rett patients and their matched healthy controls . We identified and validated somatic L1Hs insertions in both cortical neurons and non-brain tissues . In Rett patients , somatic insertions were significantly depleted in exons—mainly contributed by long genes—than healthy controls , implying that cells carrying MECP2 mutations might be defenseless against a second exonic L1Hs insertion . We observed a significant increase of somatic L1Hs insertions in the brain compared with non-brain tissues from the same individual . Compared to germline insertions , somatic insertions were less sense-depleted to transcripts , indicating that they underwent weaker selective pressure on the orientation of insertion . Our observations demonstrate that somatic L1Hs insertions contribute to genomic diversity and MeCP2 dysfunction alters their genomic patterns in Rett patients .
The term “somatic mosaicism” describes the genomic variations that occur in the somatic cells that make up the body of an individual . These variations contribute to intra-individual genetic diversity among different cells [1] . In addition to various types of cancers , somatic mosaicisms reportedly contribute to a variety of neurological disorders , including epilepsy , neurodegeneration , and hemimegalencephaly [2] . The human-specific LINE-1 ( L1Hs ) retrotransposon family is the only known family of active autonomous transposons in the human genome [3 , 4] . L1s retrotranspose through a process called target-primed reverse transcription ( TPRT ) , with the capacity for de novo insertion into new genomic locations in both germline and somatic cells [5 , 6] . Mounting evidence supports that L1Hs elements , with increased copy number in the brain relative to other tissues , contribute to neuronal diversity via somatic retrotransposition [7–12] . Recent studies reported the occurrence of somatic L1Hs insertions during neurogenesis and in non-dividing mature neurons [9 , 13] . Other studies have observed dysregulated L1Hs copy number in patients with Rett syndrome [8] and schizophrenia [14] . Methyl-CpG binding protein 2 ( MECP2 ) is the major disease-causing gene of Rett syndrome [15] . Its gene product , MeCP2 , can bind to the 5' UTR of L1 elements and represses their expression and retrotransposition [16] . While it is known that L1 expression and copy number are elevated in the brains of Mecp2 knockout mice as well as in patients with Rett syndrome [8 , 17] , little is known about the genomic distribution patterns of somatic L1Hs insertions in Rett patients and healthy individuals . In contrast to germline insertions , the effects of somatic transposon insertions depend not only on their genomic location . Rather , the specific timing , tissue , and cell lineage at which they occur profoundly influence the impact of somatic insertions [18] . Single-cell targeted sequencing approaches have been used to identify somatic insertions [7 , 11 , 12] . However , such methods typically require a large number of cells and demand considerable sequencing depth for unbiased profiling of human tissues [19 , 20] . Furthermore , owing to the rarity of somatic insertions , investigations of the clonal diversity of somatic insertions would require the sequencing of even larger numbers of cells [19] . Another limitation of single-cell sequencing approaches is that errors of allelic dropout and locus dropout , which frequently occur during the whole genome amplification ( WGA ) step of library construction , can reduce the sensitivity and specificity of somatic insertion detection . Estimates of the rate of somatic L1Hs insertions vary widely in single-cell genomics studies [21] . Bulk sequencing approach can potentially overcome these limitations and enable the genome-wide identification and quantification of somatic L1Hs insertions , but their low allele frequency in cell populations poses a great challenge to distinguishing true insertion events from technical artifacts [22] . Here , we introduced a PCR-based multiplex bulk sequencing method for sensitive enrichment and specific identification of L1Hs insertions from various types of human tissues . We used this method to perform genome-wide L1Hs insertion profiling of 20 postmortem tissues from five patients with Rett syndrome and their matched healthy controls . The aims of this study were to explore the genomic patterns of somatic L1Hs insertions in neuronal and non-neuronal samples , and to investigate whether MeCP2 dysfunction could alter the distribution of L1Hs retrotransposition in patients with Rett syndrome .
Systematic genome-wide profiling of somatic L1Hs insertions requires effective enrichment of insertion signals and specific identification of true signals from background noise . Enriching neuronal nuclei from bulk brain tissue facilitates the accurate deciphering of cell type-specific characteristic and increases the chance of identifying clonal somatic insertions that are derived from the same progenitor cell and shared by multiple neurons . Therefore , we labeled prefrontal cortex ( PFC ) neuronal nuclei using an antibody against neuron-specific marker NeuN [23] , and subsequently purified NeuN+ nuclei from postmortem human PFC by fluorescence-activated cell sorting ( FACS ) ( Fig 1A; S1A–S1D Fig; S1 Appendix ) . All initially sorted nuclei were re-analyzed with a second round of FACS , and the purity of the initial sorting was found to be > 96% ( S1E and S1F Fig; S1 Appendix ) . The integrity and purity of sorted nuclei were confirmed by fluorescence microscopy ( S2A–S2C Fig ) . To distinguish the signals of active L1Hs elements from other transposon families that are typically inactive in human , we developed a method called human active transposon sequencing ( HAT-seq ) ( Fig 1B; S3A Fig; S1 Table ) based on ATLAS [24] and several versions of high-throughput sequencing-based L1 amplification methods [7 , 25–27] . Firstly , L1Hs insertions were specifically enriched and amplified using a primer targeting the diagnostic “AC” motif of L1Hs [3 , 28] . To ameliorate the poor performance of Illumina sequencing platform for low-diversity libraries , we employed a nucleotides-shifting design by adding two , four , or six random nucleotides upstream of the L1Hs-specific primer , which greatly increased the diversity of the structure-transformed semi-amplicon library and markedly improved the sequencing quality of L1Hs 3’ end . The constructed libraries preserved information regarding the insertion direction and were sequenced by multiplexed 150 bp paired-end reads . This approach provided sequence information fully spanning the 3’ L1Hs-genome junction of each of L1Hs insertions , which enabled the identification of integration sites and facilitated in silico false-positive filtering based on both sequence features and read-count . Genomic position of each L1Hs insertion was determined by the alignment of its 3’ flanking sequence ( Fig 1C ) . A custom data analysis pipeline classified putative insertions into one of the following four categories: known reference ( KR ) germline insertions , known non-reference ( KNR ) germline insertions , unknown ( UNK ) germline insertions , and putative somatic insertions ( S3B Fig ) . To further remove technical artifacts induced by non-specific or chimeric amplification and read misalignment in next-generation sequencing , we designed a series of stringent error filters to remove them in different aspects ( Table 1 ) : 1 ) read pairs with non-specific amplification signals and incorrect 3’ truncation were removed based on the sequence of L1Hs 3’ end ( Read 2 ) ; 2 ) after merging paired-end reads into contigs , chimeric molecules with abnormal contig structures were identified by BLAST and filtered out; 3 ) reads with inconsistencies in BWA-MEM and BLAT alignments were defined as mapping errors; and 4 ) putative somatic insertion signals without multiple PCR duplicates or those present in different individuals were removed , as they were deemed likely to have resulted from sequencing errors . After applying these error filters , the remaining insertions were annotated with peak features to facilitate downstream analysis . To benchmark the performance of HAT-seq for detecting somatic L1Hs insertions , we experimentally generated a series of positive control samples with insertions at different frequencies by mixing the genomic DNA ( gDNA ) extracted from the blood samples of two unrelated adults , ACC1 and ACC2 ( see details in Methods ) . 172 ACC1 non-reference germline L1Hs insertions were identified by HAT-seq , 64 of which were confirmed to be ACC1-specific by 3’ junction PCR ( 3’ PCR ) analysis of gDNA from ACC1 and ACC2 ( Fig 2A; S2 Table; S2 Appendix ) and thus served as positive controls . Three HAT-seq libraries were generated from samples consisting of ACC2 gDNA spiked with 1% , 0 . 1% , or 0 . 01% of ACC1 gDNA . Considering that decreasing the number of cells pooled for sequencing increased the signal-to-noise ratio for detecting somatic insertions [22] , each HAT-seq library was constructed from 20 ng input ( about 3 , 000 cells ) . The zygosity of ACC1-specific L1Hs insertions was confirmed by full-length PCR: 49 of which were heterozygous , 9 of which were homozygous , and 6 of which were zygosity-undetermined ( Fig 2B; S2 Table; S2 Appendix ) . We detected all 64 ACC1-specific insertions in our positive control 1% ACC1 spike-in library , 49 ( 76 . 6% ) of which passed all of error filters and subsequently were deemed “identified” by HAT-seq . In the 0 . 1% library , we detected 23 ACC1-specific insertions ( 16 heterozygous , 4 homozygous , and 3 zygosity-undetermined ) , 17 ( 73 . 9% ) of which were identified . In the 0 . 01% library , we detected seven heterozygous ACC1-specific insertions , five ( 71 . 4% ) of which were identified . The distributions of signal counts ( reads with unique start positions ) per ACC1-specific insertion followed the Poisson distribution ( Fig 2C ) , indicating a similar probability for each of ACC1-specific insertions to be randomly sampled . In the 1% , 0 . 1% , and 0 . 01% libraries , each of ACC1-specific insertions was diluted to 30 , 3 , and 0 . 3 copies . Theoretically , by Poisson statistics , there would be 64 , 60 . 81 , and 16 . 59 ACC1-specific insertions being sampled and subsequently being used as the input of HAT-seq libraries ( see details in Methods ) . Therefore , we estimated the sensitivity of HAT-seq for somatic L1Hs insertions in 1% , 0 . 1% , and 0 . 01% libraries as 76 . 6% ( 49/64 ) , 28% ( 17/60 . 81 ) , and 30 . 1% ( 5/16 . 59 ) , respectively . Our data showed that , with about 3 , 000 cells as input , HAT-seq was able to detect somatic insertion events present in a single cell ( Fig 2D and S3 Appendix ) . To further evaluate the efficacy of our L1Hs identification pipeline , we compared the proportions of true positives and false positives after applying all the error filters . For the most stringent evaluation , only those 64 ACC1-specific germline insertions in spike-in libraries were defined as “true positives”; all other signals were defined as “false positives” , which might include both background noise and some true somatic insertions present in the blood gDNA . As shown in Fig 2E , in three positive control experiments with 1% , 0 . 1% , and 0 . 01% ACC1 gDNA spike-in , 76 . 56% , 73 . 91% , and 71 . 43% of true positives remained after all filters , whereas only 3 . 40% ( 66 ) , 6 . 90% ( 181 ) , and 7 . 70% ( 183 ) of false positives remained after all filters ( S3 Table ) . These results showed that HAT-seq performed in combination with our error filters could successfully remove most artifacts and identify very low-frequency somatic insertions in bulk DNA samples . Next , we applied HAT-seq to 20 bulk samples obtained from postmortem neuronal ( PFC neurons ) and non-neuronal tissues ( heart , eye , or fibroblast ) from five Rett syndrome patients and five neurologically normal age- , gender- , and race-matched controls ( Table 2 and S4–S7 Tables ) . A total of 9 , 181 putative somatic L1Hs insertions were identified in these 20 HAT-seq libraries ( S8 Table ) . A subset of 137 ( 1 . 49% ) of these insertions were detected by reads with multiple start positions . Considering that the random fragmentation process in HAT-seq library preparation would result in only one start position shared by all reads generated from a single cell , these 137 insertions should be present in multiple cells in the bulk tissue , and thus classified as “clonal somatic insertions” . Based on the performance evaluation of HAT-seq , the lower bound of the precision of overall somatic L1Hs insertions was 60 . 14% ( see details in Methods ) . To demonstrate the validity of these identified somatic insertions in silico , we investigated whether they had the hallmark features of TPRT-mediated retrotransposition ( see details in Methods ) . By exploiting the sequence information of L1 integration junctions , we found that such somatic insertions were significantly enriched in genomic regions containing L1 endonuclease cleavage motifs ( L1 EN motifs ) ( p < 2 . 2×10−16 , Wilcoxon rank–sum test; Fig 3A; S4 Fig; S9 Table ) . Moreover , our identified somatic insertions shared the 25-bp peak of poly-A tail length with the reference L1Hs insertions ( Fig 3B and S9 Table ) , where some of the somatic insertions with shorter tails might be explained by non-TPRT mechanism [31] . These features of somatic L1Hs insertions helped to elucidate the specificity of HAT-seq method . Owing to the rarity of each somatic insertion in the cell population and to the sensitivity limits of various analytical methods , experimental validation of somatic insertions using unamplified bulk DNA , in particular when one of the primers is complementary to numerous homologous sequences in the human genome is very challenging ( S4 Appendix ) . In theory , if a somatic insertion was unique to a single cell , it would be impossible to be detected in any replicated gDNA extracted from the same tissue . To circumvent this , we performed single-copy cloning by adapting a modified version of digital nested 3’ PCR [10] that focused exclusively on clonal somatic insertions with three or more supporting signals , whose mosaicism ( percentage of cells ) were at least 0 . 1% based on our experimental design of HAT-seq library ( Fig 3C ) . Five out of eight ( 62 . 5% ) such clonal insertion sites were confirmed via 3’ nested PCR and Sanger sequencing of cloned amplification products ( Fig 3D–3H and S10 Table ) . Four of these clonal somatic insertions were located in introns of TGM6 , CNTN4 , DIP2C , and DGKB; three were sense-oriented to transcripts . To our knowledge , no somatic insertions in non-brain tissues of healthy individuals has been reported [32] . We identified and experimentally validated a heart-specific somatic L1Hs insertion from a healthy individual UMB#1571 ( Fig 3D ) . Leveraging both the 3’ and 5’ junctions of somatic L1Hs insertions enable us to characterize the terminal site duplications ( TSDs ) and L1 endonuclease cleavage site of insertion . Because most of somatic L1Hs insertions were 5’ truncated with varied lengths , we screened and selected 22 high-quality step-wise primers covering the full-length L1Hs elements to capture their 5’ junction ( Fig 3C; S11 Table; S4 Appendix ) . Using 5’ junction nested PCR , we successfully re-captured and Sanger sequenced the 5’ junction of the heart-specific L1Hs insertion in the healthy individual ( UMB#1571 ) ( Fig 4A and S11 Table ) . We confirmed this insertion was a full-length somatic L1Hs insertion with 14 bp TSD and a cleavage site at 5’–TT/AAAG–3’ , similar to the consensus L1 EN motif 5’–TT/AAAA–3’ ( Fig 4B–4D ) . Notably , we also validated this 5’ junction by combining full-length PCR with 5’ junction PCR ( Fig 4E; see details in Methods ) . In addition , we verified one fibroblast- and another heart-specific L1Hs insertion in two patients with Rett syndrome ( Fig 3E and 3F ) . The heart-specific L1Hs insertion in the Rett patient ( UMB#1420 ) was further resolved to be a highly 5’ truncated L1Hs insertion ( ~800 bp ) with 9 bp TSD and a cleavage site at 5’–TT/TAAA–3’ ( S5 Fig and S11 Table ) . The poly-A tails of these two clonal somatic insertions were experimentally measured to be polymorphic , indicating that they may involve multiple mutations after the original somatic retrotransposition events ( Fig 3I and S10 Table ) . As previously reported [10 , 33] , poly-A tail was shown to be a highly mutable sequence element and might undergo secondary mutations in descendant cells . Furthermore , we confirmed two additional somatic L1Hs insertions from Rett patient UMB#4516 were present in PFC neurons , PFC glia , and fibroblasts ( Fig 3G and 3H and S6 Fig ) , suggesting that they might retrotranspose during early embryonic development . Notably , the intronic somatic insertion ( chr20:2392172 ) in TGM6 was a full-length L1Hs insertion with 15 bp TSD and a cleavage site at 5’–AT/AAAA–3’ ( S7 Fig and S11 Table ) . We further quantified the allele fractions of this insertion using custom droplet digital PCR ( ddPCR ) assay and found that 6 . 34% of fibroblasts and 2 . 87% of PFC neurons contained this L1Hs insertion ( S8A–S8E Fig and S10 Table ) . Our observations demonstrated that endogenous L1Hs could retrotranspose in various types of non-brain tissues during human development . Our HAT-seq bulk sequencing data enabled us to perform statistical analysis of the exonic and intronic patterns of somatic L1Hs insertions in samples from Rett patients and matched healthy controls . We found 180 somatic insertions that were integrated into exonic regions: 9 of which were located in 5’ UTR , 102 of which were located in coding regions , and 69 of which were located in 3’ UTR ( S12 Table ) . While no significant difference was observed in introns ( odds ratio [OR] = 0 . 97 , p = 0 . 44 , Fisher’s exact test ) , somatic insertions were significantly depleted in exons ( OR = 0 . 59 , p = 6 . 6×10−4 , Fisher’s exact test ) of Rett patients compared with matched healthy controls ( Fig 5A and S13 Table ) . Previous studies have shown that dysregulation of long genes ( > 100 kb ) was linked to neurological disorders , including Rett syndrome [34] and autism spectrum disorder [35] . We used our HAT-seq data to investigate somatic insertional bias in both long ( > 100 kb ) and short genes ( < 100 kb ) of Rett patients . As a result , we found significant depletion of somatic insertions in exons of long genes ( OR = 0 . 27 , p = 5 . 2×10−5 , Fisher’s exact test ) but not short genes ( OR = 0 . 76 , p = 0 . 12 , Fisher’s exact test; Fig 5B and S13 Table ) . Our speculation was that if an L1Hs inserted into the exonic regions , especially in important genes , of the MECP2 mutated cell , the cell would have a higher risk of death and subsequently be cleared up; therefore , the observed exonic depletion of L1 insertions in Rett patients might be resulted from the negative selection acting on those “lethal” exonic insertions . In contrast to germline insertions , the impact of somatic insertions depends not only on their genomic location , but also the number of cells carrying that insertion , highlighting the importance of clonal somatic insertions . We found that in cortical neurons of Rett patients , clonal somatic insertions were enriched in introns ( OR = 1 . 85 , p = 0 . 029 , Fisher’s exact test; Fig 5C and S13 Table ) ; these clonal intronic insertions were significantly enriched in the sense orientation to the transcripts ( OR = 3 . 3 , p = 0 . 0067 , Fisher’s exact test; Fig 5D and S13 Table ) . The presence of L1 insertion in the sense orientation has been reported to interfere with transcriptional elongation of co-localized genes [36] . Considering that clonal insertions are more likely to have occurred at an early stage of development and thus affect a relatively large proportion of cells , these distinct insertion pattern in cortical neurons of Rett patients might indicate potential transcriptional burden on the nervous system . The design of HAT-seq method allowed for unbiased enrichment of both somatic and germline L1Hs insertions from each of bulk DNA samples . As germline insertion had constant genomic copy number in all tissues from the same donor , we used germline insertion as endogenous control to measure the relative copy number of genome-wide somatic insertions in the brain and non-brain tissues . We quantified the relative somatic L1Hs content by calculating the L1Hs-derived read count ratio of somatic to germline insertions using HAT-seq data of each sample ( S14 Table; see details in Methods ) . Among all Rett patients and their matched controls , we observed a significant increase in the copy number of somatic L1Hs insertions in PFC neurons relative to matched non-brain tissues ( heart , eye , or fibroblast ) from the same donor ( n = 10 , p = 2 . 7×10−4 , paired t-test; Fig 6A and S8F–S8G Fig ) . We also estimated the occurrence rate of somatic L1Hs insertions based on the germline insertion copy number of each individual ( Fig 6B ) . This produced an average of 1 . 29 [95% CI: 1 . 03–1 . 55] somatic insertions per PFC neuron versus 0 . 60 [95% CI: 0 . 46–0 . 74] insertions per non-brain cell ( S14 Table ) . Our observation of higher somatic L1Hs rate in PFC neurons from healthy individuals argued for the active retrotransposition of L1Hs in the human brain [9] . One significant advantage of HAT-seq was the ability to distinguish signals of somatic insertions from the overwhelming copies of germline L1Hs insertions in the genome ( see details in Methods ) . Inconsistent with the previous qPCR result [8] , when comparing the group of Rett patients with matched healthy controls , we only observed a slight but not significant increase of somatic L1Hs insertion rate in the Rett group , with 1 . 36 [min: 0 . 89; max: 1 . 82] versus 1 . 22 [min: 0 . 63; max: 1 . 66] per PFC neuron and 0 . 66 [min: 0 . 37; max: 1 . 01] versus 0 . 54 [min: 0 . 34; max: 0 . 72] per non-brain cell ( Fig 6C and S14 Table ) . We next characterized the genome-wide germline L1Hs insertions . HAT-seq yielded greater than 320-fold enrichment for KR , KNR , and UNK L1Hs insertions ( S15 Table ) . On average , 814 KRs , 183 KNRs , and 10 UNKs were identified in each bulk sample ( Table 2 , S5–S7 Table ) . Hierarchical clustering based on L1Hs profiles correctly paired all neuronal samples with the non-neuronal tissue samples of the same individual ( Fig 6D ) . To experimentally validate the HAT-seq predicted germline insertions , we performed 3’ PCR validation on a random subset of polymorphic insertions from among the ten individuals , including 8 sites out of 160 polymorphic KRs , 20 sites out of 451 KNRs , and 2 sites out of 48 UNKs ( S7 and S16 Tables ) . As a result , all of the assayed sites were detected in 3’ PCR , with 98 . 4% ( 120/122 ) and 100% ( 168/168 ) sensitivity and specificity , respectively ( S16 Table and S5 Appendix ) . These results support that HAT-seq can reliably detect germline L1Hs insertions with high sensitivity and specificity . Previous studies have shown that intronic germline L1Hs insertions are sense-depleted [12 , 25 , 37] . As expected , the germline insertions identified in this study were significantly sense-depleted to the transcripts ( 633/1 , 544 [41%] , p = 1 . 6×10−12 , binomial test; Fig 6E and S13 Table ) . It is important to ask the question: whether such orientation bias for germline insertions is resulted from natural selection or insertional preference ? To address this , we chose somatic L1Hs insertions as internal reference to control confounding factors . We compared the orientation bias between germline and somatic L1Hs insertions in transcripts and found that germline insertions were significantly sense-depleted than somatic insertions ( OR = 0 . 79 , p = 7 . 9×10−4 , Fisher’s exact test; Fig 6E and S13 Table ) . Because somatic L1Hs insertions only affected a small proportion of cells and thus they should undergo weaker selective pressure than germline insertions , our results suggested that natural selection may play a major role in shaping the sense-depleted distribution of germline L1Hs insertions .
Here , we present HAT-seq , a bulk DNA sequencing method to profile genome-wide L1Hs insertions from physiologically normal and pathological human tissues . We demonstrated that , in addition to neuronal cells [7 , 10–13] , L1Hs also retrotransposed in a variety of non-brain tissues and cell types during normal development and contributed to the inter-cellular diversity of the human genome . Using high-throughput sequencing-based quantitative analysis , we found that somatic insertions occurred at a higher rate in brain than in non-brain tissues , consistent with previous studies [9] . Previous qPCR and single-cell genomic studies have resulted in conflicting estimates of the frequency of somatic insertions in neurons: ~80 L1 insertions per neuron [9] , < 0 . 04–0 . 6 L1 insertions per neuron [11] , 13 . 7 L1 insertions per neuron [12] , or ~0 . 58–1 somatic L1-associated variants per neuron [7] . Differential estimates might result from differences in WGA and signal enrichment methods . Using a bulk DNA sequencing approach , we estimated the rate of somatic insertions to be 0 . 63–1 . 66 L1Hs insertions per PFC neuron in healthy individuals ( Fig 6B and S14 Table ) . Clonally distributed insertions are prevalent in normal brain [10] . Increasing evidence suggests that neuronal L1s retrotransposition contributes to the susceptibility to and pathophysiology of neurological disorders , including Rett syndrome [8] , schizophrenia [14] and Alzheimer’s disease [38] . We observed that , in PFC neurons of Rett patients , clonal somatic insertions were enriched in introns , and these clonal intronic insertions were significantly enriched in the sense orientation ( Fig 5C and 5D ) . In particular , in Rett patient UMB#4516 , we found a full-length , sense-orientated , intronic somatic insertion ( chr20:2392172 ) in TGM6 ( S7 Fig and S11 Table ) , a gene associated with central nervous system development and motor function [39] , which could potentially dysregulate gene expression [36] . We found that 6 . 34% of fibroblasts and 2 . 87% of PFC neurons contained this insertion ( S8A–S8E Fig and S10 Table ) , suggesting that it might occur in the 16-cell or 32-cell stages during morula stage . Mutations in TGM6 are associated with spinocerebellar ataxia type 35 , one of a group of genetic disorders characterized by poor coordination of hands , gait , speech , and eye movements as well as frequent atrophy of the cerebellum [40–42] . According to the clinical records , UMB#4516 had slight cerebral atrophy and cerebellar degeneration , could not hold things in her hands , and her speech development ceased at 16 months of age; these phenotypes were absent in the other four patients with Rett syndrome . Taken together , our data indicated that this clonal L1Hs insertion of TGM6 might be correlated with the distinct clinical phenotype of UMB#4516 . Previous studies have provided evidence for significant selection against older L1 elements that are non-polymorphic [25 , 43] . To characterize the insertion pattern of L1 with minimal influence from selective pressure , experimental methods were developed for recovery of novel L1 insertions in cultured cells [44 , 45] . Using HAT-seq method , we were able to distinguish somatic L1Hs insertions from germline L1Hs insertions within the same individual . To determine whether the sense-depleted germline insertion was resulted from natural selection or insertional preference , we used somatic insertion as internal reference to control confounding factors such as intrinsic insertion preference and compared germline with somatic insertions . Our results suggested that natural selection shaped a sense-depleted distribution of germline L1Hs insertions in the human genome . Several PCR-based bulk sequencing methods , such as ATLAS [24] , L1-seq [25] , TIP-seq [26] , bulk SLAV-seq [7] , and ATLAS-seq [27] , have been developed to identify germline L1Hs insertions . Furthermore , L1-seq and TIP-seq have been successfully used in the identification of somatic insertions in tumors [26 , 46–49] . Due to clonal expansion during tumorigenesis , such insertions could affect numerous cells in tumors . To our knowledge , HAT-seq is the first PCR-based bulk sequencing method to identify rare somatic insertions in a subset of cells—even unique cells—in non-tumor tissues . HAT-seq provides not only the genomic positions of somatic insertions but also the allele fraction of each insertion , which is informative for inferring the timing when the insertion has occurred . The sensitivities of HAT-seq for low-frequency somatic L1Hs insertions were relatively low ( ~30% for insertions present in < 1% fraction of cells ) . One possible explanation was that some signals of insertion were lost during library construction and NGS sequencing , e . g . sonic fragmentation , clean-ups , size selection , and loading library to sequencer . Single-cell whole genome and targeted sequencing approaches have been used to identify both TPRT-mediated and endonuclease-independent insertions [7 , 10–12] , where the signal of somatic insertions can be as high as germline heterozygous insertions in single-cell level . However , such single-cell approaches cannot achieve increased sensitivity without cost [22] . For example , to detect a given insertion with 0 . 1% mosaicism , more than 1 , 000 single cells may need to be amplified and sequenced . Therefore , compared with single-cell approaches , HAT-seq was eligible to identify a large number of somatic L1Hs insertions in a more cost-effective way . Based on our experimental design , assembling overlapped read pairs into contigs can provide sequence information fully spanning the L1Hs integration sites , enabling downstream false-positive filtering based on both sequence features and read-count . However , a portion of read pairs were unable to be merged into contigs because of the inaccurate size-selection during library construction . Applying the same filtering strategy , we re-analyzed these unassembled read pairs and revealed 11 clonal insertion candidates . Further PCR experiments only validate one of these candidates ( 9% , S10 Table ) . Because the key filter “chimera within poly-A tail” was not applicable for unassembled read pairs , our sequence analysis suggested that chimeric molecules bridging within the poly-A tail was the major source of false-positives for unassembled data ( see details in Methods ) . As shown in the statistics of positive control libraries ( S2 Table ) and experimental validation , the unassembled data could provide additional signals of somatic L1Hs insertions but require careful analysis and rigorous validation to address technical artifacts . Further gains in statistical power will be benefited from increased sample size and improved efficiency of HAT-seq . Several unresolved technical challenges might constrain the total number of detectable L1Hs insertions by the current version of HAT-seq , including the identification of insertions in repetitive regions with low mappability ( such as pre-existing L1 germline insertions ) and 3’ truncated insertions . With rapid innovations in sequencing technology , higher throughput and longer read length will markedly improve the performance of HAT-seq . Future studies that profile all active retrotransposons ( i . e . , L1Hs , Alu , and SVA ) in a variety of cell types , tissues , and developmental stages will shed new light on the dynamics of somatic retrotransposition under host regulation and help to uncover their roles in human disease .
Postmortem samples of prefrontal cortex and non-brain tissues were obtained from five patients with Rett syndrome ( UMB#4882 , UMB#1815 , UMB#4852 , UMB#4516 , and UMB#1420 ) and five age- , gender- , and race-matched neurologically normal individuals ( UMB#4591 , UMB#1571 , UMB#1347 , UMB#1846 , and UMB#1455 ) through the UMB Brain and Tissue Bank ( University of Maryland , Baltimore , MD ) ( Table 2 and S4 Table ) ; written informed consent was obtained by the UMB Brain and Tissue Bank and the Lieber Institute for Brain Development ( Baltimore , MD ) . The peripheral blood samples of two unrelated individuals ( ACC1 and ACC2 ) were collected with written informed consent by Peking University . This study was approved by the Institutional Review Board ( IRB ) at Peking University ( IRB00001052-13025 ) . Nuclei were isolated and labeled for FACS based on a previous study [14] with modifications . Fresh-frozen samples were thawed gradually from liquid nitrogen by transferring to a −80°C freezer; the samples were then transferred to a −20°C freezer 1 h later . All procedures were performed at 4°C unless noted otherwise . First , 100 mg of tissue was minced into pieces , and transferred to 2 mL STKM buffer ( 250 mM sucrose , 50 mM Tris-HCl , pH 7 . 4 , 25 mM KCl , 5 mM MgCl2 ) with protein inhibitor ( cOmplete , Mini , EDTA-free Protease Inhibitor Cocktail , Roche ) . The minced tissue was soaked overnight for 8 hours and homogenized in a Potter-Elvehjem glass homogenizer ( 886000–0019; Kontes ) . To improve immunostaining and the purity of the isolated target , debris was removed by Percoll density gradient centrifugation . Brain homogenate was filtered through a 100-μm cell strainer and mixed with Percoll solution ( P1644-100ML; Sigma ) to a final concentration of 19% . A 5-mL ultracentrifuge tube ( P/N 344057; Beckman ) was layered with Percoll solutions in the following order: 0 . 4 mL of 12% Percoll , 3 mL of homogenate ( 19% Percoll ) , 0 . 8 mL of 26% Percoll , and 0 . 3 mL of 35% Percoll . The tube was then centrifuged in a SW 55 Ti rotor ( Beckman Coulter ) at 16 , 000 rpm ( 30 , 000 g ) for 10 min . Large quantities of myelin and cellular debris generated during brain homogenate preparation were removed from the single-nuclei suspension , and the floating nuclei fraction was collected from the 35% layer ( S1G–S1I Fig and S2D and S2E Fig ) . Neuronal nuclei were purified using NeuN immunostaining . The nuclei fraction was blocked in 2 . 5% bovine serum albumin ( BSA ) in phosphate buffered saline ( PBS ) for 2 hours with 6 rpm end-to-end rotation; 20 μL of the sample served as an unstained control sample for flow cytometry . Blocked nuclei were labeled with 2μL/mL PE-conjugated anti-NeuN antibody ( FCMAB317PE; Millipore ) , filtered through a 40-μm cell strainer and diluted with 1% BSA in PBS at 2 volumes of the sample . We did not stain the nuclear fraction with a fluorescent nuclear stain ( e . g . , propidium iodide [PI] or 4’ , 6-diamidino-2-phenylindole [DAPI] ) for sorting because they bind to DNA and affect quantification analysis using Qubit 2 . 0 Fluorometer ( Life Technologies ) . Single-nuclei suspension was sorted in 4-way purity mode at a flow rate less than 6 , 000 events per second using an 85-μm nozzle with a BD FACSAria II cell sorter . The collection tube was pre-coated with 1% BSA in PBS , and a small volume of 1% BSA in PBS was then pre-added to protect the nuclei from breaking down . Sorted NeuN+ and NeuN− fractions were re-analyzed by flow cytometry to verify the purity; a small portion was stained with DAPI or PI to check the purity and integrity via fluorescence and differential interference contrast ( DIC ) microscopy . Nuclei were pelleted at the bottom of the collection tube after centrifugation with a swing rotor at 1 , 000 g at 4°C for 20 min . gDNA was extracted using QIAamp DNA Mini Kit ( QIAGEN ) or QIAamp DNA Micro Kit ( QIAGEN ) according to the sorting statistic . First , 500 ng of gDNA was sonicated using Covaris S220 with the following settings: sample volume , 50 μL; water level , 12; temperature , 7°C; peak incident power , 175 W; duty factor , 5%; cycles per burst , 200; and treatment time , 55 s . DNA fragments were end-repaired , dA-tailed , and adaptor-ligated using KAPA LTP Library Preparation Kit ( KK8232; KAPA Biosystems ) . All oligonucleotides used in library preparation were synthesized by Invitrogen ( Life Technologies ) and are listed in S1 Table . Adaptor-ligated DNA ( 20 ng , ~3 , 000 cells ) served as input for PCR-based target enrichment . The PCR protocol was: 12 . 5 μL KAPA2G Robust HotStart ReadyMix ( 2× ) ( KK5702; KAPA Biosystems ) , 1 . 25 μL P7_Ns_L1Hs ( 10 μM ) , and PCR-grade water added to final volume of 23 . 75 μL . Another primer , 1 . 25 μL P5_extension ( 10 μM ) , was added when linear amplification was finished . P7_Ns_L1Hs ( 10 μM ) was an equimolar mixture of P7_N2_L1Hs , P7_N4_L1Hs , and P7_N6_L1Hs . The cycling programs were: 95°C for 5 min; 5 cycles of 95°C for 40 sec , 61°C for 15 sec , and 72°C for 15 sec; a pause at 12°C to add the P5_extension primer; 11 cycles of 95°C for 40 sec , 61°C for 15 sec , and 72°C for 15 sec; ending with 72°C for 30 sec and held at 4°C . Post-PCR cleanup was performed with 1 . 05× Agencourt AMPure XP beads ( Beckman Coulter , Inc . ) . Amplified products from the first PCR were eluted in 10 μL of Buffer EB ( QIAGEN ) and used as template in the second PCR to incorporate Illumina sequencing adapters with barcode . The PCR protocol was 12 . 5 μL KAPA2G Robust HotStart ReadyMix ( 2× ) , 1 . 25 μL P5_to_end ( 10 μM ) , 1 . 25 μL P7_extension_i7_index ( 10 μM ) , and PCR-grade water to final volume of 25 μL . The cycling program was: 95°C for 5 min; 5 cycles of 95°C for 40 sec , 60°C for 15 sec , and 72°C for 15 sec; ending with 72°C for 30 sec and held at 4°C . To deal with “bubble products” from overamplification that could hinder accurate gel-based size selection , a step of “one-round PCR” was performed by adding equal volumes of KAPA2G Robust HotStart ReadyMix ( 2× ) , P5_to_end ( 10 μM ) , P7_extension_i7_index ( 10 μM ) , and PCR-grade water added to the PCR tube to a final volume of 50 μL . The cycling program was: 95°C for 80 sec , 60°C for 30 sec , 72°C for 2 min , and held at 4°C . Post-PCR cleanup was performed with 1 . 1× Agencourt AMPure beads . Each library was eluted with 30 μL of Buffer EB and size selected ( 320–550 bp ) using Pippin Prep ( Sage Science ) . After library quality control using Agilent 2100 Bioanalyzer with High Sensitivity DNA Kit ( Agilent Technologies ) and KAPA Library Quantification Kit Illumina platforms ( KK4824 , KAPA Biosystems ) , HAT-seq libraries were paired-end sequenced ( 2*150 bp ) at Novogene , Inc . For 20 HAT-seq libraries constructed from postmortem human tissues , a total of 1 , 191 , 889 , 370 2*150 bp read pairs were generated , with an average of 59 , 594 , 469 read pairs per sample . Library details are shown in S5 Table . Schema of the HAT-seq data analysis pipeline is shown in S3B Fig . Raw data were de-multiplexed , adaptor trimmed , and base trimmed with base quality < 10 . Next , we specifically extracted L1Hs-derived read pairs based on the Read 2 sequence information . Only those with the 3’ consensus sequence of P7_Ns_L1Hs “GGGAGATATACCTAATGCTAGATGACAC” were retained and trimmed , as they had the correct HAT-seq library structure . Read 2 sequences with 95% identity to the 3’ end of the L1Hs consensus sequence were retained . For KR insertions , the retained Read 2 and their paired Read 1 were aligned to hg19 using BWA-MEM ( version: 0 . 7 . 12-r1039 , default parameters ) and those uniquely mapped reads were used for peak calling . For non-reference insertions , we first merged L1Hs insertion-derived read pairs into contigs using PEAR ( version: 0 . 9 . 6; parameters: -y 50M -j 4 -m 300 -n 70 ) and aligned contigs to hg19 with BWA-MEM ( default parameters ) which allowed for split-read mapping . All uniquely mapped contigs without any clipping were ignored in the downstream analysis as these contigs were deemed to be KR-derived reads . Only those clipped contigs and non-uniquely mapped contigs ( contigs with mapQ < 20 or unmapped contigs ) were extracted for further computational analysis to call non-reference insertions . These contigs were poly-T ( TTTTTTTT ) trimmed , leaving 3’ genomic flanking sequence of L1Hs insertions for STAR mapping to hg19 ( version2 . 4 . 2a; parameters:—outFilterMatchNminOverLread 0 . 3—outFilterScoreMinOverLread 0 . 3—scoreGapNoncan -4—scoreGapGCAG -4—scoreGapATAC -4—alignIntronMax 500 ) . For uniquely mapped reads , we marked PCR duplicates with SAMBLASTER ( version: 0 . 1 . 22 ) and used them to call non-reference insertion peaks . Peak calling was triggered where a genomic position with >1 depth was found . Adjacent peaks were merged with a maximum distance of 100 bp . We intersected peaks of each sample with the L1Hs insertions collected in RepeatMasker ( database version: 20130422; http://www . repeatmasker . org ) and annotated each overlapped peak with a total of 6 features: peak height ( reads per million mapped reads [RPM] ) , peak width ( genomic length with read depth ≥ 1 ) , signal count ( the number of unique start positions of reads aligning to the peak ) , depth of each signal ( the number of PCR duplicates for each signal with unique start position ) , genomic information of overlapped L1 elements , and its overlapping width . We employed a read-count filter to distinguish true insertions from artifacts . Many false-positive KRs were supported by reads aligned to the 3’ end of L1Hs with sufficient depth but without reads mapping to their 3’ flanking sequence . Some false positives were supported by a few chimeric molecules with low depth . Putative KR peaks of each sample were assigned when they satisfied the following criteria: a ) overlapped with annotated L1Hs regions in the human reference genome; b ) RPM > 40; c ) overlapping width > 200 bp; and d ) not in chromosome Y ( chrY ) . Peaks were called and merged as described above , with the exception that they were performed separately for reads aligning to the plus and minus strands of the reference genome since the 3’ flanking sequence preserved the insertional orientation information . We filtered out peaks that overlapped with reference L1Hs and L1 subfamilies ( L1PA2 , L1PA3 , and L1PA4 ) . We intersected the remaining peaks with the meta retrotransposon insertion polymorphisms ( MRIP ) list from euL1db [30] , and assigned overlapped peaks as putative KNRs when they satisfied the following criteria: a ) signal counts ≥ 30; b ) RPM ≥ 100; c ) at least 3 signals with a “depth of each signal” ≥ 5; and d ) not in chrY . For the remaining non-reference insertions , we implemented a series of empirical error filters to deliberately remove several types of false positives . First , we rejected reads with risk of misalignment , defined as when the BWA-MEM and BLAT alignments were inconsistent . Second , we rejected reads without an L1Hs diagnostic G motif ( position 6012 relative to the L1Hs Repbase consensus ) . Third , we rejected reads at risk of being a chimeric molecule . We applied BLAST to find the best alignments for retrotransposon and non-retrotransposon segments from hg19 . Reads were removed as a putative chimera when the sequences of two segments overlapped > 10 bp with A% ≥ 50% or overlapped 6–10 bp with A% < 50% [12] . Fourth , we rejected reads with risk of being derived from nearby reference L1Hs . We extracted 2 kb downstream of aligned non-retrotransposon segment from hg19 and aligned the full contig against this sequence by BLAT to exclude potential genomic rearrangement events [12] . To circumvent the interference of background noise , all contigs that passed these filters were extracted and remapped using STAR to acquire clean bam files for subsequent statistical analysis . Peaks were classified as putative UNKs when they satisfied the following criteria: a ) signal counts ≥ 10; b ) at least 3 signals with a “depth of each signal” ≥ 5; and c ) not in chrY . After filtering out all putative UNKs , most remaining non-reference insertions were supported with low read depth . To distinguish somatic insertions from artifacts , we regarded PCR duplicates as a marker of high-confidence somatic insertions . The rationale was that each L1Hs insertion in the template gDNA was amplified by 17 PCR cycles ( 11 + 5 + 1 ) and a portion of their duplicates should be sequenced . In contrast , technical artifacts induced by non-specific or chimeric PCR amplification were inevitable but were generated at a much lower rate . Therefore , we rejected putative peaks without PCR duplicates . As shown in S5 Table , we suggested to sequence at least 50M reads for each HAT-seq library . Finally , we rejected systematic error-prone sites shared by two or more individuals because the likelihood of recurrent somatic insertions in different individuals was presumed to be much smaller than the likelihood of systematic mapping or sequencing errors . A subset of putative somatic insertions was classified as clonal somatic insertions , which were supported by two or more PCR duplicate signals with different unique start positions . If two unique start positions differed by a shift of 1 bp , we tolerated the difference and regarded them as the same signal , for this was likely due to low base quality at the beginning of Read 1 ( 3’ flanking genomic sequence ) . Merged peak references were created for each of four insertion categories ( KR , KNR , UNK , and somatic insertions ) . Peaks were merged with a maximum distance of 100bp in a strand-specific manner . Detailed information of germline and somatic insertions across all samples was provided in S5–S8 Tables . Hierarchical clustering of germline insertions across all samples was performed with heatmap3 package in R ( https://www . r-project . org ) . Due to the wide range of size-selection during library construction , a portion of unassembled read pairs in the HAT-seq data were filtered after contig merging step . We confirmed that additional signals of somatic L1Hs insertions exist in these unassembled data . As shown in S2 Table , higher sensitivity of HAT-seq will be achieved when including unassembled reads in HAT-seq analysis . These unassembled paired-end reads do not contain the sequence information of L1-genome junction and thus were not applicable to distinguish true signals ( somatic L1Hs insertions ) from noise ( chimeric molecules ) based on L1 integration site sequence features ( hallmarks of TPRT mechanism ) . Applying the same criteria for “contig data” analysis except for the two inapplicable filters ( “chimera within poly-A tail” and “local SV” ) , we identified 11 clonal somatic insertion candidates with three or more supporting signals , whose mosaicism ( percentage of cells ) were at least 0 . 1% based on our experimental design of HAT-seq library . Among the 11 putative clonal somatic insertions , only one event was confirmed via 3’ nested PCR and Sanger sequencing ( S10 Table [unassembled data] ) . This clonal somatic insertion ( 1571_chr3:2944507 ) has the maximum count of supporting signal and was supported by both “unassembled data” and “contig data” with 6 and 18 supporting signals ( reads with different start positions ) , respectively . All false positives from unassembled data were overlapped with repeat elements , especially the 3’ end of Alu subfamilies . Eight out of the ten false positives ( 80% ) were supported by only three supporting signals . Notably , five false positives were identified from PFC neuron of UMB#4852 . This library contained higher proportion of unassembled reads than other libraries ( S5 Table [Uniquely mapped polyT trimmed read pairs using STAR] ) , indicating a higher level of background noise in this library . Previous studies reported relative quantification of L1Hs contents using TaqMan quantitative PCR ( qPCR ) [8 , 9] . However , except for its limitation by using exogenous L1 plasmid to estimate L1 copy number [50 , 51] , the qPCR assay lacks specificity for active L1 elements [22] . On one hand , L1 reverse transcription occurring in cytoplasm would confound quantification [52] . On the other hand , the qPCR assay was unable to distinguish between somatic and germline L1Hs insertions , while L1Hs copy number variation among tissues was only contributed by active somatic insertions . In HAT-seq library , genomic fragments containing L1Hs insertions served as templates and were amplified equally using the same PCR reaction conditions . A random subset of the library was subsequently sequenced and classified into somatic and germline insertion-derived reads . We quantified the relative copy number of somatic L1Hs in each tissue from the same donor by normalizing somatic L1Hs-derived read counts with germline L1Hs-derived read counts . As KNRs shared the same sequence features and non-reference insertion calling pipeline with somatic insertions , we further quantified the rate of somatic L1Hs insertions per cell based on the KNR copy number of each individual ( S14 Table ) . Given that most KNRs are heterozygous , we regarded the KNR copy number of each individual as the KNR count per cell . To demonstrate the linear PCR amplification of both germline and somatic insertions during HAT-seq library construction , we calculated the estimated rate of 64 ACC1-specific spike-in insertions in the positive control libraries . Our observed rates were 0 . 59 , 0 . 056 , and 0 . 0144 , approximated to the expected 0 . 64 , 0 . 064 , and 0 . 0064 ACC1-specific insertions per cell in 1% , 0 . 1% and 0 . 01% spike-in libraries , respectively ( S14 Table ) . Using R and Bioconductor ( https://www . bioconductor . org/ ) packages , we downloaded refGene annotations for the hg19 genome from UCSC Genome Browser and annotated the L1Hs insertions . If a gene produced multiple transcripts , we focused on the canonical transcript , the longest transcript among those with the longest coding sequence . We annotated the genomic coordination of each category of L1Hs insertions along with the human genome and applied a binomial test to compare the proportion of insertions located inside a specific region ( e . g . introns or exons ) with the expected proportion determined by the exact base-pair count of that specific region relative to the human genome . We also annotated the sense or anti-sense orientation of the peaks located in transcripts and applied a binomial test to compare the sense proportion with the expected 50% under null hypothesis . When comparing the intronic or exonic insertion proportion between Rett patients and control samples , we made a 2 × 2 contingency table and applied Fisher’s exact test; we reported the p-value , the estimate and 95% CI of OR . Similarly , Fisher’s exact tests were also applied to judge the difference in sense-oriented count between germline and somatic insertions . Statistical analysis on clonal and “unique” somatic insertions were performed using Fisher’s exact test based on their count of insertion events . All annotations and statistical analyses were conducted by an automatic pipeline in R language to ensure reproducibility . Based on the strand of insertion , we retrieved 2-kb upstream and downstream genomic sequences of each of L1Hs integration sites and calculated their minimum distance from the integration sites to one of seven typical L1 EN motifs in TPRT-mediated retrotransposition ( TTAAAA , TTAAGA , TTAGAA , TTGAAA , TTAAAG , CTAAAA , and TCAAAA ) [53] . For random sampling control , we retrieved the upstream and downstream flanking sequences of 100 , 000 random positions in the genome . Sequences consisting of >20% low mappability nucleotides ( mappability < 0 . 25 ) were removed ( a total of 8 , 634 sequences ) . Furthermore , 67 sequences consisting of >90% N were removed . We applied Wilcoxon rank-sum test to compare the absolute values of the distances between each of insertion categories . For S4 Fig , we reduced the bin size to 10bp and illustrated the y-axis with a gap break . In addition , we examined the poly-A tail length size of each L1Hs insertion . Due to our experimental design , the supporting contigs contained poly-T before the L1Hs sequence ( reverse complementary ) . Therefore , we determined the anchor position of ATTAT on each contig using a greedy algorithm , and then calculated the poly-T length by searching backward to the upstream anchor position using a scoring algorithm ( match [T] +1 , mismatch −2 , report poly-T with score ≥ 0 ) . Starting from the anchor position at the end of L1Hs 3’ UTR , our algorithm examined the poly-A tail length of L1Hs elements regardless of whether the retrotransposition event had 3’ transductions or not . All annotations and statistical analyses were conducted using Perl 5 ( https://www . perl . org ) and R 3 . 1 . 0 . Because both polymorphic germline and somatic insertions belonging to L1Hs sharing the same sequence characteristics , known concentration spike-in of polymorphic germline insertions can be regarded as true somatic insertions with known allele frequency . First , we extracted DNA from the blood of two unrelated adults , ACC1 and ACC2 . We identified 172 non-reference L1Hs insertions in ACC1 using its HAT-seq data and screened them in both ACC1 and ACC2 using 3’ PCR . 64 polymorphic L1Hs insertions confirmed to be ACC1-specific ( Fig 2A and S2 Appendix ) . The zygosity of ACC1-specific insertions was confirmed by full-length PCR with 49 ( 77% ) heterozygous insertions , 9 ( 14% ) homozygous insertions , and 6 ( 9% ) zygosity-undetermined insertions ( Fig 2B and S2 Appendix ) . As most ACC1-specific insertions were heterozygous , we used ACC1 gDNA as the spike-in , with 0 . 5 insertions per genome for each of ACC1-specific insertions . A mixed-DNA series containing 1% , 0 . 1% , and 0 . 01% ACC1 gDNA were prepared using ACC1 and ACC2 gDNA . Using 20 ng ( 3 , 000 cells ) adaptor-ligated gDNA as input , HAT-seq libraries were constructed , sequenced , and computationally analyzed in the same manner as the bulk sequencing HAT-seq libraries mentioned above , with the exception of ignoring the KNR insertion filtering . We labeled insertion sites as “detected” when they were supported by uniquely mapped reads , and “identified” when they were subsequently supported by reads that passed all stringent error filters . 64 ACC1-specific insertions were independent events . For each insertion , supporting signals could be counted based on different start positions . Because all the insertion-supporting reads originating from a single cell should have identical start position , the signal count of each insertion indicated the number of cells carrying the insertion that were sampled from the library input . The distributions of supporting signal counts ( reads with unique start positions ) per ACC1-specific insertion should follow Poisson distribution . The parameter lambda for Poisson distribution was fitted using the maximum likelihood method , and chi-squared goodness-of-fit tests were performed ( Fig 2C and S2 Table ) . For 1% , 0 . 1% , and 0 . 01% ACC1 spike-in libraries , each of 64 ACC1-specific insertions was diluted to 30 , 3 , and 0 . 3 copies . Theoretically , by Poisson statistics , there would be 64 , 60 . 81 , and 16 . 59 ACC1-specific insertions being sampled and subsequently being used as the input of HAT-seq libraries . According to the number of ACC1-specific insertions identified in 1% , 0 . 1% , and 0 . 01% libraries , the sensitivities for somatic L1Hs insertions were 76 . 6% ( 49/64 ) , 28% ( 17/60 . 81 ) , and 30 . 1% ( 5/16 . 59 ) , respectively . As shown in S3 Table , the number of false positives were 66 , 181 , and 183 in 1% , 0 . 1% , and 0 . 01% spike-in libraries , respectively . The numbers of false positives were stable in 0 . 1% and 0 . 01% libraries , whereas the lower number of false positives in 1% library should result from the lower total throughput of the library ( S5 Table ) . The percentage showed a 3 . 61-fold decrease ( from 14 . 3% to 3 . 96% ) after applying the “PCR duplicate” filter , compared to 2 . 28-fold decrease in 0 . 1% ( from 17 . 23% to 7 . 55% ) and 0 . 01% ( from 19 . 09% to 8 . 37% ) libraries , suggesting that more candidates without PCR duplicates were filtered in the 1% library due to lower sequencing throughput . In sum , our results suggested that the upper bound of the number of false positives should be 183 per library . Using the most stringent definition , the total number of false-positives in all 20 libraries was 3 , 660 out of 9 , 181 and thus the overall precision for our putative somatic L1Hs insertions was 60 . 14% . To identify ACC1-specific insertions , the 3’ PCR protocol comprised 10 ng template DNA , 10 μL 2× Taq PCR StarMix with Loading Dye ( A012; GenStar ) , 1 μL site-specific 3’ primer ( 10 μM ) , 1 μL L1Hs-AC-28 ( 10 μM ) , and PCR-grade water to 20 μL . The cycling program was: 94°C for 2 min; 35 cycles of 94°C for 30 sec , 60°C for 30 sec , 72°C for 30 sec; 72°C for 5 min; and held at 4°C . Validation primer sequences used for each candidate insertion can be found in S2 Table . To determine the zygosity of ACC1-specific insertions , the full-length PCR protocol comprised 50 ng template DNA , 1 μL PrimeSTAR GXL DNA polymerase , 5 μL PrimeSTAR GXL buffer ( 5× ) , 2 μL dNTP mixture ( 2 . 5 mM each ) , 0 . 5 μL dimethyl sulfoxide ( DMSO ) , 0 . 75 μL 5’ primer ( 10 μM ) , 0 . 75 μL 3’ primer ( 10 μM ) , and PCR-grade water added to a final volume of 25 μL . The cycling program was 98°C for 3 min; 30 cycles of 98°C for 15 sec , 58°C for 20 sec , 68°C for 2 min; 68°C for 3 min; and held at 4°C . Validation primer sequences used for each candidate insertion can be found in S2 Table . For validation of polymorphic germline L1Hs insertions , the 3’ PCR protocol comprised 10 ng template DNA , 10 μL KAPA2G Robust HotStart ReadyMix ( 2× ) , 1 μL site-specific 3’ primer ( 10 μM ) , 1 μL L1Hs-AC-28 ( 10 μM ) , and PCR-grade water to a final volume of 20 μL . The cycling parameters were 94°C for 3 min; 35 cycles of 94°C for 15 s , 58°C for 15 s , 72°C for 15 s; 72°C for 3 min; and held at 4°C . Validation primer sequences used for each candidate insertion can be found in S16 Table . All PCR products were run on 1 . 5% or 2% agarose gels , and images were analyzed using Image Lab software ( Bio-Rad ) to quantify the product sizes . | Human-specific LINE-1 ( L1Hs ) is the most active autonomous retrotransposon family in the human genome . Mounting evidence supports that L1Hs retrotransposition occurs postzygotically in the human brain cells , contributing to neuronal genomic diversity , but the extent of L1Hs-driven mosaicism in the brain is debated . In this study , we profiled genome-wide L1Hs insertions among 20 postmortem tissues from Rett patients and matched controls . We identified and validated somatic L1Hs insertions in both cortical neurons and non-brain tissues , with a higher jumping activity in the brain . We further found that MeCP2 dysfunction might alter the genomic pattern of somatic L1Hs in Rett patients . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2019 | Somatic LINE-1 retrotransposition in cortical neurons and non-brain tissues of Rett patients and healthy individuals |
Heterogeneity in the expression of various bacterial genes has been shown to result in the presence of individuals with different phenotypes within clonal bacterial populations . The genes specifying motility and flagellar functions are coordinately regulated and form a complex regulon , the flagellar regulon . Complex interplay has recently been demonstrated in the regulation of flagellar and virulence gene expression in many bacterial pathogens . We show here that FliZ , a DNA-binding protein , plays a key role in the insect pathogen , Xenorhabdus nematophila , affecting not only hemolysin production and virulence in insects , but efficient swimming motility . RNA-Seq analysis identified FliZ as a global regulatory protein controlling the expression of 278 Xenorhabdus genes either directly or indirectly . FliZ is required for the efficient expression of all flagellar genes , probably through its positive feedback loop , which controls expression of the flhDC operon , the master regulator of the flagellar circuit . FliZ also up- or downregulates the expression of numerous genes encoding non-flagellar proteins potentially involved in key steps of the Xenorhabdus lifecycle . Single-cell analysis revealed the bimodal expression of six identified markers of the FliZ regulon during exponential growth of the bacterial population . In addition , a combination of fluorescence-activated cell sorting and RT-qPCR quantification showed that this bimodality generated a mixed population of cells either expressing ( “ON state” ) or not expressing ( “OFF state” ) FliZ-dependent genes . Moreover , studies of a bacterial population exposed to a graded series of FliZ concentrations showed that FliZ functioned as a rheostat , controlling the rate of transition between the “OFF” and “ON” states in individuals . FliZ thus plays a key role in cell fate decisions , by transiently creating individuals with different potentials for motility and host interactions .
Flagella are complex surface structures that serve as the primary means of locomotion in many bacterial species and allow many bacterial pathogens to adhere to and invade cells and in some cases to secrete virulence factors [1] . More than 50 genes are involved in the biogenesis and function of a flagellum in Escherichia coli or Salmonella enterica serovar Typhimurium ( S . Typhimurium ) [2] . Flagellar gene expression is sequential mirroring the timing of the assembly process [2] , [3] . First expressed is the class I operon , flhDC , the products of which , FlhD4C2 heterohexamers , are required for the expression of all other flagellar genes [4]–[6] . The E . coli FlhD4C2 complex activates class II operons , including the structural genes for flagellar hook-basal body components ( a type III secretion system ) and the alternative sigma factor FliA [7] . The fliA gene is the first gene of the fliAZY operon in E . coli and S . Typhimurium and its product , sigma 28 , directs transcription of the class III genes encoding the filament protein called flagellin , hook-associated proteins , motor proteins and various chemotaxis proteins [8] . The central channel of the flagellar apparatus is thought to serve as a passage for both flagellar component proteins and for the flagellar regulatory protein FlgM , an anti-sigma-28 factor [5] , [9] . Thus , the accumulation of FlgM in the cell due to the prevention of its export blocks the transcription of class III genes , including that encoding flagellin . Two other genes within the flagellar regulon , fliT and fliZ , have been shown to regulate class II gene transcription in S . Typhimurium [10] . Disruption of the fliT gene increases class II gene transcription , whereas disruption of the fliZ gene decreases class II gene transcription , with no effect on class I transcription , suggesting that FliT and FliZ are negative and positive regulators , respectively . The type III secretion chaperone FliT has been shown to act as an anti-FlhD4C2 factor preventing the formation of the FlhD4C2-DNA complex and inhibiting its binding to class II promoters [11] . FliZ is encoded by a gene in the fliA operon , and orthologs are found only in the flagellar regulon of members of the family Enterobacteriaceae . The precise mechanism of action of FliZ remains unclear . It has been reported to activate class II flagellar gene expression and there is some evidence that it is involved in the posttranslational regulation of FlhD4C2 activity in S . Typhimurium [12] . However , the FliZ protein contains a region resembling the core DNA-binding domain of phage integrases [13] , [14] , suggesting that it may play a direct role in the regulation of transcription . Indeed , a primary study in Xenorhabdus nematophila ( Enterobacteriaceae ) showed that FliZ activated the transcription of class II flagellar genes by direct binding to the flhDC promoter [13] . Another study showed that FliZ indirectly activated flagellar gene expression in S . Typhimurium by binding directly to nlpC promoter and repressing the transcription of the associated ydiV gene , which encodes an anti-FlhDC factor [15] . Flagellar regulators , such as FliZ , have been implicated in processes other than flagellum synthesis . FliZ has been shown to be an abundant DNA-binding protein that inhibits gene expression mediated by RpoS in E . coli by recognizing operator sequences resembling the −10 region of RpoS-dependent promoters [14] . Previous studies have also shown that fliZ mutation significantly reduces hilA expression and intestinal S . Typhimurium colonization in mice [16] . Indeed , indirect regulation by FliZ has been shown to upregulate the expression of the SPI1 type three secretion system ( T3SS ) in S . Typhimurium , where FliZ controls HilD protein activity upstream from the HilC/RtsA/HilA transcriptional cascade [17] . As in S . Typhimurium , FliZ has been shown to mediate the coordinate regulation of flagellum synthesis and virulence in the insect pathogen X . nematophila [13] . Xenorhabdus nematophila displays complex interactions with invertebrates , has a symbiotic lifestyle with nematodes of the genus Steinernema and is pathogenic to insect larvae [18] , [19] . The successful colonization of two invertebrate hosts requires Xenorhabdus to cope with shifting host environments , by interconnecting the various gene networks [19] . Various regulatory proteins of Xenorhabdus are involved in host interactions , but it has been shown that the fliAZ operon plays a central role in controlling lipase and hemolysin production and in motility and full virulence in insects [13] . Indeed , FliA coordinates the expression of class III flagellar genes , such as the flagellin-encoding gene fliC , and the two non-flagellar genes , xlpA and xrtA , encoding a lipase and a protease , respectively [20] , whereas FliZ binds directly to the promoter regions of two different hemolysin-encoding operons , xaxAB [21] and xhlBA [22] , activating the transcription even in the absence of the FlhD4C2 complex [13] . A real-time analysis of virulence gene expression during insect infection revealed that the expression of FliZ-dependent hemolysin genes coincided with the increase in iron availability detected at the time of insect death , suggesting that iron availability is a signal governing the adaptation of X . nematophila to changes in host environments . Interestingly , this study also revealed that the expression of the fliC and xaxAB genes in Xenorhabdus was heterogeneous at the individual cell level [23] . In 1976 , Spudich and Koshland reported the existence of “non-genetic individuality” in monitoring the swimming behavior of S . Typhimurium at the level of individual cells [24] . More recently , the molecular origin of the temporal variations of chemotaxis system signaling between individual bacteria was reinvestigated , resulting in the demonstration of a role for the relative concentration of a key chemotaxis network component , CheR [25] . Furthermore , high levels of stochastic phenotypic variation have been reported for flagellar genes in S . Typhimurium [26]–[28] and Bacillus subtilis [29] . We show here that FliZ plays a key role in Xenorhabdus , not only in hemolysin activities and full virulence in insects , but also in efficient swimming motility . We demonstrate that efficient expression of the entire flagellar regulon requires the FliZ-dependent positive feedback loop controlling expression of the master operon flhDC . We also show that expression of the flagellin and FliZ-dependent hemolysin genes is heterogeneous , differing between individual cells , and that a FliZ threshold controls the rate of transition between the OFF and ON states of FliZ-dependent gene expression at the single-cell scale . FliZ-modulated bimodal gene expression generated a mixed population of cells , with different levels of FliZ-dependent gene expression , resulting in the transient production of individuals with different potentials in terms of host interactions .
Our previous transcriptional analysis revealed that FliZ was required for the coupling of motility and hemolysin expression in X . nematophila and that FliZ bound directly to the promoter regions of the hemolysin and flhDC operons , functioning as an activator [13] . We investigated the whole FliZ regulon using RNA-Seq analysis to compare the gene expression profiles of the wild type strain F1 and the isogenic fliZ mutant at mid-exponential growth phase ( OD540 = 0 . 5 ) . More than 73 million Illumina sequences ( 36-base reads ) were obtained for each sample . More than 82% of these sequences were of sufficiently high quality and could be mapped to at least one site in the X . nematophila F1 genome [30] . Transcriptomes were compared for each annotated feature between the wild type strain and the fliZ mutant ( GEO accession number: GSE47365 ) . We observed significant differences in expression between the fliZ mutant and the wild type strain for 278 coding sequences ( |log2 fold change| ≥1; adjusted P-values≤0 . 05; Table S1 ) , 235 of which were downregulated and 43 of which were upregulated in the fliZ mutant relative to the wild type strain . These genes were either isolated or clustered into 23 genomic regions scattered throughout the bacterial chromosome . The genes on which fliZ inactivation had the strongest effect are listed in Table 1 . The protein-coding genes significantly downregulated in the fliZ mutant included all 47 flagellar protein-encoding genes , clustered in three flagellar regions ( loci 13 , 14 and 17; Table S1 ) . Many other non-flagellar functional clusters were also upregulated by FliZ ( i ) the 14 genes ( xcnA–N; locus 15 ) required for the synthesis of xenocoumacin , the major antimicrobial compound produced by X . nematophila [31]; ( ii ) the pax cluster ( locus 10 ) encoding enzymes involved in synthesis of the Pax antimicrobial cyclolipopeptide [32] , [33] and ( iii ) all 16 genes ( locus 23 ) encoding putative components of a type VI secretion system [34] , [35] . Non-flagellar genes directly regulated by FliZ , such as those encoding hemolysins ( XaxAB and XhlAB ) [13] , were identified , as expected . However , more surprisingly , xptA1 , encoding XptA1 , the active component of a high-molecular weight protein toxin complex ( Tc ) , was found to be strongly regulated by FliZ , and XptA2 , a protein without insecticidal activity detected [36] , was found to be weakly regulated by FliZ . FliZ was initially described as a transcriptional activator , but we also identified individual genes and clusters of genes downregulated by this protein ( Tables 1 and S1 ) . Most of these downregulated genes are annotated as encoding hypothetical proteins of unknown function . However , FliZ also represses the transcription of a prophagic region ( loci 11 and 12 ) . The rpoS gene encoding the sigma factor σS was also found to be repressed by FliZ , consistent with the observed interference of FliZ with the expression of σS-dependent genes in E . coli [37] . We also found that FliZ downregulated the nilQR locus encoding NilR , a DNA-binding protein that , in turn , represses the transcription of nilAB and nilC required for colonization of the nematode host [38] . For validation of our differential RNA-Seq analysis , the expression level for 15 genes encoding factors potentially involved in the lifecycle of Xenorhabdus were also determined by real-time RT-PCR ( Table 1 ) . The fold-changes obtained with the two techniques were very similar , yielding a correlation coefficient ( R2 ) of 0 . 96 ( Table 1 , Figure S1 ) . Taken together , these results demonstrate that FliZ is required for the efficient expression of the entire flagellar regulon in Xenorhabdus . FliZ also serves other functions , as a positive or negative regulator of the expression of numerous genes encoding non-flagellar proteins potentially involved in key steps of the Xenorhabdus lifecycle . Mutations in the flhDC and fliAZ operons of X . nematophila have been shown to affect swimming and swarming motilities , lipase and protease production , hemolysis and insect virulence [13] , [20] , [39] . However , the relative impacts of these regulatory factors on phenotypes remain unclear . We tried to elucidate the role of FliZ , by comparing the phenotypic characteristics of the wild type and of flhD , fliAZ and fliZ mutants with and without fliZ expression , using an inducible Ptet-fliZ construct ( Table 2 ) . No difference was observed between strains in terms of antibiotic production , lecithin degradation and bromothymol blue adsorption . As expected [23] , the fliZ mutant had no hemolytic activity on sheep blood agar and displayed an attenuated virulence phenotype in insects similar to that of the fliAZ and flhD mutants ( Figure S2 ) . However , the fliZ mutant was unable to hydrolyze Tween 20 and presented a weak ability to swim on motility agar plates; both these phenotypes are known to be FliA-mediated [20] . The ectopic expression of FliZ complemented all affected phenotypes in the fliZ mutant , but it restored only hemolytic activity in the flhD and fliAZ mutants ( Table 2 ) . The absence of complementation for lipase production and motility in these strains indicates that FliZ acts indirectly on these FliA-dependent phenotypes , through the positive feedback loop exerted by FliZ on flhD expression [13] . These data demonstrate that FliZ plays a key role , not only in hemolysin activity and full virulence in insects , but also in efficient motility and lipase activity in Xenorhabdus . The fliZ mutants of E . coli and S . Typhimurium are fully motile and display only modest decreases in flagellar gene expression when grown in LB medium [15] . By contrast , the motility diameter developed by the Xenorhabdus fliZ mutant growing on LB motility agar plates was smaller than that obtained with the wild type strain , by a factor of 3 . 5 ( Figure 1A and 1B ) . However , unlike the polar fliAZ mutation , fliZ deletion did not fully abolish the swimming capacity of Xenorhabdus . The fliZ ( Ptet-fliZ ) strain recovered full motility in the presence of the inducer anhydrotetracycline ( aTc ) ( Figure 1A and 1B ) . We then investigated whether the motility defect of the fliZ mutant resulted from lower levels of flagellin production . The amount of flagellin produced by the fliZ mutant , as estimated by ELISA , was smaller than that produced by the wild type , by a factor of 3 . 2 , and was similar to the amount of FliC detected in the non motile fliAZ strain ( Figure 1C ) . As expected , expression of the Ptet-fliZ construct in the fliZ mutant fully restored flagellin production . We therefore used a PfliC-gfp[AAV] construct as a reporter for the level of expression of the flagellin gene . The GFP fluorescence signal was recorded in a bulk assay , on wild type , fliAZ and fliZ mutant cells grown for 15 h . As expected , GFP fluorescence was undetectable in the fliAZ mutant and reduced in the fliZ mutant by a factor 15 when compared to wild type ( Figure 1D ) . Complementation assays , performed with a plasmid carrying both PfliC-gfp[AAV] and Ptet-fliZ constructs , also resulted in high levels of GFP fluorescence if the fliZ strain was cultured in the presence of the aTc inducer . Together with the results of RNA-Seq experiments , these data demonstrate that the motility defect of the fliZ mutant results from a global decrease in the expression of flagellar genes , resulting in the production of low amounts of the flagellin monomer . Microscopic observations performed during insect infection have revealed that the expression of the fliC and xaxAB genes in Xenorhabdus is heterogeneous differing between individual cells [23] . We investigated the heterogeneity of fliC , xaxAB and xhlBA gene expression during bacterial growth in vitro , by measuring the expression of these genes in the wild type strain carrying the PfliC-gfp[AAV] , PxaxAB-gfp[AAV] or PxhlBA-gfp[AAV] fusion by flow cytometry . Two distinct populations ( OFF [GFP-negative cells] and ON [GFP-positive cells] ) were visualized , providing evidence of a bimodal distribution of cells , in terms of the expression of flagellin and hemolysin genes , in Xenorhabdus ( Figure 2 and S3 ) . The fliZ mutant of Xenorhabdus is less motile and has lower levels of flagellar protein than the wild type . It is therefore possible that the motility phenotype is supported by the expression of the flagellar regulon by only a small number of cells . We tested this hypothesis , by measuring the expression of the PfliC-gfp[AAV] fusion gene in the fliZ and fliAZ mutant strains , at single-cell resolution . We detected no fluorescent bacteria for the fliAZ mutant or the wild type strain carrying a promoter-less gfp gene used as a negative control ( Figure 2 ) . By contrast , GFP-positive cells were clearly detected for the fliZ mutant carrying PfliC-gfp[AAV] , albeit in much smaller numbers than for the wild type strain , corresponding to only 0 . 4% of all bacteria ( Figure 2 ) . These data suggest that the low levels of fliC gene expression observed in the fliZ mutant at the whole population level ( Figure 1 ) result from a substantial decrease in the number of bacteria expressing the flagellin gene . In S . Typhimurium , FliZ induces a switch in the kinetics of class II flagellar gene expression [28] . We investigated whether flagellar genes from classes I , II and III were expressed with a bimodal distribution in Xenorhabdus , by monitoring the expression of flhD ( class I ) , flgB and fliL ( class II ) and fliC ( class III ) during time-course study of the growth of the wild type strain . As for fliC , the expression of the class II genes , flgB and fliL , was bimodal throughout the growth of the bacteria ( Figure 3 ) . The percentages of bacteria corresponding to the OFF and ON populations for the fliL and fliC genes fluctuated strongly over time ( Figure 3 ) , with the ON population accounting for about 90% of all bacteria when the culture reached the early stationary phase . By contrast , only a single ON population was observed for the master operon flhDC , regardless of the growth stage considered ( Figure 3 ) . Thus , heterogeneous gene expression is intrinsic to the flagellar cascade and not due to external factors affecting flhDC expression . We evaluated the role of the FliZ positive feedback loop in the observed bimodal pattern of expression , by quantifying flagellar gene expression in the fliZ mutant , in which the FliZ feedback loop is inactivated . As previously observed , the percentage of fliC-expressing fliZ mutant bacteria did not exceed 1% , regardless of the growth stage considered . By contrast , the class II genes , flgB and fliL , were expressed with a bimodal distribution in both the wild type and fliZ mutant strains . However , GFP-positive populations emerged later in the fliZ mutant strain . The expression of flhD remained unimodal in the fliZ mutant cells , but was much weaker ( by a factor of 5 ) than that in the wild type strain ( Figure 3 ) . The FliZ feedback loop is , therefore , necessary for the early dynamics of class II flagellar gene expression , but dispensable for the heterogeneous expression of flagellar genes in X . nematophila . The FliZ feedback loop is not directly involved in the generation of bimodal expression patterns for class II flagellar genes , but FliZ may exert its activity through positive control over flagellar gene expression , mediating the transition between OFF and ON populations . We tested this hypothesis , by carrying out complementation assays with the plasmid carrying both PfliC-gfp[AAV] and Ptet-fliZ constructs . The addition of aTc led to a dose-dependent increase in the proportion of cells belonging to the ON population , which reached more than 96% for the highest concentrations of the inducer ( Figure 4 ) . However , the pattern of gene expression remains bimodal overtime and more specifically for the intermediate concentration of aTc ( 5 to 10 ng/ml ) . A slight increase in the amount of aTc , from 2 . 5 to 5 ng/ml , induced a shift of one third of the cells into the ON state . These results clearly demonstrate rheostatic control , by FliZ , of the rate of transition between OFF and ON states of flagellin gene expression at the individual scale . In contrast , the complementation assays of the flhD mutant with the plasmid carrying both Ptet-flhDC and PfliC-gfp[AAV] causes a more homogeneous response in cell population where almost all cells are either ON or OFF ( Figure S4 ) . These findings also suggest that the FliZ-modulated dynamic heterogeneity in flagellar gene expression may give rise to an “OFF” state in which the FliZ regulon is switched off , and an “ON” state in which FliZ-dependent genes are expressed in a controlled manner . For the validation of this hypothesis , we separately quantified the levels of FliZ-dependent gene transcripts in ON and OFF populations , after cell sorting . The wild type strain carrying the PflgB-gfp[AAV] fusion displayed a constant bimodal pattern of expression , regardless of the growth phase considered ( see Figure 3 ) . We therefore used this strain to separate GFP-negative and GFP-positive bacterial cells by fluorescence-activated cell sorting ( Figure 5A ) . As expected , the sorting of bacteria in the early exponential growth phase ( EEP ) showed flgB transcript levels to be eight times higher in the GFP-expressing population than in the GFP-negative population . A slight decrease in flhD transcription was found to be associated with significant decreases in the levels of expression of all the class II and class III flagellar genes studied , by factors of 4 to 80 ( for fliC ) in non fluorescent cells with respect to GFP-expressing cells . Non-flagellar genes encoding hemolysins ( XaxAB and XhlBA ) or protease PrtA/XrtA , which were previously shown to be upregulated by FliZ in our RNA-Seq analysis ( Table 1 ) , also displayed significant downregulation in GFP-negative bacterial cells . Only one gene downregulated by FliZ , feoB , displayed significantly higher levels of expression in non fluorescent than in fluorescent cells . However , the seven FliZ-repressed genes , including the genes encoding the regulators RpoS and NilQ , were only slightly more expressed in the OFF state ( Figure 5B ) . Overall , these data indicate that the two cellular states resulting from bimodal expression of the flagellar cascade express the FliZ regulon differentially . They also suggest that the amount of FliZ present within the cell governs its fate .
X . nematophila FliZ is a transcriptional regulator that binds in vivo to unspecified regions of DNA upstream from the flhDC master operon , thereby exerting positive feedback on fliAZ expression [13] . The RNA-Seq analysis described here demonstrated a positive impact of FliZ on the expression of all the genes belonging to the flagellar cascade . Gradual decreases in the expression levels of class I , II and III flagellar genes were observed in the fliZ mutant ( Table S1 ) . It is likely that the lower level of FlhD in absence of FliZ feedback decreases class II gene expression that in turn prevents late class III promoter transcription through the action of FlgM control ( see legend of Figure 6B for more details ) . Here , we also showed that the effects of the FliZ positive feedback loop on flhDC expression are critical for efficient motility in Xenorhabdus . Moreover , ectopic fliA expression in the fliZ mutant did not restore the full motility of X . nematophila ( G . Jubelin , unpublished data ) , highlighting the key role of the FliZ protein in flagellum-driven motility . As mentioned above , there are several major differences between the flagellar regulation circuit of X . nematophila and those of E . coli and S . Typhimurium [13] . One of these differences concerns the regulatory elements in the fliAZ promoter region . Unlike the fliAZ operon in Xenorhabdus , which is controlled principally by FlhDC [13] , the fliAZ operons of E . coli and S . Typhimurium have two promoters: a class II promoter , which is recognized by the sigma 70 RNA polymerase in the presence of the FlhD4C2 activator complex , and a class III promoter , which is recognized by the FliA/sigma 28 RNA polymerase [7] , [40] . However , the role of the class III promoter remains a matter of debate , because a recent study showed that the class III S . Typhimurium fliA transcript was not significantly translated when FliZ was produced from both the class II and class III transcripts [41] . The role of FliZ in the regulation of the flagellar circuit also differs considerably between the various enterobacterial species studied . Indeed , FliZ upregulates motility in S . Typhimurium [15] , whereas it slightly represses motility in E . coli [37] . Like that of X . nematophila , the FliZ of E . coli interferes with flhDC expression by binding to a sequence downstream from the transcriptional start site , which resembles the −10 element of a cryptic σS-dependent promoter [37] . However , this consensus sequence is not found in the promoter region of the X . nematophila flhD gene . In S . Typhimurium , FliZ activates flagellar gene expression indirectly , by binding to the promoter region of the nlpC operon , repressing the transcription of this operon , which also controls expression of the ydiV gene encoding an anti-FlhDC factor active in minimal media [15] . However , no ydiV homologs are present in the X . nematophila genome . FliZ may exert direct or indirect feedback on flhDC expression in many bacterial species , but its overall impact on motility behavior and the expression of FliZ-targeted genes differs considerably between motile bacterial species . This led us to examine the primary function of FliZ in the flagellar cascade network more closely and to suggest that changes to the FliZ regulation circuit may play a key role in evolution , allowing motile bacterial species to adapt to their specific ecological niches . Single-cell analysis revealed unexpected heterogeneity in gene expression in clonal bacterial populations in which particular gene circuits were either ON or OFF in individuals . Population heterogeneity has been reported for various phenotypes , including competence for sporulation , DNA uptake , biofilm formation and persistence in the presence of antibiotic treatment ( see [42] for a review ) . Heterogeneity has also been reported for B . subtilis , in which a motile state and a sessile state coexist in growing bacterial populations [29] . Moreover , the use of tools favoring enrichment in noisy promoters in S . Typhimurium has revealed that two promoter sequences regulating genes involved in flagellum synthesis , fliC and flgK , display the highest levels of variation [27] . Noise can be exploited under certain conditions , to generate phenotypic heterogeneity . In the presence of positive regulatory feedback , a graded expression can be converted to a binary response , in which cells express a certain gene at high or low levels [42] . At the population level , this switch-like behavior may result in a bimodal distribution of gene expression , with the stable propagation of these differences to daughter cells . This type of gene expression pattern is commonly referred to as bistability [42] . We found that the expression patterns of class II and III flagellar genes were bimodal in X . nematophila . However the phenomenon we observed in X . nematophila is not a true bistable mechanism as the bimodality is transient for the expression of some flagellar genes ( Figure 3 ) . Surprisingly , single-cell analysis revealed differences in the expression dynamics of two class II genes , both of which were strictly FlhD-dependent . Expression of the flgB-L operon remained bimodal over time , whereas the fliL-Q promoter was switched on in almost all cells in early stationary phase ( Figure 3 ) . As the transition times are probably random , noisy expression at the population level probably results in cells expressing class II genes at different times . By contrast , the expression pattern of the master operon flhDC , which is controlled by numerous regulators [19] , remained unimodal over time . Model-based predictions suggest that ‘democratic’ networks , in which a large number of genes mutually regulate each other , might limit variation in information flow by facilitating the emergence of a consensus decision . By contrast , ‘autocratic’ subnetworks may permit variation in information flow , by allowing expression levels within key regulatory hubs to differ between individuals in a population , resulting in different phenotypes [43] . Because the single class I operon , comprising the flhDC genes , is the master operon of the flagellar transcriptional hierarchy , these predictions may explain the noisy expression of the class II flagellar genes . In the absence of X . nematophila FliZ , class II gene expression is induced later than in wild type cells and follows a bimodal pattern ( Figure 3 ) . These results also show that FliZ does not primarily induce heterogeneity in the expression of the class II flagellar genes in X . nematophila . A similar transient heterogeneity in class II flagellar gene expression has been reported for wild type cells of S . Typhimurium . By contrast to our observations in Xenorhabdus , fliZ mutation in S . Typhimurium causes a homogeneous response in the individual cells of the population [28] . The authors of this previous study suggested that the autogeneous FliA positive feedback loop resulted in heterogeneous expression from class II and III promoters suggesting an important role of FliZ in regulating flagellum assembly . As X . nematophila FliA has no effect on transcription of the fliAZ operon [13] , this model cannot be applied to X . nematophila . Instead , we propose a model for X . nematophila in which the amount of FliZ within the cell has a major effect , fine-tuning the dynamics of the bimodality of flagellar gene expression . This model is based on the response of a cell population to a gradient of FliZ and shows that FliZ exerts rheostatic control over the rate of transition between the OFF and ON states of flagellin production ( Figure 4 ) . In the presence of FliZ-mediated positive feedback ( Figure 6B ) , the noisy expression of class II flagellar genes is rapidly converted to a transient binary response , in which most of the cells strongly express class II and III flagellar genes , resulting in a motility phenotype at the population level . In the fliZ mutant , the weaker input of FlhD delays class II gene expression , probably resulting in a smaller number of cells concomitantly expressing class II flagellar genes . The heterogeneity of cellular dynamics and lower output would be expected to impair completion of the basal body-hook structure , leading in turn to the downregulation of class III gene expression through FlgM , resulting in a smaller number of motile cells ( Figure 6B ) . This might explain why about 50% of the cells expressed class II genes whereas less than 1% of the cells of the fliZ mutant expressed the fliC gene . This scenario would account for the weak flagellum-driven motility of the fliZ mutant , due to the expression of class III genes by a much smaller proportion of the bacteria in the population . In addition to its role in flagellar regulation , FliZ has been shown to regulate the expression of a number of non-flagellar genes , either directly or indirectly , in X . nematophila [13] and other enteric bacteria . These genes include the pathogenicity island 1 ( SPI1 ) genes [16] , [44] , [45] and type 1 fimbrial genes [46] in S . Typhimurium . Our RNA-Seq analysis revealed that FliZ up- or downregulated the expression of genes encoding many non-flagellar proteins potentially involved in key steps of the Xenorhabdus lifecycle . As expected , FliZ was found to upregulate hemolysin gene expression by binding directly to the xaxAB and xhlAB promoter regions [13] . Through positive feedback on fliAZ expression , FliZ also modulates the expression of the FliA-dependent gene prtA ( also called xrtA ) , which encodes a protease [20] , [47] . The expression of the FliA-dependent lipase gene , xlpA [20] , [48] , was not FliZ-dependent in our RNA-Seq analysis of exponentially growing bacteria , but significant differences were observed in a stationary phase assay ( A . Lanois , unpublished data ) . We have yet to identify the bacterial factors potentially accounting for the delayed virulence pattern observed with the fliZ ( Figure S2 ) and flhD mutants [39] . We previously showed that the FliZ-dependent hemolysin XaxAB , which strongly induces necrosis and apoptosis in insect immunocompetent cells [21] , was not required for full virulence . One interesting candidate is XptA , a high-molecular weight toxin complex ( Tc ) protein with insecticidal effects found in Xenorhabdus and Photorhabdus [49] . Indeed , the Tc makes a major contribution to Xenorhabdus virulence , as demonstrated by the virulence defect of an xptD1 mutant [50] . Thus , the loss of expression of xptA in the fliZ mutant , as revealed by our RNAseq data probably explains its attenuated virulence phenotype . The repressive effects of FliZ are weak in X . nematophila . The genes annotated as FliZ-repressed include two genes encoding regulators , RpoS , the sigma factor σS , and NilR , which is indirectly involved in the colonization of the nematode host by Xenorhabdus [38] , [51] . The rpoS mutation in X . nematophila is also associated with enhanced flagellum-driven motility [38] , [51] . In E . coli , FliZ plays a key role in determining cell lifestyle: the FlhDC-controlled flagellum-based motility or a σS-dependent adhesive-sedentary lifestyle [37] , [52] . The transition between the motile state of Xenorhabdus in insects and its mutualistic state , in which it adheres to the intestinal region of a soil-dwelling nematode , may also be regulated by interplay between RpoS and FliZ . However , the RpoS-dependent regulon has yet to be deciphered in X . nematophila . Finally , we showed , by a combination of fluorescence-activated cell sorting and RT-qPCR quantification , that the two subpopulations coexisting during growth in vitro display differential expression of almost all the messenger RNA markers of the FliZ regulon . In vivo real-time expression analysis with an unstable GFP monitoring system has shown that FliZ target genes are upregulated just before the death of the insect , with expression levels peaking , at population level , in the larval cadavers . In addition , microscopic observations of insect cadavers have shown that FliZ-dependent gene expression in Xenorhabdus is heterogeneous , with differences observed between individuals [23] . FliZ-modulated bimodal expression may , therefore , lead to the generation of several subtypes of cells with different virulence potentials within an isogenic population of infecting bacteria ( Figure 6B ) . It remains unclear why Xenorhabdus generates a mixed population of cells , some of which produce flagella , whereas others do not . Bacterial flagellins elicit innate immune responses in mammals and plants [1] . However , to our knowledge , no flagellin receptor has been described in insects , and it therefore remains unclear why variation in the expression of flagellar genes might be advantageous for insect infection . In the closely related association between Photorhabdus and its nematode host , the inversion of a single promoter , mediated by a site-specific recombination event , allows the bacteria to switch from a pathogenic to a mutualistic state [53] . Further studies are therefore required , to provide a clear demonstration that the bimodal expression of virulence factors constitutes a strategy for generating specialized cell types capable of surviving in different invertebrate niches , in X . nematophila .
The strains and plasmids used in this study are listed in Table S2 . Bacteria were grown routinely in Luria-Bertani ( LB ) medium or Mot broth ( 1% tryptone , 0 . 5% NaCl , 10 mM MgSO4 ) at 28°C ( X . nematophila ) or 37°C ( E . coli ) . For motility assays , agar plates were prepared with LB broth supplemented with 0 . 35% agar . Antibiotic production , bromothymol blue adsorption , lecithinase , lipolytic and hemolytic activities were assessed as previously described [54] . When required , antibiotics were used at the following final concentrations: kanamycin , 20 mg . l−1 , gentamicin , 30 mg . l−1 and chloramphenicol , 20 mg . l−1 for E . coli strains and 15 mg . l−1 for X . nematophila . Ptet constructs were induced by adding anhydrotetracycline ( aTc ) at a final concentration of 0 . 2 ng . l−1 , unless otherwise indicated . The regions upstream and downstream from fliZ ( partial fliA and putA genes , respectively ) were amplified by PCR with the fliA-Xba-f and fliA-BamHI-r primers for the upstream region and the putA-BamHI-f and putA-XhoI-r primers for the downstream region . The two 700 bp fragments obtained were inserted , together with the omega interposon cassette from pHP45-ΩCm conferring resistance to chloramphenicol , into pJQ200KS , to introduce the ΩCam cassette between the two PCR fragments . The resulting plasmid , pGJ906 , was then used to transform E . coli strain S17 . 1 and was introduced into X . nematophila F1 in a mating experiment . Cmr and Sucr exconjugants were selected on LB agar supplemented with 4% sucrose and chloramphenicol . Omega insertion was confirmed by PCR analysis and the loss of hemolytic activity of the resulting fliZ strain was checked on sheep blood agar plates . DNA manipulations were carried out as previously described [55] . Plasmids were introduced into E . coli by transformation and transferred to X . nematophila by conjugative mating [39] . All constructs were sequenced by Millegen ( Labège , France ) . The primers used in this study ( Eurogentec ) are described in Table S3 . Total RNA was extracted with the RNeasy Protect Bacteria miniprep ( for RNA-Seq experiment ) or the RNeasy Micro kit ( for sorted cells , from Qiagen ) including DNase I incubation in accordance with the manufacturer's recommendations . For each RNA preparation , we assessed DNA contamination by carrying out a control PCR . The quantity and quality of total and messenger RNA , respectively , were assessed with a NanoDrop 2000 spectrophotometer ( Thermo Scientific ) and an Agilent 2100 Bioanalyzer with the RNA 6000 Nano LabChip kit ( Agilent ) . Material for RNA-Seq analysis was prepared by extracting total RNA from the Xenorhabdus wild type strain and the fliZ mutant grown in Mot broth ( OD540 = 0 . 5 ) ( six independent biological replicates per strain ) and pooling equal amounts of total RNA from three replicates of the same strain together , to generate two biological samples for each strain , which were subjected to two successive rounds of ribosomal RNA depletion with the Microbe Express kit ( Ambion ) according to the manufacturer's instructions . RNA-Seq libraries were constructed with the Truseq RNA sample preparation kit from Illumina . Briefly , for each sample , 100 ng of rRNA-depleted RNA was chemically fragmented . The first cDNA strand was generated by reverse transcription with random hexamer primers and SuperScript II Reverse Transcriptase ( Life Technologies ) and the second strand was then synthesized . A blunt-ended double-stranded DNA was then generated by repair techniques . A single “A” nucleotide was added to the 3′ end and ligation was carried out with Illumina's indexed adapters . After 15 cycles of PCR , libraries were validated with a DNA 1000 Labchip on a Bioanalyzer ( Agilent ) and quantified with a KAPA qPCR kit . For each sequencing lane , two libraries were pooled in equal proportions , denatured with NaOH and diluted to 8 pM before clustering . Clustering and 50 nt single-read sequencing were performed according to the manufacturer's instructions . Image analysis and base-calling were carried out with HiSeq Control Software ( HCS 1 . 5 . 15 ) and a RTA component ( RTA 1 . 13 . 48 ) . Finally , demultiplexing was carried out with CASAVA ( 1 . 8 . 1 ) . Transcriptomic high-throughput sequencing data were analyzed with a bioinformatic pipeline implemented within the Microscope platform [56] . The pipeline currently used is a “Master” shell script that launches the various parts of the analysis ( i . e . a collection of Shell/Perl/R scripts ) and checks that all tasks are completed without error . We first assessed RNA-Seq data quality by including options , such as read-trimming or the use of merging/split paired-end reads . We then mapped reads onto the contigs of the X . nematophila F1 genome sequence ( accession number: CAVM000000000 ) with the SSAHA2 package [57] , which combines the SSAHA searching algorithm ( sequence information encoded in a perfect hash function ) for identifying regions of high similarity , and the cross-match sequence alignment program , which aligns these regions , using a banded Smith-Waterman-Gotoh algorithm . An alignment score covering at least half of the read is required for a hit to be retained . We minimized the false positive discovery rate , by using SAMtools ( v . 0 . 1 . 8 ) to extract reliable alignments from SAM-formatted files . The number of reads matching each genomic object harbored by the reference genome was then calculated with the Bioconductor-Genomic Features package . If reads matched several genomic objects , the count number was weighted so as to keep the total number of reads constant . Finally , the Bioconductor-DESeq package [58] was used with default parameters for the analysis of raw count data and to determine whether expression levels differed between conditions . The complete dataset from this study has been deposited in the GEO database under accession no . GSE47365 . RT-qPCR was performed in two steps . First , the cDNA was synthesized from 1 µg of total RNA from each replicate used for RNA-Seq ( 0 . 2 µg of total RNA was used for sorted cells ) , with Super Script II Reverse Transcriptase from Invitrogen and random hexamers ( 100 ng . µl−1 ) from Applied Biosystems . We then carried out qPCR in triplicate with the LightCycler 480 SYBR Green I Master kit from Roche Diagnostics , with 1 µl of cDNA synthesis mixture ( diluted 1∶100 or 1∶20 ) and 1 µM of specific primers for the genes studied ( Table S3 ) . The enzyme was activated by heating for 10 minutes at 95°C . All qPCRs were performed in three technical replicates , with 45 cycles of 95°C for 5 s , 60°C for 5 s and 72°C for 10 s , and were monitored with the Light Cycler 480 system ( Roche ) . Melting curves were analyzed for each reaction and each curve contained a single peak . The recA gene was used as the reference housekeeping gene and 16S , mre or ampD was used as an internal control . The data for each sample are expressed relative to the expression level of recA , as follows [59]: . The relative expression ratio for a target gene was calculated on the basis of its real-time PCR efficiency ( E ) and the crossing point ( CP ) difference ( Δ ) between a sample and the control ( ΔCPcontrol – sample ) . Crossing points ( CP ) for each sample and each gene were calculated with LightCycler480 ( Roche ) software , using second-derivative maximum analysis and the CP medians of the technical replicates for each biological sample used . All relative quantifications were assessed with REST software 2009 , using the pairwise fixed randomization test with 2 , 000 permutations , with PCR efficiencies calculated with serial dilutions of a mixture of cDNAs [59] . This method provided a relative quantification of the expression of a target gene with respect to a reference gene , for the comparison of the wild type and fliZ mutant strains or of GFP-positive and GFP-negative cells . The construction of the PfliC-gfp[AAV] , PxaxAB-gfp[AAV] , PxhlBA-gfp[AAV] and PD31-gfp[AAV] fusions have been described elsewhere [23] . We used a similar method to obtain plasmids expressing the reporter gene gfp[AAV] under the control of the flhD , flgB or fliL promoter region . Briefly , DNA fragments corresponding to the flhD ( 576 bp ) , flgB ( 400 bp ) and fliL ( 223 bp ) promoters were amplified by PCR from F1 genomic DNA , with primers containing an EcoRI or BamHI restriction site . The PCR products were digested and inserted into the corresponding sites of pPROBE′-gfp[AAV] for flhD and pPROBE-gfp[AAV] for flgB and fliL , yielding PflhD-gfp[AAV] , PflgB-gfp[AAV] and PfliL-gfp[AAV] . For Ptet-MCS construction , we amplified the PLtet o-1-MCS-tetR DNA fragment from pSS012 by PCR with the Ptet-XhoI-f and 3′PROTetSeq primers and inserted it in place of gfp[AAV] in pPROBE-gfp[AAV] digested with SalI and EcoRV . We amplified the fliZ gene by PCR from F1 genomic DNA , with the LfliZ-Eco and RfliZ-Bam primers , and inserted it into Ptet-MCS digested with EcoRI and BamHI , to generate Ptet-fliZ . The same strategy was used with flhDC operon using LflhDEco2 and RflhCBam primers to generate Ptet-flhDC . Finally , the Ptet-fliZ-tetR and Ptet-flhDC-tetR DNA fragments were amplified by PCR from Ptet-fliZ or Ptet-flhDC respectively with the Tet-fliZ-f and Tet-fliZ-r primers and inserted into PfliC-gfp[AAV] digested with SalI and SbfI , to yield the PfliC-gfp[AAV] - Ptet-fliZ construct or PfliC-gfp[AAV] - Ptet-flhDC construct . Wild type , fliA and fliZ strains carrying either PfliC-gfp[AAV] or PfliC-gfp[AAV] – Ptet-fliZ constructs were cultured in black-sided , clear-bottomed 96-well plates ( Greiner ) . For each well , 20 µl of a 1/50 dilution of an overnight culture was added to 180 µl of LB supplemented with kanamycin , and 200 ng . ml−1 of aTc when required . Then , the plates were incubated , with shaking on an orbital shaker , at 28°C , in an Infinite M200 microplate reader ( Tecan ) . Absorbance at 600 nm and GFP fluorescence intensity , with excitation at 485±4 . 5 nm and emission at 520±10 nm , were measured after 15 hours of growth . Specific fluorescence was obtained by dividing fluorescence units by the absorbance value . As a control , we checked that addition of aTc did not affect the expression of PfliC-gfp[AAV] fusion in the wild type strain ( data not shown ) . Bacterial strains were grown in LB supplemented with kanamycin at 28°C . If necessary , aTc was added at the indicated concentrations after 3 h of growth ( OD540∼0 . 1 ) . At an OD540 of ∼0 . 5 ( or OD540 = 1 . 8 for PxaxAB-gfp[AAV] and PxhlBA-gfp[AAV] plasmids ) , samples were taken , washed once with PBS , diluted and immediately analyzed by flow cytometry ( FACS Canto II , BD Biosciences ) . For kinetic analyses , samples were taken at the indicated time points , washed once with PBS and bacteria were fixed by incubation in 2% formaldehyde in PBS for 15 minutes at room temperature . The cells were then washed once with PBS and bacterial pellets were stored at 4°C until flow cytometry analysis . Forward scatter ( FSC ) , side scatter ( SSC ) and GFP parameters were set to log and bi-exponential display was used for the GFP parameter . We captured a total of 30 , 000 bacteria for each sample , unless otherwise indicated , and raw data were analyzed with FlowJo version 8 . 8 . 6 software ( TreeStar ) . FliC was detected by ELISA in bacterial lysates of wild type F1 , fliA and fliZ strains carrying either Ptet-fliZ or the vector control Ptet-MCS . These strains were cultured in LB medium , and 200 ng . ml−1 of anhydrotetracycline ( aTc , Clontech ) was added , when required , during the early exponential growth phase ( OD540 = 0 . 2 ) . Two hours after induction , samples were taken , centrifuged and the bacterial pellets were resuspended in ultrapure water and lysed by three freeze–thaw cycles and sonication . For ELISA , microtiter plates ( Maxisorp Nunc-Immuno Plate ) were coated with an amount of culture supernatant ( 50 µl/well ) equivalent to 0 . 1 OD540 units of bacterial lysate . The plates were incubated overnight at room temperature , washed three times with 0 . 05% Tween 20 in PBS ( PBS-T ) and blocked by incubation with 0 . 25% BSA in PBS-T for 2 h at room temperature . Anti-FliC antibodies [60] diluted in 1% BSA in PBS-T ( 1/500 ) were added to each well and the plates were incubated for 1 h at room temperature . The plates were washed four times with PBS-T and incubated for 1 h with peroxidase-linked donkey anti-rabbit IgG ( 1/5000 dilution; GE Healthcare ) . The plates were washed four times with PBS-T , and 100 µl of 1-Step Ultra TMB-ELISA ( Pierce ) solution was added to each well . Color development was stopped after 20 minutes , by adding 100 µl of 2 M H2SO4 , and absorbance at 450 nm was measured with a microplate reader ( Tecan Infinite 200 ) . For the sorting of GFP-negative and GFP-positive subpopulations , an overnight culture of the F1 strain containing PflgB-gfp[AAV] was washed once with PBS , diluted 1∶250 in fresh LB broth supplemented with kanamycin and incubated at 28°C for 6 h ( OD540 of 0 . 3 to 0 . 8 ) . The cells were then washed once with PBS before FACS analysis . Flow-cytometric sorting was performed on a FACSAria II cell sorter system ( Becton Dickinson ) . We sorted 107 GFP-negative and GFP-positive cells at 4°C , for a maximum of 2 hours . The sorting efficiency for three independent biological samples was determined by analyzing the GFP fluorescence patterns of the subpopulations obtained . Immediately after cell sorting , RNA Protect Bacteria Reagent ( Qiagen ) was added to the cells and RNA was extracted with the RNeasy micro kit ( Qiagen ) as described above and eluted in a final volume of 15 µl . | Heterogeneity in the expression of bacterial genes may result in the presence of cells with different phenotypes in an isogenic population . The existence of such “non-genetic individuality” was the first described many years ago for the flagellum-driven swimming behavior of bacteria . In this study , we identified a new bimodal switch controlling the expression of genes involved in flagellum biosynthesis and host interactions in the insect pathogen Xenorhabdus nematophila . This switch is modulated by a transcriptional regulator called FliZ . In addition to identifying all the specific genes up- and downregulated by FliZ , we showed that the concentration of FliZ fine-tuned the expression of FliZ target genes , resulting in individuals with different potentials for bacterial locomotion , host colonization and virulence . | [
"Abstract",
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] | [] | 2013 | FliZ Is a Global Regulatory Protein Affecting the Expression of Flagellar and Virulence Genes in Individual Xenorhabdus nematophila Bacterial Cells |
This paper scrutinises pipelines for Neglected Diseases ( NDs ) , through freely accessible and at-least-weekly updated trials databases . It updates to 2012 data provided by recent publications , and integrates these analyses with information on location of trials coordinators and patients recruitment status . Additionally , it provides ( i ) disease-specific information to better understand the rational of investments in NDs , ( ii ) yearly data , to understand the investment trends . The search identified 650 clinical studies . Leishmaniasis , Arbovirus infection , and Dengue are the top three diseases by number of clinical studies . Disease diffusion risk seems to be the most important driver of the clinical trials target choice , whereas the role played by disease prevalence and unmet need is controversial . Number of trials is stable between 2005 and 2010 , with an increase in the last two years . Patient recruitment was completed for most studies ( 57 . 6% ) , and Phases II and III account for 35% and 28% of trials , respectively . The primary purpose of clinical investigations is prevention ( 49 . 3% ) , especially for infectious diseases with mosquitoes and sand flies as the vector , and treatment ( 43 . 2% ) , which is the primary target for parasitic diseases Research centres and public organisations are the most important clinical studies sponsors ( 58 . 9% ) , followed by the pharmaceutical industry ( 24 . 1% ) , foundations and non-governmental organisations ( 9 . 3% ) . Many coordinator centres are located in less affluent countries ( 43 . 7% ) , whereas OECD countries and BRICS account for 34 . 7% and 17 . 5% of trials , respectively . Information was partially missing for some parameters . Notwithstanding , and despite its descriptive nature , this research has enhanced the evidence of the literature on pipelines for NDs . Future contributions may further investigate whether trials metrics are consistent with the characteristics of the interested countries and the explicative variables of trials location , target ( disease ) choice , and the object of the trials .
Neglected diseases ( NDs ) may be defined as ancient , disabling , and poverty-promoting chronic conditions that afflict the poorest people in the developing world [1] . These diseases represent the most widespread viral , parasitic , and bacterial infections in those countries with people living on less than US $ 2 per day [2] . NDs can lead to long-term disability and poverty , as a result of impaired childhood growth and development , adverse outcome of pregnancy , and reduced productive capacity . There is not a unique list of NDs . The World Health Organization ( WHO ) [3] defines “neglected” as the 17 “…chronically endemic and epidemic-prone tropical diseases , which have a very significant negative impact on the lives of poor populations [and] remain critically neglected in the global public health agenda” . According to the Public Library of Science for Neglected Tropical Diseases ( PLoS NTD ) [4] , NDs “[neglected tropical diseases] are defined as a group of poverty-promoting chronic infectious diseases , which primarily occur in rural areas and poor urban areas of low-income and middle-income countries . They are poverty-promoting because of their impact on child health and development , pregnancy , and worker productivity , as well as their stigmatizing features” . Merging the lists suggested by the WHO and PLoS NTD , more than 40 NDs were listed ( see Box 1 ) . Table 1 illustrates the prevalence , mortality rates , and current treatment for some NDs . Precise epidemiological data are not available for all NDs . In fact , some of them are either endemic in the poorest and most rural world areas or difficult to diagnose . Soil-transmitted Helminthiasis , Schistosomiasis , Lymphatic filariasis , Trachoma , Dengue , Onchocerciasis , and Leishmaniasis are the most common NDs . They are mostly caused by parasites , poor sanitation , and other environmental factors . Their current treatments , if any , show poor effectiveness ( e . g . , the longer is the exposure to Chagas disease before treatment , the lower is the effectiveness of the combination of benznidazole and nifurtimox ) and/or important side effects . These diseases were given low priority by the pharmaceutical industry and other actors before the new millennium . According to a Wellcome Trust Report [5] , only 13 out of 1 , 393 new drugs developed during 1975 to 1999 were for NDs . However , after the 2000 WHO initiative on Millennium Development Goals ( MDGs ) , the international health policy agenda put NDs in high consideration [6] . At the same time , the industry has started to include public health objectives in their ethical responsibilities and other sectors ( governments , non-governmental organisations [NGOs] , and international health organisations ) have begun to look at the private sector as a partner . This new scenario fostered the development of Public-Private Partnerships ( PPPs ) , because joining the strengths and skills of the two parties seemed a feasible and effective way to tackle complicated and expensive public health problems [7] . Incentives and PPPs increased investments in NDs , with more than 60 projects in progress at the end of 2004 [5] . HIV/AIDS , tuberculosis and malaria were the primary diseases addressed by global fund and health interventions for all NDs [8] . After 2005 , the WHO , NGOs and foundations recognised the lack of effective global prevention and control programs to overcome NDs . WHO created the Global Plan to combat NDs . The goal of the Global Plan was to prevent , control , eliminate or eradicate NDs by 2015 [9] . However , the Global Plan did not achieve the expected goals , and the deadline to prevent and control programs was postponed to 2020 [10] . The literature has further tracked the increase in investments in R&S for NDs . Under the umbrella of the G-FINDER project , a report has investigated the amount of money invested in projects on NDs [11] . Bio Ventures for Global Health [12] has collected data on pipelines for NDs from multi-sources , including websites and reports , press releases and scientific literature , and clinical trials databases . The most recent contribution has investigated both products approved in 2000–2011 and pipelines for NDs ( derived from the NIH – National Institute for Health and WHO databases ) as of December 2011 , showing that NDs ( including malaria and tuberculosis ) account for 4% of total products launched into the market in 2000–2011 and 1% of pipelines as of December 2011 [13] . This evidence has produced new important information on investments in NDs . However not all diseases listed into the Box 1 have been covered . Additionally , the latest and most complete analysis does not provide disease-specific data . These data may be useful to understand the drivers of investments allocation . Location of trials coordinator and patients enrolment status have not been investigated or reported in most of these studies . Finally , the evolution of pipelines in time has not been considered . Our objective is to cover these information gaps and update to pipelines analysis to 2012 .
The list of NDs investigated resulted from the merging of the WHO and PLoS NTD lists ( see Box 1 ) . The disease or group names ( e . g . , Arbovirus , Hookworm , and Enteric pathogens ) were used to extract the relevant trials from the databases . We have excluded malaria and tuberculosis . They are not included into the WHO and PLOS lists of NDs and the investment in these diseases have been compared with other NDs by other authors [11] . The following access-free and at-least-weekly updated trial databases were considered: the U . S . clinical trial database ( http://www . clinicaltrials . gov/ ) , the European clinical trial database ( https://www . clinicaltrialsregister . eu/ ) , the International Standard Randomised Controlled Trial Number Register ( http://www . controlled-trials . com/isrctn/ ) , the Indian clinical trial database ( http://ctri . nic . in/Clinicaltrials/login . php ) , and the Australian clinical trial database ( http://www . anzctr . org . au/ ) . Other registries , included in the WHO list ( http://www . who . int/ictrp/network/primary/en/index . html ) , were not included in the search strategy because very few trials were extracted and most of them matched what has been found using the above-mentioned databases . In principle , the WHO clinical trial database merges the information of all trial databases , but extracting information from primary databases was preferred to be sure that the most recent trials were included . All trials databases have been accessed last time December , 31st , 2012 . Trials received from January 1st 2005 to December 31st 2012 were extracted for each disease listed in Box 1 . The following inclusion criteria were used: Clinical trials were classified and analysed according to:
The research identified 650 clinical studies . Figures are not comparable with the pipelines for important diseases in affluent countries , e . g . , cardiovascular diseases ( 15 , 232 clinical trials ) or respiratory diseases ( 10 , 063 clinical trials ) . Total number of trials has been rather constant over time , with an important increase in the last two years covered by our analysis ( 2011–2012 ) . Total number of trials have been rather constant over time , with an important increase in the last two years covered by our analysis ( 2011–2012 ) . The increase in the last two years is mainly driven by trials of WHO NDs list ( Box 1 ) . This trend may explain why we have found a lower number of trials for diarrhoeal diseases in our NDs list , than what have been found as of the end of 2011 by Pedrique and colleagues [13] ( Table 2 ) . Leishmaniasis ( 95 studies ) , arthropod-borne viruses ( Arbovirus ) infection ( 86 studies ) , and Dengue ( 76 studies ) are the top three diseases by number of clinical studies ( Figure 1 ) . These three diseases represent almost 50% of all NDs studies , followed by enteric diseases ( Salmonella , Cholera , Shigella and Escherichia coli infection ) , which cumulatively account for 18% of total trials . The group “other diseases” ( diseases with less than 9 trials ) includes , among others , Buruli Ulcer and Ascariasis , which are recognised as severe diseases . For most of the 650 trials , recruitment of patients is completed ( 57 . 6% ) ( Figure 2 ) . The patient recruitment process is ongoing for 24 . 9% of trials . Only 12 . 5% of the studies are either temporarily or definitely suspended and the trial status is unknown for 5 . 1% of the studies . Leishmaniasis ( 54 studies ) , Arbovirus infections ( 53 studies ) and Dengue ( 41 studies ) again show the largest number of completed trials . These three diseases are also the object of the highest number of trials in which patients have been enrolling . The distribution of clinical studies per development phase is strongly affected by a huge proportion ( 24 . 9% ) of missing data and trials allocated between phase I and II or phase II and III ( 6% ) ( Table 3 ) . For some diseases ( i . e . , Schistosomiasis , Leprosy , soil-transmitted Helminthiasis ) , the number of trials missing information on the trial phase exceeds the number of trials where the phase is specified . Considering trials allocated to a single phase , 100 ( 22% ) are in phase I , 135 ( 35% ) in phase II , 125 ( 28% ) in phase III and 88 ( 20% ) in phase IV . Dengue and Leishmaniasis account for 37 . 5% of trials in early phases ( I and II ) , whereas Arbovirus infections , Leishmaniasis and Rabies represent 47% of clinical trials in phase III . Phase IV studies are more frequent in Arbovirus infections , Leishmaniasis and Salmonella . The primary purpose of the identified trials is illustrated in Table 4 . The greatest proportion of clinical studies have prevention ( 41 . 4%; 49 . 3% if only trials where the purpose is specified are considered ) or treatment ( 185 trials , 36 . 3%; 43 . 2% of trials , net of whose purpose is not specified ) as the primary purpose . Prevention is the most important target for diseases in which the method of transmission is a vector such as the mosquito or sand fly , including Dengue and Arbovirus infections . For parasitic diseases , such as Leishmaniasis , soil-transmitted Helminthiasis and Chagas disease , most trials have treatment as the primary purpose . Research centres and public organisations are the most important sponsors of clinical studies on NDs ( 58 . 9% of studies ) , followed by the industry ( 24 . 1% ) , foundations and NGOs ( 9 . 3% ) ( Figure 3 ) . These figures are different from what can be found for Research and Development ( R&D ) on targets prevailing in affluent countries , where the industry plays a major role as sponsor , especially in the pre-marketing phase , and may be also co-funder of non-profit studies . Foundations play a minor role as sponsors of NDs . However , many research centres ( inside or outside universities ) receive research grants from foundations ( e . g . , the Wellcome Trust ) , thus making these groups funders , but not sponsors , of the relevant clinical studies . The National Institute of Allergy and Infectious Diseases ( NIAID ) ( within the National Institutes of Health ) and the U . S . Army Medical Research and Material Command are the most important funders among public organisations and research centres . The huge investment by the U . S . Army Medical Research centre is mainly motivated by the presence of the U . S . Army in low-income countries where NDs are endemic . The target of clinical trials sponsored by the U . S . Army Medical Research and Material Command are Arbovirus infection ( 9 studies ) , Dengue ( 7 studies ) and Leishmaniasis ( 10 studies ) , because they have a greater potential to cross national borders than other diseases . Apart from the International Centre for Diarrheal Disease Research , located in Bangladesh , and the International Vaccine Institute , the major sponsors among research centres are all located in the US and the UK . Sanofi-Aventis is the pharmaceutical company most involved as sponsor in trials for NDs , most of which are related to Arbovirus infections and Dengue , with a particular interest in vaccines and viral treatments . Novartis and Novartis Vaccines , with 22 trials , is the second largest sponsor from the pharmaceutical industry , with a focus on Rabies and Salmonella . Noticeably , many trials on Arbovirus infections are sponsored by Intercell AG , a small biotech company that develops vaccines for the prevention and treatment of infectious diseases ( especially Japanese Encephalitis ) . The Drugs for Neglected Diseases initiative ( DNDi ) is certainly the most important NGO involved in trials for NDs , with 15 studies sponsored mostly related to human African Trypanosomiasis and Leishmaniasis . Other foundations , like Oswaldo Cruz Foundation ( 6 clinical trials ) or the AB Foundation with 5 clinical trials have sponsored trials mostly on Leishmaniasis ( Table 5 ) . The last topic we have investigated is the location of the trials coordinator . Less affluent countries , and particularly emerging ones ( including BRICS - Brazil , Russia , India , China and South Africa ) , may be a target for trial location of the trials coordinator because most NDs are endemic to these countries . Additionally , some of these countries have developed a capable scientific community and their pharmaceutical market is growing faster than in affluent countries . Lower costs may be another reason for locating trials in less affluent countries , even if cost is not the most important driver of trials location of the trials coordinator [14][15] . Coordinator centres of trials on NDs are distributed among OECD countries ( 34 . 7% ) , BRICS ( 17 . 5% ) and other less affluent countries ( 43 . 7% ) ( in 4 . 1% of trials the coordinator centre is unspecified ) . Figure 4 shows the distribution of coordinator centres using country clusters adopted by the WHO classification ( http://www . who . int/about/regions/en/index . html , last access , 15th of February 2013 ) , but considering separately the BRICS group . BRICS together take first place , with India playing a leading role ( 9 . 3% of trials ) . The European Region accounts for 17 . 4% of trials , with 7 . 1% in the major EU-5 countries . The US and Canada account for 14 . 5% of trials . In other regions , Bangladesh ( 3 . 8% ) and Thailand ( 3 , 7% ) are the countries more involved in trials for NDs . Other countries are all below 3% .
Our research has confirmed the growing interest in NDs of previous analyses , with an important increase in the number of trials in 2011–12 . All NDs considered show at least one interventional clinical trial , with few exceptions , including Dracunculiasis ( Guinea-worm disease ) , food-borne Trematodiases and Myiasis . Additionally , we found a large number of studies in which patients' enrolment has been completed ( 57 . 8% of studies ) and 28% completed was in in phase III . Hence , the present pipeline is the result of an investment that started several years ago . Prevention and treatment are the objectives of 49 . 3% and 43 . 2% of studies , respectively , whereas basic science and diagnosis/screening technologies are disregarded . Prevention is the main target in virus-related diseases , whereas for non-virus related diseases , with the relevant exception of Shigella , treatment is the main target . The research has been focusing on Leishmaniasis , Dengue , Rabies , Salmonella and Cholera . Other diseases , such as Fascioliasis , Relapsing Fever , Giardiasis , Amebiasis , Echinococcosis or Yaws , have less than four trials each . The target choice may have different drivers . It seems that the prevalence of the disease is not the main driver of the research target , e . g . trials for Soil-transmitted Helminthiasis ( Ascariasis and Trichuriasis ) , which show a high prevalence , are rare , with the exception of Hookworm infection . The unmet need may be another driver . However , the evidence is rather controversial , and there are many cases not supporting this hypothesis . For example , Buruli Ulcer is still considered a ND , but it may be easily managed using a combination of antibiotics , if diagnosed early . Only four trials have been found for Buruli Ulcer between 2005 and 2012 , and all of them were focused on clarithromycin and not on early diagnosis issues . Another example is Leishmaniasis , where the primary object of most trials ( 17 in phase III and 10 in phase IV ) is to test the efficacy of drugs that are already approved and included in the WHO recommendation , whereas only one trial focuses on vaccines . The third driver may be the risk of disease diffusion . In fact , many trials were found for NDs with a high risk of diffusion due to their viral nature , including tick-born and Japanese encephalitis ( Arbovirus infection group ) , Dengue , Rabies and Salmonella . The role of pharmaceutical companies in directly sponsoring clinical research for NDs is rather limited and is very concentrated in a few companies ( Sanofi Aventis , Novartis , and Intercell AG ) , with a focus on Arbovirus infection and Dengue . Research centres and public institutions are much more involved , whereas Foundations and NGOs play a minor role . Whereas sponsors are either international organisations or concentrated in the US , BRICS are increasing their role in location of coordinator centres . This may be motivated by the higher prevalence of NDs in these countries , their emerging economies and the increasing research standards guaranteed by trials sites . The present study has some limitations: ( i ) not all trial databases were scrutinised , even if some of the excluded ones show a very low potential contribution to the dataset; ( ii ) databases are not complete for some topics: e . g . , trial phase was unspecified for 30% of trials; and ( iii ) the analysis is purely descriptive , even if some relationships have been qualitatively investigated . Despite its limitations , this study has for many aspects integrated the evidence on R&D in NDs and updated this evidence on 2012 . Future contributions may further investigate trials metrics , such as the formulation , setting of administration and length of treatment , to understand their consistency with low-income countries' characteristics [5] , and the possible explicative variables of location , target ( disease ) and object ( basic science , prevention , diagnosis/screening , treatment ) . | Neglected diseases lead to illness , long-term disability and affect economic development in poor populations . There is evidence that clinical research on neglected diseases has increased starting from the second half of the '90s . This paper aims at updating this evidence to 2012 and at integrating available data ( groups of target of the clinical projects , phase in the clinical development process , sponsors ) with other data that have not been investigated or published so far ( recruitment status of patients , and trials location of the trials coordinator ) . Our study has confirmed previous findings on the important investment in NDs , highlighting , thanks to a disease-specific approach , a particular focus on diseases with a higher diffusion risk , but not necessarily the higher prevalence and the most unmet need . In most studies , patients' recruitment has been completed , and many trials are also in the very terminal phases: this means a high probability that new treatments will be available in the next years . In addition , trial coordinator centres are increasingly located in low income countries; as a consequence , the investment in clinical research has become an opportunity to further enhance clinical and organisational expertise in these countries . | [
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"disea... | 2014 | Current Pipelines for Neglected Diseases |
In vivo direct conversion of differentiated cells holds promise for regenerative medicine; however , improving the conversion efficiency and producing functional target cells remain challenging . Ectopic Atoh1 expression in non-sensory supporting cells ( SCs ) in mouse cochleae induces their partial conversion to hair cells ( HCs ) at low efficiency . Here , we performed single-cell RNA sequencing of whole mouse sensory epithelia harvested at multiple time points after conditional overexpression of Atoh1 . Pseudotemporal ordering revealed that converted HCs ( cHCs ) are present along a conversion continuum that correlates with both endogenous and exogenous Atoh1 expression . Bulk sequencing of isolated cell populations and single-cell qPCR confirmed 51 transcription factors , including Isl1 , are differentially expressed among cHCs , SCs and HCs . In transgenic mice , co-overexpression of Atoh1 and Isl1 enhanced the HC conversion efficiency . Together , our study shows how high-resolution transcriptional profiling of direct cell conversion can identify co-reprogramming factors required for efficient conversion .
During development , pluripotent stem cells follow lineage-specific pathways to differentiate into mature cells that can be converted back to pluripotent cells by defined transcription factors ( TFs ) [1] . In addition , direct lineage conversion ( also termed transdifferentiation ) between differentiated cells has been demonstrated in heart , pancreas , brain , and other tissues through the use of defined TFs [2–5] or pharmacologic agents [6] . Such conversions have provided a deeper mechanistic understanding of development and hold promise for regenerative medicine . Several studies have used single-cell RNA-seq to identify distinct intermediate conversion states , providing valuable insights into how to improve the efficiency and complete the direct conversions [4 , 7 , 8] . The cells of the mouse inner-ear cochlear sensory epithelium ( organ of Corti ) are post-mitotic after birth and exhibit limited spontaneous regeneration that is only present during the first week after birth [9–11] . Atoh1 , a lineage-specific TF for sensory hair cells ( HCs ) , directly converts non-sensory supporting cells ( SCs ) to HCs in cochlear explant culture , as well as in vivo [12–15] . The current model is that ectopic Atoh1 induces the expression of endogenous Atoh1 in SCs to initiate the conversion . This Atoh1-mediated HC conversion is analogous to the natural HC regeneration in chicken inner ears or mammalian vestibular organs [16 , 17] . However , the Atoh1-converted HCs ( cHCs ) in mouse cochleae exhibit immature morphology and do not express several terminal differentiation markers ( e . g . , Slc26a5 encoding prestin and Ocm encoding oncomodulin ) . In addition , the process is inefficient , with conversion rates of 6%–20% [13 , 14] . Consequently , a more precise understanding of the molecular events underlying Atoh1-induced HC conversion is needed to identify additional factors required for improving the efficiency and completion of the conversion . In this study , we performed unbiased transcriptional profiling of all cells present in the organ of Corti during Atoh1-mediated SC-to-HC conversion at multiple time points in vivo . This high-resolution transcriptomic analysis revealed new mechanisms of HC conversion in vivo and identified co-reprogramming factors .
In contrast to other regenerative systems , the organ of Corti in the mature cochlea contains relatively few cells: approximately 3 , 100 HCs [18] , including both inner HCs ( IHCs ) and outer HCs ( OHCs ) , similar numbers of Deiters’ cells ( DCs ) and pillar cells ( PCs ) surrounding the OHCs , as well as several other SC subtypes surrounding the IHCs ( Fig 1A ) . Massively parallel single-cell RNA sequencing using droplet microfluidics has been shown to be an efficient strategy for acquiring transcriptional profiles from rare cells isolated from fragile structures , as was established in the initial drop-seq study of the human retina [19] . These techniques allow for the rapid and accurate quantification of 5–10% of the transcripts isolated from each cell , which can be expanded upon by identifying and grouping cells with distinct expression programs to create a composite profile of that cell state [20] . We leveraged the technology and these principles to acquire unbiased transcriptional profiles of cells present in the organ of Corti isolated from mouse cochleae . For HC conversion , we used the previously established mouse model Fgfr3-iCreER; Atoh1-HA; Chrna9-EGFP; tdTomato where ectopic expression of Atoh1-HA transgene was driven by Fgfr3-iCreER-mediated CAG promoter in DCs and PCs after tamoxifen-mediated induction at postnatal day 12 ( P12 ) and P26 and P33 cHCs were identified by their double-positivity for the reporter tdTomato driven by the Fgfr3-iCreER-mediated CAG promoter and EGFP driven by the promotor of endogenous HC gene Chrna9 [14 , 21 , 22] ( Fig 1A and 1C ) . After dissecting and dissociating cells from organs of Corti at P12 , P26 and P33 , we obtained a total of 5 , 470 single-cell RNA-seq profiles from 8 organs of Corti that had undergone tamoxifen induction , as well as those from uninduced control mice ( Fig 1B ) . We then reduced the dimensionality of the expression matrix and placed individual cells into two-dimensional space using t-Distributed Stochastic Neighbor Embedding ( tSNE ) ( S1A Fig; Figure Legends ) . Cluster analyses using shared nearest-neighbor modularity optimization-based clustering [23] revealed 21 distinct cell clusters ( Fig 1D; Figure Legends ) . Based on expression of known markers for each cochlear cell type , we designated these clusters as HCs ( Cluster 18 ) , DCs/PCs ( Cluster 11 ) , Hensen cells ( Cluster 6 ) , type I spiral ganglia ( Cluster 15 ) , tympanic membrane border cells ( Cluster 1 for mature cells and Cluster 2 for developing cells ) , Reissner's membrane cells ( Cluster 7 ) , and immune and other unknown cell clusters ( Fig 1D and 1E and S1B Fig and S1 Table ) . Focusing on the cells that undergo conversion , we first defined the cluster of DCs/PCs that expressed both endogenous Fgfr3 as well as the Fgfr3-iCreER-mediated reporter ( tdTomato ) ( Fig 1D and S1B Fig ) . Near those cells , we identified the cHCs , which expressed both the Atoh1-HA transgene , as well as endogenous Atoh1 . The clusters of endogenous OHCs and IHCs were identified based on expression of known markers Ocm and Slc17a8 , respectively . In general , the cells clustered based on cell type and the postnatal age of the mice , suggesting our experimental and analysis pipelines separated cells based on biological differences between the cells ( S1 Fig ) . Further supporting the reproducibility of our approach , relative cell cluster frequencies were similar between replicates ( S1C Fig ) . We next focused on cells undergoing conversion , which included the DC/PC ( SC ) , cHC , and HC clusters . In total , we extracted 101 SCs , 60 HCs , and 145 cHCs from the full dataset . We then performed tSNE on this subset of the data , followed by shared nearest neighbor modularity optimization based clustering , which identified 7 unique clusters ( Fig 2A , S2 Fig and S2 Table; Figure Legends ) . To further validate our approach , we found markers were restricted to the expected clusters . For example , expression of Fgfr3 and Slc17a8 was restricted to the SCs and IHCs , respectively ( S2 Fig ) . The SCs also separated into two clusters , with almost all SC1 cells coming from P12 cochleae while almost all SC2 cells were isolated from P26-33 cochleae ( Fig 2A , S1A and S2 Figs ) . As Atoh1 induction occurs at P12 , we used the SC1 cluster as the starting point for the induction of cHCs . The cHCs separated into three distinct clusters ( cHC1-3 ) ( Fig 2A ) . Closer examination of marker expression in these cells found that canonical HC markers Chrna10 and Pou4f3 were almost uniquely expressed in cHC3 , suggesting they are the most mature among cHCs and that there is a progression from SC1 to cHC1 to cHC2 to cHC3 [14 , 21] ( Fig 2C and Fig 2A ) . One important implication of this finding is that studying the previously unrecognized cHC1 and cHC2 that had undergone less complete conversion could identify factors that are needed to increase conversion efficiency . To provide additional evidence that there is a continuum of cells present during HC conversion , we next ordered the cells in pseudotime with inverse graph embedding using Monocle2 [24] . The pseudotime reconstruction places individual cells in two-dimensional space in an unsupervised manner based on the relative transcription profiles of each cell . With this approach , the cells ordered along the anticipated trajectory of HC conversion starting with SC1 to cHC1 to cHC2 to cHC3 ( Fig 2B ) . While transitioning from cHC1-2 to cHC3 , canonical HC markers ( Myo6 , Rasd2 , Chrna9 , Pvalb , Pou4f3 , Chrna10 ) started to be expressed ( Fig 2C ) . Thus , inverse graph embedding of SCs undergoing Atoh1-mediated conversion further supported a continuum from a donor SC to a target state that resembled HCs . To identify additional TFs that may be required to increase the efficiency and completion of Atoh1-mediated HC conversion , we plotted the expression of TFs that were found to be nonrandomly expressed across pseudotime using the beaming algorithm imbedded in Monocle2 ( Fig 2C ) . As expected , TFs associated with the terminal differentiation of SCs , such as Rorb , Rora , Id1 , Id4 , Id2 [25 , 26] , decreased in expression as cells began undergoing Atoh1-mediated conversion . There was then a second cluster of genes that transiently increased in expression in cHC1 , such as Hes5 , Hes1 , Sox9 , Zbtb20 and members of the AP-1 complex ( Fos , Junb , Jun ) . Subsequently , the cHC2 cluster was enriched with TFs such as Hes6 , Insm1 , Jund and Egr1 , in addition to endogenous Atoh1 and exogenous Atoh1-HA . Finally , the cHC3 cluster highly expressed known HC TFs such as Barhl1 , Lhx3 , Pou4f3 and Neurod6 [27–30] . It has been shown that the expression of endogenous Atoh1 is upregulated during naturally occurring HC conversion in zebrafish and birds [31–34] . In our data , endogenous Atoh1 increases as conversion progresses ( S2A Fig ) ; transgenic Atoh1-HA expression strongly correlates with converted HC state ( Fig 2D and S2A Fig ) , with cHC3 expressing 3 . 29 fold more Atoh1-HA than cHC1 . This is unexpected because transgenic Atoh1-HA is driven by CAG promoter in Cre-positive cells , which should presumably have ubiquitously constant levels of transcriptional activity . Moreover , quantification of endogenous Atoh1 using the 3'UTR and exogenous Atoh1-HA using the HA tag , we found a strong correlation between the expression of both genes in each cell ( R2 = 0 . 86; Fig 2E ) . These findings suggest there is a connection between endogenous Atoh1 and exogenous Atoh1-HA expression that is variable between cells but correlates strongly with the extent of conversion; the underlying mechanisms remain to be further studied . To understand correlations between these TFs during the conversion continuum , we performed gene network analysis of TFs identified in our single-cell RNA-seq that were expressed above a threshold ( i . e . , detected in at least 10 cells ) to remove noisy genes , and that also had high variance in expression across cells ( variance >0 . 4 ) ( Fig 2F ) . We identified four groups of TFs that showed high correlation within each group . As expected , Atoh1 was a central node to a large number of TFs ( i . e . , Barhl1 , Lhx3 , Gata3 , Hes6 , and Neurod6 ) ; Pou4f3 expression was also correlated to a distinct large group of TFs ( i . e . , Barhl1 , Lhx3 , Hes1 , and Zbtb20 ) . Therefore , such gene network analysis placed Atoh1 and Pou4f3 as key reprogramming factors for SC-to-HC conversion , which is supported by in vivo studies demonstrating that Pou4f3 synergistically induces Atoh1-mediated conversion or can promote conversion on its own [21] . Single-cell RNA-seq provides high-resolution readouts of transcription within a tissue . However , this comes with the tradeoff of having limited sensitivity in a given cell where only 5–10% of transcripts are captured . This limit of sensitivity is especially problematic for genes that are expressed at low levels . To validate TFs that were found to be differentially expressed between SCs , cHCs , and OHCs in the single-cell RNA-seq data , we manually isolated , with high purity , individual cells of the three cell types based on marker expression and performed RNA-seq after whole-transcriptome amplification ( S3 Table ) . These data were then compared to the single-cell RNA-seq to identify TFs consistently differentially expressed . Specifically , after enzymatic dissociation of mouse cochleae , SCs ( DCs and PCs ) were isolated at P26 based on expression of Cre-positive tdTomato reporter , cHCs at P33 based on expression of both tdTomato and HC marker Chrna9-GFP , and OHCs at P7 or P22 based on expression of OHC marker Slc26a5 ( S3A–S3D Fig ) . We also isolated IHCs at P74 ( S3B–S3F Fig ) . A total of 10 bulk RNA-seq profiles ( duplicates of five cell types ) were produced , detecting expression of 23 , 415 unique genes in at least one sample ( S3C and S3D Fig ) . The independent amplified bulk RNA-seq profiles of biological duplicates for each cell type were reproducible with a Spearman correlation of 0 . 86–0 . 89 ( S3E and S3F Fig ) . In comparison , previous studies using replicates of RNA-seq data from samples that had undergone whole-transcriptome amplifications reported a Spearman correlation between biological duplicates of 0 . 8 [35] . To further validate the genes we found to be differentially expressed at the protein level , we found that 24 genes that had previously been shown to be differentially expressed in SCs , cHCs , and OHCs by immunostaining showed consistent expression patterns among SCs , cHCs and OHCs [14] , and such patterns were also comparable to gene expression profiles determined by single-cell RNA-seq ( S3G Fig ) . To determine whether cHCs ( P33 ) resembled differentiating neonatal HCs , as indicated by immunostaining and morphological results in previous reports [14 , 21] , we estimated , based on Spearman correlation analysis of bulk RNA-seq profiles , the distances between SCs ( P26 ) , OHCs ( P7 ) , OHCs ( P22 ) , IHCs ( P74 ) , and cHCs ( P33 ) . The distance of cHCs from OHCs ( P7 ) was smaller than that from SCs , OHCs ( P22 ) , or IHCs ( S3H Fig ) . These analyses provided evidence that cHCs resembled OHCs ( P7 ) more than the mature SCs , and mature OHCs/IHCs analyzed . Cell type marker expression in SCs , cHCs , OHCs ( P7 ) , OHCs ( P22 ) , and IHCs was comparable to that observed using single-cell RNA-seq ( S3G Fig ) . To identify TFs that may promote Atoh1-mediated SC-to-HC conversion in vivo , we focused on TFs that are differentially expressed between mature SCs ( P26 ) , cHCs ( P33 ) , and mature OHCs ( P22 ) . Among 1 , 425 TFs in the mouse genome ( Animal Transcription Factor Database , http://www . bioguo . org/AnimalTFDB/ ) , we identified 90 TF genes that were differentially expressed , with statistical significance , in SCs ( P26 ) , cHCs ( P33 ) , and OHCs ( P22 ) ( Fig 3 , S4 Table ) . To confirm that these TFs represented the underlying conversion of SCs to cHCs , we performed GO enrichment analysis where the most significantly differentially expressed categories included “sensory perception of sound” , “sensory perception of mechanical stimulus” , “neuron development” , “cell projection organization” , “inner ear development” , “sensory organ development” , and “synaptic transmission” ( FDR < 0 . 05 ) . We then utilized an orthogonal strategy to validate our list of differentially expressed genes by performing single-cell multiplex RT-qPCR analysis of 89 genes in manually isolated SCs , cHCs , and OHCs . These genes included: 1 ) 52 TF genes that we had identified as differentially expressed between SCs , cHCs , and OHCs in both single-cell and bulk RNA-seq; 2 ) specific SC or HC marker genes ( i . e . , Slc26a5 , Ocm , Gfi , Fgfr3 , Cdkn1b , Gjb2 ) ; and 3 ) housekeeping genes ( i . e . , Gapdh , Actb ) to serve as controls ( S3I–S3M and S3P Fig ) . Independent sets of single cells that included 27 SCs , 25 cHCs , and 16 OHCs , were isolated after enzymatic cochlear dissociation at P33 and analyzed with the Fluidigm BioMark high-throughput qPCR system ( S3I–S3M and S3P Fig ) . Comparison of the expression of TFs in the single-cell RNA-seq , bulk RNA-seq , and single-cell RT-qPCR datasets revealed a striking concordance among the three experimental strategies ( Fig 3 ) . To identify candidate TF genes that are required for transition from the SC/cHC1/2 states to cHC3 and whose overexpression could potentially increase conversion efficiency , we first focused on TFs overexpressed in cHC3 compared to SCs . As mentioned above , the cHCs ( P33 ) manually isolated based on HC marker expression for bulk RNA-seq and single-cell qPCR are likely drawn from cHC2 and cHC3 in the single-cell RNA-seq samples . This approach revealed several TFs that could potentially increase the efficiency of conversion , including Barhl1 , Lhx3 , Nfia , and Pou4f3 , many of which were also highly expressed in cHC3 cells in our single-cell RNA-seq analysis ( Fig 2C and S2A Fig ) . As mentioned before , Pou4f3 has been shown to improve Atoh1-mediated conversion in vivo [21] . We also focused on TFs that are overexpressed in OHCs compared to cHC3 or cHCs ( P33 ) whose overexpression could promote the completion of conversion; such TFs included Sall1/3 , Ikzf2 , Isl1 , and Aff3 ( Fig 3 ) . We next sought to identify the TFs that would be most likely to potentiate Atoh1-mediated SC-to-HC conversion . To accomplish this , we examined the list of genes overexpressed in OHCs for a TF that has been shown to regulate other differentially expressed genes . Strikingly , Isl1 was previously shown to regulate the expression of six of the 16 other overexpressed TFs , including Mlxip , Zbtb38 , Aff3 , Zfp827 , and Zfp532 in cardiac pacemaker cells [36] and Tub in retinal cells [37] . To determine if Isl1 can cooperate with Atoh1 to increase the SC-to-HC conversion efficiency and completion , we first overexpressed Isl1 in neonatal mouse cochlear explants . These ex vivo models have been shown to be surrogates of in vivo cochlear SC-to-HC conversion where cells analogous to SCs in the medial region ( the greater epithelial ridge [GER] ) of the cochlear explant can be converted to cHCs by ectopic Atoh1 expression [15 , 38] . After transfecting the explants with empty ( GFP ) vector , Atoh1 , Isl1 , or both Atoh1 and Isl1 , we found a significant increase in the number of transfected GER cells converted into Myo6-expressing cHCs when both Atoh1 and Isl1 were transfected , whereas Isl1 itself did not convert the GER cells to cHCs ( Fig 4A–4D ) . The increase in the conversion rate in Atoh1-Isl1 co-transfected cochleae was nearly double that in cochleae transfected with Atoh1 alone ( 43 . 9% vs . 25 . 5% , 2-way ANOVA , p < 0 . 05 ) , while the transfection rates among the four groups were similar ( Fig 4E and 4F ) . We observed no significant difference in Ki67 staining among transfected GER cells ( GFP+ ) between explants transfected with Atoh1 alone and those transfected with Atoh1+Isl1 ( 1 . 3% vs . 1 . 1% ) , indicating no significant proliferation was induced by the co-transfection . To validate Isl1’s function in vivo , we created transgenic mouse lines to ectopically express Isl1 and to test whether the Isl1 co-expression in SCs synergistically enhances Atoh1-mediated conversion in vivo ( Fig 4H and S4 Fig ) . We inserted the CAG-loxp-stop-loxp-Isl1-IRES-mCherry fragment ( which has the same backbone as the Atoh1-HA transgenic construct ) into the mouse genome , and bred transgenic founders with the Fgfr3-iCreER mice to test Isl1 overexpression alone in PCs and DCs . In addition , we bred Isl1 transgenic founders with Fgfr3-iCreER and Atoh1-HA overexpressing mice to test the synergistic effect between Atoh1-HA and Isl1 in PCs and DCs . We used the same tamoxifen-mediated induction strategy for Atoh1- or Isl1-overexpression mice in PCs and DCs . Similar to our ex vivo explant model , in two independent transgenic founder lines , co-expressing Isl1 with Atoh1 in SCs significantly increased the conversion rate compared with Atoh1 alone ( 50% vs 13% , see Materials and Methods for conversion rate calculation ) ( Fig 4H–4J ) three weeks after tamoxifen induction ( at P12 ) . Expression of Isl1 alone , in contrast , failed to promote any conversion from SCs to HCs . Together , these ex vivo and in vivo results demonstrate that Isl1 synergistically enhances Atoh1-mediated SC-to-HC conversion by increasing the conversion rate . Moreover , the results validate our bulk , single-cell RNA-seq , and single-cell qPCR analyses , indicating that our approach can identify co-reprogramming factors that promote Atoh1-mediated SC-to-HC conversion .
Based on our multi-faceted transcriptome profiling , our cHCs start with donor SCs ( P12 ) when Atoh1-HA is ectopically expressed , gradually decrease expression of many SC-enriched genes , concomitantly gain HC signatures , and eventually reach the cHC3 state that closely resembles neonatal HCs ( i . e . , P7 OHCs ) . These results are consistent with our previous reports using morphological and immunostaining criteria [14 , 21] . In >125 individual cHCs and >161 SCs/OHCs at three independent time points ( P12 , P26 and P33 ) , the cHC conversion is a continuum , a conclusion also supported by our bulk RNA-seq and single-cell qPCR analyses . The pseudotemporal separation of the conversion enabled the logical identification of TFs that are required at different stages of conversion . Interestingly , we discover two clusters of early transition states ( cHC1/2 ) that transiently express different sets of TF genes that can potentially contribute to the initial conversion from SCs at P12 . By identifying differentially expressed TF genes between cHC3 and mature OHCs , we also list TFs that can presumably promote the completion of conversion . By gene network analysis , we discover the key TFs for conversion , including Atoh1 , Pou4f3 and their closely correlated TFs , several of which have been previously demonstrated to contribute to SC-to-HC conversion [13 , 14 , 21 , 39] . For example , Pou4f3 functionally promotes Atoh1-mediated conversion in vitro ( together with Gfi1 ) [39] and in vivo [21] , while GATA3 plays a key role in the Atoh1-mediated conversion in adult cochleae in vivo [21] . Future analyses of our high-resolution profiling can provide further insight into Atoh1-mediated conversion . For example , conversion paths are dependent on the starting donor cell’s genetic and epigenetic states , driving factors , and environment [2 , 5 , 40] . It is possible that distinct subpopulations of SCs provide the appropriate cellular context where Atoh1 has access to the required target loci to induce further conversion . Further work is needed to determine how closely early cHCs ( i . e . , cHC1/2 ) resemble otic progenitors , bypassing potentially harmful ( i . e . , tumorigenic ) aspects of the progenitor phenotype . Along with the revealed continuous process of conversion ( from SC1 to cHC3 ) , we also found that the expression of endogenous Atoh1 is progressively upregulated , with the lowest expression in the SC1 state and the highest expression in the cHC3 state . Such upregulation of endogenous Atoh1 is attributed to the ectopic expression of Atoh1-HA and the transcriptional autoregulation of the endogenous Atoh1 [31] . Interestingly , Atoh1-HA is also progressively upregulated during conversion from SC1 to cHC3 . In fact , endogenous Atoh1 and transgenic Atoh1-HA were highly correlated in >125 cHCs analyzed . It is commonly assumed that the CAG promoter drives constant high levels of gene expression in all cells at all times; however , this may not be true in the organ of Corti . In support of this , tdTomato reporter expression driven by the CAG promoter at the ROSA locus also exhibits strikingly different intensities between IHCs and OHCs at P76 ( S3B Fig ) . It is possible that both the CAG promoter for Atoh1-HA and the endogenous Atoh1 promoter are subject to transcriptional regulation by some common transcriptional or epigenetic factors [41] . It is also possible that Atoh1 and Atoh1-HA mRNAs are stabilized by common but unknown RNA binding proteins or miRNAs . Interestingly , Dicer mutant mice exhibited immature hair bundle morphology strikingly similar to that in Atoh1-HA-driven converted HCs [21 , 42] , linking miRNAs to Atoh1 mRNA stability . For most direct reprogramming , multiple TFs function together , simultaneously or consecutively , either in the initiation stage or the later stage , to determine the cell fate and to induce efficient and complete conversion [2–5] . Thus , it is critical to identify TFs that improve Atoh1-mediated conversion . Here we have identified and validated 52 TF genes , including Atoh1 , that are differentially expressed in SCs , cHCs , and OHCs . Notably , of these 52 TFs , Pou4f3 functionally promotes Atoh1-mediated conversion in vitro ( together with Gfi1 ) [39] and in vivo [21] or by itself promotes conversion in vivo [21] . Interestingly , several TFs among the 52 are involved in development and cell fate reprogramming . For example , Isl1 is critical for pacemaker cell differentiation in the heart [43] , and for motor neuron differentiation [44] . Several TFs ( Isl1 , Tub , Zbtb38 , Zfp827 , Aff3 , Mixip and Zfp532 ) have been shown to be involved in pacemaker cell conversion in the heart [43] . Moreover , overexpression of Lhx3 in cochlear nonsensory cells is suggested to lead to Isl1 suppression [45] . Isl1 is also co-expressed with Atoh1 during early cochlear development [46] . Here we provided ex vivo and in vivo evidence that Isl1 indeed synergistically enhances Atoh1-mediated conversion in the cochlea . However , it remains unknown how Isl1 promotes Atoh1-mediated conversion . Notably , by forming complexes with different TFs ( Lhx3 vs Phox2a ) , Isl1 can program ESCs to distinct cell identities ( spinal vs . cranial motor neurons ) [44 , 47] by turning on variable groups of target genes determined by its binding partners . Thus , it will be important to identify the specific Isl1 binding complex in Atoh1-induced conversion . Moreover , future studies ( e . g . single-cell RNA-seq and electrophysiology ) on the Atoh1 and Isl1-converted HCs are also needed to molecularly and biologically characterize these cells to examine whether Isl1 also promotes HC maturation in vivo . Nonetheless , these results for Isl1 ex vivo and in vivo undoubtedly validate our single-cell transcriptomic analyses and identify 51 TFs that can promote Atoh1-mediated conversion in vivo . SC-to-HC direct conversion is initially predominant in chicken HC regeneration that also starts with upregulation of Atoh1 in SCs [48] where the initial nuclear migration and other morphological cellular transformation are also similar to what we have characterized in our Atoh1-HA ectopic expression cochlear models [14 , 21] . These parallels strengthen the notion that Atoh1-mediated HC conversion in the mature cochlea recapitulates the initial phases of naturally occurring HC conversion in non-mammalian species [48] and mammalian utricles [16] . Of note , our Atoh1-mediated SC-to-HC conversion path is remarkably similar to those reprogramming paths described in other regenerative systems . Ascl1-driven reprogramming of mouse embryonic fibroblast ( MEF ) cells to induced neurons ( iN ) in vitro exhibited a continuous conversion path nearly identical to the Atoh1-mediated conversion path [8] . Both Atoh1 and Ascl1 appear to act in a similar manner where donor cells overcome threshold barriers to initiate simultaneous upregulation of target cell fate genes and downregulation of donor cell fate genes . Moreover , in the MEF-to-iN conversion in vitro , additional reprogramming factors ( Brn2 and Mytl1 ) further prevented competing myogenic programs and/or reversal to the initial donor state . While in our Atoh1-mediated SC-to-HC conversion , additional factors may play similar roles as Brn2 and Mytl1; the 51 TFs identified here may also benefit other regenerative systems by improving their efficiency and completion . Together , our studies represent a major step towards understanding cochlear HC regeneration in vivo , with the potential to further improve the ongoing ATOH1 gene therapy in the clinic for patients with hearing loss . More importantly , our approach provides a valued strategy for better studying cochlea and other regenerative systems where conversion efficiency and completion are also central challenges .
The Animal Care and Use Committee of St . Jude Children’s Research Hospital approved all protocols used in this study , and all methods were carried out in accordance with the approved guidelines . Mice were housed in a facility with a 12-h light/dark cycle and free access to food and water . Fgfr3iCreER , Atoh1-HA , Chrna9-EGFP , Ai14 and prestin-YFP mice were as described previously [22 , 49–51] . Ai14 mice ( termed here tdTomato mice ) have a loxP-flanked stop cassette followed by a CAG promoter-driven red fluorescent protein variant ( tdTomato ) in the Rosa26 locus . The Pval-Cre mice were purchased from The Jackson Laboratory ( Stock no . 008069 ) . In order to label cHCs in vivo , tamoxifen was intraperitoneally injected into Fgfr3iCreER+; Atoh1-HA+; Chrna9-EGFP+; tdTomato+ mice at 3 mg/40 g at P12-13 ( simplified as P12 ) as described before [14] . Fgfr3iCreER+; tdTomato+ mice were used to isolate SCs . We found strong tdTomato signals in IHCs but weak signals in OHCs in Pval-Cre+; tdTomato+ mice at P74 ( S3B Fig ) . Therefore , Pval-Cre+; tdTomato+ were used to collect isolated IHCs . Cochleae were dissected out , enzymatically digested , and triturated gently using fire-polished Pasteur pipettes . The enzymatic digestion was performed by incubating the cochleae in 1 mg/ml pronase ( Roche life science ) at 37°C for 40–50 min [52] . Total RNAs from handpicked fluorescently labeled isolated cells were purified as described previously [52] . The cDNAs were created and amplified using Single Primer Isothermal Amplification ( SPIA ) technology according to the manufacturer's instructions ( NuGEN , San Carlos , CA ) . Libraries for RNA sequencing were generated with Encore NGS Library Systems according to manufacturer's instructions ( NuGEN ) . The 100-bp paired-end reads were generated using an Illumina HiSeq 2000 system ( Illumina , San Diego , CA ) . Base calling was performed using Illumina Casava 1 . 7 . FASTQ adapter sequences were trimmed ( with Cutadapt ) and mapped to the mouse mm9 genome by a pipeline that serially employs STAR and BWA as described previously [53] . The mouse mm9 genomic sequence file was obtained from Genecode ( https://www . gencodegenes . org/ ) . The mapping statistics were determined using FlagStat in SAMtools [54] and the mapped reads were counted using HTSeq [55] . The count matrix was trimmed and mean of M-values ( TMM ) -normalized [56] , and the differentially expressed genes were obtained using the limma-voom package in R [57 , 58] after adjusting the p value for the false discovery rate ( FDR ) using Benjamini and Hochberg's adjustments [59] . To obtain differentially expressed transcription factors , gene sets encoding transcription factors/regulators from the Animal Transcription Factor Database ( http://www . bioguo . org/AnimalTFDB/ ) were used . Fragments per kilobase of exon per million fragments mapped ( FPKM ) value for each gene was calculated by dividing the count of reads for each gene by total read counts for each sample , multiplied by 1 , 000 , 000 , and divided by each transcript length in kb . The transcript length for each transcript was calculated by adding up the length of all exons annotated for each gene from the mm9 annotation file obtained from Genecode . All heatmaps represented in this study were drawn using the gplots package in R . In order to perform PCA analysis , count of reads were TMM-normalized and divided by the transcript length ( TMM-normalized FPKM ) , and the numbers were used for subsequent PCA analysis . The PCA analysis was computed using the prcomp function in R after genes showing no expression in any cell type were removed . GO analysis was performed using DAVID Bioinformatics Resources 6 . 7 ( https://david . ncifcrf . gov/ ) . Only nonredundant GO terms were selected by using Revigo ( http://revigo . irb . hr/ ) [60] . Spearman’s and Pearson’s correlations were computed using the cor function in R . The RNA-seq data is available at Gene Expression Omnibus ( GEO ) submission: GSE85983 ( NCBI tracking system #18023366 ) . Total RNA ( 300ng ) extracted from the inner ear of a C57BL6 mouse at P1 was converted into cDNA by using SuperScript VILO Master Mix ( Thermo Fisher Scientific ) . After adding 2× TaqMan PreAmp Master Mix ( Thermo Fisher Scientific ) and 10× primer mix , pre-amplification of target genes was performed with 14 cycles of 95°C for 15 s and 60°C for 4 min . The cDNAs were treated with Exonuclease I ( New England Biolabs , Beverley , MA ) , and the fivefold diluted cDNA was used to make a threefold dilution series of 15 concentrations . The samples were mixed with 2× SsoFast EvaGreen Supermix with Low Rox ( Bio-Rad , Richmond , CA ) and 20× DNA Binding Dye Sample Loading Reagent ( Fluidigm , South San Francisco , CA ) . They were combined with 2× Assay Loading Reagent and pooled primer pairs in the 48 . 48 Dynamic Array integrated fluidic circuit ( IFC ) ( Fluidigm ) using the BioMark IFC controller MX ( Fluidigm ) . The quantitative PCR was performed using a BioMark HD system ( Fluidigm ) . The limit of detection threshold cycle ( LOD-Ct ) value was determined from the highest Ct value obtained using a threefold dilution series of 15 concentrations . The universal LOD-Ct was obtained by calculating the median value . Handpicked fluorescent cells described above were lysed in 5 μL of lysis buffer containing 1× VILO reaction mix ( Thermo ) , 1 . 2 U/L SUPERase•In RNase Inhibitor ( Thermo Fisher Scientific ) , and 0 . 5% NP-40 . The cDNA was generated in a 6-μL reaction volume after adding 0 . 15 μL of 10× SuperScript Enzyme Mix ( Thermo Fisher Scientific ) and 0 . 12 μL of T4 Gene 32 Protein ( New England Biolabs ) . The RT reactions were performed as recommended by Fluidigm . Pre-amplification of target genes was performed , and the fivefold diluted cDNA was used for subsequent quantitative PCR analysis as described above and Cq value of each gene for each cell were obtained using Fluidigm Real-Time PCR Analysis software . The Cq values were converted to expression levels using the equation Log2 ( Ex ) = ( LOD-Ct ) value—Cq . Cochleae were dissected out and incubated in solution ( 50% accutase ( Innovative Cell Technologies , San Diego ) , 0 . 02% Trypsin ( Thermofisher ) , 125 μg/ml Thermolysin ( SIGMA-ALDRICH ) ) at 37°C for 3 min . 0 . 02 mg/ml collagenase IV ( SIGMA-ALDRICH ) and dispase ( Worthington Biochemicals Corp ) were further added to the enzyme solution and incubated for 4 min at 37°C . The enzymatically treated tissue was triturated gently using fire-polished Pasteur pipettes and aggregated cells were removed using a 40-μm strainer . The dissociated cells were centrifuged at 500 × g for 5 min and resuspended in solution ( 0 . 5% fetal bovine serum , 0 . 04% bovine serum albumin , 0 . 3 mM ethylenediaminetetraacetic acid in Hanks' balanced salt solution ) . Library construction was performed using Chromium Single Cell 3’ v2 Reagent Kits ( 10X genomics ) following the instruction manuals . Briefly , the dissociated cells were loaded into a Chromium Controller ( 10X Genomics ) and the encapsulated cells were lysed individually . Reverse transcription within each oil droplet was performed and the cDNAs were amplified . Next-generation sequencing was performed using an Illumina HiSeq 4000 system ( Illumina , San Diego , CA ) and the sequence reads were aligned to mouse reference genome mm10 using the Cell Ranger pipeline ( 10X Genomics ) . The obtained gene-barcode matrices were further analyzed using Seurat 1 . 4 . 0 . 16 in R . Briefly , cells with relatively small and large library size in individual datasets were individually removed as potential doublets and low-quality cells . Cell cycle classification was performed using scran 1 . 4 . 5 in R , and cells that were classified as being in G1 phase were chosen . To minimize batch effects , expected counts were obtained by randomly generating numbers following a binominal distribution with the minimum number of unique molecular identifiers for all the cells as a size parameter and the population of each gene as the probability . Cell cycle-related , apoptosis-related , and ribosomal protein-encoded genes were removed . Highly variable genes were chosen by calculating the average expression and dispersion for each gene using the MeanVarPlot function in the Seurat package with default parameters [23] . Subsequent PCA followed by t-Distributed Stochastic Neighbor Embedding was performed . Differentially expressed genes were identified using the FindAllMarkers function in the Seurat package with a default condition and PCA followed by tSNE analysis were further performed using the differentially expressed genes . Pseudo-temporal ordering analysis was performed using Monocle 2 . 4 . 0 [24] . Briefly , the expected counts were loaded into the Monocle package with a lowerDetectionLimit of 0 . 5 . Highly variable genes with ≥ 0 . 1 mean expression and ≥ 1 . 0 empirical dispersion for each gene were then chosen . The cell trajectories were then drawn using the remaining genes . To create a machine learning classifier , 20% and 80% of cells from each cluster were chosen as the test and training datasets using the StratifiedShuffleSplit function from scikit-learn 0 . 9 . 0 in python 3 . 5 . 3 . Expression values for each gene were transformed into the range between 0 and 1 using the MinMaxScaler function , PCA without whitening was performed using the training dataset , and the 30 principle components were chosen . Optimal parameters for the support vector machine with a radial basal function , such as the C and gamma parameters were determined using the GridSearch CV function with a default conditions ( 3-fold cross-validation ) . Predicted clusters for the remaining 20% of cells were determined using the predict function and the predicted clusters were compared to clusters determined by PCA followed by tSNE using the confusion_matrix function . The prediction accuracy of the remaining datasets ( 20% ) using the classifier was 100% , thus validating the clusters identified by tSNE . The organs of Corti from P0 . 5 FVB mice were dissected and electroporated as described previously [38] . Briefly , the organ of Corti was isolated with the basal hook region removed to allow for improved adhesion . The tissue was then transferred to 150 μL HBSS in the center of a Millipore filter membrane ( 30 mm–diameter culture plate insert; Millipore , Billerica , MA ) with the sensory epithelium facing up . Excess HBSS was carefully removed , and 5 μL of plasmid ( 1 μg/μL ) was immediately added on top of the tissue . The volumes and DNA concentrations for the transfection were kept constant for all experiments . The epithelium on the filter was then placed in the center of a dish electrode ( anode , 2 mm–diameter flat round electrode; NEPA GENE catalog no . CUY700P2E ) . A cover electrode ( cathode , 2 mm–diameter flat round electrode; NEPA GENE catalog no . CUY700P2L ) was positioned above the epithelium . Two rectangular pulses were delivered ( 28 V , 30 ms duration , with a 970-ms interval ) using the NEPA GENE CUY21EDIT Square Wave Electroporator . The organ of Corti was then left standing for 1 min , after which 1 mL of Opti-DMEM was added to the membrane . The explant was then divided in two on the filter membrane , and the apical and basal sections were transferred to separate 5-cm glass-bottom culture dishes ( Mattek ) coated with Matrigel ( Corning ) . A 2-mL volume of pre-warmed culture medium ( high-glucose DMEM , 10% fetal bovine serum , 20 ng/mL epidermal growth factor , 10 uL/mL N2 supplement , 50 μg/mL ampicillin ) was then added to each dish , and the dishes were incubated at 37°C in 5% CO2 and 95% humidity for the duration of the culture . The coding region of the murine Isl1 gene was cloned using RT-PCR of mouse cochlear total RNA and inserted right before the IRES sequence of the pCAGGS-S-stop-IRES-mCherry vector [21] . The 8 . 0kb DNA fragments of CAG-flox-stop-flox-Isl1-mCherry were taken out using PvuI and SapI and injected into the zygotes [14] . The offspring of ten founders were analyzed and five of them exhibited specific expression of mCherry in DCs/PCs at cochleae at P33 and later stages after tamoxifen injection at P12-13 when bred with Fgfr3iCreER+ . The obtained conditional transgenic mice showed no obvious abnormal cochlear morphology either with or without Isl1-induction in DCs/PCs . At least two independent lines ( #4 and #18 ) phenocopied the synergistic effects of Isl1 and Atoh1 in this study . The analysis shown in this study is from #18 . The tissues were fixed in 4% paraformaldehyde for 15 minutes at room temperature ( cultured explants ) or for overnight at 4°C ( P33 cochleae ) . After washing in PBS and , for P33 cochlea , decalcification in 120mM EDTA , the samples were blocked and permeabilized in blocking buffer ( 10% horse serum , 1% BSA , and 1% Triton X-100 in PBS ) for 1h at room temperature . They were then incubated at 4°C overnight in primary antibody solution . Primary antibodies used are as follows: Chicken anti-GFP antibody ( 1:1000 , Abcam ) , rabbit anti-myosin VI ( 1:500 Proteus Bioscience ) , rat anti-HA ( 1:75 , SIGMA ) , mouse anti-parvalbumin ( 1:500 , Sigma ) , and rabbit anti-mCherry ( 1:1000 , Abcam ) . The tissues were incubated with 1:1000 diluted secondary antibodies for 2 hours at room temperature , washed with PBS , and then mounted for imaging using ProLong Gold Antifade Reagent ( Life Technologies ) . All images were taken under a Zeiss Axiophot 2 microscope with an LSM710 confocal laser scanning image system ( Carl Zeiss , Jena , Germany ) . To analyze the transfected explant cochleae , GFP+ cells in the greater epithelial ridge ( GER ) region of the organ of Corti with no obvious abnormal shape were counted as transfected cells . Of the GFP+ cells , myosin VI+ ( MyoVI+ ) / GFP+ cells were counted as converted hair cells whereas MyoVI+/ GFP+ cells within clusters containing cells with MyoVI+/ GFP+ and with MyoVI+ were considered dislodged endogenous cells . The ratio of GFP+/ MyoVI+ cells to all GFP+ cells was presented as the conversion rate . To analyze cochleae from transgenic mice , 200-μm-long regions in the middle turn of the cochleae were chosen , as described previously [14] . HA+ or mCherry+ were ectopically expressed only in DCs and PCs in this study . The conversion rate was calculated as the percentage of Parvalbumin+/ HA+ cells in all HA+ cells ( for all Atoh1-HA mice ) , Parvalbumin+/ mCherry+ cells in all mCherry+ cells ( for all Isl1 mice ) , or parvalbumin+/ HA+/ mCherry+ cells in all HA+/ mCherry+ cells ( for all Atoh1-HA; Isl1 mice ) . | The ongoing ATOH1 gene therapy clinical trial offers promise for hearing restoration in humans . However , in animal models , Atoh1-mediated sensory regeneration is inefficient and incomplete . Here we performed high-resolution gene expression profiling of single cochlear cells at multiple time points in a mouse model whereby we discovered a continuous regeneration process that leads to the formation of immature sensory cells . We identified 51 key reprogramming transcription factors that may increase the efficiency and completion of the regeneration process and confirmed that Isl1 in transgenic mice promotes Atoh1-mediated sensory regeneration as a co-reprogramming factor . Our studies identify molecular mechanisms and novel co-reprogramming factors for sensory restoration in humans with irreversible hearing loss . | [
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"(mat... | 2018 | High-resolution transcriptional dissection of in vivo Atoh1-mediated hair cell conversion in mature cochleae identifies Isl1 as a co-reprogramming factor |
An estimated 600 million people are affected by the helminth disease schistosomiasis caused by parasites of the genus Schistosoma . There is currently only one drug recommended for treating schistosomiasis , praziquantel ( PZQ ) , which is effective against adult worms but not against the juvenile stage . In an attempt to identify improved drugs for treating the disease , we have carried out high throughput screening of a number of small molecule libraries with the aim of identifying lead compounds with balanced activity against all life stages of Schistosoma . A total of almost 300 , 000 compounds were screened using a high throughput assay based on motility of worm larvae and image analysis of assay plates . Hits were screened against juvenile and adult worms to identify broadly active compounds and against a mammalian cell line to assess cytotoxicity . A number of compounds were identified as promising leads for further chemical optimization .
Schistosomiasis is a potentially severe helminthic disease causing widespread morbidity and affecting an estimated 600 million people [1] . Humans are infected percutaneously by the cercarial larval stage shed from aquatic snails and thereafter the worms remain in the human blood stream migrating from the skin , through the lungs and maturing in the liver . The adult worms then migrate to the mesenteric veins or the vesical plexus of the bladder , depending on species , where they lay eggs , which cause inflammation and fibrosis in various organs . Praziquantel ( PZQ ) is the only drug recommended for treatment of schistosomiasis and its increasingly widespread use in mass chemotherapy campaigns means it is the mainstay of control of this infection [2] . PZQ has proved to be generally safe and effective using a single oral dose [3 , 4] , however reliance on a single drug has led to concerns over potential development of drug resistance . Although PZQ is active against adult worms of all the medically important Schistosoma species [5] , it is relatively ineffective against the juvenile stages both in vivo and in vitro [6 , 7] meaning that treatment does not eliminate all the worms in an infected patient and repeat treatment is required . Consequently there is a need for new schistosomicides with activity against all life stages and this has led to renewed interest in research into drug discovery using both phenotypic and target-based approaches . These include development and application of whole organism screens for compound testing [8] , target-based drug discovery [9] and identification of putative molecular targets by analysis of the annotated schistosome sequences [10] . We have developed a high-throughput screen ( HTS ) based on use of the larval , schistosomula , stage for primary screening of large compound libraries [11] and here we describe its use as part of an in vitro screening program prior to application of medicinal chemistry optimization . Our ultimate aim is to identify compounds with better activity against the juvenile worms than PZQ but with at least comparable activity against the adult worms . Whilst it was unlikely that high-throughput screening hits would be as efficacious as an established drug candidate , selection of hits with multi-stage efficacy is likely to give the best chance of developing a clinical candidate with the desired profile . Therefore all hits were subsequently screened against juvenile ( 3 week old ) schistosomes and adult worms as well as against mammalian cells to check for cytotoxicity . We here report the screening of almost 300 , 000 small molecule compounds provided from pharmaceutical companies , charitable organizations and commercial sources and describe a number of promising new leads for further chemical optimization .
Experimentation was carried out using the NC3Rs and ARRIVE guidelines under the United Kingdom Animal’s Scientific Procedures Act 1986 ( under project licence 60/4456 ) with approval from the London School of Hygiene and Tropical Medicine Ethics committee . Male CD1 mice ( aged 5–6 weeks ) were bred on site using SPF conditions with access of food and water ab libitum . Experiments were performed using the Puerto Rican strain of S . mansoni maintained in Biomphalaria glabrata and CD1 mice . The strain of snail was one bred by Prof Mike Doenhoff ( Nottingham University ) for high susceptibility to S . mansoni and ease of breeding in large numbers to high density . Careful attention was paid to controlling infections with rotifers which otherwise would contaminate the HTS schistosomula preparations as well as affecting snail viability . This included recovery of egg masses on short length pieces of polypropylene tubing placed into the breeding tanks and treatment of these with 3 x 10 seconds immersion in 70% ethanol with 3 minutes soaking in clean water in between alcohol treatments . Schistosomula were mechanically prepared as previously described [12] with the modification of using a 45% and 70% Percoll gradient , and the cercarial heads recovered from the 70% layer . All centrifugation steps were done at 350x g . The schistosomula generated were then incubated overnight in M169 supplemented with 100U/ml Penicillin , 300μg/ml Streptomycin , 0 . 25μg/ml Fungizone ( Amphotericin B ) ( Gibco , UK ) and 5% Foetal Calf serum . For production of S . mansoni juvenile or adult worms , mice were infected subcutaneously under mild isoflurane ( Merial Animal Health Ltd ( UK ) ) anaesthesia with , respectively 1400 ( for juveniles ) or 450 cercariae ( for adults ) . Worms were recovered from infected mice using sterile techniques by portal perfusion 3 weeks ( for juveniles ) or 6 weeks ( for adults ) post-infection using warm perfusion medium ( Dulbecco’s Modified Eagle’s Medium [DMEM] , 2mM L-glutamine , 100 Units/ml penicillin , 100μg/ml streptomycin , 20mM Hepes , 10 Units/ml heparin [Sigma , UK] [8] . Adult worms were washed free of red blood cells using the perfusion medium , and finally placed in culture in complete medium ( cDMEM: DMEM , 2mM L-glutamine , 100 Units/ml penicillin , 100μg/ml streptomycin , 10% foetal calf serum ( FCS ) at 37°C , in an atmosphere of 5% CO2 . Juvenile worms were sedimented successively following washing with cold perfusion medium and then suspended in cold cDMEM until dispensed to avoid attachment to the plastic tubes . The larval HTS was run as described previously [11] . Compound libraries were provided in 384 well plates as liquid either ( i ) small volumes in polypropylene 384 V-bottom storage plates ( Greinerbio-one ) that were diluted and 500nL dispensed into black 384 imaging plates ( ViewPlate 6007460; PerkinElmer ) using the Biomek FxP ( Beckman Coulter ) or ( ii ) in an assay-ready format from which a 40nL volume was transferred into wells of imaging plates . All test plates included the same array of control wells: medium alone [4 wells] , medium plus DMSO carrier ( negative control ) [16 wells] , 10μM Oltipraz ( OLT ) positive control [8 wells] or 10μM Praziquantel ( PZQ ) [4 wells] . Through the interactive reporting system the larval images for all control wells and wells defined as hits by the algorithm were reviewed manually to confirm viability/hit status . Periodically HTS wells suffered from yeast contamination that could not be eliminated by modifications in snail rearing and more extensive washing of the schistosomula preparations . Therefore , to prevent fungal contamination 0 . 25μg/ml ( 0 . 27μM ) of amphotericin B ( AMP-B ) was added to cultures both during the overnight culture prior to plating and during the 3 day assay . This concentration of AMP-B is below the concentrations ( 1–10μM ) reported to have lethal activity against S . mansoni schistosomula [13 , 14] . In our assays the mean ± SD phenotype and motility scores for the DMSO control larvae before and after implementation of use of AMP-B showed minimal differences . The phenotype scores were not significantly different by Student t-test and motility scores showed marginally higher values for AMP-B treated plates ( P<0 . 004 ) . The mean values for the PZQ control wells used in all screening plates were slightly lower in the presence of AMP-B for phenotype but not for motility ( phenotype: -AMP-B: -0 . 21 ± 0 . 12 and +AMP-B: -0 . 38 ± 0 . 08 ( P<0 . 001 ) ; motility: -AMP-B: -0 . 53 ± 0 . 10 and +AMP-B: -0 . 57 ± 0 . 10 ) . For the juvenile assay the concentration of worms was adjusted to 40/ml cDMEM and , prior to dispensing , the suspension was cooled on ice to prevent adherence of the worms to the plastic bottles . After swirling to resuspend worms , 150μL of suspension was added to the wells of 96 well microtitre plates ( Nunc , Thermo Fisher Scientific , UK ) . Wells were quickly checked and those with <5 worms were marked . The remainder were made up to 200μL and the deficient wells topped up with a worm suspension to ensure that all wells had ≥5 worms . The adult worm assay was as described in [8] except that 1ml cultures containing 3 worm pairs were generally used . Both the juvenile and adult worm assays were assessed for activity on days +1 and +5 . Drug effects were determined by assessing the viability of individual worms and calculating the mean percentage inhibition , a hit being defined as ≥70% reduction in viability . Activity against mammalian cells was determined using MRC-5 cells [15] . Cells were maintained in Minimum Essential Medium supplemented with glutamax ( Gibco , UK ) , 1% non-essential amino acids ( Gibco , UK ) and 10% Foetal bovine serum ( Gibco , UK ) . They were harvested by treatment with 0 . 25% Trypsin-EDTA for 3 minutes and diluted to 1x105/ml . 100μl aliquots of the cell suspension were then plated in 96 well plates ( Nunc , Thermo Fisher Scientific , UK ) . The plates were left to incubate for 24 hours at 37°C and 5% CO2 in a humidified incubator . Cytotoxicity DR assays were set up 24 hours post incubation at 5% CO2 and 37°C , with compounds prepared at 3-fold dilutions starting from 50μM . Dilutions were added to the cells such that the final % DMSO/well was 0 . 5% . Each drug concentration was tested in duplicate . On each 96 well plate there were negative control wells of media only , MRC-5 cells only and MRC-5 cells with 0 . 5% DMSO . For positive control wells doxorubicin hydrochloride ( Sigma-Aldrich , UK ) was added to the cells at 100μM concentration . The cultures were then maintained for a further 72 hours at 37°C and 5% CO2 . Drug cytotoxicity was determined using the redox indicator Alamar blue ( AbD Serotec , UK ) . 10μl of Alamar Blue was added to each well and the plates incubated for 4 hours at 37°C and 5% CO2 atmosphere . The fluorescence intensity was measured using a Spectramax Gemini plate reader ( Molecular Devices , UK ) using an excitation wavelength of 530 nm and an emission wavelength of 580 nm . The screening sequence is shown in Fig 1 . The primary HTS and subsequent juvenile and adult screens were run as single point ( SP ) assays at 10–12 . 5μM depending on the concentrations in the compound collections . Compounds were tested in duplicate . Where necessary , repeat testing was carried out on re-supplied liquid samples . All compounds of interest were re-tested as solid compounds or by repeat synthesis and activity confirmed . Compounds with activity against all three parasite stages were tested in parallel dose response ( DR ) assays against juvenile worms and MRC-5 cells . Promising compounds were then tested in the adult DR . For the parasite DR ( larvae , juveniles and adults ) the concentrations tested were two fold dilutions starting from 2x the primary screening concentrations ( i . e . 20–25μM ) . IC50 values were calculated from the drug concentration–response curves using Microsoft XLfit version 5 . 1 . 0 . 0 ( 2006–2008 ID Business Solutions Ltd ) . Results were expressed as the geometric mean IC50 . Compounds were obtained from four sources . The MMV collection was obtained in 96 well plates as 2mM solutions in DMSO and was a diverse chemical library . The Pfizer collection was obtained as 4mM solutions in DMSO and comprised a set of compounds that had previously shown activity against a variety of pathogens . The European Screening Port collection of structurally diverse compounds was obtained in 384 well plates as 2mM solutions in DMSO from European Screening Port GmbH , Schnackenburgallee 114 , D-22525 Hamburg ( European Screening Port is now Fraunhofer Institute for Molecular Biology and Applied Ecology , http://www . ime . fraunhofer . de/en . html ) . The published TCAMS collection of compounds with activity against the malaria parasite Plasmodium falciparum was provided by GSK ( www . ebi . ac . uk/chemblntd , [16] ) as 1mM solutions in DMSO . For the larger compound sets from Enamine and MMV , we had the opportunity to select plates of compounds . In doing so we were able to bias the selections we screened towards rule-of-5 like properties [17] and also to select plates containing compounds such that the overall selection was quite diverse . Indeed , the final screened collections contained no duplicate compounds . The chemical diversity of the compound collections was further investigated by comparing scatter plots ( Fig 2 ) . Although there is significant overlap in chemical property space , the scatter plots also demonstrate some differences between them .
Overall 294 , 253 compounds were tested in the HTS . Table 1 shows the mean Z’ factors for the plates in each of the different compound collections along with the mean phenotype and motility scores for the OLT and the PZQ control wells . The mean Z’ factors for both the phenotype and motility scores were consistently above 0 . 5 and essentially similar both within and between the different libraries , indicating that this is a robust assay . Similarly the mean phenotype and motility scores for both OLT and PZQ were broadly consistent within and between the different collections and as expected based on previous screening [11] . As was reported previously [11] , OLT phenotype and motility scores were consistently higher than those for PZQ indicating a more severe effect of this compound on the worms . The hit rates in the primary larval screen varied markedly for the different compound libraries ranging from 0 . 58% to almost 30% ( Table 2 ) . Given the consistent performance of the HTS throughout the screening campaign and the fact that some of the collections were screened in parallel , it is likely that the differences reflect the nature of the compound collections rather than any variation in the performance of the assay . High numbers of cercariae were used for the infection of mouse donors meaning that the juvenile assay could be run in medium throughput and provided an important triage step in the screening sequence . It was observed that the smaller juvenile worms generally proved to be more susceptible to compound inhibition than the larger worms . Following the initial screening cascade and subsequent IC50 testing , seven compounds were selected for further follow up on the basis of balanced activity against all parasite life stages , a good cytotoxicity window and structural attractiveness . Additional hits with less compelling profiles were also identified and are shown in S1 Table . The structures of the seven priority compounds and two standards ( PZQ and OLT ) which we have used in our HTS assays are shown in Fig 3 and the activity is described in Table 3 . Activity of all compounds was confirmed with solid sample which was purchased commercially if available , or re-synthesised . The activity of solid samples was in good agreement with the original liquid sample data . It was encouraging that the screening process was able to identify multiple different chemotypes with similar potency to the standard agent ( PZQ ) against juvenile worms . All of the seven hit molecules comply with the Lipinski rules-of-five for oral absorption as originally defined [17] . Some of the compounds failed the lipophilicity criteria ( ALogP >5 ) but Lipinski rules predict that this single failure is unlikely to significantly impair absorption . We were interested to know the speed of action of the selected compounds , therefore activity was examined in juvenile and adult worm assays at both 1 day and day 5 . The data in Table 4 shows that most of the compounds of interest had more rapid and complete activity against juvenile worms than PZQ which only achieved 58 . 4% motility reduction after 5 days . Two compounds , 2389 and 1716 appeared to have a delayed action with no inhibition after 1 day , but complete inhibition after 5 days . The effects of compounds on adult worms were generally similar to PZQ . The choice of series to move forward for further chemical optimisation is a crucial one in the life of any small molecule drug discovery project . Multiple factors are likely to affect ultimate success of the project including: physicochemical properties , presence of structural groups linked with toxicity , synthetic tractability , freedom to operate from an IP perspective . One of the factors affecting the decision which is sometimes overlooked is to understand what is known about existing pharmacology of the hit or compounds like it . To that end we have followed a similar process to that described by Avery et al [18] . The compounds listed in Table 2 were subjected to an analysis using a freely available workflow based on KNIME ( http://www . knime . org/downloads/overview ) , ChEMBL ( doi:10 . 1093/nar/gkr777 ) and DataWarrior ( http://www . openmolecules . org/datawarrior/download . html ) [25] . called ‘Know Your Molecule’ . The workflow is described and can be downloaded from ( http://www . mmv . org/research-development/computational-chemistry ) . A table of the findings from this is provided in S2 Table . This data indicates for example that close analogues of LSHTM-1956 have been shown to have activity against S . mansoni peroxiredoxin Prx2 at a range of potencies 100–1μM [19 , 20] . Although these analogues possess a methyl at the 2-position of the indole rather than a phenyl , it is an interesting mechanistic link to explore . Close analogues of LSHTM-1716 have shown activity in a variety of whole cell assays , but simplified analogues appear less promiscuous . Simpler analogues of LSHTM-2045 were reported active against P . falciparum at 600–750nM and released by GSK as part of the TCAMS . Encouragingly both were also recorded as inactive ( 7% and 9% inhibition at 10μM ) in a HepG2 cell line cytotoxicity . Finally , two close analogues of LSHTM-1586 are recorded as showing activity below 1μM in 9 separate screens , perhaps supporting the finding in our hands that the resynthesized compound was cytotoxic ( S1 Table ) . Although no further work has been done to confirm the reported activities , some of the data can be useful signposts to help prioritise the hits and to identify hypotheses to test in subsequent optimisation .
Based on the need to develop new drugs for improved treatment of schistosomiasis , we carried out a high throughput screening campaign with the aim of discovering compounds with balanced potency against all life stages of Schistosoma mansoni . We identified a number of novel compounds of interest and have prioritised seven of these for further follow up . We were fortunate to be able to access high quality libraries from pharma companies and other sources for this project . Surprisingly the hit rates for each compound collection were markedly different , with the Pfizer and GSK sets having significantly higher hit rates than the other libraries . There are two notable differences between compound collections . Firstly the GSK and Pfizer subsets have higher mean lipophilicity and molecular weight compared to the other collections . Secondly , the MMV and Enamine collections are diversity screening libraries , whereas the Pfizer and GSK libraries both comprise compounds which have previously been reported to have activity against one or more pathogens . Both the physicochemical property differences and different selection biases might be expected to influence hit rate in a new phenotypic screen such as this . Our ability to screen nearly 300 , 000 compounds in a short time was due to use of a high throughput larval screen [11] . To efficiently progress the hits from this screen we developed and implemented a screen using juvenile ( 21 day ) worms as we were particularly keen to identify compounds with improved drug efficacy against this stage of the parasite life cycle . The juvenile assay proved to be a very efficient medium throughput screen which was very efficient at triaging compounds prior to testing in the adult worm assay . It is encouraging that we have been able to identify a number of compounds with IC50 <10 μM against all stages of the parasite life cycle and with potency against juvenile worms comparable with praziquantel . Typically , medicinal chemistry optimisation of HTS hits can result in early leads that are >10-fold more potent [21] and advanced leads that are 100-1000-fold more potent than the original hit . A similar potency improvement was also achieved during the discovery process leading to praziquantel [22] . Therefore we are optimistic that medicinal chemistry optimisation of our hits could lead to a candidate superior to praziquantel . It is interesting to note that we identified two compounds ( 1507 and 1716 ) which like PZQ are more potent against the adult stage than juveniles , whereas a number of other compounds showed comparable activity against both stages as was also the case for oltipraz , a compound which has proved to be effective in humans but whose development and use clinically was halted due to phototoxicity [23 , 24] . This may suggest that the targets of these compounds are differentially expressed , or play greater or lesser roles in the different life stages of the parasite . Investigation of compound activity at two different time points revealed that several of our compounds appear to cause faster and more complete inhibition of juvenile worm motility compared with PZQ . It will be interesting to explore if this translates to faster cures in an in vivo efficacy model . The lower effect of PZQ on juvenile compared with adult motility may also explain the relatively poor clinical efficacy of this compound against juvenile versus adult worms . All seven of our priority compounds comply with the Lipinski rules-of-five for oral absorption [17] making them attractive starting points for further medicinal chemistry optimisation . This is particularly important as oral treatment is standard for schistosomiasis . Analysis of the structures of our seven priority compounds reveals that 1355 and 2389 have related structures , as do 1507 and 1945 . This may indicate that these pairs of compounds could have similar modes-of-action , although the mechanism of action of all our hits remain unknown at this time . To the best of our knowledge , there are no new compounds in late pre-clinical or clinical development for schistosomiasis , therefore the molecules we have identified represent an exciting starting point for drug discovery . We are currently working to optimise selected series from our hit set with the initial goal of making analogues with pharmacokinetic properties that would allow demonstration of in vivo activity and ultimately with the aim of identifying a clinical candidate . | Schistosomiasis is a parasitic infection that affects an estimated 600 million people in developing countries . Treatment is currently dependent on a single drug which is only active against the adult form of the disease . Treatment of infected patients eliminates adult worms but leaves any juveniles to survive and develop , thus continuing the infection . A drug acting equally against adult and juvenile worms is likely to be more effective at eliminating the disease . We have identified quality chemical leads with balanced activity against all life stages of the parasite and with a good window over cytotoxicity , which could form the basis of an exciting new drug discovery project . | [
"Abstract",
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"mathematics"... | 2016 | High Throughput Screening Identifies Novel Lead Compounds with Activity against Larval, Juvenile and Adult Schistosoma mansoni |
Leptospirosis , a zoonosis associated with potentially fatal consequences , has long been a grossly underreported disease in India . There is no accurate estimate of the problem of leptospirosis in non-endemic areas such as north India . In order to understand the clinical spectrum and risk factors associated with leptospirosis , we carried out a retrospective study in patients with acute febrile illness in north India over the last 5 years ( January 2004 to December 2008 ) . There was increased incidence of leptospirosis ( 11 . 7% in 2004 to 20 . 5% in 2008 ) as diagnosed by IgM ELISA and microscopic agglutination titer in paired acute and convalescent sera . The disease showed a peak during the rainy season ( August and September ) . We followed up 86 cases of leptospirosis regarding their epidemiological pattern , clinical features , laboratory parameters , complications , therapy , and outcome . Mean age of patients was 32 . 6 years ( 2 . 5 years to 78 years ) and males ( 57% ) outnumbered females ( 43% ) . Infestation of dwellings with rats ( 53 . 7% ) , working in farm lands ( 44 . 2% ) , and contact with animals ( 62 . 1% ) were commonly observed epidemiological risk factors . Outdoor workers including farmers ( 32 . 6% ) , labourers ( 11 . 6% ) , para-military personnel ( 2 . 3% ) , and sweepers ( 1 . 2% ) were commonly affected . Modified Faine's criteria could diagnose 76 cases ( 88 . 3% ) . Renal failure ( 60 . 5% ) , respiratory failure ( 20 . 9% ) , the neuroleptospirosis ( 11 . 6% ) , and disseminated intravascular coagulation ( DIC ) ( 11 . 6% ) were the commonest complications . Five patients died , giving a case fatality rate of 5 . 9% . There has been a rapid rise in the incidence of leptospirosis in north India . Severe complications such as renal failure , respiratory failure , neuroleptospirosis , and DIC are being seen with increasing frequency . Increased awareness among physicians , and early diagnosis and treatment , may reduce mortality due to leptospirosis .
Leptospirosis , a worldwide zoonosis associated with sinister complications and fatalities , has been recognized in India since 1931 [1] . It is especially rampant in southern , central , eastern and western India , where heavy monsoon , animal rearing practices , unplanned urbanization and agrarian way of life predispose to this infection [1] , [2] , [3] , [4] , [5] , [6] , [7] . Leptospirosis has long been recognized as one of the foremost causes of acute febrile illness in those parts of the country [2] , [5] . Though similar conditions exist in the north , reports of this disease from north India are few and only of recent origin ( two of them from our center ) [8] , [9] , [10] , [11] , [12] . Lack of awareness , clinical suspicion and active surveillance could be the probable reason . Leptospirosis has been a neglected disease even in developed countries like USA [13] . There is a wide spectrum of clinical presentations for leptospirosis . While most patients with Leptospira infection present only with mild fever and recover without complications , a small proportion develops various complications due to involvement of multiple organ systems . We have previously highlighted the importance of leptospirosis in pyrexia of unknown origin ( PUO ) cases [8] , [9] in our center ( Post Graduate Institute of Medical Education and Research , Chandigarh , India ) situated in north India . Being a tertiary care center , undiagnosed and complicated patients are referred from all across the northern states . Recently we observed an increase in the number of leptospirosis cases . We retrospectively reviewed the records of 86 such cases and observed the varied clinical manifestations and course of the disease in these patients . Apart from usual cases of acute icteric and anicteric febrile illnesses , severe manifestations of the disease , like neuroleptospirosis , haemorrhagic pneumonitis , and adult respiratory distress syndrome , were observed .
This present study is a retrospective review of records of leptospirosis cases diagnosed at our institute during the last 5 years ( 2004 to 2008 ) . During this period , the microbiology laboratory received 1391 blood samples from suspected cases with pyrexia of unknown origin for leptospira serology . Paired acute and early convalescent ( 10–15 days into illness ) serum samples were tested for specific anti-leptospira IgM antibody using the PanBio IgM ELISA ( Panbio diagnostics , Brisbane , Australia ) . The test procedure was performed according to the protocol provided along with the kit . The results were interpreted according to manufacturer's instructions , i . e . values <9 PanBio ELISA units were considered negative , 9–11equivocal , and >11 positive . For samples showing equivocal results , another blood sample was drawn after a period of 10 days , and the test was repeated . Negative and positive controls were kept with each test run . Microscopic agglutination test ( MAT ) could be done in a few samples only ( paired sera ) . For this test these samples were sent to the National Reference Laboratory for Leptospirosis , Port Blair , Andaman Islands , India . MAT was carried out following standard procedure [14] using 10 live leptospiral reference strains as antigens . The strains belonged to serogroups Australis , Autumnalis , Ballum , Bataviae , Canicola , Grippotyphosa , Icterohaemorrhagiae , Javanica , Pomona , and Tarassovi . The criteria for a positive MAT test was a titre of ≥1∶400 in a single sample , four-fold rise in titre or seroconversion in paired samples . Partial autopsy was done on one of the five patients who died , after obtaining informed written consent from close relatives . Representative tissue samples were processed for paraffin embedding and histological evaluation . Eighty six patients positive for leptospira serology were evaluated noting their clinical history , presentation , radiological features , laboratory parameters , management , course and outcome . Utilizing clinical , epidemiological , and laboratory parameters modified Faine's criterion was scored and assessed [13] . Scoring system by modified Faine's criteria is detailed in Table 1 . The study was approved by the Institute ethical committee , PGIMER , Chandigarh . Informed consent for blood samples and for autopsy was not needed in the study . Blood samples are received in the laboratory routinely as part of patient care . Autopsies are routinely done at our center; institute ethical committee approval is not needed for the autopsy because it was not done for research purposes .
During the study period , from 2004 to 2008 , there was a sustained rise of leptospirosis cases from ( 11 . 7% to 20 . 5% ) ( Figure 1A ) . In all we detected 232 cases of leptospirosis in the five years of study period ( 9 in 2004 , 17 in 2005 , 25 in 2006 , 74 in 2007 , and 107 in 2008 ) . Cases were more common in the months of July-October for most of the years ( Figure 1B ) . The patients resided in different states of north India , however majority of our patients were from Punjab , Haryana and Himachal Pradesh ( Figure 2 ) . Mean age of patients was 32 . 6 ( ±0 . 7 ) years with a range from 2 . 5 years to 78 years . Male patients ( 49 , 57% ) outnumbered female patients ( 39 , 43% ) . Most of the patients ( ∼70% ) were young adults in their 2nd , 3rd , and 4th decades of life ( Table 2 ) . Sixty six ( 76 . 7% ) patients were from rural areas , travelling to endemic area was suggestive in 2 patients ( Table 2 ) . Major epidemiological risk factors noted in our patients include wet environmental living conditions , lack of protective footwear , infestation of dwelling with rats , working in farm lands , contact with animals , especially cattle , bathing in public places , history of unprotected contact with dirty stagnant water , alcohol addiction , and smoking ( Table 3 ) . Most of the patients by occupation were farmers ( 28 , 32 . 6% ) , followed by housewives ( 19 , 22 . 1% ) , students ( 11 , 12 . 8% ) , labourers ( 10 , 11 . 6% ) , indoor non-manual workers ( 10 , 11 . 6% ) , para-military personal ( 2 , 2 . 3% ) , sweeper ( 1 , 1 . 2% ) , carpenter ( 1 , 1 . 2% ) ; and 4 were children below 4 years ( unemployed ) ; two of the female patients were pregnant . Fever was seen in all 86 individuals , being intermittent and associated with chills and rigor in most of the patients . Icterus , abdominal pain , hepatomegaly , muscle pain and tenderness , headache , vomiting , breathlessness , splenomegaly , subconjunctival effusion , oliguria and altered sensorium were the common manifestations of the disease . Meningism , lymphadenopathy , arthralgia were seen in fewer cases ( Table 4 ) . The most frequently observed alterations in laboratory parameters in these patients included leukocytosis , anemia , thrombocytopenia , elevated hepatic enzymes [alanine aminotransferase ( ALT ) and asparate aminotransferase ( AST ) ] in the range of 100 to 200 IU/dl , elevated serum bilirubin levels in the range of 2–8 mg/dl , thrombocytopenia , increased prothrombin time , and D-dimer positivity ( Table 5 ) . Laboratory parameters suggestive of DIC ( derangement of any three of prothrombin time , activated partial thromboplastin time , D-dimer , thrombin time , fibrinogen level , fibrinogen degradation products , and fragmented RBCs on a peripheral blood film+a low platelet count ) were present in ten patients . Modified Faine's criteria could diagnose 76 cases ( 88 . 3% ) . By far the commonest complication was renal failure ( serum creatinine >1 . 4 mg/dl ) . Other common complications observed among these patients included respiratory failure requiring mechanical ventilation , neuroleptospirosis , ascitis and pleural effusion . Laboratory confirmation of disseminated intravascular coagulation ( DIC ) could be noted in 10 cases; however , obvious external bleeding , petechiae and echymoses were seen in 19 cases ( Table 6 ) . Majority of complications occurred in the second week of illness or later , however 18 cases of renal impairment ( 34 . 5% of 52 ) , three cases of respiratory failure ( 16 . 7% of 18 ) and one case of neuroleptospirosis ( 10% of 10 ) occurred in the first week of illness . Three cases of mixed infection with P . vivax , 2 with P . falciparum , one each with dengue and hepatitis A virus were also observed . No specific clinical feature or complication correlated with the geographic area of incidence of the disease . Treatment with once a day ceftriaxone therapy was given to 66 cases , doxycycline therapy alone to 3 patients; and combined doxycycline and ceftriaxone therapy to 17 patients . All these patients had documented hospital acquired septicemia or were suspected of having such superinfection . High dose corticosteroid therapy was instituted in 7 cases , all of them with respiratory failure . Six of them survived . Of all 86 cases , 5 patients died ( 5 . 9% ) . Pathological autopsy was done in one of these cases . Leptospires were demonstrated in post-mortem kidney ( Figure 3 ) and lung specimens of the patient using the Warthin-Starry stain . By the microscopic agglutination test the following serovars gave highest titres ( >1∶400 ) against patients' sera: Pomona , Ballum , Gryppotyphosa , and Autumnalis .
The increase in leptospirosis cases during the last few years is possibly the result of greater awareness of this disease in the north and more drier parts of the country . WHO estimates the incidence of leptospirosis between 0 . 1–1 cases/100000 population/year in temperate , non-endemic areas and between 10–100 cases/100000 population/year in humid , tropical , endemic areas . Though north India receives less rainfall compared to the coastal regions and the south , most areas still receive ≥100 cm rainfall in the monsoon season between July and October . Flooding and unseasonal heavy precipitation are not uncommon like the August floods in Punjab , Bihar and Himachal Pradesh in 2008 . A large proportion of the population depends on the agrarian way of life . Intimate contact with animals , unprotected entry into waterlogged fields , and bathing in contaminated community ponds are a part of rural life in across north India [2] . These are precisely the conditions most suitable for the survival and transmission of Leptospira [2] . Thus the stupendous rise in the number cases seen in this study should not come as a surprise . Also , previous reports from Chandigarh , Ludhiana , New Delhi , and Uttar Pradesh point to the fact that leptospirosis is present all over India [8] , [9] , [10] , [11] , [12] . Reports from Italy , Bulgaria and certain centers in south India point at a decreasing incidence of leptospirosis , this is however , not true at our center [15] . Like our study , a study conducted in Chennai ( south India ) too has seen a rapid increase in leptospirosis cases between 2004 and 2006 [3] . We used the Pan Bio IgM ELISA to screen for leptospirosis in well timed acute and convalescent blood samples , and performed MAT on few samples . It is unlikely that a large number of leptospirosis cases were missed since sensitivity of PanBio IgM ELISA as used in this study has been reprted to be as high as 76–90% [16] , [17] . Though dark ground microscopy and culture in EMJH media may be performed from blood and urine samples during the acute phase of the disease , these have relatively poor sensitivity in detection of the disease [1] . In India , MAT is performed only in the reference laboratory at Andaman . Most laboratories hence prefer IgM ELISA formats for the diagnosis of leptospirosis [1] . Further this test is reactive even in early cases of leptospirosis when MAT may be negative [1] . The modified Faine's criteria could diagnose leptospirosis in 76 patients . MAT being unavailable in our center , the original Faine's criteria is not an option for our physicians . Instead the modified Faine's criterion is a useful guide . Sivakumar S et al . , 2004 modified the original Faine's criteria to include local factor like rainfall , and newer investigations like IgM ELISA and Slide Agglutination test ( SAT ) [13] . No modifications were however made to the clinical criteria . Rainfall has been added because of the observation that most cases of leptospirosis are reported in monsoon and post-monsoon period . Compared to MAT , IgM ELISA and SAT are simpler and more sensitive tests that can be used to diagnose acute leptospira infections including milder forms which are associated with low clinical scores [13] , [18] . The differential diagnosis of leptospirosis is very long , and this disease easily confuses with other viral , parasitic and bacterial infections . We suggest that the modified criterion be used by physicians in this regard . Farmers and farming labourers ( 32 . 6% and 11 . 6% in our study ) are the ones most commonly infected in the rural setting and the disease is associated with sowing and harvesting seasons and meteorological phenomena like monsoons . Presence of farming animals , and rodents , some of them leptospiral carriers , in the farmland , wet and humid environmental conditions for Leptospira survival , and frequent human agricultural and animal rearing activity form the core determinants of Leptospira transmission [2] . That leptospirosis occurs in those living in unhygienic conditions was evident when 34 . 7% of the patients lived in mud and part-mud houses and 12 . 8% gave history of entering waterlogged areas barefoot . Intermittent fever with chills and rigor was the most common manifestation; however continuous fever and lack of chills and rigor were also seen in a few patients . Though the incidence of icteric and severe disease with renal failure has decreased in certain centers of south India [15] , the present study found several cases manifesting with severe icteric disease and renal failure . Prabhakar MR et al . point out that epidemiological and clinical pattern of infectious disease change in course of time and leptospirosis is no exception to this rule [15] . We further argue that the pattern may vary from region to region . Knowledge of such changing epidemiological and clinical profile of leptospirosis is essential for successful prevention , early diagnosis and treatment [15] . Typically a biphasic illness , complications ensue in the second immune phase of the disease [1] , [19] . Renal failure is the commonest complication noted , both in anicteric and icteric leptospirosis [1] . Azotemia , oliguria and anuria commonly occur during the second week of illness , but may appear as early as 3 to 4 days after onset [1] . Cough , pleural effusion , respiratory failure , and hemorrhagic pneumonia were commonly observed in our study . Respiratory symptoms are known to occur commonly in severe leptospirosis [19] , [20] , [21] . However , when respiratory complications are predominant , chances of misdiagnosis as community acquired pneumonia increase . Pulmonary complications have especially been noted to occur early and more frequently in the Andamans , and has been associated with higher mortality [21] . The higher frequency of respiratory presentation is in contrast to our earlier studies [8] , [9] . Many patients with severe respiratory problems progress to develop multiple organ dysfunction syndrome and are admitted in intensive care [20] resulting in further complications from hospital acquired infections . Majority of our patients with a diagnosis of neuroleptospirosis presented with an encephalitic syndrome , with altered sensorium , and headache . A minority also had signs of meningism , and experienced generalized seizures . This finding is similar to the case series reported by Mathew P et al . , 2006 [22] . Neurological manifestations were seen in 10%–15% of leptospirosis patients [22] . Such manifestations are varied and often lead to misdiagnosis , unless strongly suspected . Most frequent manifestations include altered sensorium and neck stiffness [22] . More importantly leptospirosis is responsible for 5%–13% of all cases of aseptic meningitis [23] . Generalised tonic-clonic seizures with altered sensorium , encountered in 3 patients in our study , are a manifestation of the encephalitis [23] . Less common manifestations include hemiplegia [22] , [23] , intracranial bleed [23] , cerebellitis [23] , movement disorder [23] , myelitis [23] , acute flaccid paralysis including Guilain Bare syndrome [23] , mononeuritis [23] , facial palsy [22] , [23] , and neuralgias [23] . CT scan may be normal [22] , however diffuse cerebral edema may be seen in a minority of patients . An early and specific diagnosis is mandatory as effective and specific therapy is available . Mathew P et al . , 2006 observe that neuroleptospirosis should be considered in the differential diagnosis of neuroinfections associated with hepatorenal dysfunction in endemic areas [22] . Thrombocytopenia is especially common , while minority of patients also present with prolonged prothrombin time due to hypoprothrombinemia [1] , [23] . Activation of the coagulation system is an important feature of leptospirosis [23] . Direct or indirect signs of the DIC and intravascular platelet aggregation are characteristic of malignant forms of leptospirosis [23] . Concentrations of fibrinogen , D-dimer , thrombin-antithrombin III complexes , and prothrombin fragment 1 , 2 are significantly elevated in leptospirosis patients [23] . In our study , patients with leptospirosis had significantly longer prothrombin times ( 9 . 3% ) , were D-Dimer positive ( 7% ) , and had lower platelet counts ( 18 . 3% ) . In all 10 patients could be categorized as having DIC . Mild leukocytosis with a shift to the left is also commonly observed in leptospirosis [1] , [23] . 61 . 6% of our cases had an increased leucocyte count . These severe and unusual manifestations are often not recognized as leptospirosis , and other infectious etiology like viruses looked for . Such is especially common in areas wrongly thought to be non-endemic . This leads to delay in appropriate therapy and further progression of the disease . Further , renal complications can ensue even in the first week of illness [24] , although they occur more often in the second or third week of illness as observed in our study . Including leptospirosis in the differential diagnosis and instituting early empirical therapy could reduce inadvertent deaths . Treatment with high dose corticosteroids in cases with severe complications , where the immune phase of disease has begun , is controversial [25] . Only 7 of our cases were put on high dose systemic corticosteroids . Further study on the role of systemic steroids in this disease is warranted . We also describe cases with unusually high ALT and AST levels ( >200 IU/ml , 2 cases ) and very high bilirubin levels ( >8 mg/dl , 2 cases ) . Chronic alcoholism ( 32 . 7% in our study ) may predispose to additional liver damage and symptomatic disease [2] . Coinfection with malaria , dengue and other viruses may present diagnostic dilemmas to the treating physician . Inevitably such cases have severe manifestations and result in high morbidity and mortality . Once a day ceftriaxone therapy has been documented to have equal efficacy to penicillin therapy , and seemed to be the preferred therapeutic regimen in our center . The mortality in treated cases was high ( 5 . 9% ) , however it was comparable to that seen by Jayakumar M et al . ( 9 . 5% ) [26] . Severe disease and mortality from Leptospira infection is not always due to the bacteria itself , but usually due to the destructive activity of the immune system of the host [1] , [23] , [25] . Before death , irreversible multiple organ dysfunction syndrome usually sets in , although leptospiraemia has ceased .
A significant rise in the incidence of leptospirosis in north India was documented . Clinical manifestations and laboratory abnormalities were protean; severe complicated disease with renal or respiratory failure , neuroleptospirosis , and DIC was also observed . The increased awareness among physicians of protean clinical manifestations of leptospirosis and early laboratory diagnosis will help reduce morbidity and mortality associated with disease . | Leptospirosis is often not suspected by physicians in patients with acute febrile illnesses reporting from supposedly “non-endemic areas , ” including north India . Clinical manifestations are protean , and complications can affect most organ systems , including liver , kidneys , lungs , and the central nervous system . Timely diagnosis and specific therapy can reduce severity of illness and , in turn , mortality . In this study conducted at a tertiary care center in north India , we find how a much-neglected disease entity has emerged as a major cause of acute febrile illness in a so called “non-endemic area . ” Incidence is increasing yearly . The majority of patients were from a rural background , and were farmers or farm labourers . Poor hygiene , contact with animals , rat infestation of houses , and contact with stagnant dirty water are the major determinants of disease . Apart from the usual symptoms of intermittent fever with chill and rigor , hepatosplenomegaly , renal decompensation , muscle pain and tenderness , and conjunctival suffusion , signs and symptoms indicating involvement of the respiratory and central nervous systems were also commonly observed . Severe complications resulting in mortality do occur and is especially due to late suspicion among primary level physicians , and the resulting inappropriate therapy . | [
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] | 2010 | Increasing Trends of Leptospirosis in Northern India: A Clinico-Epidemiological Study |
Long noncoding RNAs ( lncRNAs ) are emerging as new players in gene regulation , but whether lncRNAs operate in the processing of miRNA primary transcript is unclear . Also , whether lncRNAs are involved in the regulation of the mitochondrial network remains to be elucidated . Here , we report that a long noncoding RNA , named mitochondrial dynamic related lncRNA ( MDRL ) , affects the processing of miR-484 primary transcript in nucleus and regulates the mitochondrial network by targeting miR-361 and miR-484 . The results showed that miR-361 that predominantly located in nucleus can directly bind to primary transcript of miR-484 ( pri-miR-484 ) and prevent its processing by Drosha into pre-miR-484 . miR-361 is able to regulate mitochondrial fission and apoptosis by regulating miR-484 levels . In exploring the underlying molecular mechanism by which miR-361 is regulated , we identified MDRL and demonstrated that it could directly bind to miR-361 and downregulate its expression levels , which promotes the processing of pri-miR-484 . MDRL inhibits mitochondrial fission and apoptosis by downregulating miR-361 , which in turn relieves inhibition of miR-484 processing by miR-361 . Our present study reveals a novel regulating model of mitochondrial fission program which is composed of MDRL , miR-361 and miR-484 . Our work not only expands the function of the lncRNA pathway in gene regulation but also establishes a new mechanism for controlling miRNA expression .
Long non-coding RNAs ( lncRNAs ) are non-protein coding transcripts longer than 200 nucleotides . A number of lncRNAs have been shown to be involved in a wide range of biological functions including RNA processing [1] , gene transcription regulation [2] , miRNAs' host genes [3] , modulation of apoptosis and invasion [4] , marker of cell fate [5] , chromatin modification [6] , etc . Also , misregulation of lncRNAs has been observed in human diseases such as cancer and neurological disorders . In addition , lncRNAs not only can act as antisense transcripts or as decoy for splicing factors leading to splicing malfunctioning [7] , [8] , but also can act as a competing endogenous RNA ( ceRNA ) in mouse and human myoblasts [9] . However , it is not yet clear whether lncRNA can be involved in the processing of pri-miRNA and the regulation of mitochondrial network . MicroRNAs ( miRNAs ) act as negative regulators of gene expression by inhibiting the translation or promoting the degradation of target mRNAs . Growing evidence has demonstrated that miRNAs can play a significant role in the regulation of development , differentiation , proliferation and apoptosis . Recent studies have identified the functional role of miRNA in numerous facets of cardiac biology , including the control of myocyte growth , contractility , fibrosis , angiogenesis , heart failure , and myocardial infarction , providing potential therapeutic targets for heart disease . To prevent and reverse myocardial infarction , it is critical to identify those miRNAs that are able to regulate myocardial infarction and to characterize their signal transduction pathways in the apoptotic cascades . Mature miRNAs execute their functions mainly in the cytoplasm . Some studies have observed that there exist mature miRNAs in the nucleus [10] , [11] . Their work demonstrated that additional sequence elements of specific miRNAs can control their posttranscriptional behavior , including the subcellular localization . Recent work reported that the miRNA pathway targets non-coding RNAs across species . It showed that let-7 could regulate its biogenesis autonomously through a conserved complementary site in its own primary transcript , creating a positive-feedback loop [11] . However , the molecular mechanism of miRNAs and their regulation model in the nucleus remains to be fully elucidated . Mitochondria are highly dynamic organelles that constantly undergo fusion and fission to form a network that spans the entire area of the cell . Mitochondrial fission and fusion are crucial for maintaining mitochondrial function and are necessary for the maintenance of organelle fidelity [12] . The disruption of mitochondrial fission and fusion has been linked to the development and progression of some diseases [13]–[17] . Most recent studies have revealed that abnormal mitochondrial fusion and fission participate in the regulation of apoptosis . Mitochondrial fusion is able to inhibit apoptosis , while mitochondrial fission is necessary for initiation of apoptosis [12] , [18]–[23] . Thus , exploring the function of mitochondrial fission and fusion regulators will unveil their roles in various pathway and diseases . Our present work revealed that nuclear miR-361 can directly bind to pri-miR-484 and inhibiting its processing into pre-miR-484 which is mediated by Drosha . miR-361 participates in the regulation of mitochondrial network and apoptosis through the miR-484 pathway . Moreover , our study further suggested that a long noncoding RNA , MDRL , can directly bind to miR-361 and act as an endogenous “sponge” which downregulates miR-361 expression levels and promotes the processing of pri-miR-484 . In short , MDRL regulates mitochondrial network and apoptosis through the miR-361 and miR-484 . Our data reveal a novel role for lncRNA and miRNA in promoting biogenesis of other miRNA primary transcript , expanding the functions of the lncRNA and miRNA in gene regulation and mitochondrial network .
Many studies have observed that there exist mature miRNAs in the nucleus [12] , [18]–[23] and recent work also reports that let-7 miRNA in the nucleus can regulate its own primary transcript through a conserved complementary site , thus creating a positive-feedback loop [11] . Our previous work has showed that transcription factor could regulate miR-484 expression [24] . To further explore other underlying mechanism responsible for miR-484 regulation under anoxia/reoxygenation ( A/R ) condition , we tested whether miRNA in the nucleus participates in the regulation of miR-484 expression . To understand which nuclear miRNA is involved in the apoptosis pathway of A/R , we performed a microarray to detect nuclear miRNAs in response to A/R treatment ( Figure 1A , Figure 1B and Table S1 ) and among these miRNAs induced by A/R , only knockdown of endogenous miR-361 ( Figure S1A ) induced an increase in the miR-484 expression levels ( Figure 1C ) . A further study confirmed that miR-361 was predominantly located in the nucleus , and miR-484 was predominantly located in the cytoplasm ( Figure 1D ) . We test whether nuclear miR-361 may directly affect the expression of miR-484 . The results showed that enforced expression of miR-361 could reduce mature miR-484 levels ( Figure 1E ) . Furthermore , miR-361 transgenic mice ( Figures S1B and S1C ) demonstrated reduced levels of miR-484 in the animal model ( Figure S1D and Figure 1F ) . Taken together , it appears that miR-361 is predominantly located in the nucleus and is able to regulate mature miR-484 levels in the cellular and the animal model . To understand the mechanism by which nuclear-located miR-361 regulates the levels of cytoplasmic mature miR-484 , we tested whether miR-361 is able to affect the levels of pri-miR-484 located in nucleus . We compared the sequences of miR-361 with that of pri-miR-484 using the bioinformatics program RNAhybrid and noticed that miR-361 is complementary to pri-miR-484 ( Figure 2A ) . Their complementary sequences led us to consider whether miR-361 can directly interact with pri-miR-484 and inhibit its processing into pre-miR-484 in the nucleus . We demonstrated that enforced expression of miR-361 resulted in the strong accumulation of pri-miR-484 ( Figure 2B ) and the reduction of pre-miR-484 ( Figure 2C ) . Knockdown of miR-361 resulted in the reduction of pri-miR-484 ( Figure 2D ) and the increase of pre-miR-484 ( Figure 2E ) . Thus , it appears that miR-361 prevents the processing of pri-miR-484 into pre-miR-484 in nucleus . Mature miRNAs are generated via a two-step processing by Drosha and Dicer . The initial processing that occurs in the nucleus is catalyzed by Drosha . The Drosha complex cleaves pri-miRNA into pre-miRNA . To further verify whether miR-361 prevents the processing of Drosha . We applied Drosha to assay the levels of pri-miR-484 and pre-miR-484 . Our data showed that enforced expression of miR-361 prevented the reduction of pri-miR-484 and inhibited the increase of pre-miR-484 induced by Drosha , and knockdown of miR-361 had an opposite effects ( Figures 2F and 2G ) . These data suggest that miR-361 prevents the processing of pri-miR-484 by Drosha into pre-miR-484 in nucleus . To further test whether the miR-361 recognition element on pri-miR-484 was responsible for miR-361 binding and inhibition of processing , we applied a biotin-avidin pull-down system to assess the direct binding of miR-361 to pri-miR-484 . The cardiomyocytes were transfected with biotinylated miR-361 , biotinylated mutant miR-361 and biotinylated negative control ( NC ) ( Figure S1E ) . Then the cells were harvested for biotin-based pull-down assay . Pri-miR-484 was co-precipitated , and the levels of pri-miR-484 in the pull-down complexes was analyzed by the qRT-PCR ( Figure 2H ) . As shown in Figure 2H , pri-miR-484 was significantly enriched in the miR-361 pull-down products as compared to the biotinylated mutant miR-361 group and negative control group , indicating that miR-361 can directly bind to pri-miR-484 in vivo . We also employed inverse pull-down assay to test whether pri-miR-484 could pull down miR-361 , a biotin-labeled-specific pri-miR-484 probe was used . The results showed that pri-miR-484 ( Figure S1F ) and miR-361 could be co-precipitated ( Figure 2I ) . Taken together , it appears that miR-361 is able to directly bind to pri-miR-484 and prevent the processing of pri-miR-484 by Drosha into pre-miR-484 . Our previous findings found that miR-484 could inhibit mitochondrial fission and apoptosis in cardiomyocytes [24] . The present result appears that miR-361 can interact with pri-miR-484 and regulate mature miR-484 levels . We thus explored the functional role of miR-361 in mitochondrial fission and apoptosis . To this end , the antagomir of miR-361 was employed to knock down endogenous miR-361 . Mitochondrial fission induced by A/R was attenuated by the knockdown of miR-361 ( Figure 3A ) . Concomitantly , apoptosis was reduced in the presence of miR-361 antagomir ( Figure 3B ) . These data indicate that miR-361 can promote mitochondrial fission and apoptosis upon A/R treatment . To understand the pathophysiological role of miR-361 , we detected whether miR-361 is involved in the pathogenesis of myocardial infarction in the animal model . miR-361 was elevated in response to ischemia/reperfusion ( Figure S2A ) . Knockdown of miR-361 resulted in a reduction in mitochondrial fission ( Figure 3C , upper panel ) and apoptosis ( Figure 3C , low panel and right panel ) . We produced miR-361 transgenic mice , and these mice exhibited increased mitochondrial fission , apoptosis ( Figure 3D ) , myocardial infarction sizes ( Figure 3E ) and potentiated cardiac dysfunction ( Figure S2B ) in response to ischemia/reperfusion ( I/R ) . Taken together , it appears that miR-361 is able to promote mitochondrial fission , apoptosis and myocardial infarction . How does miR-361 exert its effect on the mitochondrial network ? Because miR-361 is able to reduce mature miR-484 expression as shown in Figure 1 , we thus tested whether miR-484 is a mediator of miR-361 . To confirm the relationship between miR-361 and miR-484 in mitochondrial fission machinery , we used antagomir to inhibit miR-484 levels , and observed that the inhibitory effect of miR-361 knockdown on mitochondrial fission and apoptosis was attenuated in the presence of miR-484 antagomir ( Figure S3A and Figure 3F ) . Taken together , these data suggest that miR-361 targets miR-484 in the cascades of mitochondrial fission and apoptosis . Recent studies have suggested that lncRNAs may act as endogenous sponge RNA to interact with miRNAs and influence the expression of miRNA [9] , [25]–[27] . To explore the underlying mechanism responsible for miR-361 upregulation in response to A/R treatment , we tested whether lncRNA could regulate miR-361 expression . We carried out qRT-PCR to detect lncRNAs levels in response to A/R treatment . LncRNAs were chosen from the lncRNA array published online by Fantom company . Among 100 lncRNAs , AK009271 which we named mitochondrial dynamic related lncRNA ( MDRL ) , was substantially reduced ( Figure 4A ) . The MDRL is 1039 nt in length and the subcellular location showed that MDRL was expressed both in nucleus and cytoplasm ( Figure S3B ) . Further , our results showed that miR-361 levels were elevated in the cells upon knockdown of endogenous MDRL ( Figures 4B and 4C ) . To know whether MDRL can affect miR-361 activity , we constructed miR-361 sensor ( with a perfect miR-361 binding site ) . The lucifease activity of miR-361 sensor was decreased in cells treated with MDRL siRNA ( Figure 4D ) , suggesting the induction of miR-361 activity . Enforced expression of MDRL induced a reduction in miR-361 expression ( Figure 4E ) and activity ( Figure 4F ) . To further test whether MDRL may act as a miR-361 sponge , we transfected the miR-361 sensor luciferase reporter , along with adenoviral miR-361 , MDRL or β-gal . The luciferase activity showed that MDRL counteracted the effect of miR-361 ( Figure 4G ) , suggesting that MDRL is a functional sponge for miR-361 . Taken together , these data suggest that MDRL is able to regulate miR-361 levels and activity . To understand the mechanism by which MDRL regulates the levels of miR-361 , we tested whether MDRL can interact with miR-361 . We compared the sequences of MDRL with that of miR-361 using the bioinformatics program RNAhybrid and noticed that MDRL contains a target site of miR-361 ( Figure 5A ) . The wild type luciferase construct of MDRL ( Luc-MDRL-wt ) and a mutated form ( Luc-MDRL-mut ) were produced by inserting the sequence of putative miR-361 binding site into the report constructs ( Figure 5B , upper panel ) . Luciferase assay revealed that miR-361 could suppress the luciferase activity of MDRL , but it had less effect on the mutated form of MDRL compared to the wild type ( Figure 5B ) . Our results further showed that the mutated form of MDRL had no effect on miR-361 activity ( Figure S3C ) and it also lost the ability to counteract miR-361 ( Figure S3D ) . These results revealed that MDRL may interact with miR-361 by this putative binding site . Further , we applied a biotin-avidin pull-down system to test if miR-361 could pull down MDRL . Cardiomyocytes were transfected with biotinylated wild type miR-361 , biotinylated mutant miR-361 and biotinylated miR-NC ( Figure S4A ) . We found these transfections did not change MDRL levels ( Figure S4B ) . Then , Cardiomyocytes were harvested for biotin-based pull-down assay . MDRL was pulled down by wild type miR-361 as analyzed by qRT-PCR , but the introduction of mutations that disrupt base pairing between MDRL and miR-361 ( Figure 5C ) led to the inability of miR-361 to pull down MDRL ( Figure 5D ) , indicating that the recognition of miR-361 to MDRL is in a sequence-specific manner . We also employed inverse pull-down assay to test if MDRL could pull down miR-361 , a biotin-labeled-specific MDRL probe was used . The results showed that MDRL ( Figure S4C ) and miR-361 ( Figure 5E ) could be co-precipitated . Taken together , it appears that MDRL is able to directly bind to miR-361 in vivo . We further tested whether MDRL is able to regulate miR-484 expression and influence its processing . Our results showed that enforced expression of MDRL ( Figure S4D ) resulted in the decrease of pri-miR-484 ( Figure 5F ) and the accumulation of pre-miR-484 ( Figure 5G ) . Knockdown of MDRL induced the increase of pri-miR-484 ( Figure S4E ) and the decrease of pre-miR-484 ( Figure S4F ) . Knockdown of MDRL inhibited the decrease of pri-miR-484 ( Figure 5H ) and the increase of pre-miR-484 ( Figure S4G ) induced by Drosha , and the inhibitory effect of MDRL knockdown was reduced in the presence of miR-361 antagomir ( Figure 5H and Figure S4G ) . Thus , it appears that MDRL promotes the processing of pri-miR-484 into pre-miR-484 through targeting miR-361 . Our present results have demonstrated that MDRL could promote the processing of pri-miR-484 by Drosha . Thus , we tested whether MDRL is able to regulate mature miR-484 levels . Knockdown of MDRL reduced miR-484 levels ( Figure 6A ) , while overexpression of MDRL resulted in up-regulation of miR-484 expression ( Figure 6B ) . And MDRL counteracted the effect of miR-361 on miR-484 expression ( Figure 6C ) . Our previous report has demonstrated that Fis1 is a downstream target of miR-484 . The current data showed that MDRL could regulate Fis1 expression by miR-484 ( Figure S5A ) . These results indicated that MDRL may act as endogenous sponge “antagomir” of miR-361 to regulate the processing of pri-miR-484 and expression of miR-484 . To explore the functional role of MDRL , we tested that enforced expression of MDRL inhibited mitochondrial fission ( Figure 6D ) and apoptosis ( Figure 6E and Figure S5B ) induced by A/R . We also demonstrated that knockdown of MDRL induced mitochondrial fission ( Figure S5C ) and apoptosis ( Figure S5D ) . In the animal model , administration of MDRL attenuated mitochondrial fission , cell death ( Figure 6F ) and myocardial infarction sizes ( Figure 6G ) in response to ischemia/reperfusion ( I/R ) . Administration of MDRL also ameliorated cardiac function ( Figure S5E ) . Taken together , it appears that MDRL is able to prevent mitochondrial fission , apoptosis and myocardial infarction . Since MDRL is able to elevate miR-484 expression as shown in Figure 6B , we thus tested whether miR-484 is a mediator of MDRL in the mitochondrial network . To confirm the relationship between MDRL and miR-484 in mitochondrial fission machinery , we used miR-484 antagomir , and observed that the inhibitory effect of MDRL on mitochondrial fission and apoptosis was decreased in the presence of miR-484 antagomir ( Figure 6H ) . Taken together , these data suggest that MDRL targets miR-361/miR-484 in the cascades of mitochondrial fission and apoptosis ( Figure S6 ) .
Our present work revealed that miR-361 directly binds to pri-miR-484 and prevents its processing by Drosha into pre-miR-484 . miR-361 is able to regulate mitochondrial fission and apoptosis , and this regulatory effects on mitochondrial fission and apoptosis is through targeting miR-484 . Our study further revealed that MDRL directly binds to miR-361 and acts as its “sponge” , promoting the processing of pri-miR-484 . And MDRL can inhibit mitochondrial fission and apoptosis though targeting miR-361 and miR-484 . Our results provide novel evidence demonstrating that MDRL , miR-361 , miR-484 constitute an axis in the machinery of mitochondrial network . LncRNAs have been defined to have important functions in specific cell types , tissues and developmental conditions such as chromatin modification [6] , RNA processing [1] , structural scaffolds [28] and modulation of apoptosis and invasion , etc [4] . Despite the biological importance of lncRNAs , it is not yet clear whether lncRNAs is involved in the processing of primary transcript and the regulation of mitochondrial network . Our present work for the first time reveals a novel function of lncRNA participating in regulating the processing of miR-484 primary transcript and mitochondrial dynamics . Our results may provide a new clue for the understanding of lncRNAs-controlled cellular events . It has been shown that lncRNAs may act as endogenous sponge RNAs to interact with miRNAs and influence the expression of miRNA target genes . A recent report shows that the H19 lncRNA can act as a molecular sponge for the major let-7 family of miRNAs [27] . Other report demonstrates that a muscle-specific long non-coding RNA , linc-MD1 , governs the time of muscle differentiation by acting as a competing endogenous RNA ( ceRNA ) in mouse and human myoblasts [9] . Highly up-regulated liver cancer ( HULC ) may act as an endogenous ‘sponge’ , which down-regulates miR-372 leading to reducing translational repression of its target gene , PRKACB [26] . Transient knockdown and ectopic expression of HSUR 1 direct degradation of mature miR-27 in a sequence-specific and binding-dependent manner [25] . Our present study reveals that lncRNA ( MDRL ) sponges miR-361 and promoting the processing of pri-miR-484 , which inhibits mitochondrial fission and apoptosis . The discovery of a long non-coding RNA in miRNA primary processing and mitochondrial dynamics may shed new lights on understanding the complex molecular mechanism of mitochondrial network . Many research works reveal that mature miRNAs execute their functions mainly in the cytoplasm . Recently , it has been reported that miRNA also functions in nucleus [12] , [18]–[23] , but the function of nuclear miRNA remains to be fully unveiled . Mature miRNAs are generated via a two-step processing by Drosha and Dicer . The initial processing that occurs in the nucleus is catalyzed by Drosha . The Drosha complex cleaves pri-miRNA into pre-miRNA . The precise processing is pivotal to ensure the production of mature miRNA . This present work reveals that miR-361 in the nucleus can directly bind to the pri-miR-484 and prevent its processing by Drosha into pre-miR-484 , and then further inhibit the biological function of miR-484 . This finding may provide a new clue for the understanding of miRNAs-controlled gene expression . Emerging data suggest that changes in mitochondrial morphology may be relevant to various aspects of cardiovascular biology including cardiac development , heart failure , diabetes mellitus , and apoptosis . The heart function stringently depends on the ATP-generating pathways [29] , and cardiomyocytes are a good model to study mitochondrial dynamics because of the abundant existence of mitochondria . So far , it remains unclear whether lncRNA is involved in the regulation of mitochondrial dynamics . Our present work indicated that lncRNA ( MDRL ) can inhibit mitochondrial fission and apoptosis through regulating miR-361 and miR-484 . The involvement of MDRL , miR-361 and miR-484 in regulating mitochondrial networks shed new lights on the understanding of mitochondrial integrity and cardiac pathophysiology . In summary , our present study reveals that miR-361 located in nucleus can directly bind to pri-miR-484 and prevent its processing by Drosha into pre-miR-484 . miR-361 reduces mature miR-484 levels and affects mitochondrial apoptotic pathway through targeting miR-484 . Moreover , we demonstrated that MDRL acts as endogenous sponge RNA and inhibits miR-361 expression . MDRL is able to inhibit mitochondrial fission and apoptosis through targeting miR-361 and miR-484 . Thus , modulation of MDRL and miR-361 may represent novel approaches for interventional treatment of cardiac disease . This finding may provide a new clue for the understanding of lncRNAs and miRNAs-controlled cellular events .
We declare that all experiments were performed according to the protocols approved by the Animal Care Committee , Institute of Zoology , Chinese Academy of Sciences , China . Neonatal mouse cardiomyocytes were isolated and prepared as we described [30] . In brief , after dissection the hearts were washed , minced in HEPES-buffered saline solution containing 130 mM NaCl , 3 mM KCl , 1 mM NaH2PO4 , 4 mM glucose and 20 mM HEPES ( pH adjusted to 7 . 35 with NaOH ) . Tissues were then dispersed in a series of incubations at 37°C in HEPES-buffered saline solution containing 1 . 2 mg/ml pancreatin and 0 . 14 mg/ml collagenase ( Worthington ) . After centrifugation the cells were re-suspended in Dulbecco's modified Eagle medium/F-12 ( GIBCO ) containing 5% heat-inactivated horse serum , 0 . 1 mM ascorbate , insulin-transferring-sodium selenite media supplement , 100 U/ml penicillin , 100 µg/ml streptomycin , and 0 . 1 mM bromodeoxyuridine . The dissociated cells were pre-plated at 37°C for 1 h . The cells were then diluted to 1×106 cells/ml and plated in 10 µg/ml laminin-coated different culture dishes according to the specific experimental requirements . Anoxia/reoxygenation was performed as follows . Briefly , cells were placed in an anoxic chamber with a water-saturated atmosphere composed of 5% CO2 and 95% N2 . Cells were subjected to 6 hours of anoxia followed by 12 hours of reoxygenation ( 95% O2 and 5% CO2 ) . For creating miR-361 transgenic mice , a DNA fragment containing murine miR-361 was cloned to the vector , pαMHC-clone26 ( kindly provided by Dr . Zhong Zhou Yang ) , under the control of the α-myosin heavy chain ( α-MHC ) promoter . The primers used to generate miR-361 transgenic mice include , forward primer: 5′-AGAATGAGGCTAACAGGTGAGTCATC-3′; reverse primer: 5′-TGACTGGCAGACACTGGTTTCAGGTGTTAC-3′ . Microinjection was performed following standard protocols . Mitochondrial staining was carried as we and others described with modifications [19] , [26] . Briefly , cells were plated onto the cover-slips coated with 0 . 01% poly-L-lysine . After treatment they were stained for 30 min with 0 . 02 µM MitoTracker Green ( Molecular Probes ) . Mitochondria were imaged using a laser scanning confocal microscope ( Zeiss LSM510 META ) . Data are expressed as the mean ± SEM of at least three independent experiments . We evaluated the data with Student's t test . We used a one-way analysis of variance for multiple comparisons . A value of p<0 . 05 was considered significant . | Long non-coding RNAs ( lncRNAs ) have been shown to be involved in a wide range of biological functions . However , studies linking individual lncRNA to the mitochondrial fission program remain scarce . Also , it remains unknown whether lncRNAs can operate in the processing of miRNA primary transcript . Here , we provide causal evidence for the involvement of the lncRNA MDRL in the mitochondrial dynamics and the processing of miR-484 primary transcript in cardiomyocyte . We identified MDRL which can act as an endogenous ‘sponge’ that directly binds to miR-361 and downregulates its expression levels . miR-361 can directly bind to primary transcript of miR-484 and prevent its processing by Drosha into pre-miR-484 . MDRL inhibits mitochondrial fission and apoptosis by miR-361 and miR-484 . Our present study reveals a novel regulating model which is composed of MDRL , miR-361 and miR-484 . Modulation of their levels may provide a new approach for tackling myocardial infarction . | [
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"... | 2014 | MDRL lncRNA Regulates the Processing of miR-484 Primary Transcript by Targeting miR-361 |
Cytoplasmic virus like elements ( VLEs ) from Kluyveromyces lactis ( Kl ) , Pichia acaciae ( Pa ) and Debaryomyces robertsiae ( Dr ) are extremely A/T-rich ( >75% ) and encode toxic anticodon nucleases ( ACNases ) along with specific immunity proteins . Here we show that nuclear , not cytoplasmic expression of either immunity gene ( PaORF4 , KlORF3 or DrORF5 ) results in transcript fragmentation and is insufficient to establish immunity to the cognate ACNase . Since rapid amplification of 3' ends ( RACE ) as well as linker ligation of immunity transcripts expressed in the nucleus revealed polyadenylation to occur along with fragmentation , ORF-internal poly ( A ) site cleavage due to the high A/T content is likely to prevent functional expression of the immunity genes . Consistently , lowering the A/T content of PaORF4 to 55% and KlORF3 to 46% by gene synthesis entirely prevented transcript cleavage and permitted functional nuclear expression leading to full immunity against the respective ACNase toxin . Consistent with a specific adaptation of the immunity proteins to the cognate ACNases , cross-immunity to non-cognate ACNases is neither conferred by PaOrf4 nor KlOrf3 . Thus , the high A/T content of cytoplasmic VLEs minimizes the potential of functional nuclear recruitment of VLE encoded genes , in particular those involved in autoselection of the VLEs via a toxin/antitoxin principle .
Pichia acaciae and Kluyveromyces lactis each contain two cytoplasmic virus-like elements ( VLEs , also known as linear plasmids ) ; i . e . pPac1-1 ( 12 . 6 kb ) , pPac1-2 ( 6 . 8 kb ) and pGKL2 ( 13 . 5 kb ) , pGKL1 ( 8 . 9 kb ) respectively [1 , 2] . The respective larger elements display substantial similarities to each other in terms of organization and gene content . They can exist without the smaller ones as they encode all proteins required for nucleus-independent cytoplasmic replication and maintenance [3] . The smaller VLEs pPac1-2 and pGKL1 , respectively , which depend on the larger ones in terms of cytoplasmic transcription and/or replication , encode for the production of killer toxin complexes , zymocin ( pGKL1 ) and PaT ( pPac1-2 ) [reviewed in 4] . One subunit in either zymocin or PaT is highly conserved; it carries chitin binding and chitinase domains that recognize cell wall associated chitin of target cells as primary toxin receptor for subsequent import and/or activation [5 , 6 , 7] . In both zymocin and PaT , a rather hydrophobic stretch or subunit appears to manage membrane transfer of the cytotoxic subunits , PaOrf2 ( encoded by pPac1-2 ORF2 ) and γ-toxin ( encoded by pGKL1 ORF4 ) . Although they hardly show any sequence similarity , they both act as anticodon nucleases ( ACNases ) . The recently solved crystal structure of PaOrf2 revealed a unique fold , which shows no similarity to any known ribonuclease [8] . PaOrf2 specifically attacks tRNAGln in vivo and additionally cleaves in vitro tRNAGlu and tRNALys or synthetic stem-loop RNA derived from the tRNAGln sequence [8 , 9] . γ-toxin cleaves the same tRNAs in vitro , but in vivo its preferred target is tRNAGlu [10 , 11] . While γ-toxin cleaves its target tRNA once at the 3`side of the wobble uridine , PaOrf2 apparently cleaves at the same position and additionally two nucleotides upstream , as judged from the appearance of two alternative cleavage products with full length tRNA from S . cerevisiae [9 , 10] . Since PaOrf2 but not γ-toxin evades a possible repair of the tRNA halves by cellular tRNA ligases , it was speculated that the presence of two cleavage sites might allow the excision of a di-nucleotide , rendering the target tRNA non-repairable [12 , 13 , 14 , 15] . VLE cured strains of P . acaciae and K . lactis are sensitive to their own respective toxins , proving that not only the killer phenotype but also the cognate immunity are encoded by the elements [1 , 2] . Indeed , PaT immunity is conferred by the only protein encoded by pPac1-2 ( ORF4 ) that lacks a signal peptide for secretion [16] and immunity against zymocin had been postulated to be encoded by KlORF3 of pGKL1 [17] . There is hardly any homology among PaORF4 and KlORF3 , and consistent with it , no cross-protection has been observed against zymocin or PaT [16] . PaT and zymocin are the most thoroughly studied VLE encoded ACNase killer toxins , but there are other systems in yeast [reviewed in 18] , such as PiT from Pichia inositovora , a ribonuclease inducing specific fragmentation of 25S and 18S rRNAs [19] , or DrT from Debaryomyces robertsiae , an ACNase resembling PaT and cleaving tRNAGln [20] . From an evolutionary point of view , toxin and immunity functions implemented in VLEs have to be considered as players of an autoselection system rather than providing advantages for the respective host [16 , 21] , although the latter , clearly benefits from the conferred killer phenotype . Intriguingly , PaORF4 encoding PaT immunity could be heterologously functionally expressed solely from VLEs in the cytoplasm [16] , i . e . when the gene was integrated into the pGKL-system of Kluyveromyces lactis . All efforts to express the immunity phenotype with PaORF4 on nuclear episomal and centromeric vectors failed to establish self-protection against the ACNase toxin . Here , we show that PaORF4 as well as the immunity genes from other VLEs ( KlORF3 and DrORF5 ) are nevertheless transcribed when the genes are governed by a yeast nuclear promoter in episomal vector backbones , but the mRNA becomes immediately fragmented thereby preventing translation that otherwise would yield a functional immunity protein . As exemplified for PaORF4 and KlORF3 , changing the primary structure from a rather high A/T bias to a much lower degree allowed for functional nuclear immunity expression , proving that the gene’s primary sequence information is sufficient to provide ACNase self-protection and that the native ORF context ensures autoselection of the VLE .
For the three known VLE encoded ACNase toxin complexes PaT , zymocin and DrT , immunity functions were proposed to be encoded by PaORF4 , KlORF3 and DrORF5 , respectively [16 , 17 , 20] . Subsequently , PaORF4 and DrORF5 were functionally expressed from their native promoters in the cytoplasm after integration of the genes into a VLE system ( the pGKL1/2 system transferred to S . cerevisiae ) [16 , 20 , 22] . Attempts to express both immunity genes in the nucleus after fusion of the ORFs to the constitutive ADH1 promoter ( ADH1pr ) , however , did not establish toxin immunity . This is in contrast to the putative zymocin immunity gene ( KlORF3 ) which was previously identified on the basis of functional expression from the nucleus after fusion of the KlORF3 to the PGK promoter [17] . Upon expression of the PGKpr-KlORF3 construct , partial zymocin protection was observed in S . cerevisiae cells . However , the phenotype required the presence of the autonomous VLE pGKL2 while , nuclear expression in a standard S . cerevisiae strain devoid of any VLE did not confer detectable zymocin immunity [17] . To reconfirm this latter notion , we fused KlORF3 to the alternative strong constitutive promoter ADH1pr and analyzed the zymocin response of the sensitive S . cerevisiae strain BY4741 containing the ADH1pr-KlORF3 fusion in comparison to the wild type by the microdilution method . As shown in Fig 1 , zymocin sensitivity indeed remains unaltered in the presence of the ADH1pr-KlORF3 construct , supporting the conclusion that the VLE encoded immunity factors cannot be functionally expressed in the nucleus in a standard S . cerevisiae strain . In contrast , both DrORF5 and PaORF4 provide resistance to their cognate ACNase toxins ( DrT and PaT ) when expressed in the cytoplasm of a sensitive S . cerevisiae strain and in all assays conducted , complete , rather than partial immunity was observed [16 , 20] . To analyze whether the observed failure of immunity expression from the nuclear vectors was due to a barrier in transcription or due to transcript instability , we analyzed the levels of mRNAs encoding immunity ( immRNA ) and their stability . ImmRNA from S . cerevisiae strains carrying nuclear fusions of the immunity factor encoding ORF ( immORF ) and the ADH1pr was compared with immRNA from natural expression hosts , where immORFs are expressed from the cytoplasm . In all cases , cytoplasmic expression yielded stable immRNA that exceeded the size of the corresponding full-length immORF ( Fig 2 ) . In contrast , nuclear expression of immORFs produced one ( KlORF3 ) , two ( DrORF5 ) or four ( PaORF4 ) distinct signal bands in Northern blots , which were significantly smaller in size than their corresponding full-length immORF ( Fig 2 ) . Thus , while immRNAs are stable , when expressed from their cognate VLEs in the cytoplasm , they are prone to fragmentation and get particularly instable when expressed in the nucleus . The lack of full length immRNA in the latter case is in line with the observed general lack of functional ACNase immunity when immORFs are expressed in the nucleus . Since the poly ( A ) site processing machinery recognizes UA rich elements that appear to be quite diverse in S . cerevisiae [reviewed in 23] , we explored the possibility that immRNA fragmentation of the highly A/T biased transcripts could be associated with recognition of random , internal poly ( A ) sites , leading to immORF fragmentation with the addition of poly ( A ) tails at the cleavage sites . We isolated total RNA from S . cerevisiae strains expressing ADH1pr-KlORF3 , ADH1pr-PaORF4 and ADH1pr-DrORF5 and primed cDNA synthesis with a poly ( A ) -specific oligonucleotide . Such cDNAs were analyzed by PCR using immORF specific oligonucleotides , binding at the 5’ end together with an oligonucleotide complementary to the poly ( A ) -specific anchor . As a control , the ERG3 mRNA was amplified from the same cDNA preparations . For all immORFs , several 3’ RACE products were obtained , all of which were smaller than the minimum expected gene size ( Fig 3 ) , suggesting the presence of poly ( A ) stretches at the 3´ ends of the immRNA fragments . Such result agrees with the specific fragmentation of the nuclearly expressed immRNAs followed by the addition of poly ( A ) tails . Fragments of each PCR reaction were extracted from the gel and cloned; sequencing identified the fragments to perfectly match the 5`terminal regions of the respective immRNAs , which are truncated at their 3´ends and extended by attachment of several ( 16 to 66 ) adenines ( Fig 4 ) . To ensure that the data obtained from 3’RACE experiments did not result from unspecific internal priming within A-rich mRNA regions , a linker ligation method was applied to exemplarily identify the immRNA ends of KlORF3 . The linked KlORF3 fragments were amplified using a gene specific and a linker specific primer and after cloning analyzed by sequencing . All identified fragments contained a poly ( A ) tail consisting of 7–49 adenyl nucleotides . The results confirm the previous observations . Both methods identified that cleavage and polyadenylation of each of the immRNAs happens not only at one definite , but at multiple positions . For example , among the 33 sequenced PaORF4 mRNA fragments , 15 different polyadenylation sites in a region between positions 209 nt and 594 nt were mapped ( Fig 4 and S1 Fig ) . The identified positions in the mRNA truncation products and their frequency of occurrence for each immRNA are summarized in Fig 4 . Since the above results suggested the possibility that the high A/T content of immRNAs limits their nuclear expression due to ORF-internal poly ( A ) site processing , we analysed whether a reduction of the A/T content of PaORF4 and KlORF3 improves their expression from the nucleus . Synthetic variants of both genes were generated , where most of the A/U rich codons were replaced by synonymous more G/C rich codons via gene synthesis ( S2 Fig ) . As a result , the G/C content for PaORF4 increased from 21% to 45% in the synthetic variant PaORF4ms and KlORF3 increased in G/C content from 22% to 54% in the synthetic variant KlORF3ms without altering the amino acid sequence . Both variants were cloned into the same vector backbone previously used to study the native , unchanged genes , resulting in a set of multicopy plasmids carrying immORF fusions to the ADH1 promoter , where immORFs remain either unchanged in codon usage ( 78–79% A/T ) or exhibit a significantly reduced A/T content ( 46–55% ) . All constructs were expressed in a PaT or zymocin sensitive VLE-free S . cerevisiae strain and the presence of full length immRNA was comparatively analysed by RT-PCR ( Fig 5 ) . As a control , the ERG3 mRNA was detected in all strains in parallel . Full length immRNA was generally absent in the strains expressing the A/T-rich native ( non-modified ) versions of the immORFs ( Fig 5A ) , which is in agreement with the results obtained by Northern analysis ( Fig 2 ) . In striking contrast , however , full length immRNA becomes detectable when low A/T content codon usage variants PaORF4ms and KlORF3ms are expressed from the same nuclear constructs ( Fig 5B ) . Thus , lowering the A/T content clearly improves immORF expression in the nucleus compared to the natural gene variants being expressible only in the cytoplasm . To check whether such improvement also enables functional immORF expression , ACNase immunity of strains carrying the different immORF expression constructs was scored by the eclipse plate and microdilution assays ( Fig 6 ) . Consistent with previous results , expression of A/T-rich versions of PaORF4 or KlORF3 did not confer a detectable immunity phenotype to the PaT or zymocin producers , respectively . When the low A/T-content variants PaORF4ms or KlORF3ms were expressed , however , sensitivity to the cognate ACNase toxin producer was entirely lost . At the same time , the PaORF4ms expressing strain remained sensitive to the zymocin producer ( Fig 6A and Fig 6B ) and the KlORF3ms expressing strain remained sensitive to the PaT producer ( Fig 6A ) . These results indicate that although lowering the A/T contents in two functionally distinct immORFs suffices to overcome the observed nuclear expression barrier , the immunity factors , once being expressed , do not confer ACNase cross-protection to non-self yeast strains . Our KlORF3ms expression studies ( Fig 6A ) confirm the previous proposal by Tokunaga et al . , [17] that pGKL1 encoded Orf3 protein confers zymocin immunity . As KlORF3ms yields protection to the zymocin producer K . lactis in the background of S . cerevisiae strain BY4741 , there is evidently no general requirement for the presence of the pGKL2 VLE to establish immunity . In previous experiments , the immunity phenotype associated with the PGKpr-KlORF3 construct and the pGKL2 VLE was only partial , since exogenous purified zymocin induced detectable growth inhibition [17] . To check whether the pGKL2 independent immunity conferred by nuclear expression of ADH1pr-KlORF3ms also is partial , we analyzed the zymocin response using the microdilution assay . We compared KlORF3ms-induced zymocin protection in WT cells with elp3 cells not carrying any immORF . The latter condition prevents zymocin induced tRNA cleavage due to absence of the crucial wobble uridine mcm5s2-modification and confers full toxin resistance [10 , 11] . We observed no difference between the zymocin response of elp3 cells not expressing any immORF and ELP3 cells expressing KlORF3ms; only ELP3 wild type cells without KlORF3ms showed sensitivity to zymocin ( Fig 7A ) . To further check the dominant nature of KlORF3ms induced immunity and to analyze whether the KlOrf3 protein is capable of intracellular inactivation of γ-toxin , as suggested in earlier work [17] , we constructed strains co-expressing KlORF3/KlORF3ms and GAL1pr-driven , multi copy KlORF4 devoid of its signal peptide encoding region , leading to intracellular accumulation of the ACNase subunit γ-toxin ( KlOrf4 ) . As a control , both immORF constructs were introduced into the elp3 strain , where the need for an immORF is overcome by preventing tRNA cleavage in the first place . Galactose induced expression of the γ-subunit proved inhibitory to the strain expressing the A/T-rich variant KlORF3 only; as expected , the elp3 mutation prevented toxic effects of γ-toxin but also KlORF3ms entirely prevented growth inhibition by intracellular γ-toxin and the additional removal of ELP3 did not improve growth of the strain under inducing conditions ( Fig 7B ) . Thus , zymocin immunity acts , similar to the previously studied PaT and DrT immunity functions at the intracellular stage and provides true immunity rather than partial resistance , independent of any pGKL2 encoded functions . Since we detected no cross resistance of KlORF3ms expressing strains to the non-cognate ACNase toxin PaT ( Fig 6A and Fig 6B ) and PaORF4ms did not protect detectably from zymocin ( Fig 6A ) , the two immunity proteins PaOrf4 and KlOrf3 are highly specific for each of their cognate ACNase subunits .
PaT , DrT and zymocin are the three known examples of eukaryotic protein toxins with ACNase activity . All three are encoded by non-autonomous VLEs ( pPac1-2; pWR1A and pGKL1 ) persisting in the cytoplasm of different yeast species . Crucial functions for cytoplasmic transcription and DNA replication , processes normally occurring in the nucleus , are supplied in each case by a larger VLE ( pPac1-1; pWR1B and pGKL2 ) . Among these are a uniquely structured RNA polymerase [24] as well as a virus like mRNA capping enzyme [25 , 26] to generate capped , cytoplasmic mRNAs from unique cytoplasmic promoters [27 , 28 , 29] . Generally , VLE genes cannot be expressed in the nucleus due to non-recognition of the cytoplasmic promoters by the nuclear ( host encoded ) RNA polymerases . The presence of genes on pPac1-2 and pWR1A mediating immunity against PaT and DrT was previously shown by integration into the pGKL1/2 system transferred to S . cerevisiae . The parental pGKL1/2 carrying S . cerevisiae strain produces zymocin and its cognate immunity factor but was sensitive to DrT and PaT . This sensitivity was entirely lost upon integration of DrORF5 and PaORF4 , respectively [16 , 20] . Since both , DrOrf5/PaOrf4 and DrOrf3/PaOrf2 display detectable sequence homology and there is significant DrT/PaT cross protection mediated by PaOrf4 , a direct recognition of the matching ( PaOrf2 ) or nearly matching ( DrOrf3 ) ACNase by the immunity factor was suggested . In support , PaOrf4 can disable toxic in vivo effects of intracellular PaOrf2 and both proteins were shown to form a complex in vitro that inhibits the ACNase activity of PaOrf2 , resembling the mode of action of tRNase colicin immunity factors , which tightly bind and occlude the tRNase active site [8 , 30] . Similarly , in vivo studies with DrOrf5 showed that it protects against the in vivo tRNase activity of the intracellular DrOrf3 subunit [20] , hinting at a similar immunity principle as for PaOrf4 . Co-crystal structures of VLE-encoded tRNases with their cognate immunity proteins will be required to determine whether immunity factors against toxic tRNases as evolutionary diverse as prokarytotic colicins and VLE encoded killer toxins indeed share a similar mechanistic strategy to bind and occlude the tRNase active site . For zymocin , understanding of the immunity factor function had been less advanced; published data [17] indicated an as yet undefined requirement for the VLE pGKL2 to establish KlOrf3 mediated immunity and in contrast to PaT and DrT immunity functions , the zymocin immunity factor appeared to provide partial protection only . Additionally , it was suggested that the zymocin immunity factor protects from intracellular γ-toxin based on the observation that pGKL1/2 carrying cells are resistant to galactose-induced expression of a signalpeptide-less KlORF4 gene [17] , but no similar protection has been shown for the isolated KlORF3 gene . Since heterologous expression of KlORF3 precluded the use of the cytoplasmic pGKL1/2 based expression system , we encountered the general problem that even the established immunity genes PaORF4 and DrORF5 could not be expressed in the nucleus after replacement of the cytoplasmic promoter by well characterized nuclear promoters . As the same outcome was observed for the zymocin immunity gene , a general principle inhibitory to nuclear expression of these immunity genes became obvious . Since AT rich immunity genes are efficiently translated by the host’s translational machinery when the corresponding mRNA is generated in the cytoplasm by a VLE encoded transcriptional machinery but not when the same mRNA is generated in the nucleus , a nuclear transcriptional rather than a cytoplasmic translational barrier appeared to exist . Northern , RACE and linker ligation analysis now shows that nuclear expression generally ends up in fragmentation of the immRNAs which goes along with the addition of poly ( A ) tails . Poly ( A ) site recognition is thought to basically involve the presence of the AAUAAA poly ( A ) signal ( PAS ) , together with a GU-rich sequence as a downstream element [reviewed in 23] . However , unlike higher eukaryotes , yeast apparently tolerates a high degree of variation in individual poly ( A ) site recognition elements , as was derived from the analysis of expressed sequence tags generated by oligo ( dT ) primed cDNA synthesis [31] . For example , the PAS element in yeast is simply characterized by being A-rich . Thus , extremely AU-rich transcripts , such as VLE derived genes exhibit a high probability of ORF internal poly ( A ) site recognition and processing when moved to the nucleus . In support of this , we show that lowering the A/T content by defined gene synthesis is sufficient to prevent immRNA fragmentation in the nucleus allowing for functional expression of PaT and zymocin immunity phenotypes . Our analysis with the synthetic KlORF3ms construct shows that the zymocin immunity protein indeed acts intracellularly and provides true self-protection rather than partial resistance . Thus , all eukaryotic ACNase immunity proteins may , like PaOrf4 , recognize and inhibit their cognate ACNase . In support of a specific recognition , cross immunity against DrT but not zymocin can be provided by PaOrf4 , whereas KlOrf3 provides immunity solely and specifically to zymocin . Since ACNase subunits of DrT and PaT are detectably similar but no similarity exists between DrT/PaT and zymocin , such similarity/non-similarity is apparently recognized by the immunity proteins . The initial detection of partial zymocin resistance in a strain carrying PGKpr-KlORF3 in the nucleus as well as pGKL2 in the cytoplasm [17] may be correlated to the fact that the fusion of KlORF3 to the PGKpr did not eliminate the upstream conserved sequence ( UCS ) element of KlORF3 . Since UCS sequences have been shown to be sufficient for mediating cytoplasmic transcription [28] and toxin resistance was only seen when pGKL2 , i . e . the UCS-recognizing RNA polymerase was present , it appears possible , that partial zymocin immunity was due to cytoplasmic transcription of the PGKpr-KlORF3 construct , which may have been located transiently in the cytoplasm after transformation . Such transient cytoplasmic availability may constitute the basis for the observed partial zymocin resistance as opposed to full immunity in this study . Only rather recently nuclear sequences of plasmid and viral origin ( NUPAVs ) were detected which—eponymously—result from evolutionary capture of plasmid and VLE-genes by the yeast nucleus [32] . Indeed , cytoplasmic VLE based genes can frequently and repeatedly be trapped by the nucleus , as explicitly shown for pDH1A from Debaryomyces hansenii , which represents the most recent ancestor of NUPAVs so far known [33] . Taken also into consideration that some ORFs of VLEs , such as the toxin genes have been cloned and successfully expressed from nuclear vectors [19 , 34 , 35] the risk is immediately imposed on a VLE system that upon nuclear immunity gene capture autoselection is disabled , which is yet mandatory for VLE long term propagation . While chromosomally encoded yeast killer toxins are traditionally considered as factors beneficial to the producer cell due to the ability to eliminate competitors , VLE encoded toxins additionally or even predominantly serve to counterselect for spontaneous plasmid free segregants , clearly resembling the autoselective properties of bacterial toxin/antitoxin systems [16 , 21 , 36] . In Pichia acaciae , Kluyveromyces lactis and Debaryomyces robersiae toxin encoding cytoplasmic VLEs can be easily eliminated under laboratory conditions , which in all cases generates toxin sensitive segregants . Such situation differs from the vast majority of chromosomally encoded toxins , which are routinely not active against the producing species which supports that VLE encoded killer toxins function to kill spontaneous VLE-free segregants . However , such function does not exclude additional benefits to producer cells that are provided by the ability to kill other yeasts in a given environment . Since VLE-derived NUPAVs in different stages of degeneration can be detected in various yeast genomes [32 , 33] , the high A/T content of VLEs in general may serve to minimize the potential for domestication of VLE based genes by the host , which might be particularly relevant for the immunity function that , if domesticated and separated from a toxin encoding VLE , would eliminate the positive selective pressure incurred by the toxin on the maintenance of the VLE system . In other words , spontaneous VLE-free segregants would no longer be eliminated in an environment of VLE containing , toxin secreting sister cells . Interestingly , among the VLE derived NUPAVs described by Frank and Wolfe , 2009 , immunity genes constitute the largest group . We identified an additional immunity derived NUPAV in the yeast Pichia sorbitophila [37] , which appears to be almost intact and closely related to the DrORF5/PaORF4 genes ( S3 Fig ) . The gene ( Piso0_001880 ) located close to the end of chromosome F spans 729 bp and contains at its 5’end an extended region of similarity to the immORFs where , however , the ATG and the VLE promoter appear to be lost , leading to the annotation of an internal ATG as the gene’s startcodon . Importantly , Piso0_001880 has an A/T content of 75 . 5% which resembles the typical VLE characteristics and differs significantly from P . sorbitophila genome average ( 58 . 6% ) . We assume that Pios0_001880 represents a rather recent VLE derived domestication in an early stage of degeneration that may be related to the nuclear expression barrier caused by extreme A/T content . In support , no entirely intact immunity-NUPAV has been identified so far , suggesting a general incompatibility of A/T rich VLE genes with the nuclear transcript processing machinery .
The cloning host Escherichia coli DH5αF’ was grown in Luria-Bertani ( LB ) medium ( 0 . 5% yeast extract , 1% peptone , 0 . 5% NaCl ) supplemented with ampicillin ( 100 μg ml-1 ) at 37°C . Yeast strains used in this study are listed in S1 Table . They were grown either in YEPD medium ( 1% yeast extract , 2% peptone , 2% glucose ) or in yeast nitrogen base ( Difco , Detroit , MI , USA ) at 30°C . Transformation of S . cerevisiae was performed according to the PEG/lithium acetate method [38] . Plasmids used in this study are listed in S2 Table . The immORFs were amplified by PCR using total DNA of the killer yeasts P . acaciae , D . robertsiae , K . lactis as template and the primers listed in S3 Table ( PaORF4: PO4-NdeI-rv and PO4-fw , KlORF3: KlO3rev_NdeI and KlO3for , DrORF5: DrO5rev_NdeI and DrO5for ) . The PCR products were blunt-end cloned into EcoRV restricted pSK- plasmid to yield pSKPaO4 , pSKKlO3 and pSKDrO5 . The pSKDrO5 plasmid was modified by site-directed mutagenesis to remove a DrORF5-internal NdeI restriction site using the primers mut_NdeI_for and mut_NdeI_rev . The A/T decreased gene versions PaORF4ms and KlORF3ms were synthesized by GeneArt ( Regensburg , Germany ) and delivered in a vector containing NdeI and HindIII restriction sites upstream and downstream of the respective ORF . All ORFs were released from their vectors via NdeI and HindIII and cloned into a likewise restricted pSKpADH1 . The ADH1pr-immORF fusions were then ligated into the 2μ vector YEplac195 using the restriction sites KpnI and SacI ( YEPaO4 , YCPaO4 YEKlO3 , YEDrO5 , YEPaO4ms ) or SmaI and HindIII ( YEKlO3ms ) . For coexpression of KlORF3ms and γ-toxin , the EcoRI-BglII insert of pABY1643 ( GAL1pr-γ-toxin-GST; [10] ) was subcloned into EcoRI-BamHI digested YEplac181 . For the microtiter plate assay the partially purified toxins PaT and zymocin were obtained from culture supernatants of P . acaciae NRRL Y-18665 and K . lactis AWJ137 by ultrafiltration as described previously [20] . Different toxin concentrations were applied in microtiter plates as described in Klassen et al . , [39] . Cell growth was monitored photometrically in a Multiscan FC Microplate Photometer ( Thermo Fisher Scientific , Waltham , MA , USA ) at 620 nm . To check the sensitivity of a strain in the eclipse assay , a drop of 7 μl of the cell suspension was spotted on a YEPD agar plate . The killer strain was then placed at the rim of the drop and the plate was incubated at 30°C overnight . Effects of intracellularly expressed , galactose-inducible toxin subunits on the growth of certain strains were checked with the drop dilution assay . Cultures were serially diluted and 5 μl aliquots were spotted on YNB medium containing glucose ( repressing condition ) or galactose ( inducing condition ) and incubated for several days at 30°C . Zymocin containing YPD plates were prepared by spreading 300 μl of filter sterilized , concentrated supernatant ( RCF 5 ) of K . lactis AWJ137 . Total RNA of different immunity ORF expressing yeast strains was isolated after over night cultivation in YEPD medium . 1 . 5 μg of each RNA sample were separated by a denaturating 1 . 5% agarose gel electrophoresis ( 20 mM MOPS , 8 mM sodium acetate , 1 mM EDTA , 0 . 74% formaldehyde , pH 7 . 0 ) and blotted onto a positively charged nylon membrane ( Roche Diagnostic GmbH , Mannheim , Germany ) . The blotting success and RNA integrity were controlled by methylene blue staining ( 0 . 02% methylene blue , 0 . 3 M sodium acetate , pH 5 . 2 ) . Hybridization was performed at 57–61°C overnight in hybridization buffer containing 50% formamid and a DIG-labelled RNA probe specific for the mRNAs of PaORF4 , KlORF3 and DrORF5 , respectively . Probes were prepared by amplifying the gene sequences using the primers ( PaORF4_probe_for/PaORF4_probe_revT7 , KlO3proberevT7/KlO3res1 , DrO5_probe_rev_T7/DrO5_rs1 , S3 Table ) and labelled with the DIG RNA Labeling Kit ( SP6/T7 ) ( Roche Diagnostic GmbH , Mannheim , Germany ) according to the manufacturer’s instructions . For detection a phosphatase-conjuncted anti-DIG antibody and a chemilumiscent alkaline phosphatase substrate CDPstar ( Roche Diagnostic GmbH , Mannheim , Germany ) were applied , and signals were visualized by exposure to X-Ray films . For cDNA synthesis the RevertAid H minus first strand cDNA synthesis kit ( Fermentas , St . Leon-Rot , Germany ) was applied according to the manufacturer’s instructions . All primers used for cDNA synthesis are listed in S3 Table . Total RNA was isolated as previously described ( Klassen et al . , 2008 ) or , alternatively , by making use of the RNeasy Mini Kit ( Qiagen , Hilden , Germany ) according to the manufacturer’s instructions . cDNA was synthesized using random hexamer primers and total RNA as template . 1 μl of cDNA was used as the template for PCR reactions applying the Phusion High fidelity DNA polymerase ( Thermo Fisher Scientific ) and primer combinations PaO4intr-fw/PaO4intr-rv ( PaORF4 ) , PaO4ms_for/PaO4ms_rev ( PaORF4ms ) , KlO3_rs3/KlO3pf ( KlORF3 ) and KlO3ms_rev/KlO3ms_for ( KlORF3ms ) ( for primer binding positions see Fig 5C ) . Primer design for 3´ RACE experiments was based on the protocol of Scotto-Lavino et al . , [40] . Polyadenylated RNA was transcribed to cDNA using the poly ( A ) complimentary primer QT22 . The cDNA was used as template for PCR with primer Q0 ( binds to a QT22 anchor ) and an immORF specific primer at an annealing temperature of 58°C . PCR products were separated on an 1% agarose gel and extracted fragments were cloned into vector pSKpADH1 via NdeI and HindIII restriction sites . Isolated plasmids were analyzed by sequencing . For linker ligation 2 μg of total RNA were mixed with 2 μg of a phosphorylated DNA oligo nucleotide ( Oligo_5P_3ddC ) and ligated using T4 RNA ligase ( NEB , Frankfurt , Germany ) for 1 . 5 h at 37°C . Column purified reactions were used for cDNA synthesis with Oligo_rev as primer . cDNA of the immunity gene was amplified using the primers Oligo_rev and KlO3rev_NdeI as and then cloned into pSKpADH1 via NdeI and EcoRV restriction sites . Cloned fragments were analyzed by sequencing . | The rather wide-spread and extremely A/T rich yeast virus like elements ( VLEs , also termed linear plasmids ) which encode toxic anticodon nucleases ( ACNases ) ensure autoselection in the cytoplasm by preventing functional nuclear capture of the cognate immunity genes , but how ? When expressed in the nucleus , the mRNA of the VLE immunity genes is split into fragments to which poly ( A ) tails are added . Consistently , lowering the A/T content by gene synthesis prevented transcript cleavage and permitted functional nuclear expression providing full immunity against the respective ACNase toxin . Thus , internal poly ( A ) cleavage is likely to prevent functional nuclear immunity gene expression . | [
"Abstract",
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] | [] | 2015 | Autoselection of Cytoplasmic Yeast Virus Like Elements Encoding Toxin/Antitoxin Systems Involves a Nuclear Barrier for Immunity Gene Expression |
Bladder cancer is a significant health problem in rural areas of Africa and the Middle East where Schistosoma haematobium is prevalent , supporting an association between malignant transformation and infection by this blood fluke . Nevertheless , the molecular mechanisms linking these events are poorly understood . Bladder cancers in infected populations are generally diagnosed at a late stage since there is a lack of non-invasive diagnostic tools , hence enforcing the need for early carcinogenesis markers . Forty-three formalin-fixed paraffin-embedded bladder biopsies of S . haematobium-infected patients , consisting of bladder tumours , tumour adjacent mucosa and pre-malignant/malignant urothelial lesions , were screened for bladder cancer biomarkers . These included the oncoprotein p53 , the tumour proliferation rate ( Ki-67>17% ) , cell-surface cancer-associated glycan sialyl-Tn ( sTn ) and sialyl-Lewisa/x ( sLea/sLex ) , involved in immune escape and metastasis . Bladder tumours of non-S . haematobium etiology and normal urothelium were used as controls . S . haematobium-associated benign/pre-malignant lesions present alterations in p53 and sLex that were also found in bladder tumors . Similar results were observed in non-S . haematobium associated tumours , irrespectively of their histological nature , denoting some common molecular pathways . In addition , most benign/pre-malignant lesions also expressed sLea . However , proliferative phenotypes were more prevalent in lesions adjacent to bladder tumors while sLea was characteristic of sole benign/pre-malignant lesions , suggesting it may be a biomarker of early carcionogenesis associated with the parasite . A correlation was observed between the frequency of the biomarkers in the tumor and adjacent mucosa , with the exception of Ki-67 . Most S . haematobium eggs embedded in the urothelium were also positive for sLea and sLex . Reinforcing the pathologic nature of the studied biomarkers , none was observed in the healthy urothelium . This preliminary study suggests that p53 and sialylated glycans are surrogate biomarkers of bladder cancerization associated with S . haematobium , highlighting a missing link between infection and cancer development . Eggs of S . haematobium express sLea and sLex antigens in mimicry of human leukocytes glycosylation , which may play a role in the colonization and disease dissemination . These observations may help the early identification of infected patients at a higher risk of developing bladder cancer and guide the future development of non-invasive diagnostic tests .
Schistosoma haematobium , a parasitic flatworm infecting millions of people in Angola and other countries of Africa and Middle East , is responsible for the development of urinary schistosomiasis , a neglected tropical disease [1] , [2] . The World Health Organization estimates that 500 to 600 million people residing in rural agricultural and periurban areas are at risk of infection and over 200 million people are currently infected , 10% of which will experience sever health complications; [3] , [4] . The parasite has a complex life cycle consisting of two phases , one inside the human body ( the definitive host ) and another inside a snail of the genus Bulinus [5] . Free-swimming cercariae penetrate human skin when in contact with contaminated water , enter the blood stream and travel to the liver to mature into adult flukes . After a period of about three weeks the young flukes migrate to the plexuses around the urinary bladder to copulate . The eggs released by female flukes traverse the wall of the bladder causing haematuria , fibrosis and ultimately the calcification of the tissue; they are then excreted through urine [6] , [7] . However , some eggs become embedded in the bladder mucosa further contributing to chronic inflammation and granuloma formation [6] , [7] . The eruption of the eggs through the mucosa stimulates not only the establishment of chronic inflammations but also promotes the development of benign/pre-malignant bladder lesions such as urothelial hyperplasia and dysplasia that may be precursors of bladder cancer [8]–[10] . When contaminated urine comes in contact with fresh watercourses ( e . g . rivers ) , the eggs hatch , releasing free-swimming miracidia that infect the intermediate snail host . After a maturation period new cercariae are formed and released into the environment , assuring the perpetuation of infection and transmission of the disease [5] . The World Health Organization ( WHO ) International Agency for Cancer classifies S . haematobium as a Group 1 biological carcinogen , a definitive cause of cancer [11] . Epidemiological findings reveal a positive relationship between S . haematobium infection and the development of squamous cell carcinoma of the bladder , a type of bladder cancer rarely observed in western patients but prevalent in Africa and Middle East [12]–[14] . It has been observed that patients infected with the parasite have a higher risk of developing bladder cancer earlier in life than uninfected people [13] , [15] . The probability of developing cancer has been suggested to depend on the intensity ( worm burden and tissue egg burden ) and duration of infection [16] , [17] . However , despite the epidemiological data from case control studies and the geographical overlap between bladder cancer development and regions endemic for urogenital schistosomiasis [8] , [18] , few experimental evidences support this association . Nonetheless , Botelho and coworkers demonstrated recently that the exposure to soluble antigen extracts of mixed sex adult S . haematobium worms and eggs promote the tumourogenic potential of urothelial cells in vitro and in vivo [8] , [19] , [20] , and Zhong and colleagues have reported hypermethylation of several genes including RASSF1A and TIMP3 detected in urine sediments of Ghanaians with bladder pathology associated with infection with S . haematobium [21] . Further understanding of the pathobiological features underlying the association between S . haematobium and bladder cancer development are needed to support these observations . The identification of the molecular events underlying early urothelial carcinogenesis in the bladder is also warranted . This is a particular critical matter since early symptoms of schistosomiasis , which include urinary pain and hematuria , are common to those of bladder cancer . As such , they are often neglected by local communities in developing countries , where medical assistance is scarce . Therefore , bladder tumours are often diagnosed at a late stage , which is associated with decreased overall survival . The identification of biomarkers may help to control S . haematobium-associated bladder cancer in these populations . This research is based on establishing common molecular alterations among schistosoma-associated tumours and benign/pre-malignant lesions found either in tumor-adjacent mucosa or in apparently normal urothelia of cases without tumors . These lesions were screened for oncoprotein p53 that is associated with both aggressive urothelial [22]–[24] and squamous cell bladder carcinomas [25] . The proliferation rate , given by the overexpression of nuclear protein Ki-67 , and considered a prognostic marker of tumor recurrence and progression in non-muscle invasive urothelial carcinoma [26]–[28] , was also evaluated . Particular attention was further devoted to the characterization of alterations in membrane-bound glycans that accompanied malignant transformations and favor cell-to-cell detachment , migration , immune evasion and metastization [29] . This includes the sialylated antigens sialyl-Tn ( sTn; CA72-4 ) [30] , [31] , sialyl-Lea ( sLea , CA19-9 ) [32]–[34] and sialyl-Lex ( sLex ) [35] , [36] that have been observed in bladder cancer . Cancer-associated glycans can also be found in secreted proteins often shed into the bloodstream and urine , offering potential for non-invasive diagnosis [34] , [36]–[38] .
All procedures were performed after patient's written informed consent and parental consent in the cases of children and approved by the Ethics Committee of Agostinho Neto University , Luanda , Angola and the Portuguese Institute For Oncology of Porto , Portugal ( IPO-Porto ) . Clinico-pathological information was obtained from patients' clinical records and this information was anonymized . This study includes 43 Angolan patients ( 30 . 2% male and 69 . 8% female ) diagnosed as positive for S . haematobium infection in Sagrada Esperança Clinic ( Luanda , Angola ) and Hospital Américo Boavida ( Luanda , Angola ) . The median age of the patients was 33 . 5 years ( 12–82 years ) and , even though the majority resided in the rural areas around Luanda , they were born and had resided in provinces where S . haematobium is endemic . All patients presented irregularities of inner surface of bladder wall found by ultrasound scan and some of them showed a localized thickening of bladder wall protruding into the lumen . Therefore , the patients underwent cystoscopy and biopsy of the visualized mass and corresponding adjacent mucosa . The apparently normal urothelium of cases without noticeable tumour mass were also subjected to random biopsies . All biopsies of apparently normal urothelium and tumour-adjacent mucosa presented benign/pre-malignant lesions ( chronic inflammation , urothelial hyperplasia , epidermoid metaplasia or dysplasia ) . Malignant lesions included papilloma ( P ) , papillary urothelial neoplasm of low malignant potential ( PUNLMP ) and high-grade urothelial cell carcinoma ( UCC ) , squamous cell carcinomas ( SCC ) or both ( UCC+SCC ) as summarized in Table 1 . No differences were observed in age and sex distribution among the lesions/tumours . S . haematobium eggs were evident in the bladder of 27 ( 62 . 8% ) cases , from these 7 ( 26% ) presented tumours . This study also includes a retrospective series of 22 non-Schistosoma haematobium infected patients diagnosed with urothelial cell carcinoma ( 10 low-grade tumours; 12 high-grade tumours , 5 presenting muscle invasion ) and 4 squamous cell carcinomas presenting invasion of the muscularis propria , that have been previously characterized in relation to Ki-67 and sTn expressions by Ferreira et al . [31] . The patients ( 48 . 3% male and 51 . 7% female ) , mean age 69 years ( 45-89 years ) , underwent transurethral resection of the tumour in the Portuguese Institute for Oncology of Porto ( IPO-Porto , Portugal ) , between July 2011 and May 2012 . None had received prior adjuvant therapy . Six normal urothelium tissues of necropsied male individuals without bladder cancer history , within the same mean of age range , were also included . Formalin fixed paraffin embedded biopsies and tumour sections stained with hematoxylin and eosin were examined and classified by an experienced pathologist under light microscopy , with reference to the WHO's 2004 grading criteria [39] . Formalin-fixed , paraffin-embedded ( FFPE ) tissue sections were screened for p53 accumulation , proliferation ( Ki-67 ) , and cancer-associated glycans sTn , sLea , and sLex by immunohistochemistry by the streptavidin/biotin peroxidase method using mouse monoclonal antibodies . The p53 protein was determined with clone DO-7 ( Dako ) , Ki-67 with clone MIB-1 ( Dako ) , sTn with clone TKH2 [31] , sLea with clone ( Abcam ) and sLex with clone ( Abcam ) . Briefly , 3 µm sections were deparaffinized with xylene , rehydrated with graded ethanol series , microwaved for 15 min in boiling citrate buffer ( 10 mM citric acid , 0 . 05% Tween 20 , pH 6 . 0 ) , and exposed to 3% hydrogen peroxide in methanol for 20 min . After blockage with BSA ( 5% in PBS ) , the antigens were identified with UltraVision Detection System ( Thermo Scientific ) followed by incubation with 3 , 3-diaminobenzidine tetrahydrochloride ( Impact Dab , Vector ) . Finally , the slides were counterstained with haematoxylin for 1 min . Colon carcinoma , tonsil and intestinal metaplasia tissue sections were tested in parallel as positive controls for , p53 , Ki-67 and sialylated glycans , respectively . Negative control sections were included , involving sections probed with BSA ( 5% in PBS ) devoid of primary antibody . The tissues were also treated with a neuraminidase from Clostridium perfringens ( Sigma-Aldrich ) to remove the sialic acid from the glycans and screened thereafter for sTn , sLea , and sLex , as described by Ferreira et al . [31] . A semi-quantitative approach was established to score the immunohistochemical labeling based on the intensity of staining and the percentage of cells that stained positively . The immunoexpression was assessed blindly by two independent observers and validated by an experienced pathologist . Whenever there was a disagreement , the slides were reviewed , and consensus was reached . Tumours were classified as p53 positive whenever expression was higher than 5% of the tissue section , as proliferative whenever Ki-67 expression was higher than 17% , as described by Santos et al . [26] , and sTn , sLea and sLex were considered positive whenever the percentage of staining was ≥5% of the tissue sections [31] , [32] , [35] . Statistical data analysis was performed using the IBM Statistical Package for Social Sciences—SPSS for Windows ( version 20 . 0 ) . Chi-square analysis was used to compare categorical variables . Correlation between cancer associated markers expression in pre-malignant lesions and concomitant tumours whenever present was performed using Pearson correlation test . A P value of ≤0 . 05 was considered to be statistically significant .
Bladder tumours associated with S . haematobium infection were screened for the accumulation of p53 , proliferation rate ( Ki-67>17% ) and cancer-associated sialylated glycans sTn , sLea and sLex ( Fig . 1 ) . We could observe that all the biomarkers were expressed throughout the different layers of the urothelium in benign/pre-malignant lesions and also homogeneously expressed in the tumours , irrespectively of their histological classification . As presented in Table 2 , the majority of the bladder tumors exhibited p53 alterations ( 84% ) and sLex overexpression ( 74% ) . Similar percentages of p53 and sLex could also be observed in bladder tumour sections from patients non-infected with Schistosoma haematobium , irrespectively of their histological natures . Conversely , non-proliferative phenotypes predominated among low malignant lesions ( papilloma and PUNLMP; 100% of the cases ) when compared to the other groups comprehending more aggressive lesions presenting either invasion and/or high potential to invade the bladder wall ( UCC , SCC , SCC+UC; approximately 50% of the cases ) ( Table 2 ) . Contrasting with these findings , the percentage of proliferative phenotypes in the less aggressive non-schistosome associated lesions ( low grade papillary tumours ) was 30% . The percentage of proliferative phenotypes in more aggressive high grade lesions , including high grade urothelial cell and squamous cell carcinomas , was similar to described for S . haematobium infection related lesions . The sLea antigen was detected in approximately 50% of the S . haematobium-associated malignant lesions , irrespectively of their histology . These observations contrasted with the significantly higher expression of sLea observed in lesions not associated with the parasite ( 80% of the cases ) . Regarding the sTn antigen , its frequency varies among the histological groups of tumours , papilloma and UCC did not express the antigen , PUNLMP and SCC showed an equal distribution of negative and positive cases . However , the antigen was expressed by 75% of the cases presenting both an UCC and SCC phenotype , which is in accordance with our previous results for non-schistosome associated bladder tumors , where sTn antigen was present in approximately 75% of aggressive bladder tumors ( high grade papillary tumors and muscle invasive bladder cancer ) [31] . Altogether , the studied biomarkers , with the exception of sLea presented similar expressions in both schistosome and non-schistosome associated bladder tumors . Benign/pre-malignant lesions associated with S . haematobium infection , irrespective of having been isolated from urothelium without malignant lesions or tumour adjacent mucosa , were also studied ( Fig . 1 ) . The p53 protein was altered in the majority of the benign/pre-malignant lesions ( 88% ) , predominantly in those showing cellular alterations ( urothelial hyperplasia , epidermoid metaplasia , dysplasia; Table 3 ) . Approximately 40% of the cases also presented a high proliferation index ( Ki-67>17% ) , although this was more pronounced among epidermoid metaplasia ( 64 . 3% , Table 3 ) . The sTn antigen was detected in one third of the lesions ( 32 . 6% ) , however it was mainly absent in chronic inflammation cases and when compared with all the others a trend association was observed ( P = 0 . 067 ) . The sialylated Lewis antigens sLea and sLex were detected in the majority of the cases ( >80% ) . However , the percentage of sLea positive cases was higher among the cases with urothelial hyperplasia while sLex was present in all dysplasia cases ( 4/4; Table 3 ) . Furthermore , none of the normal urothelium tissues were positive for the studied biomarkers , demonstrating its cancer-associated nature . Table 4 further highlights the relationship between the studied markers in the benign/pre-malignant lesions identified in apparently normal bladder mucosa and those found in tumor adjacent mucosa . The distribution of the p53 alterations , sTn and sLex antigens overexpression was similar between the two groups . However , a higher number of non-proliferative cases were observed in sole benign/pre-malignant lesions when compared with the lesions in tumour adjacent mucosa ( 73 . 9% vs 42 . 1% , P = 0 . 037; Table 4 ) . On the other hand , sLea expression was more frequent in lesions without tumour than with concomitant tumours ( 91 . 7% vs 66 . 7%; P = 0 . 03 ) . Altogether this data shows that the majority of the benign/pre-malignant lesions associated with S . haematobium infection share alterations in p53 expression and sLex with bladder tumors . The predominance of sLea in pre-malignant lesions , in particular in bladders that do not present signs of malignant transformation , suggesting that this glycan may be a molecular alteration associated with early carcinogenesis pathways . Table 5 further shows the correlation between the expression of the biomarkers in the tumours and adjacent mucosa lesions . This showed a correlation between the expression of p53 , sTn , sLea and sLex both in the lesion and tumour , denoting that the tumour adjacent mucosa reflects the molecular alterations found in S . haematobium-associated tumours . Moreover , it was observed that 60% of the cases presented S . haematobium eggs embedded in the bladder urothelium , predominantly in benign/pre-malignant lesions without the presence of tumour ( 75% vs 45% ) . However , no associations were found between the expression of the studied markers and the presence and absence of the eggs in the bladder at the time of diagnosis . Altogether , these findings suggest that the presence of eggs in the bladder may be an early event leading to carcinogenesis . Whether the disorganization of the tissue associated with malignant transformation may favor their release into the environment , therefore explaining is lower presence in malignant tissues and consequently the lack of correlation with the studied biomarkers , warrants further investigation . It was further observed that the majority of the cases ( >75% ) presented sLea and sLea positive eggs and approximately half displayed eggs expressing the sTn antigen , suggesting some degree of mimicry of host glycosylation patterns ( Fig . 2 ) . The expression of sialylated glycans was validated by observing the loss of reactivity against anti-glycans monoclonal antibodies after treatment of the tissue with a neuraminidase . It was noteworthy that both positive and negative eggs for these antigens could be found within the same biopsy , denoting some degree of heterogeneity at this level .
In contrast to the extensive cytogenetic and molecular signatures existing for urothelial cell carcinoma , mainly found in western populations , little is known about the molecular alterations underlying the development of S . haematobium-associated bladder cancer . Nevertheless , such information is pivotal to support a definitive association between schistosomiasis and bladder cancer development that , until now , has been mostly supported by epidemiological studies . Furthermore , it may provide means for an early identification of infected populations at a risk of developing bladder cancer , which is a particular critical matter since the majority of the cases are detected at a late stage due the absence of appropriate medical facilities . Herein we have screened a series of schistosome-associated bladder tumours , their adjacent mucosa and also biopsies of apparently normal urothelia , for bladder cancer biomarkers . We also included a series of bladder tumours of non-schistosome etiology and normal urothelium sections in an attempt to highlight common molecular alterations . The majority of the patients enrolled in this study were females , and this is in clear contrast with the higher prevalence of bladder cancer among men in western populations . This may be explained by social and working habits of these populations living in the proximity of contaminated water courses . Also , the majority of the cases of bladder cancer were observed among young adults , which is rare for urothelial carcinomas of chemical etiology [40] . These findings were in accordance with previous observations from other authors [5] , [8] and support a role for the parasite in cancer development . The evaluated biomarkers included the accumulation of oncoprotein p53 [22]–[24] and tumour proliferation index given by Ki-67 overexpression [26]–[28] , two events associated with the aggressiveness of urothelial bladder cancer . Likewise , we observed alterations in p53 in the majority of schistosome-associated bladder tumours , irrespectively of their histopathological nature , which is in agreement with our findings and previous observations for non-schistosome associated tumours [25] , [41] . In addition , these alterations were not observable in the normal urothelium , denoting its cancer-related nature . The association between the accumulation of p53 in the urothelium and infection with S . haematobium reinforces the notion that the parasite may contribute to profound alterations in urothelial cells , ultimately leading to aggressive forms of cancer . This hypothesis is further supported by the observations reported by Botelho and colleagues [8] , [19] . According to these authors , the exposure to S . haematobium antigens down-regulates cell apoptotic pathways , which would ultimately lead to the development of cancer [42] . Regarding proliferation , Ki-67 overexpression was lower in low malignant potential lesions when compared to urothelial and squamous cell carcinomas . This is in accordance with previous findings associating the degree of severity of bladder malignant lesions with higher proliferation degrees and potential to evolve to more aggressive forms of cancer [26] , [31] . Since several markers were evaluated and a large numbers of comparisons were performed , the false discovery rate should be considered . Nevertheless , if all the null hypotheses are true , 5% of the comparisons are expected to present uncorrected P values lower than 0 . 05 by chance alone . However , our study present P values under 0 . 05 in 20% of the comparisons , thereby demonstrating the statistic value of the observations . The expression of cancer-associated cell-surface sialylated glycans sTn , sLea and sLex was addressed , to our knowledge , for the first time in S . haematobium-associated bladder tumours . Glycosylation is the main and more complex posttranslational modification of membrane-bound and secreted proteins . Glycans plays a key role in protein folding and stability [43] , mediate several physiological and pathological conditions , which include cell-cell adhesion , host-pathogen interactions , cell differentiation , migration and cell trafficking , signaling and immune recognition [44] , [45] . During malignant transformation , some cells change their glycosylation profile in response to microenvironment challenge , namely paracrine signaling , hypoxia among other events [29] . The sTn antigen , resulting from a premature stop in the O-glycosylation of proteins by sialylation , has been found associated with high-grade bladder non-muscle invasive papillary tumours and muscle invasive lesions [30] , [31] . It has been found to enhance bladder cancer cells capability to invade and migrate [31] and acts as a suppressor of effective dendritic cell immune responses against bladder cancer cells [46] . Despite the low number of cases , this study suggests that the sTn antigen is predominantly expressed by more aggressive forms of S . haematiobum associated tumours ( UCC+SCC ) . These observations are in accordance with our previous results from non-schistosome associated bladder tumors , were sTn expression is predominant found in high grade papillary tumors and muscle invasive bladder cancer of non-schistosome etiology [31] . The sialylated Lewis blood group determinants sLea and sLex may be found as terminal structures of both proteins and lipids and have been associated with metastatic potential and poor overall survival in several solid tumours [47]–[50] . The sLea antigen has also been found both in pre-malignant bladder lesions , non-invasive and invasive bladder urothelial carcinomas [32]–[34]; however no association with recurrence , invasion or metastasis has been reported . On the other hand , its structural isomer sLex antigen was observed in muscle invasive urothelial carcinomas associated with invasion , metastasis and recurrence [35] , [36] . This study now demonstrates that the majority of schistosome-associated bladder tumours expressed the sLex antigen , suggesting a high degree of malignant potential . However , no defined expression pattern could be drawn for sLea Similarly , we also found significant overexpression of the sLex antigen in non-schistosome associated tumours but also a more pronounced expression of sLea . However , none of these antigens were not observed in the healthy urothelium , reinforcing its malignant nature . The analysis of benign/pre-malignant lesions , irrespectively of their origin , showed a predominance of p53 , sLea and sLex positive cases . Noteworthy , sole lesions were predominantly non-proliferative when compared to lesions in the vicinity of tumours , suggesting that high proliferation may be mainly a characteristic of the tumour . Whether proliferative benign/pre-malignant lesions present a higher risk of evolving to bladder cancer warrants validation in future studies . On the other hand , the sLea antigen was predominantly expressed among sole lesions , denoting this antigen may constitute a marker of early bladder carcinogenesis mediated by S . haematobium . Reinforcing this observations , Kajiwara et al . has described that sLea is inversely associated with the grade of atypia while its non-sialylated form DU-PAN-2 correlates with the grade of atypia in urothelial carcinomas; these authors also observed that the disappearance of sLea and the presence of DU-PAN-2 correlates with high malignant potential [36] . We further observed that the expression of cancer-associated antigens in the tumour was correlated with the expression denoting a field effect that affects the entire bladder . Again , this correlation was not observed for proliferation , reinforcing this event see ms to be mainly a characteristic of malignant lesions . Taken together , these observations highlight that pre-malignant lesions present molecular alterations associated with malignancy , and that p53 and sLex are surrogate markers of bladder cancerization associated with infection with schistosomes . More studies should be conducted to validate the potential of sLea has a surrogate marker of infection that may be helpful in the monitoring of asymptomatic colonization . A glycoproteomic/lipidomic characterization of bladder tumours is ongoing , which is expected to provide their necessary insights about the biologic role of the studied glycans in bladder cancer . These observations are also likely to be of consequence in the clinic of great importance since benign and pre-malignant lesions such as those included in this study are challenging to diagnose by cystoscopy . We emphasize the potential of glycans in context , as they can be found at the cell-surface , thus easily accessible to antibodies and other carbohydrate ligands and consequently be explored in cancer detection imaging [51] , [52] . They are often secreted into the blood stream and urine and therefore readily accessible in non-invasive diagnosis [34] , [37] , [38] , [53] . Non-invasive diagnostic procedures are critical as they facilitate large scale screening of the populations in endemic regions where imaging/radiological facilities are not likely to be available . Glycans are also important mediators in the colonization of humans by parasites , as they provide means for efficient adhesion and immune escape [54] , [55] . As such , we have also addressed the expression of cancer-associated glycans in eggs of S . haematobium embedded in the bladders . We observed , for the first time , that the parasite eggs express sLea and sLex antigens , in mimicry of human leukocytes . These glycans are specific ligands for E-selectin , a cell adhesion molecule expressed only on endothelial cells and activated by cytokines , such as IL-1 and TNF-α , released by damaged cells during the course of inflammation [56] , [57] . Cytokines induce the overexpression of E-selectin by endothelial cells on nearby blood vessels that are responsible for recruiting leukocytes in a sLea/sLex-mediated manner [56] , [57] . These glycans bind weakly to E-selectin which allows leukocytes to “roll” along the internal surface of the blood vessel into the injury site by shear forces of blood flow [58] . Similar events may drive the recruitment of S . haematobium eggs to the bladder wall , a critical step in the developmental cycle of this pathogen . Similar strategies have been observed in nature , namely by the Gram-negative Porphyromonas gingivalis to adhere to human umbilical vein endothelial cells [59] . Several authors have also hypothesized that E-selectin-sialylated glycans interactions may contribute to the hematogenous dissemination of sLea/sLex expressing tumour cells and explain its association with metastasis [29] , [47] , [49] , [60] . Similarly , for bladder tumors , the identification of the parasite glycoproteins and/or glycolipids presenting these alterations may bring insights on this matter and ultimately contribute to design strategies to control infection . In addition , the identification of the glycoproteins and/or glycolipids presenting these alterations may yield insights into this infection-associated cancer and ultimately contribute to design strategies to control infection . Glycoproteomic studies will greatly benefit from the recent mapping of the parasite genome [61] . We further report that some eggs express the sTn antigen , an oncofetal antigen that , we and others have shown to play a key role in immune escape [46] . The sLex expression has also been found to reduce the susceptibility of tumour cells to hepatic sinusoidal lymphocyte-mediated killing , and thus , may facilitate the ability of the tumor cells to metastasize to the liver [62] . Similarly , the expression of these glycans by S . haematobium may provide the necessary means for immune escape either by modulation of the immune system or by molecular mimicry of the host , a common survival strategy among parasites [63] , [64] . To conclude p53 and sialylated Lewis blood group determinants may be surrogate markers of cancerization associated with chronic infection with S . haematobium . By drawing attention to common molecular pathways underlying these two events , this study provides one of the missing links associating parasite infection and cancer development . Further studies which include a larger sample of Schistosoma haematobium positive cases will be needed to determine a panel of biomarkers with the potential to identify bladder cancer precursor lesions Finally , this report provides insights on the glycosylation patterns of S . haematobium eggs and discusses a possible model for the recruitment of eggs to the bladder wall that suggests this schistosome has evolved glycosylation patterns that mimic those of its human host . These insights may help guiding the development of novel therapeutic strategy , namely glycoconjugate vaccines . | Epidemiological studies associate infection with S . haematobium , an endemic parasitic flatworm in Africa and the Middle East , with the development of bladder cancer . Nevertheless , little molecular evidence exists supporting this association . This work draws attention to the common molecular pathways underlying these two events , highlighting a potentially unreported link between infection and cancer development . It has been demonstrated that a panel of biomarkers commonly associated with aggressive forms of bladder cancer is also present in non-malignant tissues infected with the parasite . This may offer a means of early identification of people with this parasitic infection who are at risk of developing of bladder cancer , and may guide the establishment of non-invasive diagnostic tests . Furthermore , we observed that parasite eggs mimic the molecular nature of human cells , providing a possible mechanism of immune escape and persistent infection . Such knowledge is considered pivotal to develop novel therapeutic strategies . | [
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"glycobiolog... | 2014 | P53 and Cancer-Associated Sialylated Glycans Are Surrogate Markers of Cancerization of the Bladder Associated with Schistosoma haematobium Infection |
In the search for a cure for HIV-1 infection , histone deacetylase inhibitors ( HDACi ) are being investigated as activators of latently infected CD4 T cells to promote their targeting by cytotoxic T-lymphocytes ( CTL ) . However , HDACi may also inhibit CTL function , suggesting different immunotherapy approaches may need to be explored . Here , we study the impact of different HDACi on both Natural Killer ( NK ) and CTL targeting of HIV-1 infected cells . We found HDACi down-regulated HLA class I expression independently of HIV-1 Nef which , without significantly compromising CTL function , led to enhanced targeting by NK cells . HDACi-treated HIV-1-infected CD4 T cells were also more effectively cleared than untreated controls during NK co-culture . However , HDACi impaired NK function , reducing degranulation and killing capacity . Depending on the HDACi and dose , this impairment could counteract the benefit gained by treating infected target cells . These data suggest that following HDACi-induced HLA class I down-regulation NK cells kill HIV-1-infected cells , although HDACi-mediated NK cell inhibition may negate this effect . Our data emphasize the importance of studying the effects of potential interventions on both targets and effectors .
Antiretroviral therapy ( ART ) is capable of controlling viraemia in HIV-1-infected individuals to undetectable levels . However , ART is not a cure . A pool of latently infected cells persists , despite therapy , for the lifetime of the individual . One approach to target this latent reservoir is to activate latently-infected cells to stimulate virus production followed by cytopathic , immune-mediated , or other interventions to induce the death of the infected cells , a strategy that has been called ‘shock and kill’ [1] . One drug class being explored to activate HIV-1 transcription is histone deacetylase inhibitors ( HDACi ) that include vorinostat ( SAHA ) , romidepsin , and panobinostat . These drugs can induce HIV-1 expression without globally activating the immune system [2] , and in clinical trials have resulted in increased levels of unspliced cell associated HIV-1 RNA [3 , 4] and increased viraemia [5] . However , HIV-1 expression alone may not be sufficient for viral clearance as infected cells may persist despite producing HIV-1 proteins [6] . Therefore an additional ‘kill’ strategy will be required for successful clearance of infected cells . Although CD8+ve cytotoxic T cells ( CTL ) kill HIV-1-infected cells and are the most likely effectors in ‘shock and kill , ’ HDACi may inhibit the CTL response [7] . Additionally , HDACi can also induce HLA class I down-regulation , potentially impairing antigen presentation to CTL . For example , HDACi may induce HIV-1 Nef expression which would subsequently lead to HLA class I down-regulation [8] , or HDACi may directly affect HLA class I levels independently of Nef as has been found in several cell lines [9–11] . Unless this can be addressed , these data suggest that the CTL response may not be sufficient for killing cells after HDACi-induced HIV-1 expression . Natural killer ( NK ) cells also clear infected cells , particularly when HLA class I expression is reduced , and so theoretically may provide an alternative approach to eliminating cells stimulated from latency by HDACi . Here , we show that the HDACi vorinostat , panobinostat , and romidepsin down-regulate HLA class I levels ex vivo independently of HIV-1 Nef to levels sufficient to lead to NK cell degranulation , even without HIV-1 infection . HDACi treatment of infected cells also led to increased NK cell mediated clearance . However , NK cell function was inhibited following treatment with HDACi , indicating that the negative effects of HDACi on NK cells will need to be addressed before NK cells can be used as effective agents of reservoir clearance in clinical trials .
We first examined the impact of HDACi on HLA class I in uninfected primary CD4 T cells ( as cell lines may not accurately reflect in vivo effects ) . We initially cultured uninfected CD4 T cells for 24 hours in the presence of 1μM vorinostat , 100nM panobinostat , or 10nM romidepsin . All three HDACi significantly lowered HLA class I expression in uninfected CD4 T cells compared to untreated controls . The MFI ( mean+/- S . D . ) for untreated cells was 8464 +/-4797 , compared with vorinostat 5851 +/- 4587 ( p = 0 . 04 ) , panobinostat 5724 +/- 4786 ( p = 0 . 01 ) , and romidepsin 4920 +/-3094 ( p = 0 . 0003 ) ( Fig 1A ) . Converting these data to percentage expression confirmed a substantial reduction in HLA class I expression compared with normal levels ( % , S . D . ) : 66 . 2+/-15 . 2% for vorinostat , 64 . 2 +/- 18% for panobinostat , and 58 . 5+/-11% for romidepsin ( Fig 1B ) . Using lower , more clinically relevant doses of vorinostat and panobinostat ( 333nM and 20nM , respectively ) [7] also resulted in a significant reduction in HLA class I expression in primary CD4 T cells ( vorinostat p = 0 . 03 , panobinostat p = 0 . 003 ) ( Fig 1C ) to approximately 77 . 7+/- 17 . 5% and 74 . 2+/-17 . 2% of untreated levels respectively ( Fig 1D ) . We verified the results over a range of drug concentrations ( S1 Fig ) and confirmed the results were not caused by cell toxicity , particularly at the clinically relevant doses where the average viability of each of the HDACi treated cells was >93% compared to untreated controls ( S1 Fig ) . As this effect was demonstrated in uninfected primary cells , the mechanism was HIV-1 Nef-independent . We therefore sought to determine the mechanism behind the lowered HLA class I expression . As HDACi affect transcriptional profiles , we first measured the expression of HLA class I mRNA in HDACi treated uninfected primary cells , under the same culture conditions used above . We found no significant difference in levels of HLA class I mRNA in cells treated with 1μM vorinostat compared to DMSO treated controls ( Fig 2A ) . Similar results were found using panobinostat ( S2A Fig ) . We also measured β2 microglobulin RNA levels following HDACi treatment–a protein that is also required for HLA class I cell surface presentation . Similar to HLA class I RNA , we found no significant difference in β2 microglobulin levels after treatment with vorinostat or panobinostat ( Figs 2B and S2B ) . Having demonstrated decreased surface protein expression of HLA class I in the absence of a decrease in HLA Class I or β2 microglobulin mRNA levels , we tested whether HDACi led to internalization of HLA class I . To do this , total levels of HLA Class I were assayed following cell permeabilisation to measure both intracellular and surface amounts . All HDACi tested significantly lowered total levels of HLA class I ( vorinostat p = 0 . 04 , panobinostat p = 0 . 007 , romidepsin p = 0 . 02 ) , suggesting that HDACi lead to a global reduction in HLA class I , rather than just expression at the cell surface ( Fig 2C ) . To understand the relationship between intracellular and extracellular HLA Class I levels following HDACi treatment , we conducted a time-course experiment measuring both over a 24 hour period following 1μM vorinostat treatment . Although total HLA class I levels declined steadily over 24 hours , vorinostat led to a brief increase in extracellular levels after 4 hours of treatment before declining again 12 hours post treatment and continuing to decline thereafter ( Fig 2D ) . This observed rapid effect of HDACi on HLA class I is consistent with the timing of increased cell associated HIV-1 RNA detected in clinical studies [4] . These time-course data indicate that HLA class I was not rapidly internalized after HDACi treatment as this would have led to a decrease in extracellular levels without changing total levels of HLA class I . As the reproducible HDACi reduction of HLA Class I was impacting total protein levels rather than inducing internalization , we tested whether HDACi were causing enhanced proteasomal degradation of HLA class I , using the proteasome inhibitors MG132 or bortezomib with or without HDACi . However , all proteasome inhibitors tested down-regulated HLA class I to a greater degree than the HDACi ( S3 Fig ) , so any HDACi-specific inhibition could not be distinguished . Finally , we wanted to test the duration of the HDACi effect on HLA class I levels . We treated cells for 24 hours with HDACi and then either left them in culture with drug or washed them three times and cultured them in media alone . Extracellular HLA class I levels were then measured . All vorinostat treated cells had normal levels of HLA class I 24 hours after washing out the drug while panobinostat treated cells took up to 48 hours post washout for normal levels to return ( Fig 2E ) suggesting a dynamic regulation of HLA class I levels rather than the sustained level of transcription described by others [4] . Having shown HLA class I down-regulation in uninfected CD4 T cells , we turned to CD4 T cells from HIV-1-infected individuals receiving ART . We isolated fresh CD4 T cells from participants ( n = 8 ) in the HEATHER ( ‘HIV Reservoir Targeting with Early Antiretroviral Therapy’ ) cohort of individuals treated with ART since primary HIV infection ( PHI ) ( S1 Table ) . Cells were treated for 24 hours with 3 doses of vorinostat , panobinostat , and romidepsin as well as the PKC activator prostratin ( Fig 3 ) . We found that clinically relevant doses of panobinostat and romidepsin significantly reduced HLA class I levels in patient samples ( p = 0 . 002 for both HDACi ) while prostratin significantly increased HLA class I expression ( p<0 . 0001 ) . Vorinostat did not significantly reduce HLA class I expression . These results were not the result of cellular toxicity as the HDACi-treated patient samples had similar viability to the HDACi untreated samples ( S4 Fig ) . Of note , the mean percent reduction of HLA class I expression in healthy controls and ART treated patients were similar for both 20nM panobinostat ( 74 . 2% vs . 67 . 4% respectively ) and 10nM romidepsin ( 67 . 4% vs 60 . 71% respectively ) . As HDACi down-regulate HLA class I in CD4 T cells , we tested whether this would impact CTL recognition under optimised conditions . HLA class I A*02 expressing CD4 T cells were infected with HIV-1 LAI , and treated with or without 100nM panobinostat or 10nM romidepsin . We first confirmed HDACi reduced HLA Class I expression in our in vitro infected cells ( S5 Fig ) . Then , infected cells were co-cultured with CD8 T cells that were transduced to express a TCR with a high affinity for the HIV-1 Gag peptide SLYNTVATL ( SL9 ) , which is expressed on the majority of HLA A*02 infected cells [12] . HIV-1 p24 expression before and after co-culture was assayed as a measure of CTL killing . The percentage of cells expressing p24 was reduced post co-culture consistent with CTL targeting ( Fig 4 ) , but with no significant difference between the untreated and the HDACi-treated CD4 T cells , and no p24 reduction when HLA A*02 negative donors were used ( Fig 4B ) . There was no evidence for any significant effect on viability with HDACi treatment on in vitro infected CD4 T cells ( S4 Fig p>0 . 1 ) , although there was donor-to-donor variation . ( In particular , in two samples of the 100nM panobinostat treated cells there was a substantial reduction in viability ( ~60% of untreated ) ) . Of note , where there was variation in target viability , this did not impact p24 reduction . Additionally , each killing assay with HDACi had its own control where CTL were not added to control for drug toxicity . HLA Class I down-regulation should result in increased NK cell targeting of HDACi treated CD4 T cells . We , therefore , co-cultured primary NK cells at a 1:1 E:T ratio for 5 hours with autologous , uninfected CD4 T cells treated or not with 100nM panobinostat and tested for NK cell degranulation by measuring extracellular CD107a . There was significantly more NK degranulation following co-culture with treated versus untreated targets ( p = 0 . 029; untreated ( % , S . D . ) 1 . 8 +/- 0 . 9 vs panobinostat 4 . 1% +/- 0 . 6 ) ( Fig 5A and 5B ) . This degranulation was not due to an HDACi effect on NK cells as controls without CD4 T cell targets did not show increased CD107a expression ( Fig 5A ) . We tested several E:T ratios to confirm the CD4 T cells were the cause of NK degranulation and compared the level of degranulation to co-cultures using K562 cells , which express no HLA class I , as targets ( S6 Fig ) . NK degranulation decreased as fewer CD4 T cells were added , and co-culture with K562 cells led to more degranulation than with HDACi treated CD4 T cells ( S6D Fig ) , consistent with residual HLA class I expression on CD4 T cells after HDACi treatment . We repeated the co-culture experiments using several doses of vorinostat , panobinostat , and romidepsin ( S6A–S6C Fig ) including 20nM panobinostat and 10nM romidepsin , which also led to significantly more NK cell degranulation ( p = 0 . 01 panobinostat , p = 0 . 0005 romidepsin ) ( Fig 5C ) . Only the highest vorinostat dose showed a similar increase in degranulation while the clinically relevant doses did not ( S6A Fig ) . In addition to degranulation , NK cells cultured with HDACi treated CD4 T cells showed increased IFN-γ and TNF-α production , compared with almost no detectable cytokine levels ( similar to levels of NK cells alone ) when cultured with untreated CD4 T cells ( Figs 5D and S6E ) . As HDACi treatment of CD4 T cells led to increased NK degranulation and cytokine production , we tested whether this translated into increased target killing . CD4 T cells infected with HIV-1 LAI were cultured with or without 100nM panobinostat for 24 hours , at 48 hours post infection . We then co-cultured these infected targets overnight with non-target uninfected CD4 T cells ( NT ) with or without NK effectors at an E:T:NT ratio of 10:1:1 and measured HIV-1 p24 expression ( Fig 6A ) . There was a significantly larger reduction in p24 expression in infected CD4 T cells treated with panobinostat than those without HDACi treatment ( p = 0 . 03 , Fig 6A ) . As NK cells might inhibit p24 expression without killing infected cells [13] , we confirmed that the reduction in p24 reflected actual killing of infected targets by performing a FATAL assay , as previously described [14] ( Fig 6B and 6C ) . Briefly , infected targets were stained with a Cell Trace Violet ( CTV ) dye while non-target cells were stained with a Cell Trace CFSE dye . The ratio of targets to non-targets with and without NK cells was calculated , and the change in this ratio after NK co-culture was converted to percentage killing . NK cells preferentially targeted and killed infected cells , evidenced by a lower target to non-target ratio after NK cell co-culture ( Fig 6B and 6C ) . A significantly larger fraction of infected cells were cleared following treatment with 100nM and 20nM panobinostat ( p = 0 . 014 , Fig 6C and p = 0 . 03 , Fig 6D , respectively ) and 10nM romidepsin ( p = 0 . 01 Fig 6D ) , compared to untreated controls , but not with vorinostat . Increased target killing was unlikely to be due to increased HIV-1 production as there was no evidence for higher levels of p24 in HDACi treated cells before NK co-culture despite higher levels of HIV-1 RNA ( S7 Fig ) , consistent with some prior work showing HDACi alone did not induce HIV-1 expression in ex vivo stimulated patient samples [15] . We performed the same experiments with a range of HDACi concentrations with similar results ( S8 Fig ) . Expression of NK activating ligands MIC A/B , ULBP1 and ULBP2 on CD4 T cells was measured to determine potential mechanisms of the preferential killing of infected cells . CD4 T cells were isolated from healthy donors and half were infected , as above . After 48h of infection both uninfected and infected cells were stimulated with 20nM panobinostat or 10nM romidepsin using untreated cells as a negative control . Cells were stained for MIC A/B , ULBP1 and ULBP2 , all ligands for the activating NKG2D receptor found on NK cells . HDACi treatment led to increased MIC A/B expression on uninfected cells with a greater increase with HIV-1 infection ( Fig 6E ) , although there was no difference in ULBP1 or 2 levels with or without HDACi treatment ( Fig 6F and 6G respectively ) suggesting MIC A/B levels could explain the preferential killing of infected cells through the NKG2D pathway . The inhibitory effects of HDACi on CTL highlight the importance of examining the effects of HDACi not only on potential targets but also on effectors [7] . We therefore examined the direct effect of HDACi on NK function , by testing the ability of 100nM panobinostat treated NK cells to kill HDACi treated infected targets . HDACi-treated NK cells killed significantly fewer cells than untreated NK cells [untreated ( % killed , S . D . ) 60 . 0+/- 5 . 8 vs panobinostat 23 . 4 +/- 15 . 5; p = 0 . 029] ( Fig 7A ) . 333nM vorinostat , 20nM panobinostat and 10nM romidepsin also reduced NK killing , although this was only significant for the latter two ( p = 0 . 006 , p = 0 . 03 respectively ) ( Fig 7B ) . We performed the same experiments using several dilutions of each HDACi with evidence for a dose-related response ( S9 Fig ) . These results were likely not due to cytotoxic effects of HDACi as only the highest , non-physiological doses impacted cell viability ( S9 Fig ) . To determine the mechanism behind the reduced killing we first measured the ability of HDACi treated NK cells to degranulate in response to the HLA class I negative cell line K562 . We co-cultured NK cells treated or not with 333nM vorinostat , 20nM panobinostat and 10nM romidepsin with K562 cells at a 1:1 ratio for five hours and measured extracellular CD107a expression . Both panobinostat and romidepsin treatment resulted in significantly less degranulation than untreated controls ( Fig 7C p = 0 . 001 , p = 0 . 003 respectively ) , while vorinostat treated cells did not ( Fig 7C ) . Experiments were repeated with a wide range of HDACi doses and supported a dose-related response ( S10 Fig ) . We tested whether reduced degranulation resulted in less killing of K562 cells , using the FATAL assay with K562 as targets , THP1 cells as non-targets and NK cells treated with or without 333nM vorinostat , 20nM panobinostat , or 10nM romidepsin as effectors . HDACi that resulted in less degranulation also resulted in less killing of K562 cells ( Fig 7D ) . To understand if HDACi might phenotypically alter NK cells , making them less effective killers , we began by examining CD16 which is associated with cytotoxicity [16] . 100nM panobinostat significantly reduced CD16 expression ( p = 0 . 01 ) while lower doses of panobinostat and romidepsin did not ( S11A Fig ) . We also examined expression of the NK activating receptors NKp46 and NKG2D . Similar to CD16 expression , only 100nM panobinostat significantly reduced NKp46 expression ( p = 0 . 0002 , S11B Fig ) . However , both doses of panobinostat significantly reduced NKG2D expression ( p = 0 . 001 , p = 0 . 04; S11C Fig ) while romidepsin did not , suggesting that different HDACi can have different effects on NK phenotype while still inhibiting NK function . As HDACi directly inhibited NK cell function , but also rendered HIV-1-infected CD4 T cells more susceptible to killing , we wanted to determine the overall effect when both were treated . For five individual donors , we treated either both or neither CD4 T and NK cells with 20nM panobinostat . Overall , there was no significant difference in killing between the two , suggesting the negative effects of treating NK cells with HDACi cancelled out any benefits of treating targets cells with panobinostat ( p = 0 . 95 Fig 8A ) . However , there was donor-to-donor variation: two of five donors showed higher levels of killing , two showed reduced killing and one donor showed no difference . To determine if this result was drug or dose dependent , the experiment was repeated with a further five donors using two doses of vorinostat ( 333nM and 50nM ) , panobinostat ( 20nM and 5nM ) , and romidepsin ( 10nM and 5nM ) as well as 300nM prostratin . Prostratin significantly enhanced NK killing ( p = 0 . 005 ) despite significant effects on cell viability ( S9G Fig ) , while there was weak evidence for both 10nM and 5nM doses of romidepsin enhancing killing ( p = 0 . 08 and p = 0 . 1 respectively ) ( Fig 8B ) . Interestingly , for both vorinostat and panobinostat there was again evidence for donor-donor variation while romidepsin showed no such variation ( Fig 8B ) . Overall , these data suggest that different HDACi have varied effects on NK mediated killing when both effectors and targets are treated , but that romidepsin may allow more effective NK-directed clearance of the HIV-1 reservoir .
In this study we examined the effects of HDACi on HLA class I expression and the impact on CTL and NK cell targeting . We found that all tested HDACi ( vorinostat , panobinostat , and romidepsin ) down-regulated HLA class I expression in a Nef-independent manner . Although HLA class I down-regulation did not significantly impact CTL recognition , it did lead to increased NK degranulation upon co-culture and significantly increased NK cell mediated killing of targets . However , when NK cells were treated with HDACi their killing capacity was reduced . Overall , our results provide insights into the use and pitfalls of NK cells as potential effectors in HDACi based ‘kick and kill’ approaches . It was surprising that all HDACi tested down-regulated HLA class I as previous studies have found HDACi can up-regulate HLA class I and II molecules on tumour cells and cell lines [9 , 17] . However , these studies were not performed using healthy CD4 T cells , and it is possible that HDACi have different effects depending on cell type and health . While HLA class I down-regulation was HIV-1 independent , it is possible that this could lead to preferential targeting of HIV-1 infected cells as NK cell targeting is dictated by a balance of activating and inhibitory signals [18] , which may themselves be impacted by HIV-1 . Our data showing enhanced MIC A/B expression in infected cells treated with HDACi compared to uninfected HDACi treated cells supports this possibility . We saw a similar reduction in HLA class I in cells from patients on ART treated ex vivo with panobinostat and romidepsin . Importantly , the transitory nature of the down-regulation may minimize the potential negative consequences of lower levels of HLA class I in patients . HLA class I down-regulation led us to examine the effects of HDACi on CTL and NK targeting of infected cells . Prior work showed HDACi limited CTL function in vitro upon treatment of CD8 T cells with HDACi [7] . Here , we tested whether HLA class I down-regulation upon HDACi treatment of infected targets might also affect CTL recognition . We found no significant difference in CTL recognition of HDACi treated targets suggesting lower HLA class I levels might not affect CTL clearance to a substantial degree . However , our studies utilized a high affinity SL9 TCR [12] that might not be as impacted by lower levels of HLA class I expression as other TCRs . As the effects of HDACi on both targets and effectors will impact clearance of HIV-1 infected cells , we examined how HDACi affected both CD4 T cells and NK cells , respectively . Interestingly , HDACi treatment on these two cell types had opposite effects on HIV-1 clearance . CD4 T cells ( targets ) treated with HDACi were more likely to be cleared than untreated cells . Increased clearance could be due to a combination of factors including lower HLA class I levels and higher levels of NK activating ligands such as MIC A/B . Previous data have shown increased levels of soluble MIC A during HIV-1 infection [19] as well as reduced levels of MIC A on the surface of CD4 T cells in HIV-1 infected patients [20 , 21] possibly due to MIC A/B shedding by HIV-1 infected cells [19 , 22] . Higher MIC A/B levels caused by HDACi thus might allow killing through MIC mediated pathways . While work has shown the importance of ULBP1 and ULBP2 in NK mediated killing of HIV-1 infected cells [21] we found no significant difference in their expression after HDACi treatment . HDACi treatment of NK cells ( the effectors ) , on the other hand , reduced HIV-1 clearance . This might be due to reduced degranulation or changes to NK phenotype such NKp46 , NKG2D and CD16 expression . We found that different HDACi had varying effects on NK cell phenotype . Both high and low doses of panobinostat reduced NKG2D expression on NK cells , but romidepsin did not . This may be due to the fact that panobinostat is a pan-HDAC inhibitor while romidepsin potently inhibits HDAC 1 and HDAC 2 . Despite this differential effect on NK phenotype , both romidepsin and panobinostat treatment of NK cells reduced HIV-1 clearance . This suggests that reduced levels of degranulation may have played a larger role than NKG2D levels in reducing NK mediated killing . As HDACi impact effectors and targets differently , it was important to know whether treating both would lead to increased or decreased killing . Treating both had variable effects depending on the HDACi used—vorinostat and panobinostat showed donor-donor variation but romidepsin trended towards increased clearance of infected cells with all five donors showing increased NK mediated killing . Previous studies using tumour cells as targets also found treating NK cells with HDACi may decrease their function [23] . However , prior work showed that treating both cancer cells and NK cells with HDACi still led to enhanced tumor cell killing [24 , 25] . It is therefore important to examine this balancing act in the appropriate system , due to inherent differences in cancer and HIV-1-infected cells . All of our experiments were performed in vitro or ex vivo . The effects of HDACi on NK or CTL targeting/function in vivo are still unknown , although analyses of clinical trials currently underway ( e . g the RIVER study [26] ) will shed light on this . While the limited dosing schedule and half-life of the HDACi in vivo may limit effects on the immune system , the short duration of HLA class I down-regulation after removal of HDACi may make it difficult to take advantage of the enhanced susceptibility of HDACi treated CD4 T cells to NK clearance . In summary , our experiments reaffirm the importance of studying the effects of latency reversing agents ( LRA ) on multiple components of the immune response including targets and multiple effectors in a complex system where several cell types interact . Overall , in the search for an effective method to target the HIV-1 reservoir , our data suggest that HDACi alone will not be effective within a ‘kick and kill’ strategy , but that other co-administered agents are likely to be required to enhance efficacy or negate any inhibitory effects .
Primary human CD4 T cells used in this study were isolated from leukocyte cones obtained through anonymous donation to NHS Blood and Transplant ( UK ) after informed , written consent and approval by NHS Blood and Transplant and the National Research Ethics Service Oxfordshire Research Ethics Committee . The HEATHER ( ‘HIV Reservoir targeting with Early Antiretroviral Therapy’ ) study was approved by the West Midlands—South Birmingham Research Ethics Committee reference 14/WM/1104 . Ethical approvals include use of samples for the studies described . All samples were analysed anonymously . CD4 T cells were negatively selected from PBMC using the EasySEP human CD4 T cell enrichment kit ( StemCell Technologies ) per the manufacturer’s protocol . Cells were then cultured in RPMI containing 10%FCS , penicillin/streptomycin and L-glutamine ( R10 ) . HDACi treated cells were cultured in 1μM or 333nM vorinostat ( Sigma ) , 100nM or 20nM panobinostat ( Cambridge Bioscience ) , or 10nM romidepsin ( Abcam ) . For vorinostat ( Sigma ) , we chose a range of 50nM– 1 μM . The high dose of 1μM was chosen as it was used in in vitro latency experiments and the peak serum concentration of vorinostat in vivo was reported to be 1 . 2μM; however as the free concentration of vorinostat was only 360nM [27] , we used 333nM as our physiologically relevant dose . For panobinostat ( Cambridge Bioscience ) , we chose a range from 5-100nM . The mean clinical Cmax of panobinostat at the 20mg p . o . dosing of the CLEAR trial is 40nM [28]and the steady-state plasma concentration is 15-22nM [7] so we chose 20nM as our physiological dose . For romidepsin ( Abcam ) , we chose a range of 5-100nM to match our panobinostat range . The peak serum concentration for clinical doses is 698nM with free drug concentration being 56nM [27] . We thus chose 10nM , which incorporated our cell line toxicity studies . We used 300nM prostratin based on other ex vivo studies [29] . MT4 cells were transfected with a HIV-1 LAI encoding plasmid via electroporation . Viral supernatant was then harvested 9 days post transfection . Isolated CD4 T cells were spinoculated with this LAI supernatant ( 100μL per 1e6 cells , MOI 1 . 8 ) for 2 hours at 1200xg as in [30] . Cells were then washed twice with R10 , resuspended and cultured in R10 with 1 . 25μM saquinavir . If infected cells were treated with HDACi , drugs were added 48 hours post infection and left in culture for 24 hours . CD4 T cells treated or not with HDACi were stained first with the LIVE/DEAD fixable near-IR dead cell stain kit ( Life Technologies ) as per the manufacturer’s protocol along with HLA class I staining using a HLA-ABC clone w6/32 APC antibody ( eBioscience ) . For intracellular staining ( total HLA class I ) , cells were stained with live/dead stain as above and then fixed with 2% paraformaldehyde for 30 minutes . Cells were then washed and then simultaneously permeabilized with 0 . 05% saponin and stained with HLA-ABC APC for 40 minutes . For the time-course experiment , untreated and HDACi treated cells were stained for extracellular and total HLA class I at t = 0 , 4 , 6 , 8 , 12 , 18 , and 24 hours post HDACi treatment . CD4 T cells were cultured in R10 , treated with DMSO or treated with HDACi as above . RNA was isolated using the RNeasy Mini Kit ( Qiagen ) . HLA class I RNA was measured using previously described primers 5’-CCTACGACGGCAAGGATTAC-3’ and 5’-TGCCAGGTCAGTGTGATCTC-3’ [31] . B2 microglobulin was measured using the forward primer 5’-TCAATGTCGGATGGATGAAA and the reverse primer 5’-GTGCTCGCGCTACTCTCTCT [32] . Expression levels were then normalized to 18s rRNA levels measured using the previously described primers 5’TCGAGGCCCTGTAATTGGAA-3’ and 5’GAGTCCTGCGTCGAGAGAGC [33] . All qPCR reactions were performed with a Roche Lightcycler 480 using the Lightcycler 480 SYBR Green I Master Mix ( Roche ) using the following program: 1 cycle of 95°C for 10 minutes followed by 45 cycles of 95°C for 10 seconds , 55°C for 25 seconds , and 72°C for 30 seconds . HLA Class I A*02 positive CD4 T cells were infected and treated or not with 100nM panobinostat and 10nM romidepsin as above . HLA A*02 negative donors were used as a control . CD8 T cells transduced with TCRs recognizing the HIV-1 SL9 gag peptide were supplied by Adaptimmune . Infected CD4 T cells were stained using the Cell Trace Violet Cell Proliferation Kit ( Life Technologies ) as per the manufacturer’s protocol . Non-target uninfected CD4 T cells were stained using the Cell Trace CFSE Cell Proliferation Kit ( Life Technologies ) per the manufacturer’s protocol . CD8 T cells were then co-cultured overnight with targets and non-targets at a E:T:NT ratio of 1:1:1 . To measure p24 , cells were fixed and permeabilized ( as above ) and stained using the KC57 PE antibody ( Beckman Coulter ) . p24 expression was then measured in target cells . PBMC were cultured for 24 hours in R10 with or without HDACi . NK cells were then negatively selected using the EasySep Human NK Cell Enrichment Kit ( StemCell Technologies ) as per the manufacturer’s protocol . Uninfected CD4 T cells were isolated as above and treated or not with HDACi at the indicated doses . Meanwhile NK cells were isolated as above . NK and CD4 T cells were cultured at a 1:1 , 1:0 . 2 , or 1:0 . 01 ratio for 5 hours at 37°C in the presence of an anti-CD107a PE-Cy7 antibody ( Biolegend ) . Experiments using K562 cells were performed at a 1:1 ratio for 5 hours as with CD4 T cells . For both CD4 T and K562 experiments , cells were then washed and stained with a panel of: LIVE/DEAD fixable near-IR dead cell stain kit ( Life Technologies ) , anti-CD3 eflour450 ( eBioscience ) , anti-CD56 APC ( Miltenyi ) , anti-CD16 FITC ( Biolegend ) , anti-CD14 APC-Cy7 ( Biolegend ) , anti-CD19 APC Cy7 ( Biolegend ) . NK cells were gated on APC Cy7 negative cells ( live , CD14- , CD19- ) , CD3 negative , CD56 positive cells . Uninfected CD4 T cells were isolated as above and treated or not with 333nM vorinostat , 20nM panobinostat or 10nM romidepsin . Meanwhile NK cells were isolated as above . NK and CD4 T cells were cultured at a 1:1 ratio for 5 hours at 37°C . After the first hour of incubation 10μM brefeldin A ( Sigma Aldrich ) was added to all cells . After the 5 hour incubation cells were extracellularly stained for CD3 and CD56 as above and intracellularly stained with IFN-γ PE-Cy7 and TNF-α PerCP-Cy5 . 5 . CD4 T cells were isolated as above . Half of the cells ( targets ) were infected with HIV-1 as above . The other half was cultured in R10 ( non-targets ) . 48h post infection , infected cells were treated or not with 100nM panobinostat , 20nM panobinostat , or 10nM romidepsin for 24h . Meanwhile NK cells were isolated as above . 72h post infection targets were stained using the Cell Trace Violet Cell Proliferation Kit ( Life Technologies ) as per the manufacturer’s protocol . Non-targets were stained using the Cell Trace CFSE Cell Proliferation Kit ( Life Technologies ) per the manufacturer’s protocol . NK cells were then co-cultured overnight with targets and non-targets at a E:T:NT ratio of 10:1:1 . In control wells NK cells were not added . After co-culture , cells were stained with LIVE/DEAD fixable near-IR dead cell stain kit ( Life Technologies ) . p24 was measured as above . For the FATAL assay , live cells were gated for targets and non-targets . The number of non-targets:targets ( using cell counts from FlowJo ) in the wells without NK cells was converted to a NT:T ratio . This ratio was then used to predict the number of targets expected in the wells with NK cells based on their non-target number . The difference between the actual and expected number of targets was then converted into a percent targets killed ( % cytotoxicity ) . For FATAL assays using K562 cells the experiments were performed as above with K562 cells as the targets and THP1 cells as the non-targets and were done using a 1:1:1: E:T:NT ratio overnight . CD4 T cells were isolated as above . One fraction of cells was infected HIV as above for 48 hours in the presence of saquinavir while the second fraction of cells was left uninfected in culture . After 48 hours , both uninfected and infected cells were treated with or without 20nM panobinostat or 10nM romidepsin after which they were stained with one of two antibody cocktails: 1 ) LIVE/DEAD fixable near-IR dead stain ( Life Technologies ) and MIC A/B PE ( BD Biosciences ) 2 ) LIVE/DEAD ULBP1 PE ( R&D Systems ) and ULBP2 FITC ( Biorbyt ) . Antibody function was confirmed in Jurkat cells . PBMC were cultured for 24h in the absence or presence of 100nM panobinostat , 20nM panobinostat , or 10nM romidepsin . After culture , wells were stained with a panel of: LIVE/DEAD fixable near-IR dead cell stain kit ( Life Technologies ) , anti-CD3 eflour450 ( eBioscience ) , anti-CD16 APC ( Cambridge Bioscience ) , anti-CD56 FITC ( Cambridge Bioscience ) , anti-CD14 APC-Cy7 ( Biolegend ) , anti-CD19 APC Cy7 ( Biolegend ) , NKG2D PECy7 ( Cambridge Bioscience ) , and CD335 ( NKp46 ) PE ( Miltenyi Biotec ) . NK cells were gated on APC Cy7 negative cells ( live , CD14- , CD19 ) , CD3 negative , CD56 positive cells . All statistical tests were performed using GraphPad Prism 6 ( GraphPad Sofware , Inc . ) . | Antiretroviral therapy successfully controls HIV-1 viraemia and can restore life expectancy to within normal limits . However , antiretroviral therapy is not a cure as HIV-1 persists in a treatment-resistant latent reservoir . Therapy also comes with a high cost , side effects , and a lifetime commitment to pills . Therefore , there is growing interest in finding a cure . One proposed strategy is to use novel agents to stimulate HIV-1 transcription in latently-infected cells , after which the cells could be targeted and cleared by the immune system . Of current interest are the latency-reversing histone deacetylase inhibitors ( HDACi ) , with CD8 T cells and NK cells potentially serving as the HIV-1 killing agents . However , previous studies suggested HDACi negatively affected CD8 T cell function , compromising their role as killers . Here we studied whether NK cells might serve as effective killers of HDACi treated CD4 T cells , especially as HDACi may induce HLA class I down-regulation . We found that although treating infected cells with HDACi resulted in HLA class I down-regulation and enhanced their visibility to NK cells , HDACi inhibited NK cell function overall , suggesting additional killing strategies will be required . | [
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"imm... | 2016 | Histone Deacetylase Inhibitors Enhance CD4 T Cell Susceptibility to NK Cell Killing but Reduce NK Cell Function |
The adaptive immune system depends on specific antigen receptors , immunoglobulins ( Ig ) in B lymphocytes and T cell receptors ( TCR ) in T lymphocytes . Adaptive responses to immune challenge are based on the expression of a single species of antigen receptor per cell; and in B cells , this is mediated in part by allelic exclusion at the Ig heavy ( H ) chain locus . How allelic exclusion is regulated is unclear; we considered that sharks , the oldest vertebrates possessing the Ig/TCR-based immune system , would yield insights not previously approachable and reveal the primordial basis of the regulation of allelic exclusion . Sharks have an IgH locus organization consisting of 15–200 independently rearranging miniloci ( VH-D1-D2-JH-Cμ ) , a gene organization that is considered ancestral to the tetrapod and bony fish IgH locus . We found that rearrangement takes place only within a minilocus , and the recombining gene segments are assembled simultaneously and randomly . Only one or few H chain genes were fully rearranged in each shark B cell , whereas the other loci retained their germline configuration . In contrast , most IgH were partially rearranged in every thymocyte ( developing T cell ) examined , but no IgH transcripts were detected . The distinction between B and T cells in their IgH configurations and transcription reveals a heretofore unsuspected chromatin state permissive for rearrangement in precursor lymphocytes , and suggests that controlled limitation of B cell lineage-specific factors mediate regulated rearrangement and allelic exclusion . This regulation may be shared by higher vertebrates in which additional mechanistic and regulatory elements have evolved with their structurally complex IgH locus .
In the nurse shark , Ginglymostoma cirratum , there are about 15 IgM H chain loci per genome , and every functional gene contains one VH , two D , and one JH gene segments located within 2 kb ( [22]; Figure 1 , bottom ) . These miniloci are located at least 120 kb apart , and aside from two IgH genes depicted in Figure 1 , their linkage relationships are not known [23] . Among outbred individuals there can be 9–12 active IgH , classified into subfamilies called Groups 1–5 . A detailed characterization of two functional loci [22–24] and 78 of their rearrangements show that V ( D ) J recombination took place within the minilocus ( [22 , 23] and V . Lee and E . Hsu , unpublished data ) . There do not seem to be long-distance recombination events between the widely separated IgH loci or , presumably , a major role for chromatin contraction in nurse shark IgH rearrangement . To elucidate the rules for V ( D ) J recombination in the shark , we first investigated rearrangement patterns at the two defined shark H chain loci , asking whether differential VH , D , and JH activation existed in the short ( ∼400 bp ) intersegmental distances . We have found that all combinations are possible , and a completed VDJ is accomplished during one stage only , as if it were like the initial D to J step in mammals . Our results also confirmed that long-distance recombination between different IgH loci in B cells is rare , if it exists . Thus , two elements thought to be intrinsic to regulating the rearrangement process and resulting in allelic exclusion in mammals—ordered long distance recombination and chromatin contraction—are absent in sharks . Thus these findings tell us that certain mechanics of the rearrangement process can be dissociated from the phenomenon of allelic exclusion and that the two processes developed separately in evolution . We investigated rearrangement in shark lymphocytes at the population and the single-cell level and established that H chain exclusion does occur in shark B cells , where only one or a few of its many IgH loci rearrange in any one cell . We also looked at IgH loci in shark thymocytes ( precursor T cells , see Text S1 ) . Although T cells do not express Ig , the IgH genes were extensively , although partially , rearranged; Ig transcripts were not detected . The differences between B cells and thymocytes demonstrated here suggest there exists in precursors to B and T cells an IgH chromatin state already permitting rearrangement , but in B cells it is further potentiated by lineage-specific factors , leading to efficient recombination at one or a few H chain genes and results in H chain exclusion . We propose that the molecular basis establishing allelic exclusion was achieved in the earliest vertebrates possessing Ig genes , and it is independent of the wide variation in Ig gene number observed in different species .
The experiments are summarized as follows . We first focused on how rearrangement takes place in one IgH subfamily , Group 2 , in tissues and isolated cell populations . We demonstrated that partially and fully rearranged VH sequences can be amplified from lymphoid tissue DNA , but not from red blood cell ( RBC ) control DNA ( Figure 2 ) . All anticipated genomic rearrangement configurations were obtained ( Table 1 ) . These data demonstrated that rearrangement in sharks is different from the ordered , two-stage process observed in mammals . Lymphoid tissue can carry B cells , which express IgM , and T cells , which do not . The initial experiments revealed the unexpected finding that somatic IgH recombination is also present in the thymus , an almost exclusively T cell–containing tissue . We further investigated this finding , using genomic Southern analyses . Compared to RBC and heart DNA , new—somatically rearranged—bands were observed in all lymphoid tissue DNA tested . These new bands were demonstrated to be V ( D ) J rearrangements mapping to predictable locations ( Figure 3 ) . The recombined bands were identified by probes detecting all VH or only the Group 2 subfamily . A comparison of surface IgM-positive ( sIgM+ ) B cell DNA with thymus DNA showed that their respective patterns differed in the quantity and quality of the rearranged bands ( Figure 4 ) . Thus , we discovered that V ( D ) J recombination is much more extensive but mostly incomplete in T cells . This result was confirmed through investigating the status of all functional IgH loci in thymocytes and in B cells by single-cell genomic PCR ( Figure 5 ) . As predicted from the genomic Southern analysis results , only a few rearrangements could be obtained from single B cells , and these were fully rearranged VDJ; the other IgH loci were in the nonrearranged , or germline ( GL ) , configuration ( Figure 6 and Table 3 ) . Unlike in thymocytes , partial rearrangements ( Figure 7 ) were infrequent in B cells . These results show that in the developing B cell , there was a limited number of genes activated to rearrange , but once initiated , the recombination process went efficiently to completion . In contrast , multiple and mostly incomplete Ig rearrangements were found in single thymocytes ( Table 2 ) , and neither Ig H chain transcripts nor L chain expression and rearrangement could be detected in the thymus ( Figure 8 ) . This ability of DNA to act as substrate for RAG in the absence of transcription suggests a previously unknown state of chromatin activation . It was possible to detect this state only in an animal with multiple , independently rearranging sites , but such an observation signals that RAG may act on nontranscribing loci in other organisms as well . We propose that IgH in all shark precursor lymphocytes can be acted upon by RAG recombinase but that B lineage-specific factors are responsible for regulated rearrangement—and H chain exclusion—in the B cells . PCR primers ( Int/JH2; Figure 2 ) targeting the leader intron of Group 2 VH and the JH gene segment amplified DNA sequences of 1 . 6 kb from an individual shark ( -JS ) whole blood DNA ( Figure 2B , lane 2 ) ; this band contained the two functional Group 2 genes in the nonrearranged , or GL , configuration ( see PCR and probes , [22] ) . The Int/JH2 primers amplified the same 1 . 6-kb fragment from erythrocyte DNA in other genetically unrelated individuals ( Figure 2B , lanes 4 and 6 ) , demonstrating that Group 2 GL gene segments are in the organizational configuration depicted in Figure 1 . The intersegmental distances in Group 2 as well as other IgM genes are all about 400 bp ( Figure 1 ) . A single , initial somatic rearrangement event ( 1R ) , such as joining of V to D1 , would delete this interval and reduce the total V to J genomic span detected by the Int/JH2 primers to about 1 , 200 bp . Likewise , two rearrangement events ( 2R ) would give rise to a PCR product of about 800 bp , and three rearrangements ( 3R ) 400 bp . From lymphoid ( spleen and peripheral blood leukocytes [PBL] ) genomic DNA , a ladder of PCR products hybridizing to the vh probe ( Figure 2 ) can be detected , corresponding to the anticipated sizes of partial and completed genomic rearrangements ( Figure 2B , lanes 1 , 3 , and 5 ) . The arrows at the left of lane 1 point to the fragments that were later cloned from all three sharks and identified as having one , two , or three rearrangements . Splenic lymphocytes and PBL from adult sharks do not express RAG recombinase ( [25]; W . Feng and E . Hsu , unpublished data ) , so that these rearrangement intermediates would be relics from earlier stages of lymphocyte differentiation . PCR products obtained from shark-JS splenic DNA were cloned , and the insert sequences are classified by size in Table 1 . Within each size group , different rearrangement combinations were found , but some are more frequent than others . The junctions ( Figure S1 ) show that each clone is unique , with the typical diversity generated by trimming and N/P region addition . In order to determine which cells contained these rearrangements , surface IgM-expressing ( sIgM+ ) cells were isolated from PBL ( see Figure S2 ) . The PCR reactions performed on this population and the thymus both amplified sequences that showed higher frequency of VH to D1 joining ( Table 1 ) , but all combinations exist . The fully rearranged VDJ ( i . e . , VDDJ ) from the sIgM+ cells tended to be in-frame , whereas in those from the spleen and thymus , the nonfunctional ones are in the majority ( last two rows , Table 1 ) . Thus , it appeared that IgH recombination had occurred in both precursor T and B lymphocytes . The pattern of rearrangement for Group 2 , as demonstrated by the frequencies of the intermediate configurations , was similar in all samples . Although the 5′ primer is specific for Group 2 , the 3′ primer could target any IgH JH . If rearrangement occurred between Group 2 VH and another locus , it would have been possible to detect non-Group 2 intersegmental sequence in the partial configurations shown in Table 1 . ( All but one of the partially recombined clones had rearranged within Group 2 loci , as ascertained by sequencing or restriction enzyme analyses . In one thymus VDD-J clone , the VH originated from Group 2 , but the D2 , D2-J intersegmental sequence and JH were from Group 1; only four nucleotides belong to the D1 of either Group . If this sequence were a PCR artifact , the area of homology would have to have been in the N region sequence between VH and D1 or D1 and D2 . We screened a total of 58 thymic VDD-J [unpublished data] , but this was the only apparent instance of interlocus rearrangement outside these Group 2 IgH . ) Two kinds of probes were used during screening , all of which had been derived from a GL Group 2 bacteriophage clone ( Materials and Methods ) : the vh probe and the intersegmental probes , vd2 , dd2 , and dj2 ( Figure 2 , top ) . The latter were used to detect the infrequent recombination intermediates in these experiments ( Table 1 , footnote a; e . g . , the Group 2 V-D-DJ configuration is vd2+ , dd2+ , dj2− ) . On genomic Southern blots , they proved to be specific for Group 2 only , whereas in contrast , the vh probe cross-hybridizes with nurse shark V gene segments from all subfamilies ( see Figure S3 ) . In the following genomic Southern blotting experiments , these probes were used to detect rearrangement globally ( vh probe ) as well as specifically at three Group 2 IgH ( vd2 , dj2 probe ) in lymphoid tissue DNA . All the genes encoding nurse shark IgM H chain have been cloned and the functional genes can be classified into five subfamilies , Groups 1–5 ( see legend , Figure 3 ) ; the VH gene segments share >75% nucleotide identity [24] . The various vh-hybridizing bands in RBC DNA can be correlated with anticipated fragment sizes after BamHI/NcoI digestion ( Figure 3 , RBC lane in vh panel with map ) . Although the DNA amounts are similar in RBC and PBL lanes ( Figure 3 , right , ns3v panel ) , there are novel vh bands in the PBL sample ( Figure 3 , PBL lane in vh panel ) . Compared to RBC DNA , the bands at 1 , 500 bp and 700 bp in PBL are more intense , and a new band appears at 1 , 100 bp . These three bands correspond to predicted configurations of rearranged DNA from the various IgH , but mostly from Groups 2 and 4 ( Figure 3 , 1R-3R in blue ) . At the same time , the 1 . 9-kb band encompassing the Groups 2+4 GL gene segments in PBL is ca . 23% less than that of the RBC counterpart ( see Figure 3 , legend ) , demonstrating loss of the GL band after acquisition of rearranged configurations . We observed that the relative amount of DNA rearranged was different between thymus ( predominantly T cell ) and sIgM+ cells ( B cells from PBL ) . Although the images in Figure 4 are from X-ray films , phosphorimager analyses were performed for a quantitative analysis . We centered our analyses on depletion of the 1 . 9-kb band because it is a single GL configuration of known genes Group 2+4 , whereas “gain” measurements cannot be so clearly resolved . For instance , gain of signal in the 1 . 5-kb region means a combination of Group 2/4 1R plus nonrearranged GL Group 5 , but minus an unknown amount of loss by Group 5 rearrangement . To obtain a rough idea of the proportion of rearranged IgH in B cells only , the DNA from sIgM+ cells from shark-GR PBL was compared to DNA from its RBC ( Figure 4A ) . The “flow-through” sample is from the population mostly depleted of sIgM+ cells and consists of thrombocytes , granulocytes , and lymphocytes ( T cells and some B cells that slipped through ) . An obvious difference between sIgM+ and the flow-through population is the greater intensity of the 700-bp band in the former ( Figure 4A ) . This band mostly contains 3R species , suggesting that most Group 2+4 rearrangements in B cells are VDDJ . There is a 19% signal reduction of the 1 . 9-kb Group 2+4 GL band in the sIgM+ lane . The “flow-through” DNA also contained few rearrangements , as assessed by both loss of GL ( 8% ) and gain of rearranged bands . However , unlike the sIgM+ sample , the “flow-through” was a mixture of cell types , and lymphocytes in PBL can range from 5%–30% . DNA from shark-JS spleen , thymus , whole blood , and heart were compared . There were rearranged Ig bands in spleen and thymus , but these were not detected in blood or heart DNA ( Figure 4B ) . The frequency of lymphocytes in whole blood is very low ( 0 . 02%–0 . 12% , 1 PBL/250 RBC ) , and in shark-JS , the heart tissue was bled out . The amounts of DNA in the first three lanes in Figure 4B are similar , and a comparison of the intensities of the 1 , 500-bp , 1 , 100-bp , and 700-bp bands between the spleen and thymus samples in Figure 4B suggests that more Ig rearrangements were present in the thymus DNA . Indeed , upon calculation , 60% of the thymus vh-hybridizing GL 1 . 9-kb Group 2+4 band was depleted . In summary , in one B cell-enriched sample ( sIgM+ cells from PBL ) , 19% of Group 2+4 genes were rearranged and mostly to VDDJ , whereas in thymus , 60% of Group 2+4 genes were rearranged , mostly to intermediate configurations . In order to analyze these blotted DNA samples in more detail , we performed hybridizations with probes that detect only Group 2 IgH ( G2A , G2B , and pseudogene G2C ) . The resulting bands can be correlated with Group 2 rearrangement intermediates characterized in Table 1 ( Figures 4C , vd2 , and 4D , dj2 ) . The 1 , 500-bp ( VD-D-J/V-DD-J ) and 1 , 100-bp ( VDD-J ) bands detected by dj2 probe in thymus appear to be as intense as the 1 . 9-kb Group 2 GL signal and reflect the high frequency of these events ( Table 1 ) . Again , using the GL band as an internal reference , the Group 2 vd2 signal demonstrates that other Group 2 configurations ( V-DD-J/V-D-DJ at 1 , 500 bp and V-DDJ at 1 , 100 bp ) do exist but are less frequent , consistent with results from Table 1 . All the previous experiments were performed on mixed and purified cell populations , and although we can anticipate the general trend in T cells ( many and partial rearrangements ) and in B cells ( few and completed rearrangements ) , this remains to be shown at the individual cell level . Single-cell analysis was made possible by previous studies in which all the GL IgH sequences in nurse shark have been characterized [22 , 23] so that degenerate , universal primers could be synthesized , targeting and detecting only the functional genes . Likewise , it was possible to devise primers specific for each Group , just as Int was specific for Group 2 genes . We first focused on thymocytes . We picked single thymocytes , performed single-cell PCR with the universal primers in a two-stage assay ( Materials and Methods ) and demonstrated the existence of multiple IgH rearrangements . For controls , an erythrocyte was picked after every two thymocytes . Out of 24 RBC , five failed to amplify and the remaining 19 showed only the GL bands . Of the 48 thymocytes , 44 had a variety of 1R , 2R , and 3R bands . Figure 5 ( top ) shows the results from the first 18 cells after the second round of PCR with nested universal primers . Other nested PCR was also performed with Group-specific primers to Group 2 ( Figure 5 , middle panel , and Figure S4 ) , Group 1 ( Figure S5 ) , Group 4 ( Figure S6 ) , and Group 5 ( Figure S7 ) . The summary of these results is shown in Table 2 . For the most part , little GL sequence can be detected , except in the RBC controls , suggesting either that most of the IgH had rearranged or that the many rearrangements caused the longer GL fragments to be out-competed . Either possibility is the result of widespread IgH rearrangement in the single thymocyte . The various anticipated rearrangements could be cloned from any thymocyte ( Table 2 , footnote ) . The thymocyte result is in contrast to what we obtained in B cells ( Figure 5 , top and bottom , respectively ) . Using the identical PCR conditions and reagents , the PCR performed with the universal primers on surface L chain–positive B cells produced predominantly 3R bands . Moreover , GL bands were also present in almost every one of these samples . When several samples of B cell 3R fragments were analyzed on denaturing gels , they appeared to consist of only one or two species per sample ( Figure S8 ) . We went on to identify the rearranged and nonrearranged IgH in single B cells . Using the Group-specific primers , we performed nested PCR on the first-round products of the single B cells ( Figure 6 , A amplifications ) and found that each B cell carried one or only a few rearrangements . Each 3R band was cloned and the number of VDJ species determined per cell ( detailed in Figure S9 and legend ) . We also amplified nonrearranged GL sequence from each cell by using Group-specific primers directed to the intersegmental regions ( Figure 6 , B amplifications ) and identified the genes in each fragment by restriction enzyme sites . These tests were tailored for the donor , shark-GR , all of whose IgH were isolated and sequenced for this experiment ( Figure S10 ) . Table 3 summarizes the results from 13 B cells . The CDR3 sequences of these VDJ are shown in Figure S11; the rearrangements in Table 3 are indicated as out-of-frame ( VDJø ) or in-frame ( VDJ+ ) or nonfunctional ( in-frame but containing stops , VDJn ) . All 13 B cells contained 3R rearrangements , and one cell ( KS23 ) carried a 2R species as well ( Figure 7 ) . We have shown a remarkable disparity between T cells and B cells in IgH gene configuration . In thymocytes , there are multiple and mostly partial IgH rearrangements per cell . Although we cannot claim to detect every VDJ rearrangement present in a B cell , the many IgH genes that remain in GL configuration support the observation that few IgH were rearranged in a single B cell . Many VDJ in Table 3 are out-of-frame or contain stops , consistent with there being only one functional VDJ per cell . In one cell , KM13 , we found two VDJ that were both in-frame ( G1; G4CG ) and carried no stops in CDR3 ( Table 3 ) , whereas the third VDJ is out-of-frame ( G2A ) . One of the former ( G4CG ) encodes a CDR3 of 24 amino acids , an aberration among nurse shark cDNA CDR3 , which range from 4–17 codons ( average 11 . 6 codons , n = 64 ) in one study [24] and 7–16 codons in another ( adult G4 cDNA , average 11 . 3 codons , n = 41 , W . Feng and E . Hsu , unpublished data ) . However , the G1 VDJ not only contains a CDR3 of average size ( 11 codons ) but is also the only one that has been hypermutated , and its mutations show evidence of positive selection ( National Center for Biotechnology Information [NCBI; http://www . ncbi . nlm . nih . gov/] accession number EU719628 ) . There are eight substitutions , with only those in the CDRs resulting in replacement changes . Three point mutation changes in FR2 and FR3 are synonymous , but the CDR1 point mutation ( R to W ) , and the point mutation ( Q to K ) and 3-bp tandem mutation ( S to R ) in CDR2 all result in nonconserved changes . Tandem mutations are characteristic of the nurse shark hypermutation process [22] , and the frequency of PCR-induced changes after 70 cycles in these studies is 0 . 14% ( 13/10 , 371 bp ) , or less than one change per 400-bp VDJ fragment . We do not know whether the VDJ with the 24-codon CDR3 encodes an IgM protein , but it is clearly not part of the selection process acting on this hypermutating B cell . Perhaps , considering their very different CDR3 sizes , there is L chain preference for one polypeptide enabling its expression . We then ask , how often do two rearrangements result in similar CDR3 ? There are four cells ( KM5 , KM13 , KS3 , and KS23 ) in which more than one VDJ is present although most of these are nonfunctional . The junction sizes range widely . The number of nucleotides between TGT in the VH flank and TGG in the JH flank are 34 bp/45 bp in the KM5 VDJ , 39 bp/44 bp/78 bp in KM13 , 24 bp/59 bp/72 bp in KS3 , and 33 bp/41 bp in KS23 ( Figure S11 ) . With six flanks trimmed and three sites for N region addition per VDJ , it seems unlikely that any two VDJ in a B cell , even if both are potentially functional , would have such similar CDR3 sequence content and loop sizes that they would combine equally well with the available L chain . Thus , constraints operating at two levels—the combination of the random nature of V ( D ) J rearrangement and L chain compatibility—serve to enforce H chain exclusion . We propose that rearrangement ceases with the production of a successful H and L chain combination . There are few partially rearranged IgH present in B cells , as the 2R in KS23 . Here , the constellation of in-frame ( presumed functional ) G4 VDJ , the out-of-frame G2A VDJ , and the partially rearranged G2A 2R allele suggest that there was a signal for cessation of rearrangement for the G2A in VDD-J configuration once a viable μ protein was generated . Ig transcripts from functional and nonfunctional rearrangements can be cloned from B cell-containing shark-JS lymphoid tissue using Int/JH2; we found that the use of a primer in leader intron selects for Ig transcripts unspliced in this region , the majority of which are from aberrant ( out-of-frame , partially rearranged ) genes . The 3R ( VDDJ ) sequences were obtained from spleen cDNA , and many were mutated regardless of whether they were productive VDJ or not . Of the 17 2R events we cloned , two were VD-DJ , and one of them carried several mutations in the V region although not in the D-D intergenic sequence . Of 15 independent VDD-J clones , nine were mutants , of which seven contained substitutions throughout V and the D-J intergenic sequence . The mutation patterns are typical of the type previously described in shark Ig , consisting of point and tandem mutations [26] . One such example , A36 , is shown in Figure S12 . In contrast , there is very little Ig mRNA in shark-JS thymus , as observed by northern blotting ( Figure 8 ) , whereas these and other probes for nurse shark L chain isotypes detect abundant mRNA in spleen and epigonal organ . TCR β chain is abundant in thymus RNA . Reverse transcriptase PCR ( RT-PCR ) experiments using Int/JH2 to detect Group 2 2R in thymus cDNA were negative ( unpublished data ) . Given the extent of thymic IgH rearrangement described in the preceding section , we conclude that if Ig transcription does occur in precursor T cells , the RNA species are at extremely low levels . As the IgH rearrangements in thymus were a surprising observation , we investigated whether Ig L chain genes were also active in any way . The nurse shark L chains are encoded by three isotypes , NS3 , NS4 , and NS5 [27] . NS4 is most abundant ( about 60 to 70 IgL ) , consists of both rearranging and germline-joined loci , and contributes about 90% of the L chain cDNA clones; neither NS4 nor the germline-joined NS3 could be detected in thymus RNA ( Figure 8 ) . In NS5 , there are four genes , two of which can rearrange; they each consist of one VL and one JL gene segment and one C exon . Whereas somatically rearranged NS5 genomic sequences can be amplified from any source that contains B cells , none was observed in the shark-JS thymus DNA sample ( Figure S13 ) . The rearranged NS5 band in the control spleen sample was visually apparent in ethidium gels . In thymus , few if any NS5 genes somatically rearrange , and certainly not on the scale of the IgH . Thus , like in mouse , Ig rearrangements in thymocytes involve only the H chain loci .
The mechanisms that contribute to generating H chain exclusion—differential chromatin domain activation , locus contraction—have evolved with and are a consequence of the complex mammalian Ig organization . In this study , we have shown that these processes are not necessary to effect H chain exclusion in all vertebrates . Our model , the nurse shark , provides a naturally minimalist IgH locus with four rearranging gene segments . Because rearrangement can be initiated by any gene segment pair , it seems unlikely that the spatially close V , D , and J elements are regulated separately from each other or subject to different chromatin accessibility constraints . Preliminary data from non-Group 2 subfamilies show that rearrangement patterns can vary considerably; for instance , in Group 5 , V-DDJ is a prominent configuration that is rarely observed for Group 2 ( Tables 1 and 2 ) . Such observations suggest that , once the gene is accessible to recombinase , a preferred order of rearrangement is probably governed by locus-specific factors , for instance , the relative recombination efficiency of particular RSS pairs . With one possible exception , the 97 1R/2R rearrangements isolated in this study ( Table 1 ) occurred within the minilocus , supporting conclusions drawn from cDNA observations . Long-distance recombination events and sequential chromatin activation do not occur during the shark IgH V ( D ) J recombination process , demonstrating that in the absence of major aspects of the complex pathways described for mouse allelic exclusion , H chain exclusion will still be managed by limitation of rearrangement . We established in this report that IgM receptors appear to be clonally expressed in nurse shark and likely all elasmobranch fishes . In one study in the clearnose skate [28] one to three different CDR3 μ junctions were obtained by RT-PCR from single cells . Unfortunately , most of the 100–200 clearnose skate IgH are not characterized , and a number of them are germline-joined VDJ , which make these results difficult to evaluate . We have classified all nurse shark Ig H chain genes in a BAC library and determined those that are functional [23] . Our PCR primers target these genes only . We found ten functional H chain genes in the individual shark-GR and detected in its B lymphocytes one to three VDJ rearrangements per cell . At best , only one VDJ per cell was potentially functional . The other IgH were nonproductive VDJ or in GL configuration . We believe that most elasmobranch B cells express one dominant H chain mRNA and one IgM receptor . Eason and coworkers [28] hypothesized that one gene is activated at a time , like in the multigene olfactory receptor system . A mechanistic connection seems unlikely , in the absence of an evolutionary relationship between genes encoding Ig superfamily and seven transmembrane domain proteins . From our studies , it appears that either a few IgH loci are rearranging at the same time in the pro-B cell or there are sequential “tries” before a viable H chain protein is generated . The answer is possibly in between . Partially rearranged 1R and 2R configurations do exist in B cells as best demonstrated by cloning of mutated cDNA ( Figure S12 ) and the 2R species detected in B cell KS23 ( Figure 7 and Table 3 ) . The relic incomplete rearrangement configurations in B cells might suggest a feedback mechanism that functions with staggered initiation of rearrangement among loci . Alternatively , a few IgH are fully activated to rearrange , more or less simultaneously; hence the infrequent laggard 2R in the population . In such a scenario , there would be a limited but clear possibility for allelic inclusion . That we do not find many such examples suggests that the probability for two viable rearrangements is low , and as illustrated in the case of KM13 , L chain preference DNA might permit only one H chain polypeptide for the receptor . As in mammals , ongoing shark IgH rearrangement probably ceases with the formation of a functional VDJ and expression of the IgM receptor . If L chain rearrangement occurs subsequently , the H chain loci might be transiently inactivated , as occurs with the non-expressed allele in mouse [20] . We have speculated that H and L chain rearrangement occur simultaneously in shark [29] , and all rearrangement ceases with the formation of a viable cell surface receptor . However , there currently is no experimental evidence favoring either possibility . The question remains , how are 15 or 100 IgH loci to be regulated if more than one gene can be activated per cell ? In point of fact , genetically manipulated model systems with more than two H chain genes have been studied . In interspecies hybrid tetraploid and triploid Xenopus [30] and in mice triallelic for IgH [31] allelic exclusion of H chain was observed , despite the increased number of potentially competing genes . There is no reason to believe that in these animals Ig expression is regulated any differently than their diploid version . If that is the case , H chain exclusion is initiated by nonsynchronously occurring rearrangement , and it does not matter how many available genes there are . It is generally accepted that the crucial step differentiating two alleles or multiple genes should be at V to DJ stage [32] . However , in the shark , there is no such second stage; the asynchrony must occur at the initiating step of rearrangement . Liang and coworkers [33] inserted a GFP reporter into the kappa locus to mark its activation and found that the gene was transcribed at an unexpectedly low frequency in pre-B cells . They suggested that allelic exclusion at the kappa locus is based on probabilistic enhancer activation . Possibly a predetermined allele preference [34 , 35] contributes to the initial choice , but it was also suggested [33] that a competition for transcription factors would forestall activation of the second gene . We observed few but mostly fully recombined IgH per shark B cell and propose that there are limiting amounts of trans-factors that target a gene for highly efficient , processive rearrangement , such that however many IgH genes are in the genome , recombination in B cells does not commence at the same time at more than one location . The focused activity at a few IgH also may have the effect of draining other components from general use . Since shark IgH genes lack the usually well-conserved upstream octamer motif [22 , 36] , their trans-factors must differ from and are not competed for by L chain genes if they rearrange at the same time . The first compatible and viable H and L chain combination forming a receptor will generate the feedback signal . If by chance more than one viable H chain is produced at the same time , they may be differentiated by their ability to pair with the available L chain . The surprising finding in these studies is V ( D ) J recombination at multiple IgH loci in every thymocyte , and despite the numerous H chain rearrangements present , Ig transcripts are not detected . The majority of thymic IgH are left incomplete as 1R or 2R , further underlining the difference of their estate from that in B cells . Since these IgH genes are not transcribed as in B cells , despite the extensive rearrangement , and are mostly not fully recombined , essential components are obviously lacking in thymocytes . Taken altogether , we propose that in those thymocytes which are in the process of actively recombining their TCR genes also harbor IgH in a rearrangement-permissive state , and this is possibly a prelude to full activation of the chromatin , which can only be achieved in the presence of B lineage-specific components that would include IgH transcription factors . Since cDNAs of rare , aberrant rearrangements of Ig V gene segments to TCRγ have been observed in a shark thymocyte cDNA library ( M . Criscitiello and M . Flajnik , unpublished data ) , we conclude that factors capable of binding the Ig promoter ( and perhaps eliciting local chromatin remodeling [37] ) could be present in thymocytes . Most recently , transcription has been shown correlated with rearrangement competence and induction of chromatin changes [11] . One commentary [38] speculated on the connection between transcription , chromatin remodeling , and recruitment of RAG , pointing out that RAG2 contains a methyl lysine-binding region that may act as a reader for the histone code of the chromatin and thus may act differentially depending upon the pattern of the histone modifications . It is currently thought that the formation of a Ig/TCR promoter-enhancer holocomplex , consisting of a complex of nuclear factors-DNA interaction , directs the chromatin remodeling and DNA modifications that promote chromatin interaction with RAG [39 , 40] . We propose that the limited number of rearranged IgH per shark B cell is a result of infrequent formation of the holocomplex , which contains lineage-specific factors . These ideas are summarized in Figure 9 . In the absence of B cell-specific factors participating in this holocomplex , IgH in shark precursor lymphocytes may still achieve alternative states of accessibility that are not optimal but not prohibiting for unregulated rearrangement . We propose that a quasi-activated level of chromatin accessibility can exist , supports interaction with RAG , and has distinguishable characteristics .
Shark-JS , -GR , -J , -Y , -BL , and -PI ( G . cirratum ) were captured off the coast of the Florida Keys and maintained in artificial seawater at approximately 28 °C in large indoor tanks at the National Aquarium at Baltimore . Shark-GR was 7 y of age at the time of bleeding . Whole blood was obtained from the caudal sinus and passed through a Ficoll gradient to separate PBL from RBC . Shark-JS was about 5–6 y of age when sacrificed , and its organs were harvested and frozen . Shark-PI was 3–4 y of age . The thymus was dissociated , passed through a cell strainer mesh ( Falcon 2235 ) , and subjected to magnetic cell sorting ( see below ) . DNA and RNA were obtained from PBL and frozen tissues using routine procedures . DNA can be extracted from the RBC , which are nucleated . There are three Group 2 genes , G2A–C , formerly called V2–4 in [18] ( NCBI accession numbers DQ192493 , DQ192494 , and DQ857389 ) . Mismatches in the primers caused the pseudogene ( G2C ) not to be amplified in this study . Group 2-specific primers used to detect recombined DNA of G2A and G2B include oligonucleotides targeting the leader ( V18–1 ) or the leader intron ( Int , 5′-ATTCAGCAATCAGATAAT-3′ ) . These were paired with primers detecting sequence 3′ of D1 ( RSS-D1 , 5′-GAATGAGGATGTCGGTAT-3′ ) or in JH ( JH2 , 5′- TCACGGTCACCATGGT-3′ ) . Primers for the intersegmental sequence between the two D genes ( IntDD-F , 5′-GACGATTCAGAACATAGC-3′ ) and the unique 5′end of the G2-V2 Cμ2 ( V18C2–3′ , 5′-CGGAGGGTCACCGTTTCC-3′ ) detected transcripts from partially rearranged Group 2 . The names of the probes are in lower case ( e . g . , ns3v probe to the NS3 L chain V region gene , ns4c probe to NS4 L chain C exon ) . The vh probe ( vh: V18–1 5′-ACCAGAATGACGACGATG-3′ and V18–2 5′-GTCTTCGATCTTCAGGC-3′ , 461 bp ) , although derived from a Group 2 sequence , cross-hybridizes with all nurse shark VH [18] . Locus-specific probes can be obtained by using the intersegmental sequences , which are relatively nonconserved among the H chain subfamilies . Probes ( Figure 2 ) for the V-D and D-J intervening DNA ( vd2: primers IntVD-F 5′-GTACATTGCACCGTAAAC-3′ and IntVD-R 5′-CGCTCATTCTCTGTTC-3′ , 352-bp PCR product; dj2: IntDJ-F 5′-ACAGTGCAGTGTTTACT-3′ and IntDJ-R , 5′-TCACGGTAAATCGTCATC-3′ , 239 bp ) that were generated from the bacteriophage V18 [22] carrying a G2A gene will detect only the three Group 2 loci , G2A , G2B , and G2C ( Figure S3 ) . A probe to the conserved cDNA Cμ membrane sequence was also obtained ( mem: mem1 , 5′-GATTCGATAGATCACACT-3′ and mem2 , 5′-AAACAGGACTGATTGTAT- 3′ , 216 bp ) . Probes to the three nurse shark L chain types NS4 [41] , NS3 [26] , and NS5 [29] were described previously , and probe names specify whether they detect the V or C sequence ( ns3v , ns3c , etc . ) ns3v hybridizes to the germline-joined VJ genes of the nonrearranging NS3 L chains [26] and used to standardize DNA on genomic Southern blots because the position of the 3 . 7-kb band did not overlap with any of the H chain probes . Nurse shark TCRβ C region was cloned from genomic DNA ( TCRB-CF , 5′-TCACCAGCAGAGCTGAGA-3′; TCRB-CR , 5′-ATACAGGATGCTCTTGCA-3′ ) . Shark nucleotide diphosphate kinase ( ndpk: NDK-F , 5′GGTAACAAGGAACGAACC-3′; NDK-R , 5′-AAAGTTAGTTTATTGTAG-3′ ) was cloned using PCR primers derived from the available sequence ( accession number M63964 ) [42] . The blots were subjected to autoradiography , and signal intensities of bands were quantified using a Storm 860 phosphorimaging system with ImageQuant software ( Molecular Dynamics ) . For IgM+ selection , the buffy coat was resuspended in a mixture of shark IgM-specific mAbs ( CB5 , CB11 , and CB16; [43] ) , and then with goat-anti-mouse IgG Microbeads ( Miltenyi Biotec ) . Approximately 1 . 5–5 × 107 cells were collected after two rounds of column purification ( Miltenyi Biotec ) . The negative population was collected as the “flow-through” from the first round of magnetic activated cell sorting ( MACS ) . The positive cells were small and round ( lymphocyte-like ) cells , whereas the negative population contained cells of different shapes and sizes . Thymocytes were mixed in medium containing a mAb specific for nurse shark NS4 L chain C region ( LK14; [44]; E . Hsu , unpublished data ) , and the L chain-negative cells were collected as “flow-through” from the MACS LS column . Cells were collected after magnetic cell sorting , and RBC were obtained from the same individual for negative controls . Single cells were picked by hand under an inverted microscope with a finely drawn microcapillary pipette ( Fisherbrand , #21-164-2G ) . MAC-sorted lymphocytes were picked , alternating with RBC from another dish; the pipette was rinsed three times in between . The cell was deposited in a 1–1 . 5-μl volume in shark PBS; 5 μl of lysis solution ( 1× PCR buffer , 10 mM DTT , 0 . 5% NP40 ) was added , topped by mineral oil , and the tubes were heated at 65 °C for one minute to break the nuclear membrane . The tubes were stored at −20 °C until needed . One hundred microliters of 1× PCR solution with dNTP , 0 . 5 units AmpliTaq ( Roche ) and primers targeting the VH and JH sequences of Groups 1–5 ( two 5′ primers: 20% GR1 , 5′-GTTTCTCTACCTCAGCAAT-3′ and 80% GR2–5 , 5′-GTTAGTCTMCCTCTGGAAT-3′ with the 3′ primer JH5 , 5′-TCACIGTCACCATGGT-3′ ) were added and the reactions run for 39 cycles at 95 °C 1 min , 58 °C 1 min , 72 °C 1 min , and in the 40th cycle the elongation step was prolonged to 15 min . In the nested reaction , one microliter of the PCR products was added to 50 μl of a second mixture containing two 5′ primers ( 20% VG1 , 5′-AAGGTGTCCAATCGCAA-3′ and 80% VG2–5 , 5′-AAGGTGTCCAGTCGGAG 3′ ) with the 3′ primer JH6 ( 5′-TCACCATGGTYCCTTGT-3′ ) , and this reaction was run for 20–30 cycles at 95 °C 1 min , 54 °C , 1 min , 72 °C 1 min; again the elongation step was prolonged in the last cycle . The DNA patterns were identical for 20 , 25 , and 30 cycles . The universal 5′ primers used in the first PCR round are located in the leader intron whereas the nested universal 5′ primers are in FR1 of the VH , about 60 bp downstream . There are two sets of nested Group-specific reactions used to analyze the B cells , one set to identify the 3R fragments observed above , the other to ascertain which IgH remained in GL configuration . For both , the first-round PCR samples were subjected to ExoSAP-IT ( USB ) to remove remaining “GR” primers . Six microliters of the PCR sample was incubated with 2 μl of ExoSAP-IT for 15 min at 37 °C , followed by inactivation for 15 min at 80 °C . One microliter of the product was used in a 50-μl PCR reaction . For nested reactions to ascertain VDJ identity , the 5′ primers targeted unique , Group-specific sequences in the leader intron , up to 15 bp downstream of the GR primers ( G1: Fam1 , 5′-AATGTAAAAGACTCAGCC-3′ used at 58 °C; G2: Int , at 58 °C; G3: GR3N2 , 5′-TCATGGATTTTTTCATCT-3′ at 54 °C; G4: Fam4 , 5′-AATCATTTCATCAGTAAC-3′ at 54 3 °C; G5: Fam5 , 5′-GGCTCAGGATTCATTTCG-3′ at 54 °C ) . In combination with the JH6 primer , 3R products of 400–440 bp were amplified . The Group-specific primers for GL configuration targeted intersegmental sequences in V-D and D-J . Both 5′ and 3′ primers are specific for the Group , and PCR products of 1 . 1–1 . 2 kb were obtained at 58 °C: G1 ( G1DF: 5′-CTGTGCAAAAAGCCACG-3′ , G1JR: 5′-TGTCCCCAGTGATCAAG-3′; 1 , 226 bp ) ; G2 ( FD2–1: 5′-CACTTTGTACATTGCACC-3′ , RD2: 5′-AATAACTGGCTCTGCACG-3′; 1 , 154 bp ) ; G3 ( G3DF: 5′-AACAATGGCTGGACACG-3′; G3JR: 5′-CCCCAGTTACCGAAGTC-3′; 1 , 242 bp ) ; G4 ( G4DF: 5′-ACCACAGAACGAGGAAG-3′; DR3/4: 5′-GCAAAACAAAATCACGAC-3′; 1 , 143–1 , 147 bp ) ; G5 ( G5DF: 5′-AACAACGGGTGGACCCG-3′; G5JR: 5′-TTGTCCCCAGTAACCGG-3′; 1 , 224 bp ) . The cycling parameters for all nested reactions were the same , except for the annealing temperatures . | Lymphocytes provide a limitless repertoire of antigen receptors , but each lymphocyte expresses only one kind of receptor per cell in order to provide specific recognition and response to pathogen invasion . The restriction , called allelic exclusion , operates in tetrapod vertebrates from frogs to human beings . In mouse , immunoglobulin ( Ig ) heavy chain ( H ) exclusion depends on ordered activation of component parts of the highly complex , three-megabase IgH locus in a process that differentiates between the two alleles . However , the regulation and mechanisms ensuring allelic exclusion remain uncertain . Sharks represent the earliest vertebrates with an adaptive immune system; their IgH organization , consisting of multiple miniloci , is considered primitive and ancestral to the classical IgH locus in other vertebrates . We show that allelic exclusion nonetheless exists in shark B lymphocytes , although attained by alternative means . Thus , major aspects of the complex pathway described for allelic exclusion in mammals evolved with their IgH organization . Elucidating shared and divergent regulatory processes allows us to gain insight into the basis and evolution of allelic exclusion , which provides the foundation for the functioning of the adaptive immune system . | [
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"genetics",
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] | 2008 | Immunoglobulin Heavy Chain Exclusion in the Shark |
Immune defense is energetically costly , and thus an effective response requires metabolic adaptation of the organism to reallocate energy from storage , growth , and development towards the immune system . We employ the natural infection of Drosophila with a parasitoid wasp to study energy regulation during immune response . To combat the invasion , the host must produce specialized immune cells ( lamellocytes ) that destroy the parasitoid egg . We show that a significant portion of nutrients are allocated to differentiating lamellocytes when they would otherwise be used for development . This systemic metabolic switch is mediated by extracellular adenosine released from immune cells . The switch is crucial for an effective immune response . Preventing adenosine transport from immune cells or blocking adenosine receptor precludes the metabolic switch and the deceleration of development , dramatically reducing host resistance . Adenosine thus serves as a signal that the “selfish” immune cells send during infection to secure more energy at the expense of other tissues .
Immune response is energetically costly [1 , 2] . Immune cells , upon activation , favor glycolysis over oxidative phosphorylation for fast , albeit inefficient , energy generation and macromolecule synthesis [3 , 4] . This metabolic shift requires extra glucose as glycolysis produces much less ATP than does oxidative phosphorylation [5] . Therefore , at the organismal level , the energy shifts from storage and nonimmune processes towards the needs of the immune system [6–9] . Regulation of energy during the immune response is critical—full response requires a significant amount of energy , and inability to provide it with nutrients can lead to immune system suppression and reduced resistance [10–12] . In mammalian systems , the inflammatory cytokines TNF-α , IFN-γ , IL-1 , and IL-6 are released upon recognition of the pathogen and , besides modulating immune functions , they also stimulate energy release [2 , 13–16] . Immune cells must respond rapidly to the activating signals , and thus they change their metabolism , which involves , at least in mammalian systems , the preferential use of aerobic glycolysis , known as the Warburg effect [3 , 4 , 17] . The increased demand for energy by the immune system requires , both in vertebrates and invertebrates , adaptation of the whole organism , which is associated with an overall metabolic suppression and a systemic insulin resistance in all tissues except the immune cells [2 , 12 , 18] . The importance of the systemic regulation of energy is demonstrated by examples of certain infections leading to depletion of energy reserves ( wasting ) and eventually death of the organism [15 , 19] . Despite the importance of the systemic regulation of energy , we have only fragmentary knowledge about the molecular mechanisms involved in the regulation of energy during immune response at the organismal level and about the communication between different parts of the organism mediating the shift of energy from storage and growth towards immunity [12 , 20 , 21] . Extracellular adenosine ( e-Ado ) is a signal originating from damaged or stressed tissues . Acting as an energy sensor , e-Ado is released from metabolically stressed cells with depleted ATP [22 , 23] or made from extracellular ATP leaking from damaged tissues [24] . e-Ado then works as a local or systemic hormone , adjusting metabolism by acting either via adenosine receptors or by the uptake into the cells and conversion to AMP activating AMP-activated protein kinase ( AMPK ) [24 , 25] . These actions lead to a suppression of energy consuming processes [22 , 26–29] and to a release of energy from stores [30] . Damaged tissues and metabolically stressed cells are very likely to occur during immune response and thus it is not surprising that elevated levels of e-Ado are also detected , for example , during sepsis in humans [31] . The capacity of e-Ado to regulate energy metabolism , to “measure” the level of tissue and organismal stress , and to adapt the energy use to the actual situation all make e-Ado a perfect candidate for an energy regulator during immune response . However , the mode of e-Ado action under immune challenge is unclear , as the role of e-Ado in energy regulation has mainly been studied in relation to anoxia in anoxia-tolerant organisms such as turtles and hypoxia and ischemia in rodent models and human patients [22 , 30] , while e-Ado has thus far been associated with mammalian immune response only through its immunomodulatory and anti-inflammatory function [24 , 32] . We , and others , have previously shown that adenosine regulatory and signaling network in Drosophila is similar to mammalian systems [33–37] . In addition , we have shown that e-Ado regulates energy metabolism in Drosophila . Increase of e-Ado levels caused by a deficiency of adenosine deaminase-related growth factor A ( ADGF-A ) leads to hyperglycemia and reduced energy storage [38] . We have also found that the regulation of e-Ado by ADGF-A is particularly important during parasitoid wasp infection in Drosophila larvae; ADGF-A is strongly expressed in immune cells that encapsulate the invading wasp egg [39] . These findings further support a potential role of e-Ado in energy regulation during immune response . Here , we use the parasitoid wasp infection as a model to study the energy regulation during immune reaction . Parasitoid wasps inject their eggs into Drosophila larvae , and if the fly larva does not destroy the egg in time , the hatched wasp larva will consume the host [40] . The fly larva recognizes the egg and mounts a robust immune response that involves proliferation and differentiation of specialized immune cells , lamellocytes , which eventually encapsulate the parasitoid egg . Using this immune response as a model , we traced the dietary glucose destinations , measured selected metabolites and gene expressions , and analyzed host resistance and the impact of the immune response on its development . We describe here the systemic changes in energy metabolism during the immune challenge and the role of e-Ado in the regulation of these changes . We have found that e-Ado , released from the immune cells , mediates a metabolic switch characterized by the suppression of nutrient storage and developmental growth in favor of the immune defense . This metabolic switch—a tradeoff between development and defense—is crucial for the resistance to infection . In Drosophila larvae lacking adenosine signaling , development is not suppressed , and the resistance dramatically drops .
The endoparasitoid wasp Leptopilina boulardi injects its egg in early third-instar Drosophila larva . The egg , usually hiding in gut folds , is first recognized by the host-circulating hemocytes ( Fig 1A ) and the recognition triggers immune response [40] . This involves production of specialized cells called lamellocytes ( Fig 1A and 1B ) within the first 24 h postinfection ( hpi; 0 hpi is the time of infection and corresponds to 72 h after egg laying; the time in hpi is also used for the uninfected control ) . Lamellocytes are then released into circulation , and the egg gets encapsulated with subsequent melanization by 48 hpi ( Fig 1A ) . Production of lamellocytes involves a transient proliferation of prohemocytes in the lymph gland and their terminal differentiation into lamellocytes [41] . The efficiency of egg encapsulation depends on the ability to produce lamellocytes and thus varies among different genetic strains of Drosophila [42 , 43] . Our model was based on the Canton S strain of Drosophila melanogaster bearing the w1118 mutation ( hereafter w ) , which served as a control genotype in all our experiments ( the term “control” is reserved hereafter for uninfected situations , i . e . , control w means uninfected w larvae ) . On average , 42% of these w host larvae succeeded to destroy the wasp egg and 38% survived to adulthood while 42% parasitoids developed to adult wasps ( Fig 1C ) . Parasitoid-infected third-instar larvae experienced a 15% developmental delay , pupating on average 7 h later than uninfected controls ( Fig 1D ) . Such a delay might result from redistribution of energy from development towards immune defense . We therefore examined various energy aspects during infection . Without infection , circulating glucose was kept below 0 . 04 μg per μg protein ( Fig 2A ) . Both the glycogen and triacylglycerol ( TAG ) stores kept increasing , while circulating and tissue trehalose levels remained steady ( Fig 2A ) . Trehalose is a nonreducing disaccharide source of glucose , which is liberated by the action of trehalase [44] . To trace the fate of glucose , we employed dietary radiolabeled D[U-14C]-glucose . The glucose-derived 14C became evenly distributed in the larvae among saccharides , proteins , and lipids ( Fig 2B ) . About 84% of 14C was found in developing tissues ( Fig 2C ) . We divided the organism here in a simplified way into the immune system ( represented by cellular immunity , the most important defense against parasitoids , including circulating hemocytes and lymph gland ) , the circulation ( hemolymph ) , and the rest of the tissues representing mainly development , growth , and energy storage . In infected larvae , the accumulation of TAG and glycogen reserves ceased ( Fig 2A ) . This was accompanied by down-regulation of glycogen synthase ( CG6904; FlyBase ID: FBgn0266064 ) and up-regulation of glycogen phosphorylase expression ( CG7254; FlyBase ID: FBgn0004507 ) ( Fig 3A ) . The amount of tissue trehalose decreased ( Fig 2A ) , and less dietary glucose was incorporated into lipids and proteins ( Fig 2B and S2 Fig ) . These hallmarks of suppressed energy storage and growth were corroborated by reduced incorporation of 14C into developing tissues from 84% in uninfected larvae to 77% at 6 hpi and 63% at 18 hpi ( Fig 2C and S2 Fig ) . The above effects were associated with hyperglycemia as indicated by elevated hemolymph glucose and 14C at the expense of developing tissues ( Fig 2A and 2C ) . Incorporation of 14C into lipids and proteins ( at the whole organism level ) was also suppressed during infection ( Fig 2B ) , which was accompanied by down-regulation of specific glycolytic enzyme genes in the fat body ( Fig 3B and S3 Fig ) . The diversion of metabolism from building energy reserves and from fat body glycolysis was thus in agreement with extra 14C in the carbohydrate form and with the increase of circulating glucose and trehalose . Circulating trehalose peaked at 6 hpi ( Fig 2A ) concomitantly with increased expression of a trehalose transporter in the fat body , the organ where trehalose is produced ( Fig 3C ) . At the same time , the immune cells changed their behavior during infection in the opposite direction , leading to increased energy consumption . Around one-tenth of 14C is normally allocated to immune cells , leaving almost 90% to the rest of the organism , but immune cells demanded up to one-third of nutrients during immune response ( Fig 2C ) . Expression of several glycolytic genes including lactate dehydrogenase Impl3 ( CG10160; FlyBase ID: FBgn0001258 ) increased both in the circulating hemocytes and in the lymph gland ( Figs 3B , S4 , and S5 ) . This resembled the glucose-demanding aerobic glycolysis , the Warburg effect , in activated mammalian immune cells . Both the lymph gland and the circulating hemocytes expressed elevated amounts of glucose transporter Glut1 ( CG43946; FlyBase ID: FBgn0264574 ) and trehalose transporter Tret1-1 ( CG30035; FlyBase ID: FBgn0050035 ) mRNAs ( Fig 3D ) . Interestingly , later during infection ( 12–18 hpi ) , the circulating hemocytes together with already differentiated lamellocytes strongly increased expression of both Tret1-1 and trehalase ( CG9364; FlyBase ID: FBgn0003748 ) ( Fig 3D ) . This suggests that differentiated immune cells preferentially uptake energy in the form of trehalose , which may be linked to the decline of circulating trehalose after 6 hpi ( Fig 2A ) . These results demonstrate a shift of energy distribution away from storage and growth , first towards circulating glucose and trehalose , and then towards the immune cells ( Fig 2 ) . We have previously shown that e-Ado increases circulating glucose via adenosine receptor ( AdoR; CG9753; FlyBase ID: FBgn0039747 ) signaling [38] . Here , we tested if e-Ado was involved in the observed effects of infection on the metabolic shift . While the circulating glucose increased more than 2-fold during infection in w larvae , this increase was suppressed in adoR ( FlyBase ID: FBal0191589 ) mutant larvae ( Fig 4A ) , indicating that AdoR was indeed necessary for the energy redistribution during infection . Therefore , we compared the number of lamellocytes as a measure of immune response . While w larvae produced 5–6 thousand lamellocytes by 24 hpi , the adoR mutants contained less than a third of this amount ( Fig 4B ) . Yet the adoR mutants were clearly capable of differentiating functional lamellocytes that displayed normal morphology , expressed a lamellocyte-specific MSNF9>GFP marker ( FlyBase ID:FBtp0064497 ) , and were capable of encapsulating the wasp egg ( Fig 4C and S16 Fig ) . Therefore , adoR larvae were impaired in efficiency or speed of lamellocyte production , and this corresponded with their reduced resistance against the parasitoid invasion relative to w larvae . Indeed , the adoR mutants were three times less successful at neutralizing the wasp eggs and surviving to adult flies ( Fig 4C ) . Thus , AdoR signaling is crucial for effective immune defense against the parasitoid . The impaired defense in the adoR mutants was not due to affected recognition of the wasp egg , as the number of plasmatocytes attached to the egg surface within the first few hpi was similar in w and adoR larvae ( S7 Fig ) . Therefore , we tested if shortage of energy could be the problem as suggested by failure to increase circulating sugar levels in adoR larvae ( Fig 4A ) . When we fed these larvae a high-glucose diet ( 12% instead of the regular 5% ) , the hemolymph glucose significantly increased even without infection in both w and adoR larvae ( Fig 4D ) . This dietary treatment significantly increased the number of lamellocytes in the infected adoR larvae ( Fig 4B ) , suggesting that it was the lack of energy causing inefficient differentiation of lamellocytes in the absence of AdoR . Interestingly , adding glucose to the diet did not further increase the level of circulating glucose during infection . In fact , the increase induced by infection was greater than that achieved with dietary glucose ( Fig 4D ) , and consistently the number of lamellocytes in infected w larvae was the same on both diets ( Fig 4B ) . Since the glucose increase induced by the dietary treatment was not as high as the one induced by the infection , the number of lamellocytes in adoR did not reach , even on the high-glucose diet , the levels observed in w ( Fig 4B ) . This suggests that the glucose available in circulation is the limiting factor for the lamellocyte differentiation . Upon infection , more glucose was retained in the saccharide fraction in the w larvae ( Fig 2B ) , indicating that this glucose was available for energy needs of the immune response and less used for storage and growth . Little ( at 6 hpi ) or no ( 18 hpi ) such retention was observed in adoR mutants ( Fig 5A and S2 Fig ) , suggesting that storage and/or growth were not suppressed during infection in the absence of AdoR . This notion was supported by the relative distribution of 14C among individual tissues ( Fig 5B ) . The distribution was the same in uninfected w and adoR animals . The incorporation of 14C did not change at 6 hpi in infected adoR ( as opposed to w ) , and the shift from storage and growth ( red part ) towards immune cells ( blue part ) was much smaller in infected adoR compared to w at 18 hpi ( Fig 5B ) . Importantly , the comparison of relative distribution of 14C into tissues was allowed by equal total uptake of 14C-glucose from diet in w and adoR larvae ( S8 Fig ) . Interestingly , the comparison of absolute numbers of 14C entering the system also revealed anorexia during infection ( lower uptake of 14C; S8 Fig ) , supporting a common observation during immune responses [45] . This anorexia did not seem to depend on AdoR . Besides the lymph gland with slightly lower 14C in adoR mutants , the tissue distribution of 14C was similar in uninfected w and adoR larvae at both time points ( Fig 5B and S10 Fig ) . Upon infection , only the brain and imaginal disc complex and fat body of w larvae contained significantly less 14C while hemolymph contained significantly more 14C at 6 hpi ( Fig 5B and S9 Fig ) . While 14C incorporation into brain+discs significantly decreased in w larvae , it did not change in the adoR mutant upon infection ( Fig 5B and S9 Fig ) , demonstrating that the suppression of developmental growth , which occurred during infection , was missing in adoR . This is supported by the measurement of the wing imaginal disc growth . While the growth of discs was significantly delayed in w control upon infection , the delay did not occur in the adoR mutant ( Fig 5C ) . Similarly , the delay in development observed in infected w ( as measured by pupation rate ) did not occur in adoR , which pupated as there would be no infection ( Fig 5D ) . At 18 hpi , all tissues were affected by infection , significantly increasing 14C in immune cells and hemolymph and decreasing in the rest ( Fig 5B and S9 Fig ) . In all cases but gut , the changes were significantly smaller in adoR than in w ( S10 Fig ) , indicating that the AdoR signaling was involved in the overall suppression of nonimmune processes . The missing suppression of development in adoR larvae resulted in shortage of energy available for the immune system as documented first by almost no increase of 14C in the hemolymph at 6 hpi and then by much lower 14C incorporation into the immune cells at 18 hpi compared to infected w larvae ( Fig 5B ) . Weak suppression of nonimmune processes in the absence of AdoR may also be linked to the missing peak of circulating trehalose at 6 hpi ( Fig 5E and S2 Fig ) . Functional AdoR signaling seems to lower glucose transport and to increase trehalose transport in the fat body ( suggested by expression levels of the respective transporter genes; S11 Fig ) , leading to increased trehalose at 6 hpi . The trehalose peak probably serves as a reservoir for fast glucose production , which will be increasingly needed for immune defense . The rapid lamellocyte differentiation is lagging in adoR larvae , likely reflecting lower consumption of trehalose relative to w larvae ( Fig 5E ) . The AdoR signaling reallocates energy towards immune defense , suggesting that e-Ado is released upon immune challenge . Therefore , we next wanted to determine the source of e-Ado during wasp invasion . We individually knocked down the Equilibrative nucleoside transporters , ENT1 ( CG11907; FlyBase ID: FBgn0031250 ) and ENT2 ( CG31911; FlyBase ID: FBgn0263916 ) , which are expressed in Drosophila larvae [36 , 46] . We delivered RNAi to various tissues utilizing the Gal4-UAS system [47] , and as a simple readout we used lamellocyte count at 24 hpi ( S12 Fig ) . Among the tested combinations , only ENT2 knockdown driven by Srp-Gal4 ( FlyBase ID: FBtp0020112 ) in cells of the hematopoietic lineage achieved a reduction in the number of lamellocytes that resembled the effect of adoR deficiency ( Figs 4B , 6A , and S12 ) . Srp-Gal4 was expressed in all hematopoietic cells , including the circulating hemocytes and all cells of the lymph gland that also contained precursors of lamellocytes ( S13 Fig ) . In contrast , knocking down ENT2 in already differentiated hemocytes ( by Hml-Gal4 and Upd3-Gal4 drivers; FlyBase ID: FBtp0040877 and FBtp0020110 ) did not affect the lamellocyte number ( S12 Fig ) . ENT2 mRNA was abundant in the lymph gland and brain but weakly expressed in circulating hemocytes and virtually undetected in the fat body ( Fig 6B ) . During infection , ENT2 expression increased in all these tissues except the fat body ( Fig 6B ) and , consistently , ENT2 RNAi delivered using a fat body-specific C7-Gal4 driver did not affect the number of lamellocytes ( S12 Fig ) . The increasing expression of ENT2 during infection in the brain leaves a possibility that the nervous system contributes e-Ado; however , undetectable expression of Srp-Gal4 in the brain , except for minor signal in some nerve cords ( S13 Fig ) , makes the observed effects of ENT2 removal attributable to the immune cells . The results above suggest that Ado transport from immune cells , including the differentiating ones , is important for efficient lamellocyte differentiation . As in the case of adoR mutation ( Fig 4B ) , the loss of lamellocytes was rescued by increasing dietary glucose in the Srp>ENT2-RNAi larvae ( Fig 6A ) . Similarly to adoR mutation , ENT2 knockdown in immune cells also cancelled changes in nutrient distribution that normally take place in infected w larvae; there was no peak of circulating trehalose at 6 hpi and no increase in circulating glucose ( Fig 6C and S2 Fig ) . The partition of 14C into saccharides , proteins , and lipids also resembled the pattern seen in adoR mutant larvae ( compare Fig 6D with Fig 5A and S2 Fig with S14 Fig ) . Together , the above data indicate that deficiency in e-Ado release and in its receptor , AdoR , consistently lead to the same failure of energy reallocation during immune challenge . Indeed , like loss of AdoR , knocking down ENT2 also reduced the host resistance against wasp invasion ( Fig 6E ) , while the normal developmental delay observed in w controls upon infection did not occur in Srp>ENT2-RNAi larvae ( Fig 6F ) . Interestingly , pupation occurred earlier in Srp>ENT2-RNAi compared to w or adoR animals even without infection ( Fig 6F ) ; the size of pupae was unaffected implying faster growth instead of precocious pupation of Srp>ENT2-RNAi . While glycogen storage was suppressed similarly upon infection in adoR mutant and w larvae ( Fig 5E ) , there was no significant difference in glycogen content between infected and uninfected Srp>ENT2-RNAi larvae ( Fig 6C ) . Even more apparent was the effect on lipid storage where the accumulation of TAG in the fat body was suppressed both in w and adoR but not at all in Srp>ENT2-RNAi larvae ( Fig 6G ) . Blocking Ado transport thus led to continued nutrient storage even upon immune challenge , suggesting that energy storage during infection might be regulated by e-Ado independently of AdoR .
An overall metabolic suppression is a common host response to infection [18 , 12 , 6] . A likely purpose for the suppression is to conserve energy for the immune response that is energetically costly [2 , 12] . The defense of the Drosophila larva against the parasitoid wasp requires a rapid production of specialized immune cells ( lamellocytes ) that encapsulate the parasitoid egg . This has provided us with a unique in vivo model to study the metabolic changes and their regulation during immune response . We show here that the production of lamellocytes is an energetically demanding process , and that a systemic metabolic switch is required for their effective differentiation . This switch includes ( 1 ) suppression of energy storage and developmental growth , ( 2 ) retaining more energy in circulation , and ( 3 ) increased consumption of energy by the immune system ( Fig 7 ) . Suppression of energy storage ( glycogen and lipids ) and suppression of growth , as documented by slower growth of imaginal discs , lead to a developmental delay . We show here that e-Ado is a signal mediating this metabolic switch . Blocking this signal then demonstrates that the metabolic switch is crucial for an effective immune response . Without this signal , development and growth proceed at a normal speed , thus reducing energy available to the immune cells . Insufficiency of immune cells due to the shortage of energy then leads to a drastically reduced resistance against the parasitoid . Experimental interference with e-Ado or its receptor , AdoR , thus demonstrates the importance of tradeoff between development and immune response , and identifies e-Ado as a signal responsible for the switch . Blocking Ado transport from immune cells by knocking down the equilibrative nucleoside transporter ENT2 identified the differentiating immune cells as an important source of the signal for the metabolic switch . This suggests that the immune cells could autonomously regulate energy influx based on their acute needs . Ado is a fine sensor of the cellular energy state , as it becomes produced when the ATP:AMP ratio decreases [23] . This scenario is appealing mainly because immune cells dramatically change their metabolism upon activation , leading to increased aerobic glycolysis akin to the Warburg effect [3 , 4] . Our expression analysis of glycolytic genes , glucose and trehalose transporters , and 14C uptake by immune cells suggested a similar behavior for the differentiating immune cells upon wasp attack . The ability to rapidly react to a metabolic stress could be why ENT2 is strongly expressed in the lymph gland and the brain , both privileged organs from the energy point of view . AdoR signaling is important for the suppression of developmental growth . Normally , infection leads to lower consumption of energy by the brain and imaginal discs ( later also by other tissues ) , but the consumption continues in adoR-deficient larvae as if they were uninfected . At the same time , AdoR signaling seems to lower glucose transport and to increase trehalose transport in the fat body as inferred from expression levels of the respective transporter genes . The fat body is the site where trehalose is produced from glucose [44]; trehalose is then released back to the hemolymph , and more so during infection . The adoR mutation causes a misbalance of glucose and trehalose transport in the fat body , causing more nutrients to be retained there . The effect of AdoR signaling on the fat body combined with the suppression of developmental growth leads to hyperglycemia that in turn ensures enough energy to supply the immune cells . If the growth suppression fails to occur , as in the adoR mutant , the immune cells are unable to compete with developing tissues that consume the majority of energy . By analogy to the selfish brain theory [48] , “selfish” immune cells may usurp energy to themselves by way of AdoR-mediated silencing of nonimmune processes . Our work thus brings experimental evidence and explains the molecular mechanism for recently published theoretical concept of selfish immune system [49] . Interestingly , the AdoR signaling does not mediate the suppression of energy storage ( glycogen and TAG ) during infection . However , increasing glycogen and TAG stores in infected Srp>ENT2-RNAi larvae with blocked Ado transport from immune cells indicates that the storage suppression is also under e-Ado control but through an AdoR-independent mechanism . Such a mechanism , which needs to be further studied , may involve e-Ado uptake , conversion to AMP by adenosine kinase , and activation of AMPK [25] . The Srp>ENT2-RNAi larvae proceeded faster through development not only during infection but even without infection when compared to control larvae . This suggests that the regulation of energy storage by e-Ado may play a role even during normal development . e-Ado signaling was previously associated with regulation of hemocyte differentiation , and blocking the AdoR signaling was suggested to lower the differentiation in the lymph gland under noninfectious conditions [50] . The hallmark of lamellocyte differentiation upon parasitoid wasp infection is the turning off the Jak-Stat signaling in the medullary zone of the lymph gland containing the prohemocytes [51] . Expression of cytokine Upd3 ( CG33542; FlyBase ID: FBgn0053542 ) is down-regulated , and the ratio of Jak-Stat receptor Domeless ( CG14226; FlyBase ID: FBgn0043903 ) and its negative coreceptor Latran ( CG14225; FlyBase ID: FBgn0031055 ) is switched upon wasp infection leading to turning off the Jak-Stat and to induction of lamellocyte differentiation [52] . The expression patterns of Upd3 , Domeless and Latran mRNAs normally and during infection are unaffected both in adoR and Srp>ENT2-RNAi ( S15 Fig ) , indicating that the induction of lamellocyte differentiation is functional in these lines . In addition , the lymph glands develop normally in both adoR and Srp>ENT2-RNAi ( [50] and S17 Fig ) . Our results demonstrate that the adoR and Srp>ENT2-RNAi larvae are capable of lamellocyte differentiation; they are just less effective , and the reason is most likely the lack of energy as indicated by the rescue of this phenotype with extra dietary glucose . An important part of the global energy switch observed upon parasitoid invasion is the AdoR-mediated suppression of developmental growth . Although AdoR is relatively strongly expressed in imaginal discs [34] , we do not know if it is the tissue-autonomous signaling of AdoR , or whether AdoR acts systemically on metabolism as AdoR is also strongly expressed in the larval endocrine glands and brain; both scenarios may apply simultaneously . It is known that the activation of adenosine receptor leads to metabolic suppression—at the individual cell level , the activation can inhibit growth of tumor cells [26] , but it can also cause a systemic suppression during anoxia [28 , 29] or torpor [27] . Our work demonstrates that the AdoR-mediated suppression plays an important role also during immune response . It will be important to identify the target cells and signaling cascades mediating the observed suppression in future studies . We show here that the metabolic switch is mediated by e-Ado and that the switch is crucial for an effective immune response . It is of interest to see if this e-Ado role is common to other organisms including humans . e-Ado plays the same role in energy regulation in flies and mammalian systems [30 , 38] . For example , sepsis is associated with hyperglycemia and insulin resistance as well as with increased e-Ado [31 , 53] , suggesting that e-Ado could indeed mediate the systemic metabolic switch in higher organisms . However , analyzing this role of e-Ado in mammals will be complicated by the existence of multiple adenosine receptors with partly contradicting functions [54 , 55] and by diverse roles of e-Ado in immunomodulation [24 , 56 , 57] . In conclusion , our study demonstrates that extracellular adenosine , released from immune cells , mediates a systemic metabolic switch leading to suppression of energy storage and developmental growth , thus leaving more energy to the immune cells . This switch is crucial for the effective immune response and blocking adenosine signaling drastically reduces host resistance to the pathogen . This may resemble a selfish brain theory in a way that the immune system , like the brain , is a privileged part of the organism , capable of suppressing energy consumption by other tissues in its own interest . Such a selfish immune system [49] would use e-Ado as a signal to appropriate extra energy resources during immune challenge .
All strains were backcrossed at least ten times to w1118 genetic background; w1118 was used as a control in all experiments . adoR mutant was homozygous for adoR1 mutation ( FBal0191589 ) . RNAi lines originated from VDRC: UAS-Ent1-RNAi ( ID 109885 ) and UAS-Ent2-RNAi ( ID 100464 ) . SrpD-Gal4 , Upd3-Gal4 , and MSNF9-GFP were obtained from Michele Crozatier , HmlΔ-Gal4 from Bruno Lemaitre and C7-Gal4 from Marek Jindra . Flies were grown on cornmeal medium ( 8% cornmeal , 5% glucose , 4% yeast , 1% agar ) at 25°C . For dietary treatment , larvae were transferred upon infection to cornmeal diet with 12% instead of 5% glucose . Early 3rd instar larvae were infected by parasitoid wasp L . boulardi . Weak infection ( 1–2 eggs per larva ) was used for resistance and pupation analysis; strong infection ( 4–7 eggs per larva ) was used in all other cases . To determine pupation rate and resistance to parasitoids , infected and control larvae were placed into fresh vials ( 1 experiment = 30 larvae per vial , 3 vials per genotype; 4 independent experiments ) . Pupation rate was determined by counting newly appeared pupae every 6 h and incremental percentage of number of pupae per total number of infected and control larvae at a particular time point postinfection was plotted; Log-rank survival analysis was used for comparison . For resistance , we first dissected 20 larvae per experiment from each genotype to count fully melanized wasp eggs ( winning host ) or surviving wasp larvae ( winning parasitoid ) . Second , we counted all emerged adult flies as surviving the infection and flies without any egg ( i . e . , uninfected individuals ) were excluded from the total number in the experiment . Adult wasps emerged from the vial were counted as adult parasitoid winners . Expression was analyzed by quantitative real-time PCR . Samples were collected from three independent infection experiments with three technical replicates for each experiment . Expression was normalized to Ribosomal protein Rp49 . Larvae were fed either 73 h AEL or 91 h AEL for 20 min a diet containing D[U-14C]-glucose ( 10 . 6 Gbq/mmol; Amersham Biosciences ) in yeast . Samples were collected 5 h later . Each sample contained tissues from 30 larvae—all hemolymph was collected by ripping larvae in PBS , centrifuging them , and dividing them into pelleted hemocytes and hemolymph fractions; brains with attached discs and wing discs , whole guts , whole fat bodies , and lymph glands were separated by dissection , and the rest were used as carcass . Macromolecular fractions were separated from tissue homogenates according to [58] for saccharides and lipids and by TCA treatment for proteins . Part of the homogenate was used for measurement of total absorbed amount of 14C molecules . Number of 14C disintegrations per minute was detected by liquid scintillator . Glucose , trehalose , and glycogen were measured as described [59] , using GAGO-20 kit ( Sigma ) . Lipids extracted with chlorophorm:methanol were quantified by HPLC and mass spectrometry . Wing discs were dissected from larvae at 90 h AEL ( 18 hpi ) , and their size was determined from micrographs by FIJI software . Data were analyzed by GraphPad Prism 6 ( GraphPad Software , Inc . ) . Extended Materials and Methods are available in S1 Text . | The immune response is energetically costly and often requires adaption of the whole organism to ensure it receives enough energy . It is not well understood how distribution of energy resources within the organism is regulated during an immune response . To understand this better , we used parasitoid wasp infection of fruit fly larvae—the host larvae have 48 h before they pupate to destroy the infecting “alien” or face destruction by the parasitoid that will consume the developing pupa . Here we find a signal , generated by the host immune cells , which mediates a systemic energy switch . This signal—adenosine—suppresses processes driving larval to pupal development of the host , thereby freeing up energy for the immune system . We show that the resulting developmental delay in the fruit fly larvae is crucial for an efficient immune response; without the adenosine signal , resistance to the parasitoid drops drastically . Generation of this signal by immune cells demonstrates that in response to external stressors , the immune system can mobilize reallocation to itself of energy and nutrients from the rest of the organism . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Extracellular Adenosine Mediates a Systemic Metabolic Switch during Immune Response |
Plasmodium vivax is the geographically most widespread human malaria parasite . To analyze patterns of microsatellite diversity and population structure across countries of different transmission intensity , genotyping data from 11 microsatellite markers was either generated or compiled from 841 isolates from four continents collected in 1999–2008 . Diversity was highest in South-East Asia ( mean allelic richness 10 . 0–12 . 8 ) , intermediate in the South Pacific ( 8 . 1–9 . 9 ) Madagascar and Sudan ( 7 . 9–8 . 4 ) , and lowest in South America and Central Asia ( 5 . 5–7 . 2 ) . A reduced panel of only 3 markers was sufficient to identify approx . 90% of all haplotypes in South Pacific , African and SE-Asian populations , but only 60–80% in Latin American populations , suggesting that typing of 2–6 markers , depending on the level of endemicity , is sufficient for epidemiological studies . Clustering analysis showed distinct clusters in Peru and Brazil , but little sub-structuring was observed within Africa , SE-Asia or the South Pacific . Isolates from Uzbekistan were exceptional , as a near-clonal parasite population was observed that was clearly separated from all other populations ( FST>0 . 2 ) . Outside Central Asia FST values were highest ( 0 . 11–0 . 16 ) between South American and all other populations , and lowest ( 0 . 04–0 . 07 ) between populations from South-East Asia and the South Pacific . These comparisons between P . vivax populations from four continents indicated that not only transmission intensity , but also geographical isolation affect diversity and population structure . However , the high effective population size results in slow changes of these parameters . This persistency must be taken into account when assessing the impact of control programs on the genetic structure of parasite populations .
Plasmodium vivax is the human malaria parasite with the largest geographical expansion , and the predominant malaria parasite outside of Africa [1] . Transmission intensity ( according to annual parasite incidence as a surrogate measure ) ranges from very low and seasonal in temperate zones and in countries approaching malaria elimination to very high mainly in Asian and South Pacific countries [1] . Prior to malaria control starting early in the 20th century , P . vivax transmission even occurred in large parts of Europe , Russia and the US [2] . P . vivax is difficult to control due to relapsing liver stages , fast and constant formation of gametocytes and a large proportion of asymptomatic carriers contributing to transmission [3 , 4] . As a consequence , P . vivax has become the predominant malaria parasite in several countries where P . falciparum transmission has been successfully reduced [5 , 6] . Along with other parameters , parasite diversity can be used to assess the effect of interventions , as reduced transmission is expected to result in reduced diversity . This relationship was observed for P . falciparum [7 , 8] . Moreover , knowledge on parasite diversity is the basis to study gene flow between populations , or to track the source of imported infections [9] . Thus , global comparisons of population genetic data help to develop and validate molecular tools for surveillance of antimalarial interventions . Typing of highly polymorphic microsatellites has proven useful to describe the diversity and structure of parasite populations [10–14] , to study patterns of relapses [15–17] , multiplicity and molecular force of infection [18 , 19] , and for distinguishing reinfection from recrudescence in drug trials [20–22] . Several studies reported extensive P . vivax microsatellite diversity even in regions of moderate endemicity , and high multiplicity of infection was frequently observed . While some P . vivax populations showed pronounced structuring on small geographical scale [10 , 13] , this was not the case for other populations [11 , 23] . Differences in method of sampling with respect to geographical space as well as different panels of markers used for genotyping make direct comparison of results difficult [24] . Lacking so far was a comprehensive comparison of P . vivax diversity based on samples collected across many different sites and typed with the same set of markers . Therefore we compiled data from published studies that included samples from Peru [10] , Brazil [14 , 21] , Sudan [25] , Cambodia [26] , Vietnam [27] , Papua New Guinea ( PNG ) and Solomon Islands [11] , and complemented this dataset with previously unpublished typing results from Central America and Mexico , Madagascar and Central Asia ( Armenia , Azerbaijan and Uzbekistan ) . All samples were typed with 11 published microsatellite markers [28] . This global data set included 841 isolates from regions of different levels of transmission intensity ( Fig 1A and Table 1 ) and representing 6 out of 9 malaria transmission zones recently shown to differ in relapse patterns [29] . The highest P . vivax prevalence ever has been recorded in the lowlands of PNG ( e . g . reaching >50% by PCR in children in East Sepik Province [19] ) . These South Pacific parasite populations are relatively isolated due to limited migration of human hosts . In Southeast ( SE ) -Asia transmission is also high , yet often focal [30] . Migration of hosts is high SE-Asia and a major complication of eradication efforts . In Latin America transmission is lower , but increased since the 1960s when the number of P . vivax cases was very low due to successful spraying campaigns [5] . Transmission in Central America is low , and parasite populations are separated from those in South America by the Isthmus of Panama , with no road connecting South and Central America . Also within Central America sub-structuring is likely; e . g . different P . vivax subpopulations were observed in Mexico , following vector distribution [31] . In Africa , P . vivax transmission occurs mostly in Madagascar and East Africa , i . e . Ethiopia and Sudan . In other parts of Sub-Saharan Africa P . vivax transmission is very low as most individuals carry the Duffy-negative blood type , which largely prevents P . vivax infection [32] . Thus , Madagascan parasites are isolated from other P . vivax populations in northern Africa , and transmission in Madagascar is relatively low . In Central Asia , transmission is very low; in Uzbekistan malaria had been eradicated in 1961 , but was reintroduced later , and is characterized by small outbreaks in border areas [33] . The wide distribution of parasite populations included in this study permit for the first time assessing P . vivax microsatellite population structure on a global scale . Previous studies on P . vivax population structure across different continents mostly genotyped polymorphic antigens [34 , 35] or mitochondrial DNA [36 , 37] . These markers differ from microsatellites because antigens are under immune selection and mitochondria are maternally inherited , thus excluding recombination . It remains unclear whether similar results would be obtained from analyses of mtDNA , antigen-coding genes or of putatively neutral microsatellites . The present study allowed a global comparison of parasite microsatellite diversity and population structure made possible by harmonization of methods , and the definition of a minimal subset of markers for epidemiological studies .
Informed written consent was obtained from all individuals or their parents or guardians prior to the study . Details on ethical approval are published for samples from Peru [10] , Brazil [14 , 21] , Sudan [25] , Vietnam [27] , Cambodia [26] and the South Pacific [11] . For samples collected from returning travelers to the US ( samples from Central America , Mexico , Africa , India and Indonesia ) the study protocol was approved by the Ethical Committee for Research with Human Subjects of the Institute of Biomedical Sciences , University of São Paulo ( 960/CEP ) . In Madagascar , the study protocol was approved by the Ethics Committee of the Ministry of Health of Madagascar ( 007/SANPF/2007 ) . The study was approved by the WEHI Human Research Ethics Committee . Details of samples included in this study are given in Table 1 . In case of cohort studies , only 1 sample per individual was included . From Africa , India and Indonesia samples from returning travelers were utilized [38] . While the number of travelers’ samples was too small to assess intra-population diversity , linkage disequilibrium , or FST compared to other populations , they were still useful for clustering analysis and principal component analysis ( PCA ) . Samples from Central America and Mexico had been collected from travelers returning from several countries spanning from Panama to Mexico . Due to the limited number of isolates and lack of precise information on sample origin ( some samples derived from travelers visiting several countries ) , these isolates were combined as ‘Central American’ population , despite possible sub-structuring . From Armenia and Azerbaijan 14 isolates were available; these were combined because migration between both countries is frequent and it was not always clear in which of the two countries the infection had been acquired . From Uzbekistan 20 isolates were available . For calculation of allelic richness all Central Asian countries were pooled to reach the required number of 25 samples . All samples were typed with the same set of 11 published microsatellite markers [28] . Three additional markers of that panel were excluded . MS3 and MS16 had not been typed in all populations , and MS8 showed signals of positive selection and variation in allele sizing between labs ( details below ) . Microsatellites are considered neutral markers; however , some of them might lie within coding regions or be linked to genes under selection . Selection had been tested using Lositan software [39] . Only marker MS8 showed a weak signal for positive selection . A relationship between P . vivax microsatellite alleles and clinical disease or acquired immunity has never been reported , thus it was not to be expected that different age groups sampled across populations or different proportions of febrile and asymptomatic individuals would influence population genetic parameters . Minor differences in typing protocols did not affect the results; i . e . samples from PNG , Solomon Islands , Madagascar Sudan and Central Asia were amplified by nested PCR , while a single round of PCR was performed on all other samples . As the nested PCR primers were identical to the single-round primers , allele sizes can be compared directly . From each lab a subset of samples was typed again starting from DNA to ensure comparability of results . Allele lengths were very consistent for all markers , except for MS8 , where variation of up to 1 . 5 base pairs was observed . Due to this variation and possible positive selection MS8 was excluded from analysis . In case of multi-clone infections the predominant peak only was included into the analysis . Occasionally this can result in incorrect haplotype assembly . This affects analysis such as linkage disequilibrium or clustering , where individual haplotypes are needed . Diversity and FST values are not affected because allelic frequencies are assessed at population level . Clustering analysis and calculation of LD were repeated with only those samples that harbored a single allele at each marker . To obtain sufficient samples from Madagascar and Cambodia , we permitted in the analysis also isolates with >1 alleles at one of the markers . It should be noted that isolates of low multiplicity were selected for genotyping for several parasite populations . Thus the difference between the number of samples of the full data set ( including predominant peak haplotypes ) and the number of only single clone infections does not reflect the proportion of multiple clone infections . Alleles were binned using TANDEM software [40] and formatted using PGDspider [41] . Expected heterozygosity ( HE ) of markers and allelic richness were calculated using FSTAT [42] . HE is the chance that two unrelated parasites carry a different allele of a given marker , and allelic richness is a measure of alleles per each marker adjusted for the different numbers of isolates per site . When the effective population size is reduced , e . g . as consequence of intensified malaria control , rare alleles are expected to disappear first . As a result , allelic richness changes more rapidly than HE , as the influence of low-frequency alleles on HE is small . The number of unique haplotypes was calculated by Dropout [43] and linkage disequilibrium ( LD ) by LIAN 3 . 5 with 100 , 000-fold re-sampling [44] . LIAN compares the observed association of markers to the values expected for random association based on the population diversity . Only unique haplotypes with no missing data were included for calculating LD , resulting in 633 samples in this analysis . Three of the markers used ( MS2 , MS4 , MS5 ) localize to chromosome 6 and two markers ( MS12 , MS15 ) to chromosome 5 , thus these markers are physically linked . To assess linkage disequilibrium irrespective of physical linkage of markers , LD was also calculated excluding markers MS2 , MS4 and MS12 , i . e . with 8 markers located on 8 different chromosomes . As compared to the 11-marker panel , fewer isolates had to be excluded due to missing data , but identical 8-marker haplotypes occurred more often , resulting in 637 samples for this analysis . Relatedness between haplotypes was assessed by pairwise comparison of all samples within a population and calculating the proportion of shared alleles . Only samples with at least 7 markers available for comparison were included . Effective population size Ne ( i . e . the estimated number of unique haplotypes circulating in each site ) was calculated using step-wise mutations models ( SMM ) as well as infinite allele models ( IAM ) , using mutation rates observed in P . falciparum studies of 1 . 59*10−4 ( 95% confidence interval = 3 . 7*10−4 , 6 . 98*10−5 ) [45] . While some of the markers harbor simple tri-nucleotide repeats , and SMM are likely applicable , other markers contain more complex repeat structures ( e . g . MS2 , MS6 , MS10 , MS20 ) and IAM are more appropriate [46] , thus both values are given . The software STRUCTURE was used to assess clustering of isolates [47] . This method detects clusters without prior information on the origin of samples . Twenty iterations for K = 1 to K = 12 ( K being the number of clusters ) were run , each with a burn-in period of 10’000 steps and then 100’000 MCMC iterations . A method developed through simulation studies [48] was applied to estimate the most likely number of clusters . In addition , the optimal number of clusters was assessed using the program STRUCTURAMA [49] . FST values among populations were calculated using FSTAT [42] . To compute Principal Components Analysis ( PCA ) the smartPCA application of EIGENSOFT was used [50] . In contrast to STRUCTURE analysis , PCA attempts to maximize variance between populations based on the known origin of samples . While smartPCA was designed for SNPs it can be used with microsatellites; each microsatellite allele was treated as a SNP . Preliminary analysis had shown no population substructuring between samples collected in Brazil in 2004 and 2006 [21] , in the lowlands of PNG [11] and in Madagascar , thus samples were combined for calculation of FST values and PCA . In the absence of the same measures of transmission intensity for all populations , such as entomological inoculation rate , force of infection or parasite prevalence , populations were broadly classified as low , medium and high transmission ( Fig 2 ) .
Pronounced differences in population diversity were observed . While expected heterozygosity ( HE ) was generally high , it was strongly reduced in isolates from Uzbekistan . With the exception of Uzbekistan , HE of all except 2 markers ( MS5 and MS7 ) was >0 . 5 in all populations ( Table 2 , supplementary S1 File ) . Mean HE of 11 markers was lowest in Uzbekistan ( 0 . 52 ) , followed by Azerbaijan ( 0 . 67 ) and South America ( 0 . 68–0 . 71 ) , intermediate in Africa and the South Pacific ( 0 . 77–0 . 83 ) , and highest in South-East Asia ( 0 . 84–0 . 87 ) . The differences between continents were slightly higher when only those markers with lower overall diversity were assessed ( MS1 , MS4 , MS5 , MS7 , MS12 , mean HE<0 . 75 ) . Mean HE ranged from 0 . 53 in Uzbekistan and 0 . 6 in Peru to 0 . 85 in Cambodia . Mean allelic richness showed a similar pattern ( Table 2 and Fig 2 ) with lowest values in South America ( 5 . 5–7 . 0 alleles/locus ) and Central Asia ( 7 . 1 alleles/locus ) , intermediate values in Africa and the South Pacific ( 7 . 9–9 . 9 alleles/locus ) , and highest in Cambodia ( 13 . 4 alleles/locus ) . When all isolates and all markers were analyzed , linkage disequilibrium ( LD ) was strong and significant in South American , Madagascan , Central Asian and SE-Asian populations ( Table 3 ) . No or limited LD was detected in Central America , Sudan and the South Pacific . Trends were similar when only single-clone infections were analyzed , with exception of Peru and Madagascar , where LD observed in the analysis of all data was no longer detected . Overall levels of LD were lower in the single-clone data set . This can be explained by the reduction in sample size , since similarly low levels of LD were observed in an equally low number of randomly selected multi-clone infections . Most representative results were obtained when only 1 marker per chromosome was included , thus excluding physical linkage of markers ( total of 8 markers ) . All populations in the South Pacific , Peru , Central America and Sudan were in full linkage equilibrium ( Table 3 and Fig 2 ) . Effective population Ne size was about twice as high in Cambodia and Vietnam ( IAM: 6378 and 5553 ) as compared to South America ( 2423–2829 , Table 4 ) . Values for Madagascar , Sudan and the South Pacific were intermediate , and low for Azerbaijan ( 2422 ) and Uzbekistan ( 1606 ) . Estimates based on SMM were 2–3 fold higher . Samples from Central America and Mexico were highly diverse and showed high Ne ( 5159 ) , likely because they originated from different countries and thus represent different subpopulations . Across all populations a total of 759 individual haplotypes were found in 818 isolates . 11-loci haplotypes were shared only within the same country . Again isolates from Uzbekistan were unusual , as 10 out of 20 isolates were identical and another two shared the same haplotype . Ten haplotypes were observed more than once in Peru , 5 and 2 in Brazil in 2004 and 2006 ( plus 1 shared between the 2004 and 2006 data set ) , 5 in Sudan , 2 in Azerbaijan , 2 in Vietnam ( 1 observed 5 times ) , and 1 in PNG . 730/818 haplotypes were singletons . Relatedness among haplotypes was calculated for each population ( Fig 2 ) . In the pairwise comparisons isolates from Cambodia and Vietnam shared the same allele on average in 12 . 6% and 15 . 0% of markers ( i . e . a mean of 1 . 4 and 1 . 65 out of 11 microsatellites carried the same allele ) . Samples from Central American , African and South Pacific populations shared the same allele in 16 . 5–22 . 5% of markers . Relatedness was 29 . 3–32 . 5% in South American populations , and in Azerbaijan 33 . 3% and in Uzbekistan 48 . 5% of markers shared the same allele . To assess discrimination power of a smaller panel of markers compared to the full set of markers , microsatellites were removed successively and haplotype counts recorded from each individual population , as well as for the full dataset ( Fig 3 ) . MS4 was removed first , as no data for this marker could be obtained from 70 isolates . The remaining 10 markers were sequentially removed according to their diversity across all populations , starting with the least diverse one . Only taking into account haplotypes shared within populations , a set of 4 markers ( MS2 , MS9 , MS15 , MS20 ) identified 710/760 ( 93 . 4% ) haplotypes and 3 markers ( MS2 , MS15 , MS20 ) still identified 665/760 ( 87 . 5% ) haplotypes . By reducing to 2 markers ( i . e . MS15 and MS10 ) , the number of haplotypes dropped considerably to 513/760 ( 67 . 5% ) . The proportion of haplotypes lost by omitting markers was highest in South American populations . When using only 3 markers , 13–38% of haplotypes were lost in South American populations , but <10% in populations from SE-Asia and the South Pacific ( Fig 3 ) . Taking into account haplotypes observed in several populations , a single haplotype was shared between Peru and Vietnam when the panel was reduced to 5 markers . With four markers 15 additional haplotypes were shared . While some were shared between populations from the same continent ( 5 identical 4-loci haplotypes in 251 isolates from PNG ) , 11 haplotypes occurred on different continents ( e . g . Peru/Azerbaijan , Madagascar/Vietnam , Brazil/Cambodia/Vietnam , Sudan/Solomon Islands ) . As a consequence , when haplotypes shared between populations were taken into account , 4 markers identified 687/759 ( 91 . 5% ) haplotypes , and 3 markers 595/759 ( 78 . 4% ) haplotypes ( black line in Fig 3 , panel 1 ) . Clustering analysis indicated clearly distinct clusters , mainly following geographical lines ( Fig 1B ) . First South American samples ( but not those from Central America ) and samples from Azerbaijan were separated from all other populations , with admixture in samples from Central America and Africa . When the number of clusters ( K ) was 3 , the South Pacific populations formed a separate cluster , as well as the Brazilian samples , while samples from Peru , Central America , Madagascar and Asia clustered together . Peruvian samples formed a separate cluster when K was set to 4 , K = 5 led to the separation of Madagascar , Sudan and Azerbaijan from SE-Asia . Central American samples formed an individual cluster when K = 6 . The clonal population in Uzbekistan clustered with different populations in individual STRUCTURE runs for a given value of K , indicating that no clear relationship to any other population was observed . When the optimal number of clusters was calculated as described [48] , high values were observed for K = 2 , K = 5 and K = 7 ( Fig 4 ) . In addition the program STRUCTURAMA was used to assess the best number of clusters . The 99% CI for estimates of cluster number showed a wide range ( 79–115 clusters ) and was thus not informative As the clear separation of Latin American and South Pacific isolates from those from Africa and Asia might interfere with more subtle population structure within Africa and Asia , clustering analysis by STRUCTURE was repeated including the latter isolates only ( Fig 5A ) . For K = 2 African and Azerbaijani isolates clustered separately from SE-Asian ones . Indian isolates clustered with Africa . As with the full set of isolates , the clonal samples from Uzbekistan clustered with different populations in individual runs for the same number of clusters ( e . g . with Africa or with SE-Asia if K = 2 ) . For K = 3 these isolates formed a separate cluster . Both within Africa and SE-Asia admixture was high , and when the number of clusters was set to 4 or 5 , the separation between countries was limited . Analysis with STRUCTURE was repeated for single clone infections only . Results were similar as for the full data set ( Fig 5B ) . Results of clustering analysis were confirmed by FST values ( Table 5 ) . Between Brazil and Peru FST values were high ( 0 . 16 ) , as well as when South American populations were compared to non-American populations ( 0 . 11–0 . 29 ) . Differences between Cambodia , Vietnam and South Pacific populations were lowest ( 0 . 026–0 . 066 ) . The highest FST value was observed between Azerbaijan and Uzbekistan ( 0 . 37 ) , and all values for comparisons between Uzbekistan and other populations were >0 . 2 . FST between Madagascar and Sudan was low ( 0 . 07 ) . FST values were also compared between continents or sub-continents , i . e . South America , Africa , Central Asia , SE-Asia and the South Pacific ( Table 6 ) . FST was highest between South America and the South Pacific as well as between Central Asia and all other populations ( 0 . 11 ) , and lowest between SE-Asia and the South Pacific ( 0 . 042 ) and SE-Asia and Africa ( 0 . 058 ) . In principal component analysis ( PCA ) , PC1 differentiated isolates from Brazil and Peru on the one hand and the South Pacific on the other hand from a cluster containing African and Asian isolates ( Fig 6 ) . PC2 separated isolates from Peru und Brazil . In summary , clustering analysis , FST values and PCA showed similar results . South American populations were separated from all others , and populations from Brazil and Peru formed clearly separated groups with little admixture . African , SE-Asian and South Pacific populations each formed a large cluster with little sub-structuring . Samples from Central America , Indonesia and India grouped with SE-Asia .
Diversity of a marker , expressed as expected heterozygosity ( HE ) , is a key criterion for choosing a subset of microsatellite markers for a variety of genotyping questions [51] . Markers of a lower diversity are more suitable to assess population structure , as with less diverse markers , a smaller number of isolates is needed to detect genetic differences among populations . In contrast , many applied genotyping studies aim at distinguishing between “identical” and “different” clones . Samples from treatment failures in a drug trial for example require distinction between new infection versus recrudescence ( reappearance of a pre-treatment clone ) [18–22] . In this scenario the phylogenetic relationship of genotypes observed is not of interest . Such applied typing tasks often include large numbers of isolates , and thus a minimal set of markers is desirable that is able to reduce the genotyping workload and price without impairing the discrimination power for distinguishing clones . For phylogeographical studies a larger panel of markers is needed . The same is required for tracking the origin of imported malaria cases . The step-wise removal of markers showed that as little as 3 highly diverse markers were sufficient to detect around 90% of all haplotypes in most populations , only in South American populations up to 40% of haplotypes were missed . Thus , typing only 2–3 markers in SE-Asia , the South Pacific and Africa , and 4–6 markers in South America would lead to only a small underestimation of multiplicity of infection or of treatment efficiency ( when a clone during follow-up could not be distinguished from the clone at baseline and a new infection was taken for a recrudescence ) . Other limitations affect drug trial results , such as imperfect detectability of minority clones [52] and as a consequence substantial day-to-day variation in the alleles detected [53] . Longitudinal P . vivax studies involving genotyping are also complicated by relapsing hypnozoites , which can be homologous or heterologous to the clone at baseline [16 , 54] . Longitudinal studies can also be affected by re-infection with a genotype observed earlier in the same individual . This is a particular threat when the overall diversity in the parasite population is low , as observed in Uzbekistan , or in case of clonal expansion during an outbreak [46] . Thus , when the population diversity is intermediate or high , using a reduced panel of markers is acceptable as this reduces the ability to differentiate clones only in a minor way and is justified in view of substantial savings in time and costs . Overall parasite diversity measured as HE or allelic richness was high and reflected transmission levels . It was lowest in South America and Central Asia , where transmission is low , and highest in SE-Asia . In the South Pacific diversity was intermediate despite highest levels of transmission ( prevalence >50% in Ilaita [19] ) . This could be due to the relative geographical isolation of South Pacific populations , and thus limited introduction of parasites from other regions . In contrast migration of infected hosts is high among SE-Asian countries , thus high parasite diversity can be maintained , even when transmission is reduced . Linkage disequilibrium can be the result of selfing in the mosquito of male and female gametes from the same parasite clone [55] , as opposed to recombination of different clones resulting in a break up of linkage . Recombination can occur if a mosquito feeds on a host infected with different parasite clones . The level of LD is expected to decrease with increasing transmission intensity , while diversity is expected to increase . In line with this expectation , LD was detected in Brazil , Central Asia and Madagascar , where transmission is low [1] . It is , however , noteworthy that levels of diversity did not fully correlate with transmission intensity . Highest diversity and strong LD was observed in SE-Asia . In the South Pacific an intermediate diversity but no LD was found . Different processes influence diversity and LD; likely the local ecology add to the high parasite diversity in SE-Asia , while the high proportion of individuals carrying multi-clone infections in the South Pacific ( up to 75% of all infected [19] ) lead to high levels of parasite recombination . High LD is also expected when closely related parasites are sampled . Yet no LD was observed in Ilaita in PNG , despite most dense sampling of all populations with 132 isolates collected across hamlets approx . 5 km from each other [56] . Incorrect assembly of multi-locus haplotypes in multi-clone infections within a host would be expected to lead to incorrect low levels of LD , but the opposite trend was observed: lower LD was found when only single-clone infections were included in the analysis . This unexpected finding was attributed to the smaller sample size after removing multi-clone infections . Analysis of population structure revealed significant FST values between all populations . While clustering analysis and PCA differentiated among those populations separated by high FST values , no within-continent subdivision was observed in the South Pacific , Africa and SE-Asia . The difference in clustering between populations from Latin American ( clearly separated clusters in Peru , Brazil and Central America and Mexico ) on the one hand and from the South Pacific ( no subdivision between different provinces in PNG and Solomon Islands ) or Africa ( no subdivision between Madagascar and Sudan ) on the other hand is striking , given comparable distances between sites . While high levels of human migration in SE-Asia could explain parasite gene flow , no clusters were found in the South Pacific despite limited human movement ( only air and sea transport between East Sepik and Madang provinces , and between PNG and Solomon Islands ) . Likewise limited sub-structuring was evident between Madagascar and Sudan , despite open sea and countries with very low P . vivax transmission separating sampling locations . In Central Asia , clear subdivision was observed between parasites east and west of the Caspian Sea . Parasites from Azerbaijan and Armenia clustered with those from Africa , while parasites from Uzbekistan were highly clonal and formed a separate cluster . Distinct parasite subpopulations might be the result of expansion of parasite clones after the near elimination of malaria in the second half of the 20th century in some countries . In Latin America they could also reflect different independent introductions of P . vivax , as it has been shown for P . falciparum [57] . In Uzbekistan P . vivax had been reintroduced after its elimination , most likely from other Central Asian countries . In contrast , in SE-Asia and the South Pacific P . vivax was present much longer than in Latin America , and even during the peak of the eradications campaigns in the 1960s prevalence remained high [58] . Central American and Mexican samples were exceptional as they were highly diverse and LD was low despite low transmission intensity . This is most likely caused by the fact that these samples represent different isolated subpopulations over a large geographical range . Further studies involving additional parasite populations and different molecular markers are needed to establish differences between South and Central American samples , to understand why Central America clusters with SE-Asia , and to identify potential routes for P . vivax colonization of South and Central America . Beside microsatellites , other molecular markers have been used to assess P . vivax population structure , most importantly mtDNA and polymorphic antigens . In agreement with the present study , sequencing of mtDNA identified a separate subgroup in Latin America ( highest support of all subgroups in Bayesian tree analysis ) [36] , as well as highly diverse populations in Asia and the South Pacific [37] . However , no pronounced separation between South-Pacific , SE-Asian and some South American isolates was observed using mtDNA [36] . Isolates from South and Central America had been found in the same subgroup , yet , only 3 haplotypes from Central America were sequenced [36] . The same study found different subgroups in East Asia ( China and Korea ) and SE-Asia ( Cambodia , Thailand and Indonesia ) , plus a third subgroup including isolates from locations across Asia and PNG . The present study includes no isolates from China or Korea , and only few from Indonesia , thus no such structure within East Asia could be found . In concordance with microsatellite results , mtDNA diversity was highest in SE-Asia and high in the South Pacific [36] . In contrast to microsatellite-derived measurements of diversity , mtDNA diversity was lower for Madagascar , Central America and Africa , whereby results of the latter two populations are likely affected by a very small sample size [36] . The same study indicated that overall P . vivax diversity in Latin America was as high as in SE-Asia , despite locally reduced diversity , a result confirmed by analysis of 3 whole-genome sequences from Latin America [59] . The present study found microsatellite diversity across Central America and Mexico to be similarly high as that of SE-Asia , but when isolates from Peru and Brazil were combined diversity remained lower . SNPs in antigens are expected to be under strong balancing selection , limiting their use to study the underlying population structure . In line with this , many antigens showed strong clustering but in contrast to microsatellites many clusters were shared between continents [34 , 35] . Like microsatellites , AMA-1 and MSP-1 alleles from South Pacific populations showed very little admixture with any other parasite populations [34 , 60] . DBP-II alleles in contrast were more evenly distributed across continents [35] . A study using putatively neutral SNPs covering a 200-kb genomic region confirmed subdivision between Brazil and SE-Asia [61] , and a barcode of 42 SNPs across the genome was recently published and tested on a small number of clinical samples from three continents [62] . However , this barcode was not yet tested for assessing local population structure or for typing asymptomatic , low-density infections . Continuous malaria control is expected to reduce parasite diversity and effective population size , and to increase differences between populations due to clonal expansion of remaining parasite strains [63] . Indeed , near-clonal expansion of parasites has been observed for P . falciparum in the highlands of PNG [8] , in Solomon Islands [64] and in South America [7] . Likewise , Artemisinin-resistant clones have expanded in SE-Asia [65] . In striking contrast to these findings , nearly all studies assessing P . vivax diversity found high parasite diversity , even in countries now aiming to eliminate malaria [66–69] . The clonal expansion in Uzbekistan , a country that had successfully eliminated malaria in the 1960-ies , is the first such population structure reported for P . vivax . Low microsatellite diversity was also found in South Korea , where transmission has been low for decades and the parasite population is relatively isolated [70] , as well as from a rural , isolated site in Peru [71] . The high P . vivax diversity in countries with low transmission likely indicates a high underlying effective population size and thus a large number of infected individuals . Two hallmarks of P . vivax biology add to this , namely hypnozoites in the liver , and a large proportion of asymptomatic , low-density infections that escape screenings conducted by light microscopy or rapid diagnostic test and thus a substantially underestimated parasite reservoir [4] . The isolates studied here were collected prior to the renewed call for malaria elimination . Only few studies have typed samples collected after up-scaling control measures , but diversity remained high [72–74] . It seems that control has little short-term effect on population size , and diversity measures changes slowly as long as the effective population size remains high ( above 100 genetically distinct parasite clones ) [75] . Therefore diversity measures will only be useful to assess the impact of control programs once transmission is very low after several years of intensified control . In recent years malaria control has been intensified reducing prevalence and incidence in PNG [76] , many Asian countries [69 , 77] and South America [78] . It will be important to evaluate whether reduced prevalence is paralleled by increased sub-structuring on small scale , i . e . breaking up of the South Pacific and SE-Asian clusters , indicating local hotspots of transmission . A pronounced reduction of genetic diversity and increase in population structure will implicate success of control and interruption of parasite gene flow from neighboring populations . In previously malaria-free regions , microsatellite typing can help to study outbreaks . Because of their high discrimination power between clones , genotyping outbreak samples can clarify whether a single clone was imported and spread across a local region , or whether steady gene flow from neighboring regions with ongoing transmission occurs , resulting in a diverse parasite population [9] . Microsatellite typing remains an important tool to study P . vivax , as it can be done in any lab equipped for PCR . For epidemiological studies and drug trials , a limited set of 2–6 markers , depending on transmission intensity , provides sufficient resolution to distinguish individual clones . The full panel of 11 microsatellite markers showed clear population structure on a global scale , and differences in diversity reflect transmission intensity and isolation of parasite populations . These population genetic measures could potentially be used as tools to measure the impact of control programs; however , due to the large effective population size even in countries of moderate endemicity , these parameters are likely to change slowly . | Plasmodium vivax is the predominant malaria parasite in Latin America , Asia and the South Pacific . Different factors are expected to shape diversity and population structure across continents , e . g . transmission intensity which is much lower in South America as compared to Southeast-Asia and the South Pacific , or geographical isolation of P . vivax populations in the South Pacific . We have compiled data from 841 isolates from South and Central America , Africa , Central Asia , Southeast-Asia and the South Pacific typed with a panel of 11 microsatellite markers . Diversity was highest in Southeast-Asia , where transmission is intermediate-high and migration of infected hosts is high , and lowest in South America and Central Asia where malaria transmission is low and focal . Reducing the panel of microsatellites showed that 2–6 markers are sufficient for genotyping for most drug trials and epidemiological studies , as these markers can identify >90% of all haplotypes . Parasites clustered according to continental origin , with high population differentiation between South American and Central Asian populations and the other populations , and lowest differences between Southeast-Asia and the South Pacific . Current attempts to reduce malaria transmission might change this pattern , but only after transmission is reduced for an extended period of time . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Plasmodium vivax Diversity and Population Structure across Four Continents |
Paromomycin ( PMM ) has recently been introduced for treatment of visceral leishmaniasis in India . Although no clinical resistance has yet been reported , proactive vigilance should be warranted . The present in vitro study compared the outcome and stability of experimental PMM-resistance induction on promastigotes and intracellular amastigotes . Cloned antimony-resistant L . donovani field isolates from India and Nepal were exposed to stepwise increasing concentrations of PMM ( up to 500 µM ) , either as promastigotes or intracellular amastigotes . One resulting resistant strain was cloned and checked for stability of resistance by drug-free in vitro passage as promastigotes for 20 weeks or a single in vivo passage in the golden hamster . Resistance selection in promastigotes took about 25 weeks to reach the maximal 97 µM inclusion level that did not affect normal growth . Comparison of the IC50 values between the parent and the selected strains revealed a 9 to 11-fold resistance for the Indian and 3 to 5-fold for the Nepalese strains whereby the resistant phenotype was also maintained at the level of the amastigote . Applying PMM pressure to intracellular amastigotes produced resistance after just two selection cycles ( IC50 = 199 µM ) compared to the parent strain ( IC50 = 45 µM ) . In the amastigote-induced strains/clones , lower PMM susceptibilities were seen only in amastigotes and not at all in promastigotes . This resistance phenotype remained stable after serial in vitro passage as promastigote for 20 weeks and after a single in vivo passage in the hamster . This study clearly demonstrates that a different PMM-resistance phenotype is obtained whether drug selection is applied to promastigotes or intracellular amastigotes . These findings may have important relevance to resistance mechanism investigations and the likelihood of resistance development and detection in the field .
Visceral leishmaniasis ( VL ) is a neglected and poverty-related disease that causes significant morbidity and mortality . Treatment options are quite limited and the development of resistance to antimonials ( Sb ) has added to the problem [1] . To counter this evolution , the Kala-Azar Elimination programme was officially launched in India and Nepal in 2005 [2] , [3] and advocates the use of miltefosine ( MIL ) as first-line alternative to Sb . However , other drugs are still required for treating treatment failures . The aminoglycoside antibiotic paromomycin ( PMM ) was shown to be highly effective either as mono-therapy or in combination with other drugs , be well-tolerated and currently the cheapest drug available [4] , [5] . PMM has recently been licensed for the treatment of VL in India as an injectable alternative to amphotericin B and as a potential substitute for Sb [6] . It was shown that PMM is not hampered by Sb-resistance [7] , but appropriate measures should certainly be taken to assure its long-term effectiveness . Resistance in the field has not been reported yet , but this issue needs to be proactively addressed in laboratory studies to help steer decisions on future treatment policies , diagnosis and epidemiological resistance monitoring . Specific and stable resistance to PMM has experimentally been induced in L . donovani promastigotes in vitro , providing initial basic knowledge on putative PMM resistance mechanisms , characterized by an altered mitochondrial energy metabolism [8] and reduced accumulation due to a significant reduction in initial binding to the cell surface [9] . The resistant lines were still infective to macrophages in vitro and for mice [10] , raising concerns about the transmission potential of resistant parasites . These studies used laboratory strains which are well characterized but may have diverged substantially from current field isolates and thus react differently to drug pressure [11] . More importantly , resistance was induced on promastigotes that are not the relevant stage subjected to natural drug pressure and in addition are biochemically different from amastigotes , rendering their predictive value at least questionable . The aim of this study was to compare resistance-induction protocols on promastigotes and intracellular amastigotes and use cloned recent field isolates with a defined Sb-resistance background instead of drug-susceptible laboratory strains . The stability of the ensuing PMM-resistant clones was subsequently checked in vitro and in vivo .
This study using laboratory rodents was carried out in strict accordance with the guidelines that are in force in the countries of the research partners and was approved by the ethical committees the research institutes of the authors: a/University of Antwerp , Belgium ( UA ) ECD 2010–17 ( 18-8-2010 ) and adopting the EC Directive 2010/63/EU; b/National Institute of Pathology , India ( ICMR ) : Committee for the Purpose of Control and Supervision on Experiments on Animals ( CPCSEA ) , registration number 102-1999/CPCSEA ( 28-4-1999 ) , and c/University of Strathclyde , UK ( SU ) : UK Home Office project license number 60/3740 . Swiss mice ( UA ) , age-matched in-house inbred BALB/c mice ( SU , ICMR ) and golden hamsters ( UA ) were kept on a regular rodent diet and given drinking water ad libitum . Mice were used to collect primary peritoneal macrophages ( MPM ) as previously described [12] . Clones of clinical isolates of L . donovani were used as parent strain for drug selection and were obtained from the Institute of Tropical Medicine Antwerp within the frame of the EC Kaladrug-R project: MHOM/IN/09/BHU568/0 cl-1 and MHOM/IN/09/BHU573/0 cl-3 from an endemic region in Bihar State India and within the frame of the EC Leishnatdrug-R project: MHOM/NP/03/BPK087/0 cl-11 and MHOM/NP/03/BPK275/0 cl-18 from an endemic region in Nepal . The parent isolates were collected from bone marrow aspirates of patients unresponsive to Sb treatment ( except BPK087/0 isolated from a patient that finally cured at 12 months follow-up ) and typed as L . donovani based on a CPB-PCR-RFLP assay [13] . Primary isolation of promastigotes was done on Tobie's blood agar medium at 26°C . In the laboratory , promastigote cultures were maintained at room temperature in HOMEM ( Invitrogen ) supplemented with 200 mM L-glutamine , 16 . 5 mM NaHCO3 , 10% heat-inactivated FCS and 40 mg/L adenine , 3 mg/L folic acid , 2 mg/L D-biotin and 2 . 5 mg/L hemin . . The number of passages was kept as low as possible . Pure crystalline PMM ( paromomycin-sulphate USP ) was obtained from Sigma-Aldrich or Gland Pharma , India . SbIII ( potassium antimonyl tartrate trihydrate ) was purchased from Sigma-Aldrich whereas MIL and SbV ( sodium-stibogluconate ) were kindly provided by WHO-TDR . Because the SbIII and SbV formulations contain different amounts of active constituent , their concentration is expressed in equivalents ( µg/ml eq . ) : 1 mg potassium antimonyl tartrate trihydrate contains 0 . 361 mg SbIII eq . and 1 mg sodium stibogluconate contains 0 . 313 mg SbV eq . Stock solutions of SbIII and SbV were prepared in pre-heated PBS at 37°C and stored at −20°C for max 3 months . MIL and PMM were dissolved in MilliQ-water and stored at 4°C . Promastigotes of the four strains were passaged in vitro with a stepwise increase in the concentration of PMM ( 8 , 16 , 32 , 64 and 97 µM ) in the HOMEM culture medium . Parasites were considered adapted to the increased concentration when they could grow at a same rate as the parent wild type parasite . Adaptation was stopped at 97 µM as higher concentrations proved to affect normal growth . During the stepwise induction of the Indian strains , the intermediate stage parasites were also passaged through ( non-treated ) macrophages ( MPM , J774 cell line ) to maintain infectivity ( Table 1 ) . Log-phase ( day 4 ) promastigotes ( 100 µl parasite suspension ) were seeded into the wells of 96-well plates at 5×105 parasites/well and incubated with 100 µl medium alone ( untreated controls = 100% growth ) or serial dilutions of PMM in medium . After 72-hour incubation at 25°C , 20 µl of a 0 . 0125% ( w/v ) resazurin ( Sigma-Aldrich ) in PBS solution was added to each well and the plates were incubated for a further 18 h after which cell viability was measured fluorimetrically ( λexc 550 nm; λem 590 nm ) . The results are expressed as the percentage reduction in the parasite viability compared to that in untreated control wells , and the 50% inhibitory concentration ( IC50 ) was calculated using Statview Software . All experiments were performed at least twice in quadruplicates . Only the parent clone MHOM/NP/03/BPK275/0 cl-18 was used for the resistance selection experiments on amastigotes . At the start , the in vitro resistance profile against antimony ( SbIII and SbV ) , MIL and PMM had already been determined using previously described methods [14] . In brief , the clone was highly resistant to both SbV ( IC50>77 µg/µl eq . ) and SbIII ( IC50 = 51 . 1±0 . 7 µg/µl eq . ) and fully sensitive to MIL ( IC50 = 1 . 8±0 . 1 µM ) and PMM ( IC50 = 45 . 0±5 . 6 µM ) ( Table 2 ) . The principle of the selection method ( Fig . 1 ) was to maintain the highest possible PMM drug pressure in successive cycles of intracellular amastigotes , alternated with non-exposed promastigote cycles to expand the selected population for the subsequent infection . Prior experiments had already indicated that PMM is well-tolerated by primary mouse macrophages up to 500 µM , which became the upper in-test dose-range for selection . Late stationary-phase promastigotes were used to infect primary mouse macrophages grown in RPMI-1640 medium . After removing the non-internalized parasites , the infected cells were exposed to PMM serial 2-fold dilutions starting from 500 µM . During the whole selection process , no Sb drug pressure was exerted . All tests were carried out in two parallel 96-well plates and incubated at 37°C and 5% CO2 . Five days after infection , one plate was Giemsa-stained to enumerate the intracellular parasite burdens , while the medium in the second plate was replaced by HOMEM promastigote growth medium after scraping the macrophages to mechanically release amastigotes surviving the highest drug concentration and allow back-transformation into promastigotes . Once promastigote growth was observed in the wells , further expansion was done in 25 ml tissue culture bottles at 25°C in HOMEM promastigote medium until stationary growth phase was reached . This population enriched in metacyclics was then used for the next infection round of macrophages under PMM pressure . After each selection cycle , the level of resistance was determined using the standard intracellular amastigote susceptibility test [14] . The selection cycles were repeated until the maximum level of resistance was reached . In the first selection process promastigotes were exposed to levels of 62 . 5 µM PMM and the second cycle promastigotes were collected out of 125 µM . This PMM-selected strain was finally cloned for further follow-up studies . When conducting this protocol without exposing the internalized amastigotes to PMM pressure , no changes in PMM susceptibility were found . Since a single promastigote frequently fails to multiply , spent growth medium was used to enhance the cloning efficiency ( unpublished observation ) . The spent medium was prepared by collecting supernatant of a logarithmic-phase ( 3-day ) promastigote culture , centrifugation and filtration through a 0 . 22 µm filter . A ‘micro-drop’ method was used as cloning procedure . Briefly , an appropriate ‘donor dilution’ of a 3-day old promastigote culture was prepared from which the micro-drops were to be taken . In an ‘acceptor’ 96-well plate , 8 µl of HOMEM medium was placed to the side of the well to avoid rapid evaporation of the micro-drop , placed in the middle of the well with a fine needle by touching the bottom of the well . The presence of drops with a single promastigote was checked microscopically by two independent observers and 100 µl spent medium+100 µl HOMEM culture medium were added to the well . This procedure was continued until all 96-wells of the ‘acceptor’ plate were complete . The discarded wells with none or more than 1 promastigote were filled with 200 µl water and the plate was wrapped in parafilm to avoid evaporation . After incubation at 25°C for one week , growth was microscopically checked and established clones were transferred to a 25 ml tissue culture bottle for further routine culture . The in vitro susceptibility against PMM , SbV , SbIII and MIL was evaluated for each established clone , both as promastigote as well as intracellular amastigote ( Table 2 ) . The clones 1 , 8 , 11 and 13 were subjected to long term in vitro drug-free sub-cultivation to evaluate the stability of the PMM-resistant phenotype ( Table 3 ) . Routine sub-cultivation of promastigotes was done twice weekly for 20 weeks with in vitro susceptibility testing as intracellular amastigotes every two weeks . The in vivo stability of the PMM-resistant phenotype was evaluated by infecting hamsters with 2×107 late-stationary promastigotes of the respective clones . After eight weeks , a liver biopsy was taken for microscopic estimation of the parasite burden . When the biopsy revealed severe infection , the animal was sacrificed to collect spleen-derived amastigotes that were used to run the standard in vitro intracellular amastigote susceptibility assay and determination of IC50 values .
The selection process for the Indian and the Nepalese strains was very comparable and respectively took 32 weeks and 26 weeks to reach the maximal 97 µM inclusion level of PMM that did not affect normal growth . While the growth rate of the finally selected strains proved to be fully comparable to that of parent non-selected strains ( growth curves not shown ) , comparison of the IC50 values between the parent and the resistance-selected strains revealed large differences , being about 9 to 11-fold for the Indian strains and about 3 to 5-fold for de Nepalese strains . Subsequent evaluation in the macrophage susceptibility assay showed that the resistant phenotype was maintained at the level of the amastigote , although not in a linear manner . The differences between MPM and J774 host cells were minimal ( Table 1 ) . High PMM drug pressure on intracellular amastigotes very quickly selected for decreased susceptibility since already within one cycle , PMM-susceptibility showed a >3-fold decrease ( IC50 = 130 . 8 µM ) , while the second selection cycle resulted into an additional 1 . 4-fold decrease ( IC50 = 199 µM ) compared to the parent source strain ( IC50 = 45 µM ) ( Table 2 ) . Additional selection cycles did not result in any further significant increase of the IC50 ( data not shown ) . From the final PMM R-selected culture , 14 clones could be established ( Table 2 ) . Subsequent susceptibility profiling revealed that the population had become polyclonal during the selection process with several clones being highly resistant to PMM with tolerance levels up to 7 to 9× compared to the parent strain ( clones 1 , 8 , 11 , 13 ) , while a few others ( clone 6 and 14 ) were still fully susceptible . The remaining clones showed intermediate susceptibility . The susceptibility to the other reference drugs remained unchanged compared to the parent clone: full resistance to SbV ( IC50>77 µg/ml eq . ) and SbIII ( IC50 range 42 . 4–61 . 4 µg/ml eq . ) and full susceptibility to MIL ( IC50 range = 1 . 1–4 . 2 µM ) . Quite surprisingly , promastigotes of all 14 clones remained fully susceptible to PMM with IC50 values ranging between 10 . 5–23 . 5 µM , which sharply contrasts with the resistant amastigote phenotype . The stability of the resistant phenotype was checked for the non-selected and selected parent strain and for the highly resistant clones 1 , 8 , 11 and 13 adopting in vitro passage as promastigote for 20 weeks and by in vivo passage in the hamster ( Table 3 ) . The promastigote susceptibility of the induced clones increased less than 2-fold after 20 weeks . Passage in the hamster produced some but minor variability in the IC50 values . Moreover , the Sb and MIL in vitro phenotypes also remained unchanged ( data not shown ) .
Paromomycin is currently considered as a promising new antileishmania drug for the management of VL , and has already been extensively studied in clinical trials for its potential as monotherapy [15] or as combination therapy with antimonials [16] , [17] . However , monotherapy holds a direct and enhanced risk for the development of drug-resistance , and even combination therapy is not devoid of risks particularly in foci where Sb-resistance has already emerged . With regard to the latter , Bihar State has become the primary testing ground for new therapeutic approaches in VL [18] . For example , AmB is recommended as first-line drug but this recommendation may fail to be implemented in practice due to inadequate medical infrastructure [19] , [20] . The recently launched Kala-Azar elimination programme accommodates this by offering a fully integrated approach in which MIL has obtained a place among the first-line treatment options [3] . Despite the fact that PMM has already been widely considered as a valuable adjunct to current therapeutic options because of its high efficacy and tolerability [6] , [21] , yet relatively few studies focused on emergence and epidemiological monitoring of resistance . Hence , there is an immediate need to gain pro-active knowledge about PMM-resistance in case monotherapy would become more widely implemented in low endemic areas or as part of combination therapy in high endemic areas . Since PMM-resistant clinical isolates are not yet available , the present in vitro laboratory study induced PMM-resistance experimentally , considering drug selection pressure on both the promastigote and the intracellular amastigote stage . In view of its proven added value in combination with antimonials [16] and because of the high prevalence of Sb-resistant parasites in the region , clinical isolates with established Sb-resistant background were used as parent strains for selection . Applying drug pressure to promastigotes in a stepwise manner resulted in resistance after 26 to 32 weeks producing levels that were 9 to 11-fold for the Indian strains and about 3 to 5-fold for de Nepalese strains ( Table 1 ) . Similar to previous observations , the resistant phenotype is maintained upon infection of macrophages , although not in a linear fashion . The growth rates of the susceptible and PMM-resistant promastigotes were fully comparable ( data not shown ) , which contrasts with literature data [10] . However , it is important to note that resistance data on promastigotes should always be treated with some scepticism since this is not the stage that will eventually become exposed to the drug in addition to the ample evidence of their differences to amastigotes , not only biochemically [22]–[24] but also for drug susceptibility [25] . For this reason , a specific protocol needed to be developed ( Fig . 1 ) to exert drug selection pressure on the intracellular amastigote that is the sole target in the vertebrate host . Quite unexpectedly and in contrast to our observations in promastigotes , selection of resistance at the intracellular amastigote level was rapidly achieved , with a maximum already being obtained after just two selection cycles . This produced a population that tolerated up to 4 times higher PMM concentrations ( 199±8 . 5 µM ) compared to the original parent clone ( 45 . 0±5 . 6 µM ) , although the PMM-selected parasites retained a SbV , SbIII and MIL susceptibility profile that was similar to the parental line ( Table 2 ) . Taking note of the fact that the parent strain was fully resistant to SbV and SbIII and that some specific changes such as phospholipid composition [26] , [27] and membrane fluidity [28] have been described in Sb-resistant strains , further work would be needed to explore if this could have influenced the outcome of selection and whether this would have been different if a fully Sb-sensitive strain would have been used . Anyhow , more strains would deserve to be investigated for PMM resistance induction potential . The particular value of this ‘intracellular amastigote’ selection protocol lies in the fact that it more closely mimics the conditions as they develop in the field , namely drug pressure at the amastigote level in the mammalian host and disruption of drug pressure at the promastigote level in the vector . The very quick selection of PMM resistance using this model may indeed be a worrying observation , but the parasites were exposed to a huge selection pressure ( 500 µM = 308 µg/ml ) which possibly may never occur under the actual clinical use conditions of the drug . For example during the standard treatment course at 15 mg/kg daily for 21 days , peak plasma concentrations were obtained within about 30–90 minutes with steady-state PMM concentrations of about 20 µg/ml [4] . Anyhow , these data provide strong and convincing evidence on the propensity of rapid resistance development if PMM would be used in monotherapy and endorse the stringent need for close epidemiological monitoring . Although the selection was initiated from a cloned parent strain , the ensuing PMM-selected population had become polyclonal again containing sub-clones of varying PMM susceptibility ( Table 2 ) . Most clones showed comparable susceptibility to the selected parent strain ( IC50 = 130–213 µM ) , a few were more resistant ( clones 1 , 8 , 11 , 13: IC50>300 µM ) but a few were still fully susceptible ( clones 6 , 14: IC50<72 µM ) . With regard to the latter , it is difficult to explain how these susceptible clones were able to persist in the parent population that was subjected to high levels of PMM . Although yet never described for protozoa , one might speculate on the existence of mixed phenotype ‘organized’ populations and ‘persisters’ as has been described for bacteria and yeasts [29] or on the occurrence of multiple mutations as recently described for L . major [30] . Consistent with the parent strain , all clones remained susceptible to MIL and resistant to Sb , the latter being related to the fact that resistance was selected against an established SbV/SbIII-resistant ( R/R ) background . Another probably more unexpected observation was that the amastigote-induced resistant strain/clones only showed reduced PMM susceptibility at the intracellular amastigote and not at all at the promastigote stage , which sharply contrasts with the observations in the induced promastigotes that maintained the resistant phenotype as amastigote upon infection of the macrophage . This clearly demonstrates that induction of resistance may evolve differently in axenic promastigotes compared to intracellular amastigotes and hence supports the notion that the promastigote susceptibility assay should be avoided for PMM resistance monitoring purposes . This also triggers the question whether the initial observations on the mode of action and resistance [8] , [9] adequately cover the whole set of resistance mechanisms in the amastigote since promastigotes were used in these studies . In the absence of detailed mode-of-action studies , it remains difficult to speculate on putative mechanisms . Finally , the stability of the induced PMM-resistance was checked after in vitro serial passage for 20 weeks as promastigote and after a single in vivo passage in the hamster ( Table 3 ) . Even though a small decrease of PMM tolerance was observed after 20 weeks , the PMM-resistant phenotype persisted in the clones , tolerating up to 3× more PMM compared to the parent strain . The IC50 of the parent strain itself dropped slightly back from 199±8 . 5 µM to 82 . 5±4 . 3 µM ( Table 2 ) , suggesting that susceptible organisms with higher fitness may have increased their proportion in the passaged population after 20 weeks . Unfortunately , the relative fitness of the different clones was not evaluated in this study . More importantly , passage in the hamster did not alter the drug susceptibility phenotype , although some minor variation in PMM susceptibility was found . Even the Sb R/R phenotype remained stable after all these manipulations . Whether such a selection will actually occur in the field is still unknown , but the speed and stability of the induced PMM resistance certainly represents an area of concern , particularly because the standard promastigote susceptibility assay may not reveal the true situation in the field . More studies are now needed to verify whether these in vitro findings bear direct relevance to the epidemiological situation in areas where PMM is being used to treat VL . Factors that also need to be taken into account are the pharmacodynamics and -kinetics of PMM . The parasites induced in this study were subjected to extreme high concentrations of PMM far beyond the normal therapeutic plasma concentration . This interpretation may on the one hand support the position that induction/selection of PMM-resistance in the field may not develop that quickly because of the lower selection pressure; on the other hand , the pharmacodynamics and pharmacokinetics within the macrophage host cell are largely unknown . In conclusion , these observations strongly endorse the need to adopt strong treatment policies to ensure long-term efficacy of PMM . Stable PMM-resistant parasites could rapidly be induced in vitro using a novel amastigote selection model that mimics more closely the situation in the human patient . Whether the in vitro phenotype translates to in vivo treatment failure upon PMM treatment remains to be investigated , for example in the VL hamster model . Other follow-up research should include L . infantum and a larger number of strains , including Sb-susceptible and MIL-resistant isolates . | Leishmaniasis is caused by protozoan parasites of the genus Leishmania and is transmitted by inoculation of infective promastigotes by the female sand fly . In the mammalian host , amastigotes live inside macrophage cells which may lead to various clinical symptoms . First-line treatment relies mainly on antimonials and miltefosine; however , drug resistance is a growing problem . The antibiotic paromomycin ( PMM ) has recently been added as treatment option , but it is now essential to proactively assess the likelihood of resistance development to safeguard its long term effectiveness . Since ‘resistant’ patient isolates are not yet available , we artificially selected for PMM resistance using two different in vitro protocols with drug pressure on either the extracellular promastigote or on the intracellular amastigote stage . Resistance in promastigotes was obtained after about 25 weeks and persisted in the intracellular amastigote . High levels of resistance were obtained within two selection cycles on amastigotes , but with the unexpected observation that the promastigotes remained fully susceptible . In addition , the resistance proved to be stable . We could clearly demonstrate that a different PMM-resistance is obtained dependent on the ‘stage-selection’ protocol . These findings have important relevance to resistance mechanism investigations and the likelihood of resistance development and detection in the field . | [
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] | 2012 | Experimental Induction of Paromomycin Resistance in Antimony-Resistant Strains of L. donovani: Outcome Dependent on In Vitro Selection Protocol |
Common forms of atherosclerosis involve multiple genetic and environmental factors . While human genome-wide association studies have identified numerous loci contributing to coronary artery disease and its risk factors , these studies are unable to control environmental factors or examine detailed molecular traits in relevant tissues . We now report a study of natural variations contributing to atherosclerosis and related traits in over 100 inbred strains of mice from the Hybrid Mouse Diversity Panel ( HMDP ) . The mice were made hyperlipidemic by transgenic expression of human apolipoprotein E-Leiden ( APOE-Leiden ) and human cholesteryl ester transfer protein ( CETP ) . The mice were examined for lesion size and morphology as well as plasma lipid , insulin and glucose levels , and blood cell profiles . A subset of mice was studied for plasma levels of metabolites and cytokines . We also measured global transcript levels in aorta and liver . Finally , the uptake of acetylated LDL by macrophages from HMDP mice was quantitatively examined . Loci contributing to the traits were mapped using association analysis , and relationships among traits were examined using correlation and statistical modeling . A number of conclusions emerged . First , relationships among atherosclerosis and the risk factors in mice resemble those found in humans . Second , a number of trait-loci were identified , including some overlapping with previous human and mouse studies . Third , gene expression data enabled enrichment analysis of pathways contributing to atherosclerosis and prioritization of candidate genes at associated loci in both mice and humans . Fourth , the data provided a number of mechanistic inferences; for example , we detected no association between macrophage uptake of acetylated LDL and atherosclerosis . Fifth , broad sense heritability for atherosclerosis was much larger than narrow sense heritability , indicating an important role for gene-by-gene interactions . Sixth , stepwise linear regression showed that the combined variations in plasma metabolites , including LDL/VLDL-cholesterol , trimethylamine N-oxide ( TMAO ) , arginine , glucose and insulin , account for approximately 30 to 40% of the variation in atherosclerotic lesion area . Overall , our data provide a rich resource for studies of complex interactions underlying atherosclerosis .
Inheritance plays an important role in the pathogenesis of coronary artery disease ( CAD ) , the leading cause of death in the developed world [1–4] . Recent genome-wide association studies ( GWAS ) , involving hundreds of thousands of individuals have identified numerous loci contributing to CAD traits and to risk factors such as blood lipoprotein levels and blood pressure . A major challenge at present is to identify the causal genes at those loci and to understand the mechanisms by which they contribute to disease [5 , 6] . Most of the loci identified do not contain known candidates; for example , data from nearly 200 , 000 people identified 46 genetic loci associated with CAD , but only 17 of these loci contain genes for known risk factors such as lipids and blood pressure [5] . In a few cases , novel loci contributing to CAD have been successfully dissected using a combination of human and experimental mouse studies [7–9] . Such GWAS and follow-up studies do , however , have some important limitations . In particular , they are poorly powered to examine gene-by-gene and environmental interactions or to identify rare variants . Consequently , for most traits that have been studied , even very large association studies explain a small fraction of the heritability of the traits [9 , 10] . A complementary approach to studying common forms of CAD is to use naturally occurring variations in experimental organisms such as rats or mice . Important advantages include the ability to control the environment and to monitor both clinical and molecular phenotypes in detail . Over the past 20 years , quantitative trait locus ( QTL ) analysis has identified hundreds of loci for common disease traits . Unfortunately , this has led to the identification of relatively few genes and novel pathways , primarily because of the low resolution of linkage analysis [11] . However , with the recent sequencing of many mouse strains , it has become feasible to carry out high-resolution mapping in mice using association rather than linkage [12–15] . We now report association analysis of atherosclerosis and related traits in a population of over 100 common inbred strains , termed the Hybrid Mouse Diversity Panel ( HMDP ) . The panel allows mapping of complex traits by association analysis , providing resolution at least an order of magnitude better than that achieved by traditional linkage analysis [12] . The approach has now been used to identify novel genes for a number of traits , including several examples in which the genes have been validated using transgenic models [13 , 14 , 16 , 17] . Because the development of significant atherosclerotic lesions in mice requires a hyperlipidemia background , we have bred each of the strains to a common strain ( C57BL/6J ) that donated transgenes for two dyslipidemia-inducing mutations: human apolipoprotein E-Leiden ( APOE-Leiden ) and human cholesteryl ester transfer protein ( CETP ) . Genetic differences among the F1 animals arise only from sequence variations present in the individual recipient strains . We term this set of F1 animals the “Ath-HMDP” . Association analysis was used to identify and map phenotypic traits correlated with these known sequence variations . We have analyzed the progeny using a multi-phenotype layered “systems genetics” approach , involving the analysis of molecular as well as clinical phenotypes . The results provide a view of the genetic architecture of atherosclerosis in mice and enable statistical modeling of pathways underlying atherosclerosis . They also serve as a resource for future studies .
One difficulty in applying an association strategy to atherosclerosis in a panel of mice is that a sensitizing mutation resulting in hyperlipidemia is required for the development of substantial lesions . The most widely used models are apolipoprotein E null ( Apoe-/- ) [18 , 19] and LDL receptor null ( Ldlr-/- ) mice [20] . Both of these act in a recessive manner with respect to lesion development , and breeding either of these mutations to a homozygous state in numerous strains for an association study is not practical . To circumvent this problem , we developed a strategy in which C57BL/6J mice carrying dominant hyperlipidemia-inducing transgenes were bred to a panel of different strains followed by analysis of atherosclerosis and related traits in the heterozygous ( F1 ) mice ( Fig 1A ) . For this purpose , we employed transgenes for human APOE-Leiden and CETP , both of which had been previously used to promote atherosclerosis development in mice in a dominant manner [21 , 22] . We found that the combination of both transgenes provided the most robust development of lesions in both female and male mice ( Fig 1B ) . We also examined various diets and chose a diet containing 1 . 0% cholesterol since it significantly enhanced lesion development in females although no significant effect was observed in the smaller lesions observed in male mice ( Fig 1B ) . Our overall experimental design is summarized in Fig 1A . We generated mice heterozygous for the APOE-Leiden transgene and homozygous for the CETP transgene on a C57BL/6J background and bred these to each of about 100 HMDP mouse strains with selection of F1 mice expressing both transgenes . Ideally , donor mice homozygous for both transgenes would have been used in these studies , but we found that homozygous APOE-Leiden mice were poor breeders . The F1 mice ( which we term “Ath-HMDP” ) were maintained on a chow diet until 8 weeks of age and then challenged with a “Western style” high-fat cholesterol containing diet for an additional 16 weeks , at which time they were euthanized and trait phenotypes examined . In addition to atherosclerosis , we examined a variety of metabolic traits that have been associated with atherosclerosis in human populations , including plasma lipid levels , insulin/glucose levels , blood cell levels , obesity and TMAO levels [23] . We then performed high-resolution association mapping and correlation analyses for the traits and integrated the data to identify candidate genes and pathways . We measured levels of plasma lipids in the Ath-HMDP mice on a chow diet at 8 weeks of age as well as after feeding the mice the high fat high cholesterol diet from 8 weeks to 24 weeks of age . Strain-average plasma levels of total cholesterol , LDL/VLDL-cholesterol , HDL-cholesterol and total triglyceride levels varied considerably among the strains ( S1 Fig ) . In fact , for individual mice at 24 weeks , HDL-cholesterol levels ranged from undetectable levels to 220 mg/dl , VLDL/LDL-cholesterol levels ranged from 10 to 2 , 800 mg/dl , and triglyceride levels ranged from 10 to 2 , 700 mg/dl . As expected , the diet greatly elevated the levels of total- and LDL/VLDL-cholesterol in all the strains , but there remained a strong correlation between values on the chow and Western diets ( S2 Fig ) . The correlations of each class of lipids with atherosclerotic lesion area in females are shown in S3 Fig . Atherosclerotic lesion size was negatively associated with HDL-cholesterol levels ( but only suggestively , p = 0 . 08 ) and positively associated with LDL/VLDL-cholesterol levels ( p = 0 . 004 ) ( Panels A and B in S3 Fig ) . Triglyceride levels were not significantly associated with lesion area ( p = 0 . 41 ) ( Panel C in S3 Fig ) . The strengths of associations in male mice are shown in S1 Table . The levels of plasma glucose , insulin ( presented in S4 Fig ) and a measure of insulin resistance ( HOMA-IR ) [17 , 24] , were determined . There were large variations in all three parameters , with insulin levels ranging from undetectable levels to over 9 , 800 pg/ml . Glucose levels ranged from 126 mg/dl to nearly 450 mg/dl , with 75% of the mice having blood glucose levels in excess of 211 mg/dl . Correlation of glucose with atherosclerotic lesion size reached significance ( p = 0 . 016 ) in males but not in females ( p = 0 . 329 ) ( Panel E in S3 Fig and S1 Table ) . HOMA-IR , a measure of insulin resistance calculated from glucose and insulin values , ranged between 1 . 2 and 54 . Correlation of HOMA-IR with atherosclerotic lesion size was modest ( p = 0 . 06 ) in females but not significant in males ( p = 0 . 44 ) ( Panel F in S3 Fig , S1 Table ) . The levels of body fat , examined using nuclear magnetic resonance ( NMR ) spectroscopy , ranged from <10% to nearly 50% adiposity ( S2 Table ) . There was no significant relationship between adiposity and the extent of atherosclerosis among the male mice ( r = -0 . 099 , p = 0 . 381 ) but a significant inverse relationship among female mice ( r = -0 . 353 , p = 0 . 001 ) ( S1 Table ) . This inverse relationship is consistent with studies in genetically obese ( ob/ob and db/db ) mice which have reduced atherosclerosis [25] . This may have to do with increased lipoprotein particle size in obese mice . For example , lipoprotein lipase deficient mice have severe hypertriglyceridemia without increased atherosclerosis [26] . Using association analysis with correction for population structure , we mapped major loci contributing to lipoprotein levels in the Ath-HMDP . The regions identified as most likely to contain the causal gene ( or genes ) were those in strong linkage disequilibrium with the peak SNP ( determined by calculated r2 SNP correlations greater than 0 . 8 ) and exceeding the FDR cutoff of 5% , corresponding to the association p-value of 1 . 3×10−5 [27] . A list of all significant loci ( FDR < 5% ) is provided in S3 Table , but we highlight several of the associations for plasma lipids identified in females . A significant association was identified for VLDL/DL cholesterol on Chr 18 at 21 Mb ( Figs 2A and 3A ) , HDL cholesterol on Chr 5 at ~120 Mb ( Figs 2B and 3B ) , and triglycerides on Chr 1 at ~135 Mb ( Figs 2C and 3C ) . The QTL for triglycerides is novel , while the Chr 18 peak has previously been associated with HDL cholesterol but not VLDL/LDL or total cholesterol [28] and the Chr 5 peak is within the QTL boundaries of 2 previously reported HDL QTL , Hdlq8 and Chldq12 [28 , 29] . Some loci observed on chow diets are obscured on the hyperlipidemia background; for example , on a chow diet , the Apoa2 locus on Chr 1 is a major determinant of HDL-cholesterol levels [12] suggesting that gene x diet interactions influence disease susceptibility . Fig 4 shows the distribution of atherosclerotic lesion areas in the proximal aorta in the Ath-HMDP mice . Strain-average lesion areas in females ranged as high as 400 , 000 μm2 in several recombinant inbred ( RI ) strains to less than 2 , 500 μm2 for resistant strains ( Fig 4A ) . Strain-average lesion areas in males tended to be much lower but also varied widely , from about 170 , 000 μm2 to negligible ( Fig 4B ) . Strain C57BL/6J mice , with lesion sizes of about 150 , 000 μm2/section for females and 19 , 000 μm2/section for males , were approximately intermediate in the panel of strains . The sizes of lesions correlated significantly between males and females ( r = 0 . 47 , p = 2 . 61 X 10−5 ) ( Fig 4C ) but , clearly , the ratio of lesion sizes in the sexes varied widely , indicating the existence of gene-by-sex interactions . The range of lesion-areas is consistent with previous results from a small number of strains on either Apoe-/- or Ldlr-/- backgrounds as well as genetic crosses [30 , 31] . We were also interested in the site specific regulation of atherosclerosis and dissected the brachiocephalic arteries from the first 300 mice , but did not observe significant lesion development . We thus focused our efforts on the aortic root . We next sought to determine the loci contributing to lesion development . Using an FDR cutoff of 5% , corresponding to the association p-value of 1 . 3×10−5 [27] , we were able to identify 4 genome-wide significant loci in females and 1 locus in males significantly associated with atherosclerotic lesion size ( Fig 5A and 5B ) . The locus on Chr 9 , encompassing about 1 Mb between 46 and 47 Mb of the chromosome , was observed in both sexes ( Fig 5C and 5D ) and corresponds to a locus previously identified in genetic crosses between strains C57BL/6J and C3H/HeJ [32] . High-resolution regional plots of the other 3 loci in females are presented in S5 Fig . The two loci on Chr 2 are clearly distinct because conditioning on one does not affect the association for the other ( S6 Fig ) . Due to differences in breeding , the number of F1 mice differed considerably between strains ( S2 Table ) . When we performed association analysis using only strains with 3 or more mice , the significant regions exhibited similar p-values but a suggestive peak on Chr 10 became significant ( S7 Fig ) . Recent studies have highlighted the relationship between blood cells and cardiovascular disease[33–35] . We quantitated blood cell levels and correlated these with the extent of lesion development . The number of granulocytes , monocytes and percent granulocytes were positively correlated with lesions but did not reach statistical significance ( S4 Table ) . The percentage of white cells that were monocytes was significantly and positively correlated with atherosclerosis ( r = 0 . 231 , p = 0 . 023 ) ( S4 Table ) . We mapped major loci contributing to the blood cell levels ( S3 Table ) , and identified multiple loci affecting these traits . We detected an association for the percentage of white blood cells that were monocytes on Chr 8 at 36 Mb and total monocyte counts were regulated by loci on Chr 9 and 13 ( S3 Table ) . Cytokines are clearly important in atherosclerosis as indicated by transgenic studies in mice as well as human GWAS studies [4] . Although cytokines usually act in a local manner , we sought to examine circulating levels as a possible indicator of cytokine expression , using an immune capture microbead system . A list of the cytokines that were quantitated ( including their full names and abbreviations ) is presented in S5 Table . Those that could be accurately quantitated in a large fraction of strains included KC , G-CSF , IL-10 , MCP-1 , MIG , MIP-1a , and MIP-1b . All varied widely among the strains and their levels are shown in Fig 6A and S8 Fig . Of these , only KC exhibited a significant correlation with atherosclerosis ( r = 0 . 24 , p = 0 . 023 ) ( Fig 6C ) . KC levels mapped to a locus on Chr 1 ( Fig 6B ) that has been previously shown to be associated with multiple cardiovascular risk factors [3] . The uptake of modified LDL and VLDL particles by macrophages to produce cholesterol-loaded foam cells is a key aspect of atherosclerosis . To examine whether genetic differences in macrophage loading might contribute to lesion development , we quantitated the uptake of acetylated LDL ( AcLDL ) by peritoneal macrophages from the HMDP mice . Peritoneal macrophages appear to be a suitable surrogate since they show expression of surface markers typically found in atherosclerotic lesion macrophages such as SR-A , CD36 , CD68 , F4/80 , MOMA-2 , Ly6C , and CD11b [36–40] . Macrophages were incubated with DiI-AcLDL for 4 hours in 96-well microtiter plates at a density of 3×105 cells/well , washed , and the fluorescence level quantitated using a plate reader . In our initial studies , we observed reproducible differences in the uptake of AcLDL labeled with the fluorescent dye DiI ( Panels A and B in S9 Fig ) . Moreover , we observed that uptake was relatively linear over a 24-hour period ( Panel C in S9 Fig ) . Representative cultures are shown in Fig 7A . The uptake varied greatly among the strains over an approximately 5-fold range ( Fig 7B ) , and we identified two significant loci controlling the loading of macrophages by AcLDL , on Chr 6 at 149 Mb , rs30709278 ( p = 3 . 82 x10-7 ) and on Chr 7 at 13 Mb , rs37775929 ( p = 8 . 22 x10-7 ) ( Fig 7C ) . These loci do not contain the candidate genes Cd36 or Msr1 , previously implicated in cholesterol loading of macrophages , nor did we observe significant correlation between the mRNA levels of Cd36 and Msr1 with AcLDL loading [41] . Moreover , the uptake of AcLDL was not correlated with atherosclerosis ( Fig 7D ) , suggesting that AcLDL is not a good surrogate for the modified LDL found in lesions , or that cholesterol loading is not a limiting process in lesion development . To determine whether genetic factors contributed to lesion morphology as well as lesion size , we examined lesions with antibodies specific for macrophages ( CD68 ) or smooth muscle cells ( SM-α actin ) . We chose to examine the five progenitors of the four recombinant inbred strain-sets that comprise the HMDP ( C57BL/6J , DBA/2J , C3H/HeJ , A/J , and BALB/cJ ) since they provide much of the power for the association analyses . Representative sections are shown in Fig 8A–8J . Three histological sections from each of 5 female mice were quantitated for fraction of lesion area that shows staining for SM-α actin or CD68 and the results are plotted in Fig 8K and 8L , respectively . There were significant differences in CD68 staining , presumably indicating alterations in inflammatory pathways . We did not detect significant differences in the percentage of lesions staining positive for alpha actin staining . Genetic contributions to lesion morphology have been noted in some previous studies in mice [42–44] . We quantitated the levels of 31 polar metabolites in plasma of a subset of female strains . Of these , butyryl-carnitine , choline , TMAO , arginine , citrulline and ornithine exhibited significant correlations with atherosclerosis , and their strain average levels and correlations with atherosclerosis are presented in S10 Fig and Fig 9 . Most notably , the recently discovered risk factor for atherosclerosis , TMAO [23] , was positively correlated with atherosclerosis , r = 0 . 29 , p = 0 . 006 ( Fig 9C ) . On the other hand , the levels of choline , a metabolic precursor of TMAO [45] , were negatively correlated with atherosclerosis ( Fig 9A ) . The inter-related metabolites arginine , ornithine and citrulline are involved in the formation of nitric oxide ( NO ) , an important regulator of vascular tone , blood flow and pressure [46–48] . Ornithine , which was negatively correlated with lesion area ( r = -0 . 40 , p = 0 . 002 ) ( Fig 9F ) , is a breakdown product of arginine via the enzyme arginase I . Levels of arginine were positively correlated with lesion area ( r = 0 . 32 , p = 0 . 015 ) ( Fig 9D ) as were levels of citrulline ( r = 0 . 30 , p = 0 . 02 ) ( Fig 9E ) . As discussed below , we quantitated global gene expression in livers of female mice from 96 strains in the Ath-HMDP using Affymetrix HT-MG 430 PM microarrays . Several genes known to regulate these metabolites were highly associated with plasma concentrations . For example , hepatic expression of Arg1 , encoding arginase 1 , was highly negatively correlated with ornithine ( r = -0 . 45 , p = 0 . 0003 ) and positively correlated with arginine levels ( r = 0 . 323 , p = 0 . 013 ) . This suggests either that the ornithine and arginine in plasma are not of hepatic origin or that there is feedback regulation of Arg1 . It is noteworthy that the levels of arginase I in macrophages are a marker of M2 ( anti-inflammatory ) as compared to M1 ( pro-inflammatory ) macrophages [49 , 50] . We performed association analyses to identify loci regulating circulating metabolites associated with atherosclerosis . Notably we identified loci for TMAO levels on Chr 10 at 98 Mb , ( p = 6×10−6 ) and on Chr 16 at 94 Mb ( p = 1×10−6 ) ( S3 Table ) . Arginine was associated with a major locus on Chr 7 at 44Mb , rs32257964 ( p = 2×10−8 ) but also exhibited complex regulation with additional loci on Chrs 1 , 4 , 11 and 13 ( S3 Table ) . There are 13 hepatic genes with eQTL that are in strong LD with the associated SNP at the Chr 7 locus . Of these , Nosip is an interesting candidate as it has a strong cis-eQTL ( p = 8 . 7×10−22 ) and overexpression of Nosip has previously been shown to reduce NO synthesis in-vitro [51] . To help identify pathways underlying atherosclerosis , we quantitated global gene expression in livers and aortas of three individual female mice for each strain using Affymetrix HT-MG 430 PM microarrays . For this analysis , we used only strains where 3 or more female mice were available ( 104 strains for aortas and 96 strains for livers ) . S6 Table lists genes whose expression is most strongly positively or negatively correlated with atherosclerotic lesion size . These genes were analyzed using DAVID to test for enrichment of Gene Ontology ( GO ) categories . Overall there were broad differences in the function of genes correlated with atherosclerosis in the liver and the aorta . The genes most highly correlated with atherosclerosis were enriched for immune response genes in the liver and for defects in smooth muscle cell function and structure in the aorta ( Table 1 ) . We also analyzed the expression data using the weighted gene co-expression network analysis ( WGCNA ) [52] to model co-expression networks in the aorta and liver and to understand the association of gene networks with lesion size . WGCNA is a global analysis aimed at identifying genetic pathways associated with clinical traits , in this case atherosclerosis , and is used to aggregate gene expression into groups of highly co-expressed genes , called modules . The first principle component of each module was then related to atherosclerosis to identify gene clusters associated with the disease . We identified 43 and 45 co-expressed gene modules , ranging in size from 15 to 794 genes , in the aorta and liver , respectively . The most significantly correlated aortic module ( darkorange2 , r = 0 . 46 , correlation p = 4 . 3×10−5 ) contained 155 genes ( Fig 10A ) . This module was significantly enriched for the Gene Ontology “acyl-CoA metabolic process” category ( enrichment p = 2 . 6×10−6 ) . Among the genes in the module , Wdr73 was a hub , exhibiting the strongest connections with all the other genes ( Fig 10B ) . Wdr73 has been implicated in a human GWAS of periodontitis [53] and in Galloway-Mowat syndrome , a rare autosomal-recessive condition characterized by nephrotic syndrome associated with microcephaly and neurological impairment [54] . However , the function of this gene is not known and its role in atherosclerosis has not been studied . In the liver , the most significantly correlated module ( orange , r = 0 . 46 , correlation p = 3 . 5×10−6 ) contained 56 genes ( Fig 10C ) and was highly enriched for the Gene Ontology “defense response to virus” category ( enrichment p = 5 . 2×10−23 ) . The hub gene of the module was Mx1 which encodes a guanosine triphosphate ( GTP ) -metabolizing protein that is induced by type I and type II interferons ( Fig 10D ) . The module also contained several inflammatory genes , such as Irf7 ( interferon regulatory factor 7 ) , Ifit2 and Ifit3 ( interferon-induced protein with tetratricopeptide repeats 2 and 3 ) . To model causal interactions , help prioritize candidate genes at GWAS loci and examine gene-by-gene interactions , we mapped loci controlling gene expression traits ( eQTL ) in aorta and liver . We utilized the FaST-LMM algorithm to perform association while correcting for population structure [55] . Approximately 200 , 000 SNPs that segregate among the HMDP strains were chosen based on minor allele frequency greater than 10% [56] . Loci in which peak SNPs mapped within 2Mb of the gene whose expression was regulated were considered “local” SNPs while SNPs mapping elsewhere were considered “distal” and presumably trans-acting eQTL . We calculated the significant p-value cutoff for local and distal associations separately for both tissues . In aorta , we identified a total of 3 , 718 local eQTLs and 5 , 837 distant eQTLs at p-values of 8 . 4×10−4 and 1 . 5×10−6 , respectively , corresponding to 1% FDR . In liver , we identified a total of 3 , 599 local eQTLs and 3 , 912 distant eQTLs at p-values of 9 . 0×10−4 and 1 . 6×10−6 , respectively , corresponding to 1% FDR . Plots in which the location of the significantly associated SNP is graphed against the location of the regulated gene are presented in S11 Fig ( liver ) and S12 Fig ( aorta ) . To prioritize candidate genes in the atherosclerosis loci discussed above , we examined each of the annotated genes for local eQTL . Three of the 4 loci were associated with the expression levels of local genes ( 2Mb on either side of the locus ) in aorta and liver ( S7 Table ) . For example , in the Chr 9 locus , examination of hepatic eQTL identified 4 candidates; Nnmt ( p = 1 . 27×10−8 ) , 2310030G06Rik ( p = 5 . 42×10−7 ) , Alg9 ( p = 1 . 05×10−6 ) , and 1110032A03Rik ( p = 2 . 27×10−6 ) , whose eQTL are regulated by a SNP in high LD ( r2>0 . 5 ) with the associated SNP for atherosclerosis . The less stringent cutoff for r2 was chosen to ensure that that no strong eQTL contributing to the atherosclerosis phenotype would be missed , based on the rationale that the causal gene might reside immediately outside the LD block but be regulated by an enhancer within the block . The expression of all four of these genes had significant correlations with lesion size ( r = 0 . 25–0 . 37 , p = 1 . 2×10−2–1 . 9×10−4 ) . Three of the genes also exhibited significant eQTL in aorta; Nnmt ( p = 1 . 01×10−11 ) , Bco2 ( p = 1 . 61×10−4 ) , 1110032A03Rik ( p = 2 . 18×10−4 ) , and their expression levels were correlated with atherosclerosis ( r = 0 . 26–0 . 39 , p = 2 . 6×10−2–6 . 4×10−4 ) ( Panel B in S11 Fig ) . In both tissues , Nnmt had the most significant local eQTL ( Panel B in S11 Fig and Panel B in S12 Fig ) . In the Chr 5 locus , there are 4 hepatic genes with eQTL that are in strong LD with the atherosclerosis associated SNP at the locus: Nub1 ( p = 1 . 65×10−16 ) , Nos3 , ( p = 3 . 08×10−6 ) , Cdk5 ( p = 2 . 48×10−5 ) , and Abcb8 ( p = 7 . 46×10−4 ) ( S5 Fig ) . Of these genes , only Nub1 replicates in the aorta ( p = 1 . 37×10−15 ) . While the distal Chr 2 locus was associated with the expression level of two genes in liver , Dtwd1 ( p = 1 . 43×10−13 ) and Bub1 ( p = 4 . 62×10−4 ) , their abundances were not correlated with lesion size ( r = 0 . 02–0 . 08 , p = 0 . 43–0 . 85 ) . None of the loci appear to exhibit interactions with any risk factors examined in this study , including plasma lipids , metabolites , or the cytokines . “Heritability” is the fraction of trait variance that is due to genetic factors [57] . We estimated the heritability of the traits in our study using two different approaches , one that determines heritability due to additive genetic variance , termed “narrow sense heritability” [58] and the second that estimates total heritability , termed “broad sense heritability” . Broad sense heritability was calculated using an R package [59] . The estimates of narrow sense heritability were based on sharing of genomic regions identical by descent . We have recently performed high density genotyping of all the HMDP strains [56] and we used these data to determine genome sharing . Broad sense heritability was estimated based on the reproducibility of trait measurements in different individuals of the strain , a measure of the trait variance due to environmental factors . This estimate includes non-additive factors such as dominance and gene-by-gene interactions . The resulting heritabilities are shown in Table 2 . Particularly noteworthy is the fact that , in the case of atherosclerotic lesion area , the broad sense heritability is much larger than the narrow sense heritability ( 0 . 63 vs . 0 . 31 ) , suggesting that non-additive factors such as gene-by-gene interactions are important . Simple inspection of the F1 atherosclerosis data supports this conclusion . Since all the mice are F1 heterozygotes with C57BL/6J ( which have lesions of about 150 , 000 μm2 in females and 19 , 000 μm2 in males ) as one parent , an additive model would be inconsistent with lesion areas less than about 75 , 000 μm2 for females and 9 , 000 μm2 for males . Yet , some strains have lesions areas less than a few thousand μm2 . Similar evidence of non-additive inheritance was also observed for plasma lipids and a number of other traits studied . ( For examples , see Panel D in S1 Fig , Panel C in S8 Fig and Panel C in S10 Fig ) For certain traits , the narrow sense heritability exceed the broad sense heritability ( Table 2 ) . This undoubtedly reflects errors in the estimates . In particular , certain traits were examined in only a subset of the mice , compromising estimates of broad sense heritability that are based on reproducibility of measurements within a strain . Overall , these heritability estimates are somewhat lower than observed in some previous HMDP studies [15] . This may be due in part to the fact that the mice were F1 heterozygotes with C57BL/6J being one of the parental strains , thus reducing genetic diversity . Also , the weak correlation of HDL-C with atherosclerosis may reflect , in part , the low heritability of the HDL-C trait . Human GWAS studies employing tens of thousands of individuals are highly powered to identify loci for complex traits , including CAD and its risk factors . To date , about 150 genome-wide significant and suggestive loci have been identified for CAD in approximately 200 , 000 individuals [60] . Most often , these loci will contain multiple genes in linkage disequilibrium , and thus a first step in further analysis is to identify which of the genes is causally linked to the trait . The data generated in this report can be used for this purpose provided that the human candidate has a functional genetic variation in the mouse ( S8 Table ) . For example , the GUCY1B3 gene locus has been associated with atherosclerosis and its role has been validated by studies of rare variants in human families [60–62] . In the Ath-HMDP , Gucy1b3 has a strong cis-eQTL in aorta ( p = 9 . 7x10-6 ) as well as other tissues and its expression in aorta is negatively associated with atherosclerosis ( r = 0 . 23 , p = 0 . 04 ) , consistent with human studies . In some cases , our data suggest that a novel gene at a GWAS locus might be responsible . For example , human SNP rs2075650 in Chr 19 has been associated with increased CAD risk and is near the APOE/APOC1 genes whose roles in atherosclerosis are well established . However , our data show a very significant correlation between the aortic expression of another gene located at the same locus , Pvrl2 , and lesion size ( r = 0 . 55 , p = 3 . 0×10−7 ) ( Fig 11B ) . There is also a weaker but significant correlation in the liver in the opposite direction ( r = -0 . 27 , p = 0 . 007 ) ( Fig 11C , S6 Table ) . This gene encodes a protein that is part of the adherens junctions [63] , and PVRL2 mRNA and protein were shown to be elevated in the vessel wall of diseased human carotid arteries [64] and lesioned mouse aortas [65] . In addition to genes identified by GWAS approaches , the data from the Ath-HMDP should be useful to identify rare variants affecting atherosclerosis . For example , recent exome sequencing analysis of a highly affected family identified mutations in the gene CCT7 that impairs guanylyl cyclase signaling and increased risk of myocardial infarction [62] and its role has been validated in mouse knockout studies [60 , 61] . Our data show very significant correlations between aortic expression of Cct7 and atherosclerosis ( r = -0 . 39 , p = 6 . 1x10-4 ) ( S6 Table ) while hepatic expression was not correlated with lesion size ( r = 0 . 003 , p = 0 . 97 ) . Our systems genetics approach , in which a variety of plasma metabolites were measured across a population varying for atherosclerosis while holding environmental factors relatively constant , enables the identification of diagnostic markers based on genetic variation . Using a series of plasma metabolites that exhibited at least suggestive correlation with atherosclerosis , we carried out stepwise linear regression [66] to estimate the percent variation in lesion area attributable to each ( Table 3 ) . These metabolites explain a very significant fraction of disease variance , 31 . 8% in females and 38 . 9% in males . One striking difference between the sexes is the lack of impact of TMAO in males . This is likely explained by the fact that male mice show greatly reduced TMAO levels due to repression by testosterone of FMO3 , the enzyme that metabolizes TMA to TMAO [67] . The explanation for the sex difference in butyryl-carnitine is unclear as its levels are similar in both sexes . Arginine is strongly associated with lesion area in both sexes presumably reflecting its relationship to NO biosynthesis [68] . It is also of interest that , in this study , HDL-cholesterol levels do not appear to be a significant factor , consistent with Mendelian randomization studies which suggest that HDL-cholesterol levels are not causal for the disease [69] .
Over the last 35 years several groups , including ours , have investigated common genetic variations affecting atherosclerosis in mice . In the current study we have examined susceptibility to atherosclerosis and a number of related traits on a hyperlipidemic background in >1 , 800 mice across 100 strains . The results provide a broad view of the physiological and molecular interactions underlying atherosclerosis in this model organism . A number of conclusions have emerged , as discussed below , and the data reported here should serve as a resource for future gene discovery and mechanistic studies . Toward this end , all of our data can be accessed at our website ( systemsgenetics . ucla . edu ) or from the authors . Using association rather than linkage analysis , we have mapped loci for a number of clinically relevant traits with excellent resolution . Of particular interest are traits such as TMAO levels which have proven difficult to address using human GWAS studies , presumably due to the major impact of environmental factors [70] . Although the underlying genes may differ between mice and humans ( for example , we did not observe overlap between the four atherosclerosis loci in our mouse population and human GWAS loci for CAD ) , there appears to be conservation of the underlying pathways . For example , we find a high level of concordance of the major risk factors for atherosclerosis in mice and humans , including the levels of plasma LDL-cholesterol and TMAO . Because our population of mouse strains is relatively small ( as compared to human GWAS studies ) , we are powered to identify only the loci with largest effect sizes ( several percent of total trait variance ) using GWAS [12] . A complementary approach for understanding pathways contributing to complex traits in mice is “systems genetics” ( or “integrative genetics” ) [10] . This approach utilizes correlation and mathematical modeling of multi-level phenotypic data to help identify the underlying pathways . We have carried out preliminary systems genetics analyses of these data , including enrichment of annotated biologic pathways in the genes most correlated with clinical traits and co-expression network modeling . Such analyses are difficult to perform directly in human subjects because of the difficulty of accessing tissues such as blood vessels and liver . Using our systems genetics approach we identify several important characteristics of atherosclerosis . One is its sheer complexity . We carefully phenotyped the Ath-HMDP for a variety of traits and prioritized these based on their correlation to atherosclerosis . These included plasma lipids , metabolites , and cytokines and cellular uptake of modified lipids by thioglycollate-elicited macrophages . Each of these phenotypes are themselves complex traits and we identify significant loci for each of them . Our results are consistent with an infinitesimal model of common diseases , in which genetic variations in hundreds or thousands of genes determine genetic susceptibility to disease . There were several surprising findings in this study . The first is the importance of non-additive genetic variance in atherosclerosis and some other traits . This was clearly reflected in the F1 data and in the comparison of broad sense as compared to narrow sense heritability . This indicates the existence of important non-additive interactions , such as gene-by-gene and gene-by-environment interactions , which have been difficult to pursue in human studies [71 , 72] . Our overall estimates of heritability are similar to estimates in human populations , generally in the range of 0 . 4 to 0 . 5 as reviewed previously [73] . We also note the dramatic differences in lesion size between male and female mice . We and others have observed sexually dimorphic results in previous genetic crosses but not to the extent observed in the current study [32 , 74–76] . Understanding how genetic and hormonal factors affect lesion development could identify gender specific susceptibility pathways . Consistent pathways were identified in our gene set enrichment analyses , a primary one being the NO pathway . The endothelial nitric oxide synthase gene ( eNOS ) , a strong candidate at the Chr 5 locus , and plasma levels of arginine , the substrate for NO production , are correlated with atherosclerosis and are regulated by a locus on Chr 7 that contains the gene Nosip1 . Further understanding how NO metabolism is regulated may identify novel targets for atherosclerosis treatment . A number of human epidemiologic studies have revealed associations between arginine and asymmetrical dimethyl arginine , an endogenous NO inhibitor , and cardiovascular events , including myocardial infarction and stroke [77–79] . In addition to arginine , our metabolomics analyses revealed associations between atherosclerosis and several plasma metabolites . The association between TMAO and atherosclerosis adds to the previous evidence that TMAO is involved in atherosclerosis . It is noteworthy that TMAO was a significant factor only in females , which have significantly higher levels of TMAO than males due to repression of FMO3 by testosterone [67] . Briefly , TMAO is derived from dietary choline or carnitine which are metabolized by gut microbiota to trimethylamine . This is , in turn , absorbed into the circulation and oxidized in liver by FMO3 to TMAO [45 , 67] . In addition , we found that the levels of choline were inversely associated with atherosclerosis . The negative association with choline could be due to differences between strains in the catabolism of choline to trimethylamine by gut bacteria . Those strains exhibiting increased catabolism would exhibit elevated TMAO production and increased atherosclerosis . By contrast , in humans where differences in plasma choline levels are likely determined primarily by dietary intake , the positive correlation between plasma choline and atherosclerosis is consistent with increased TMAO derived from higher levels of choline substrate [23] . The loci identified for TMAO levels did not contain any obvious candidate genes . It is possible that the loci for TMAO actually influence the composition of gut bacteria responsible for choline or carnitine catabolism . The explanation for the association of butyryl-carnitine with atherosclerosis is unclear . Elevated levels of the metabolite are associated with rare forms of short-chain acyl-CoA dehydrogenase deficiencies [80] . In-vitro lipid loading of macrophages using AcLDL has been widely used as a surrogate assay for foam cell formation , a critical event in atherosclerotic lesion formation [81] . The studies performed here were designed to examine this trait in the context of common genetic variation . While we did identify loci associated with lipid loading , they are distinct from loci identified as regulating atherosclerosis in the Ath-HMDP . Nor did the loci correspond to any known genes involved in cholesterol transport , such as scavenger receptor A1 or CD36 , which have been shown to affect atherosclerosis in some loss of function studies [82 , 83] but not others [84] . Furthermore we did not observe a correlation between lipid loading and atherosclerosis susceptibility , suggesting either that AcLDL is a poor surrogate for the modified , aggregated LDL that is produced in atherosclerotic lesions or that the rate of lipid loading is not limiting for atherosclerosis development . These identified loci may , however , be of basic interest with respect to pathways contributing to cholesterol metabolism and transport . While LDL that has been oxidized in-vitro ( oxLDL ) would be a more suitable ligand for these studies , we used AcLDL for our experiments because there are large variations in uptake between batches of oxLDL as well as variations in the same oxLDL batch over time . A previous study of AcLDL loading of macrophages from two strains differing markedly in atherosclerosis susceptibility ( DBA/2J and AKR/J ) observed some striking differences in transcriptional responses associated with lysosome and ER stress pathways [85] . Inflammation is a hallmark of atherosclerosis [86] and cytokines and chemokines have been directly linked to lesion development using gain or loss of function studies in mice [87] . Our results revealed significant associations between atherosclerosis and the plasma levels of the cytokine KC ( keratinocyte-derived chemokine ) , a ligand for Cxcr2 and encoded by the gene Cxcl1 , located on Chr 5 . The homolog of KC in humans is IL-8 , a critical cytokine that is elevated in CAD patients [88] and also predictive of future events in apparently healthy subjects [89] . Mechanistic studies in mice have demonstrated that increased expression of KC promotes atherosclerosis by altering monocyte and neutrophil accumulation [90] , probably reflecting KC’s ( IL-8’s ) role in monocyte and neutrophil recruitment [91 , 92] . GWAS analysis of KC/IL-8 levels in the Ath-HMDP mice identified a locus on Chr 1 with a large effect within a locus previously identified for a variety of cardiovascular risk factors including atherosclerosis , body weight , and plasma levels of HDL , glucose and triglycerides . The Apoa2 gene has been identified as the causal variant underlying the lipid variations at this locus [93] but it is not clear that genetic variation in ApoA2 is directly responsible for all of these phenotypes . We note several positional candidates for this locus including Slamf7 and Slamf6 ( members of the Signaling Lymphocyte Activation Molecules family that mediate NK cell activation ) and Ifi203 and Ifi204 ( interferon activated genes 203 and 204 ) . Of these , only Ifi203 has an eQTL ( 3 . 2×10−24 ) regulated by a SNP in high linkage disequilibrium ( r2>0 . 5 ) with the associated SNP for KC . It also has expression levels significantly correlated with KC levels ( p = 0 . 003 ) . We also analyzed blood cell levels in the Ath-HMDP mice and observed that the number of monocytes as percent of leukocytes was significantly associated with atherosclerosis . This is consistent with human population studies showing that increased monocyte numbers are associated with disease [94] . We also identified loci , associated with monocyte levels; it is noteworthy that these differed from those previously identified in mice on chow diets [95] . Finally , our data constitute a resource for elucidating pathways contributing to atherosclerosis . For example , they should be useful in prioritizing candidate genes at human GWAS loci for atherosclerosis and related traits . More than a third of the genes present on the arrays used here exhibited significant genetic variation in expression in aorta and liver . For example we show that the vascular expression of Pvrl2 , a positional candidate from human GWAS studies , is highly correlated with atherosclerosis . Another example is the strong association of expression levels of Cdkn2b with atherosclerosis ( S8 Table ) . This gene resides in the human chromosome 9p21 region that is the most strongly associated with atherosclerosis and our data suggest that it may have a causal role [96] . The resource is also being expanded to include additional expression , metabolome , and microbiome data ( in progress ) . In conclusion , our study provides a comprehensive systems genetic analysis of traits relevant to atherosclerosis in a population of common inbred strains of mice . The results are generally consistent with human epidemiologic studies , as many of the factors associated with atherosclerosis in human populations were replicated in mice . They also identify a number of novel factors and candidate gens that can now be experimentally examined . These data as well as other HMDP studies , including gene-gene and gene-trait correlations and clinical trait and clinical trait and transcript mapping , can be accessed in a user-friendly web-based interface at http://systems . genetics . ucla . edu/data .
Results can be accessed at http://systems . genetics . ucla . edu/data All microarray data from this study are deposited in the NCBI GEO ( http://www . ncbi . nlm . nih . gov/geo/ ) under the accession number GSE66570 . Mice carrying the human transgene for cholesteryl ester transfer protein ( CETP ) on a C57BL/6 background were obtained from The Jackson Laboratory ( Stock Number:003904 ) . Mice carrying the human ApoE3 Leiden variant were kindly provided by Dr . L . Havekes [97] . For purposes of these experiments , we interbred these mice to create a strain carrying both transgenes and these were bred to females from about 100 common inbred and recombinant inbred strains purchased from The Jackson Laboratory . The locations and copy numbers for the transgenes are unknown . However , while there may be insertion effects on expression of the transgenes or neighboring genes , all F1 animals received their transgenes from the same transgenic mice so that such insertion effects should be uniform across the Ath-HMDP panel . Male and female progeny were genotyped for the presence of both transgenes and , at the age of about 8 weeks , were placed on a “Western Style” synthetic high fat diet ( 33 kcal % fat from cocoa butter ) supplemented with 1% cholesterol ( Research Diets D10042101 ) ( see S9 Table ) . After 16 weeks on this diet , animals were euthanized for the collection of tissue . Animals were maintained on a 12hr light-dark cycle , 6AM-6PM with ad libitum access to water and chow or experimental diet . Euthanasia of all mice was carried out using deep anesthesia with isoflurane vapor followed by cervical dislocation , a procedure consistent with recommendations of the AVA . All animal work was conducted according to relevant national and international guidelines and was approved by the UCLA Animal Research Committee , the UCLA IACUC . Plasma was collected from the retro orbital plexus under isoflurane vapor anesthesia immediately before starting the experimental diet at about 8 weeks of age and again at the time of euthanasia at approximately 24 weeks of age . In both cases , the animals were fasted for 4h beginning at 6AM . Blood was collected using heparinized glass capillary tubes into plasma collection tubes with EDTA ( Becton Dickerson ) . Blood was kept on ice until centrifuged and the separated plasmas were frozen at -80°C in aliquots for subsequent analysis . Plasma lipid profiles were measured by colorimetric analysis as previously described [98 , 99] . Quantification of plasma cytokines was carried out in a multiplexed immune-capture microbead system ( Milliplex Mouse Cytokine / Chemokine Magnetic Bead Panel MCYTOMAG-70K ( EMD Millipore , Billerica , MA ) ) as per manufacturer’s instructions . Cytokines profiled were: G-CSF , GM-CSF , IFNr , IL-1α , IL-1β , IL-2 , IL-4 , IL-6 , IL-7 , IL-10 , IL-12 ( p40 ) , IL-12 ( p70 ) , IL-13 , IL-15 , IP-10 , KC , MCP-1 , MIP-1α , MIP-1β , M-CSF , MIP-2 , MIG , RANTES and TNFα . Plasma insulin was measured using the mouse insulin ELISA kit ( 80-INSMS-E01 ) from Alpco ( Salem , New Hampshire ) as per manufacturer’s instructions . Blood for hematology analysis was collected from mice from the retro-orbital plexus under isoflurane anesthesia . Complete blood cell profiling was carried out using the Heska ( Loveland , CO ) HemaTrue ( TM ) Veterinary Hematology Analyzer . Blood was collected in 20 μl EDTA-coated glass capillaries and processed using standard procedures as per instructions from Heska . Lesion area in the proximal aorta was carried out as previously described [93 , 100] . Briefly , the aorta was flushed with PBS and embedded in OCT . Frozen sections ( 10 μm ) were stained with Oil Red O and lesion area quantified in every 3rd section through the proximal aorta . Lesion size was not normally distributed and was transformed using the Yeo-Johnson transformation [101] . For immunohistochemical staining , primary antibody for α-smooth muscle actin ( α-SMA ) ( Rabbit anti alpha smooth muscle actin abcam # ab32575 ) or CD68 ( for macrophages; Rat anti–mouse CD68 , Bio-Rad # MCA 1957 ) was applied on tissue sections from frozen , OCT-embedded proximal aortas . Frozen tissue sections were fixed in acetone ( 15min at 4°C ) , washed , blocked with 5% normal goat serum in 3% BSA ( 3h at room temperature ) , washed with PBS and incubated with the primary antibody for 1 h at room temperature and then overnight at 4°C . After washing with PBS , secondary antibody ( α-SMA: Goat anti rabbit ( Vector cat # BA-1000 ) or CD68: Goat anti rat ( Vector cat # BA-9401 ) ) was added for 1h at room temperature . With intervening washes , slides were then treated with ABC solution ( Vector Laboratories Cat# AK-5000 , 1h at room temperature ) , Vector red substrate ( Vector Cat# SK-5100 , 10-60min ) , Hematoxylin ( Thermoscientific #7211 , 2 min ) , Bluing reagent ( Thermoscientific #7301 , 1–2 minutes ) and 0 . 01% fast green ( Sigma #F7258 , 30–60 seconds ) and then dried and mounted with glycerol gelatin solution from Sigma ( Cat# GG1 ) . Slides stained for CD-68 macrophages , or for α-smooth muscle actin were analyzed for percent of lesion area with positive immuno-reactivity based on visual scoring of a superimposed grid . We measured the following metabolites using MS/MS TOF as described [102]: acetyl-carnitine , arginine , asymmetric dimethylarginine ( ADMA ) , betaine , butyrobetaine , butyryl-carnitine , carnitine , choline , citrulline , creatinine , crotonobetaine , gamma-butyrobetaine , hexanoyl-carnitine , isoleucine , leucine , Lysine , methyllysine , monomethylarginine ( MMA ) , octenoyl-carnitine , ornithine , pentanoyl-carnitine , phenylalanine , propionyl-carnitine , symmetric dimethylarginine ( SDMA ) , trans-crotonobetaine , trimethylamine ( TMA ) , trimethylamine N-oxide ( TMAO ) , tyrosine and valine . Stable isotope dilution LC/MS/MS was used for quantification of plasma analytes . Four volumes of methanol containing isotope-labeled internal standards were added to 1 volume of plasma to precipitate protein . The supernatant after centrifugation was analyzed by injection onto a silica column interfaced with an API 4000 Q-TRAP mass spectrometer ( AB SCIEX , Framingham , MA ) [102] . A discontinuous gradient was generated to resolve the analytes by mixing solvent A ( 0 . 1% propanoic acid in water ) with solvent B ( 0 . 1% acetic acid in methanol ) [102] . Analytes and the isotope labeled internal standards were monitored in positive MRM MS mode using characteristic precursor–product ion transitions . The parameters for the ion monitoring were optimized for each analyte . Various concentrations of analytes were spiked into the control plasma sample to prepare the calibration curves for quantification of analytes . Body composition was measured a day or two before euthanasia by NMR using the Brüker minispec ( Brüker Biospin Corp , Billerica , MA ) and software from Echo MRI ( Houston , TX ) [103] . Whole aorta from the arch to the mid-abdomen was cleaned of peri-adventitial adipose and snap-frozen at the time of euthanasia , and total RNA isolated using the Qiagen ( Valencia , CA ) RNeasy kit , as described [104] . Genome wide expression profiles were determined by hybridization to Affymetrix HT-MG_430 PM microarrays on a subset of female mice from 104 strains ( N = 1 to 10 aorta per strain ) . The liver was carefully dissected and a 50-μg aliquot from the left lobe was immediately frozen at the time of euthanasia and total RNA isolated using the Qiagen ( Valencia , CA ) RNeasy kit ( cat# 74104 ) , as described [104] . Genome wide expression profiles were determined by hybridization to Affymetrix HT-MG_430 PM microarrays on a subset of female mice from 96 strains ( N = 1 to 3 liver samples per strain ) . To assess reliability of results from these microarrays , we compared expression in livers of Ath-HMDP F1 mice carrying a recipient genome from C57Bl/6J , A/J , DBA/2J or BALB/cJ with RNA-seq data for the same strains ( n = 3 mice per strain ) . The correlation between these two approaches was quite strong , ( approximately r = 0 . 72 , p< 1 . x10-16 for each strain ) similar to a recently published comparison of microarray and RNA-seq data [105] . Primary macrophages were harvested from four mice per strain , by intraperitoneal lavage four days following intraperitoneal injection of 1 . 5ml 4%Thioglycollate ( BD , Sparks , MD ) . All mice were injected with the same batch of thioglycollate . Cells from each strain were pooled , and plated in replicate wells ( n≥ 4 ) of 96-well black plates ( Fisher , Pittsburgh , PA ) at a cell density of 3×105 cells per well in DMEM with 20% FBS at 37°C and 5% CO2 . After overnight culture , cell media was replaced with 1% FBS DMEM media for controls or with media plus 10μg/mL DiI-acetylated LDL ( Biomedical Technologies , Ward Hill , MA ) , or media plus 10μg/mL DiI-acetylated LDL and 200μg/mL unlabeled acetylated LDL ( Biomedical Technologies ) . Four hours later , these media were removed and wells were washed 3 times with PBS and measured for DiI fluorescence ( Excitation at 530 nm; Emission at 590 nm ) . Network analysis was performed using the WGCNA R package [52] . An extensive overview of WGCNA , including numerous tutorials , can be found at http://www . genetics . ucla . edu/labs/horvath/Co-expressionNetwork/ and this method has been extensively used to create co-expression networks [52 , 106–110] . To generate a co-expression network for all probes , an adjacency matrix is created by first calculating the pairwise gene-gene correlations and then raising the Pearson correlation to the 10th and 6th power for aorta and liver , respectively . The power was selected using the scale-free topology criterion , which is determined by the function “pickSoftThreshold” in the WGCNA package [52 , 111] . Network connectivity ( k . total ) of the genes was calculated as the sum of the connection strengths with all other network genes . A TOM-based dissimilarity measure was used for hierarchical clustering of the genes . Gene modules corresponded to the branches of the resulting dendogram and were defined using the “Dynamic Hybrid” branch cutting algorithm [112] . The parameters for module generation were as follows: “cut height” parameter was set to 0 . 99 and the “minimum module size” parameter was set to 30 . Gene significance ( GS ) for each gene was determined and is defined as the correlation between lesion size and expression of probes . Module significance ( MS ) was calculated as the mean GS for all module genes . Modules that were most significantly correlated with lesion size in aorta and liver were visualized using Cytoscape [113] . We performed a Gene Ontology ( GO ) enrichment analysis for network modules using the Database for Annotation , Visualization and Integrated Discovery ( DAVID ) using the functional annotation clustering option [114] . Functional annotation clustering combines single categories with a significant overlap in gene content and then assigns an enrichment score ( ES; defined as the–log10 of the geometric mean of the unadjusted p-values for each single term in the cluster ) to each cluster . The Mouse Diversity Genotyping Array [115] was used to genotype ~150 classical and recombinant inbred mouse strains . After eliminating SNPs flagged as having poor quality , approximately 450 , 000 SNPs formed our starting set of genotypes . These SNPs were then filtered using the following criteria: the minor allele frequency could not be below 10% and the missing genotype frequency could not exceed 10% . Since a different subset of our 150 strains was used in each of our studies ( which was dependent on strain availability at the time ) this filtering was performed separately for each study . Associations were performed using FaST-LMM [55] , a linear mixed model method that is able to account for population structure . To improve power , when testing all the SNPs on chromosome N for association , the kinship matrix was constructed using the SNPs from all other chromosomes besides N . This procedure allows us to include the SNP being tested for association in the regression equation only once . Also , any bias from measuring different numbers of mice for a given trait should be accounted for by the kinship matrix of the FaST-LMM algorithm . The biweight midcorrelation statistic is analogous to the Pearson correlation coefficient , but has the advantage of being robust to outliers . We used the bicor function implemented in the WGCNA R package [116] to calculate transcript-transcript correlations , transcript-trait correlations , and trait-trait correlations . These analyses were performed at the strain level to account for the variable number of mice collected for each trait . eQTL were defined as local or cis if the peak association was within a 4Mb interval , flanking 2Mb on either side of the genomic start site of the gene . eQTL were defined as distant or trans by selecting the peak association per chromosome per gene , excluding loci that mapped in cis . We calculate false discovery rates using the qvalue package in R . For each gene , we selected all association p-values in the 4 Mb interval , and calculated q-values using all the p-values for all genes . We estimated the FDR separately for each treatment and selected FDR<5% . We estimated the FDR separately for each tissue and selected FDR<5% as follows: aorta eQTL , the median 1% FDR cutoff was 8 . 4×10−4 , and 5% FDR cutoff was 6 . 4×10−3 . The corresponding results for the liver data were 1% FDR = 9 . 4×10−4 and 5% FDR = 6 . 7×10−3 . Due to the computational complexity associated with evaluating q-values for over 2 billion p-values , we computed the FDRs by taking the median FDR for 100 samples , each containing 100 million randomly selected p-values from the original calculated association p-values [117] . We estimated the FDR separately for each tissue and selected FDR as follows: aorta eQTL , the median 1% FDR cutoff was p ≤ 1 . 5×10−6 , and 5% FDR cutoff was 1 . 3×10−5 . The corresponding results for the liver data were 1% FDR = 1 . 6×10−6 and p ≤ 5% FDR = 1 . 30×10−5 . We set 1 . 3×10−5 as our threshold of significance for all molecular and clinical phenotypes . We used forward stepwise regression [66] to identify those metabolites that appeared to be the most effective predictors of lesion area . To avoid potential issues with over-fitting , we only used as candidate predictors those traits which had at least suggestive correlation with lesion area . These included plasma levels of choline , arginine , butyryl-carnitine , citrulline , TMAO , ornithine , KC , VLDL+LDL , HDL , TG , insulin , glucose , and the calculated parameter HOMA-IR . | While recent genetic association studies in human populations have succeeded in identifying genetic loci that contribute to coronary artery disease ( CAD ) and related phenotypes , these loci explain only a small fraction of the genetic variation in CAD and associated traits . Here , we present a complementary approach using association analysis of atherosclerotic traits among inbred strains of mice . A strength of this approach is that it enables in-depth phenotypic characterization including gene expression and metabolic profiling across a variety of tissues , and integration of these molecular phenotypes with coronary artery disease itself . A striking finding was the large fraction of atherosclerosis that was explained by genetic interactions . Association analysis allowed us to identify genetic loci for atherosclerotic lesion area as well as transcript , cytokine and metabolite levels , and relationships among the traits were examined by correlation and network modeling . The plasma metabolites associated with atherosclerosis in mice , namely , LDL/VLDL-cholesterol , TMAO , arginine , glucose and insulin , overlapped with those observed in humans and accounted for approximately 30 to 40% of the observed variation in atherosclerotic lesion area . In summary , our data provide a detailed overview of the genetic architecture of atherosclerosis in mice and a rich resource for studies of the complex genetic and metabolic interactions that underlie the disease . | [
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] | [] | 2015 | Genetic Architecture of Atherosclerosis in Mice: A Systems Genetics Analysis of Common Inbred Strains |
The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient’s genetic makeup . Although the highest quality databases require manual curation , text mining tools can facilitate the curation process , increasing accuracy , coverage , and productivity . However , to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature . In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature . Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content , but from a global context as well ( from the Internet and from all literature in PubMed ) . Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach . We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases ( breast cancer , prostate cancer , pancreatic cancer , lung cancer , acute myeloid leukemia , Alzheimer’s disease , hemochromatosis , age-related macular degeneration ( AMD ) , diabetes mellitus , and cystic fibrosis ) . We then evaluate our approach in two ways: ( 1 ) a direct comparison with the state of the art using benchmark datasets; ( 2 ) a validation study comparing the results of our approach with entries in a popular human-curated database ( UniProt ) for each of the previously mentioned diseases . In the benchmark comparison , our full approach achieves a 28% improvement in F1-measure ( from 0 . 62 to 0 . 79 ) over the state-of-the-art results . For the validation study with UniProt Knowledgebase ( KB ) , we present a thorough analysis of the results and errors . Across all diseases , our approach returned 272 triplets ( disease-gene-variant ) that overlapped with entries in UniProt and 5 , 384 triplets without overlap in UniProt . Analysis of the overlapping triplets and of a stratified sample of the non-overlapping triplets revealed accuracies of 93% and 80% for the respective categories ( cumulative accuracy , 77% ) . We conclude that our process represents an important and broadly applicable improvement to the state of the art for curation of disease-gene-variant relationships .
Many genetic mutations protect or predispose individuals to disease [1] . The practice of precision medicine involves identifying such mutations in patients and modifying patient treatment to reflect each individual’s unique physiologic risks and strengths [2] . Databases of gene-disease relationships play a key role in this process by acting as a reference to which providers may refer to determine the significance of their patients’ mutations [3 , 4] . In a similar way , these databases also play a key role in translational research [5 , 6] . Currently , the highest quality databases require manual curation , often in conjunction with support from automated systems [7 , 8] . Creating entries in these databases requires substantial human investment . For example , in the UniProt Knowledgebase ( UniProtKB ) , each mutation receives a multitude of annotations providing information about gene function , role in disease , and molecular interactions , and other things [9] . These manual curation efforts are critical because the biomedical literature is a unique source of genotype-phenotype information . Curations in genotype-phenotype databases are also important components of synthesized knowledge bases of clinically actionable genetic information such as the Reference Variant Store ( RVS ) [6] . Yet , the high cost of expert curation of UniProtKB and databases alike is a rate-limiting factor for content coverage and updates . Computational approaches to gene curation could potentially relieve the bottleneck of human resources in disease mutation annotation . Fully automating the curation system remains beyond the capacity of even state-of-the-art text mining systems , but automation of parts of the process is feasible . A separate , yet important role of text mining in database gene curation is that text mining tools can shed perspective on the scope and breadth of current databases by summarizing the entirety of relevant information in the biomedical literature . Identification of genotype-phenotype relationships is a key concern in both clinical and research communities . Several well-known databases employ manual curation of biomedical literature to provide comprehensive coverage of such relationships in humans . Examples of these include OMIM [10] , HGMD [11] , Comparative Toxicogenomics Database ( CTD ) [12] , GHR ( http://ghr . nlm . nih . gov/ ) and UniProtKB [9] . Recent efforts in the direction of ( semi- ) automated approaches to facilitate database curation of genotype-phenotype relationships include extraction of sequence variation information from biomedical text . Overall , most methods confine their scope to mutation entity extraction without exploring the relationships of those mutations to other entities , such as diseases or genes . Examples of mutation recognition tools include MutationFinder [13] , tmVar [14] , and several others [15] . Several groups , however , have addressed variant relationship mining in text . One early , notable method developed by Kuipers et al [16] introduced an automatic method for extracting and validating mutations for a single disease–Fabry disease . Since their approach finds mutations on a single gene ( GLA ) at Xq22 . 1 for Fabry disease , it uses regular expression to identify mutation mentions and assumes them to be related to the disease and gene . Other early approaches to relationship extraction include , MuGeX [17] , EnzyMiner [18] , and OSIRIS [19] . Each of these has been reported with limitations and over-specialization [20] . More recently , Hakenberg et al developed an approach to mining a variety of pharmacogenomic relationships from PubMed abstracts [21] . Their work is particularly noteworthy in its comparison of text-mined results to a manually curated database–PharmGKB . Laurila et al mined functional impact information about gene variants [22] , and Macintyre et al created a rule-based approach to identifying gene-disease and gene-variant relationships from literature for the purpose of investigating the impact of intergenic ( non-coding ) variants [23] . Of all the works on mutation relationship extraction , one of the most notable is the EMU tool developed by Doughty et al [20] . EMU provides a semi-automated approach to extract disease-related mutations from PubMed abstracts and full text . This work , which truly addresses broad genotype-phenotype relationship extraction is most comparable to our present work . In all the above automated approaches , there are two common limitations: ( a ) disease-to-mutation relationships in the text are not explicitly detected or utilized for extraction; rather , a relationship is typically assumed ( i . e . co-occurrence ) ; ( b ) none of the above mentioned approaches explicitly focuses on extracting a three-way relationship between gene , mutation and the disease from the text . Regarding the latter limitation , the EMU tool can extract three-way relationships from text , but its primary focus is mutation extraction . Its gene and disease association extraction functionality is limited . Likewise , the works by Macintyre et al [23] and Hakenberg et al [21] include extraction of both gene-disease relations and gene-variant relations , but extraction of the entire triplet is not expressly evaluated . We have previously approached parts of this problem as well . One work [24] used a machine learning approach to determine disease-mutation association from PubMed abstracts . In another study [25] we experimented with crowd-based human judgment to determine binary associations between genes and mutations in PubMed abstracts . These previous works separately addressed identification of disease-mutation associations and gene-mutation associations , but neither attempted extraction of complete disease-gene-variant triplets . Developing an efficient , robust and fully automated approach to extract a full three-way relationship or triplet of disease-gene-variant from text is still challenging for several reasons . Firstly , correctly mining complex bio-entities from biomedical literature has been a long-standing challenge . Secondly , mining three-way relationships is even more complicated than mining two-way relationships . The challenges in gene-variant-disease extraction are heightened due to several factors concerning natural language processing and information presentation in biomedical literature . An example explaining such challenges is shown in Fig 1 . As shown in the figure , a common challenge of text mining this information is that a single abstract may contain references to multiple diseases , genes , and mutations . The conventional , co-occurrence-based approach of assuming hereditary hemochromatosis ( HH ) to be related to all the mutations found in an abstract frequently results in false positives , as it does in this example . For this reason it is important to examine the text to establish gene-mutation and disease-mutation relationships . Fig 1 contains an abstract retrieved through a PubMed search for HH with various entities annotated using PubTator . In this abstract all named variants belong to the ATP7B gene and have a proposed association with Wilson disease ( not HH ) . Researchers used known HH-causing variants in the HFE gene ( none of which are mentioned in the abstract ) to identify people in a population of Wilson disease patients who may also have had HH . A third disease–congenital spherocytosis–is cited as a confounding factor in their analysis . The author’s ultimate conclusion is that their methodologies were insufficient to definitively demonstrate a defining association between Wilson disease and variants of the gene in question . In this paper we propose a novel , end-to-end approach to automate the extraction of disease-gene-variant triplets from biomedical literature . We employed our previously published supervised machine-learning approach to detect relationships between disease and mutation mentions in text . Then we mined gene association information using a novel approach that leverages global context ( using PubMed and the Web via Bing search ) followed by gene sequence validation in order to identify an exact gene match for the mutation . The end result of the proposed approach is a disease-gene-variant triplet . We describe the development of this system and analyze its performance in extracting gene mutations for a set of ten common diseases set forth in the list provided in Doughty et al [20] ( breast cancer , prostate cancer , pancreatic cancer , lung cancer , acute myeloid leukemia , Alzheimer’s disease , hemochromatosis , age-related macular degeneration ( AMD ) , diabetes mellitus , and cystic fibrosis ) . The performance of the proposed approach is evaluated in two ways: ( 1 ) direct comparison with the previous EMU approach using a benchmark dataset; ( 2 ) validation with a popular human-curated database ( UniProtKB ) . The main contributions of this work are as follows:
The dataset used in this work is comprised of PubMed articles . Although the technique developed is applicable for any disease , we have analyzed and presented results for a set of ten diseases [20] . For each disease , we assembled a “Disease_corpus” by collecting a list of PMIDs from PubMed using the following query: “disease_name [tiab] AND English [26] AND has_abstract [filter]” . For each PMID in the Disease_corpus , we collected the PubMed title , abstract and annotation results for gene , mutation and disease mentions via PubTator [27] . In PubTator , the gene , mutation and disease annotations were extracted by GNormPlus [28] , tmVar [14] and DNorm [29] , respectively . Our approach for identifying gene-disease-mutation relationships is portrayed schematically in Fig 2 and can be summarized as follows: Step 1: Identify all diseases , genes , and mutations in PubMed abstracts; Step 2: Associate mutations with a given disease from all articles in PubMed about that disease; and Step 3: Link the proteins or genes with each mutation using an aggregation of information extracted from PubMed , the Web ( using Bing search engine features ) , and sequence analysis . This process results in a list of triplets of the form <disease , gene , variant> . Details of each step are as follows:
The performance of the proposed approach is first evaluated for disease-gene-variant triplet extraction from biomedical literature on the benchmark datasets for prostate and breast cancer used to report the performance of the EMU approach [20] . These datasets consist of manually annotated lists of disease-gene-variant triplets from 203 and 141 PubMed articles for breast and prostate cancer respectively . Using these benchmark datasets , we report the accuracies of our approach in standard measures ( precision , recall and F-measure ) and compare the accuracy of our text-mined triplets ( cross-validation based ) directly with the results of EMU . Table 1 displays a comparison between our proposed approach and the previous state of the art ( EMU ) . The table includes results with and without sequence filters as well as results for a simple co-occurrence relationship extraction baseline that uses our entity tagging tools . Since EMU also used a co-occurrence approach but with different entity tagging tools , this co-occurrence column allows a comparison of the performance of the different entity tagging tools . As shown in Table 1 , our entity tagging tools performed similarly to those of EMU . Without sequence filters , our approach achieves marked improvement over the results of EMU in precision and overall F1-measure on both datasets , although EMU has better recall . Employing a sequence filter improves the precision of EMU at the expense of substantial decreases in recall ( The EMU with sequence filter removes gene-variant pairs that fail the protein sequence validation check , reducing the overall number of mutations extracted by EMU and therefore the recall also decreases ) , but this tradeoff does not occur in our approach . Rather , the use of a sequence filter improves all three metrics . Overall , for the prostate cancer dataset , our full approach achieves 39% improvement in precision ( from 0 . 59 to 0 . 82 ) over the EMU approach with a sequence filter incorporated . Incorporation of a sequence filter improves the precision at the expense of recall for EMU , but for our approach , the addition of a sequence filter has the opposite effect–both precision and recall improve with the addition of the filter . This is because our approach evaluates each gene in the candidate list of genes and thus increases the likelihood that the final gene match is correct . Consequently , the overall F1-measure is 28% higher ( from 0 . 62 to 0 . 794 ) than EMU’s F1-measure . Similarly for the breast cancer dataset , we find that the precision of the proposed approach is 22% higher ( from 0 . 61 to 0 . 742 ) than the precision value for EMU , and the F1-measure is 15% higher ( from 0 . 64 to 0 . 74 ) . This is a significant improvement in the state of the art for disease-gene-variant triplet extraction . Moreover the balanced performance of our approach offers practical advantages for database curation: achieving high precision at the cost of a small decrease in recall suggests that the extracted results contain very few errors ( false positives ) with comparable coverage ( recall ) . To assess the potential of our approach in assisting database curation , we performed an extrinsic analysis by comparing our text-mined results against curated relationships for a total of ten diseases . UniProtKB–the Universal Protein Resource Knowledge Base–is a database of protein sequence and annotation data produced in Switzerland through collaboration between the European Bioinformatics Institute ( EMBL-EBI ) , the Swiss Institute of Bioinformatics ( SIB ) and the Protein Information Resource ( PIR ) [38] . Its scope includes all human genes and function-altering gene variants along with any diseases caused by those variants [39] . Data collection from UniProtKB is explained in detail in the supplementary material , S4 Text . The raw output of our algorithm across the literature for these ten diseases can be found in the supplementary material ( S1 Data ) along with a thorough analysis of these results in S5 Text . We compare the text-mined results with the UniProt curated set . As shown in Fig 4 , the red bars denote the counts of text-mined results for each disease , and the blue bars denote the counts of curated variants for each disease in the UniProtKB dataset . As is apparent in the figure , text mining extracts a significantly larger number of triplets than exist in UniProtKB curations . From UniProtKB , we collected 1 , 529 unique gene-variant pairs for the ten diseases . In comparison , we extracted 5 , 656 gene-variant pairs from the literature using our text mining approach for the same diseases . We divided the UniProtKB entries and text-mined disease-gene-variant triplets into three separate groups by their overlap and evaluated the proposed approach differently in each group ( shown in Fig 5 ) . We evaluated the accuracy ( in precision ) of gene-variant pairs that were only found through text mining via human annotation of a stratified random sample of the results ( Analysis 1 ) . Overlapping mutations were evaluated directly with UniProtKB with respect to their gene association ( Analysis 2 ) . Finally , we analyzed gene-variant pairs that were unique to UniProtKB–potential false negatives for our approach ( Analysis 3 ) .
Based on the aforementioned three analyses of the text-mined mutations and the UniProt curated mutations , we state the following findings: One reason that may explain the relatively low overlap between the results of our approach and the curations in UniProtKB is a difference in institutional focus . UniProtKB only curates gene variants that result in alterations of protein function , and while our approach does identify function-altering variants , it also includes a much broader range of associations , including disease-causing , protective , and treatment-response associations . Although we have presented our findings as a comparative analysis with data from the well-known UniProtKB , we nevertheless consider that the text mining results will act as a complimentary support to curators of any database to enhance the efficiency of the curation of disease mutations and genes . For instance , the uncurated text mined results may represent priority candidates for human curators during triage . A key step in making our approach useful for such databases will be developing additional text mining filters that can permit curators to retrieve custom sets of gene-variant-disease triplets with accompanying evidence that will best suit their institutional objectives . An important consideration in the field of automated mining of gene-variant-disease associations from literature is that nomenclature standards for gene variants have evolved over time as researchers have understood new levels of genetic complexity [40–42] . One trend has been a movement to describe all variants by the sequence of the coding DNA strand and avoid other levels of description ( i . e . mRNA and protein ) . As mentioned previously , our approach is fairly agnostic to variant nomenclature alterations because of the robust nature of tmVar’s mutation identification algorithm–we extract all types and descriptions of variants . Nevertheless , in this work , we concentrated on protein sequence nomenclature largely to facilitate comparison with the UniProtKB database . Since our algorithm incorporates a sequence filter and since protein sequences are notoriously variable , it is possible that this processing step may have removed correct variant associations with slightly different protein sequence numbering from our results . This could also explain some of the difference in overlap between our text-mined results and the curated associations in UniProtKB . Normalization of variant mentions in literature is an important next-step for automated extraction of genotype-phenotype relations . Discrepancies between human annotators: As mentioned previously , two human annotators evaluated each text-mined result in approximately 40% of the sample set for each of the ten diseases . Following independent evaluation of each variant triplet , the annotators met and discussed the variants that they had rated differently and reached a consensus . A comparison of these specific instances revealed several trends . For example , disagreements were more common when sentence syntax was complex ( e . g . PMID 22774841 , disease: hemochromatosis , variant: W779X , gene: ATP7B –final judgment: no association ) , when the article in question addressed a disease related to but distinct from the disease in question ( e . g . PMIDs 21853126 , 21680267 and 18580449; disease: pancreatic cancer; variant: P86S , gene GCGR–final judgment: true association ) , when disagreement exists in the published literature about the significance of a given variant ( e . g . PMID: 23397959 , disease: AML , variant: K751Q , gene: ERCC2 ( XPD ) versus PMID: 24486506 for the same disease and variant–final judgment: true association ) , or when the genetic variants returned via text mining were the result of experimental modifications ( e . g . PMID 18595696 , disease: cystic fibrosis , variant: K1250A , gene: CFTR–final judgment: true association ) .
We identify a few areas of work that may enhance our approach and improve its utility for future research and other applications . First , although our approach robustly identifies gene variant mentions of different types across multiple nomenclature styles , we do not currently normalize variants . To avoid duplicate references in this work we have constrained our evaluation to only variant mentions at a protein level . Our work could be improved if we were to normalize all gene variant references to a single notation format , preferably to a complementary DNA sequence . Such normalization would facilitate future comparisons with data sources and also increase the utility of the sequence validation step in our approach . Still , this sequence validation step will only be possible for substitutions and deletions and not for insertion-type variants . This limitation did not affect our analysis in this work since UniProtKB only curates substitution variants and does not curate insertions or deletions . In future studies , we may need to incorporate a sequence validation method that will permit validation of insertions as well as substitutions and deletions . Another important limitation of the current approach is that it mines information only from abstracts and not full text or supplementary material , which have been shown to be an important source of genetic variant information [43] . An extension to full text will require more advanced systems to overcome the additional noise in the full text and tables . As shown in the results of Analysis 3 , we miss a large proportion of the UniProtKB mutations because they either appear in full text or supplementary material . For these reasons , an extension of the current work to full text is one of the important future steps of our efforts . Two potential resource for developing this extension are the Variome Corpus , which contains ten full-text articles with manual annotations applied according to the Variome Annotation Schema guidelines [44] , and the Biomedical entity Relation ONtotlogy COrpus ( BRONCO ) , which contains a large collection of annotated relationships between genes , variants , drugs , and cell lines from the full text of 108 articles [45] . Finally , this proposed framework directly uses several state-of-the-art tools ( like tmVar , DNorm and GNormPlus ) . Future advances in the respective domains of these tools would enable increased performance of the proposed approach . This study has been helpful to excavate several examples which will serve as feedback to the independent machineries of mutation , gene , and disease annotation systems . In conclusion , we have shown that our approach for text mining disease mutations and their associated genes from the biomedical literature is successful . We have also shown that the training step for this approach is generalizable among different types of diseases . Our approach can thus apply broadly to a variety of diseases . The intrinsic evaluation shows that our approach achieves state-of-the-art performance and compares favorably to a competitive system . Our comparative analysis with real-world curation data confirms the accuracy of our approach and demonstrates that text-mined results may be potentially useful for expanding the coverage of curation and improving curation quality . | To provide personalized health care it is important to understand patients’ genomic variations and the effect these variants have in protecting or predisposing patients to disease . Several projects aim at providing this information by manually curating such genotype-phenotype relationships in organized databases using data from clinical trials and biomedical literature . However , the exponentially increasing size of biomedical literature and the limited ability of manual curators to discover the genotype-phenotype relationships “hidden” in text has led to delays in keeping such databases updated with the current findings . The result is a bottleneck in leveraging valuable information that is currently available to develop personalized health care solutions . In the past , a few computational techniques have attempted to speed up the curation efforts by using text mining techniques to automatically mine genotype-phenotype information from biomedical literature . However , such computational approaches have not been able to achieve accuracy levels sufficient to make them appealing for practical use . In this work , we present a highly accurate machine-learning-based text mining approach for mining complete genotype-phenotype relationships from biomedical literature . We test the performance of this approach on ten well-known diseases and demonstrate the validity of our approach and its potential utility for practical purposes . We are currently working towards generating genotype-phenotype relationships for all PubMed data with the goal of developing an exhaustive database of all the known diseases in life science . We believe that this work will provide very important and needed support for implementation of personalized health care using genomic data . | [
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"molecular"... | 2016 | Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine |
The recent genealogical history of human populations is a complex mosaic formed by individual migration , large-scale population movements , and other demographic events . Population genomics datasets can provide a window into this recent history , as rare traces of recent shared genetic ancestry are detectable due to long segments of shared genomic material . We make use of genomic data for 2 , 257 Europeans ( in the Population Reference Sample [POPRES] dataset ) to conduct one of the first surveys of recent genealogical ancestry over the past 3 , 000 years at a continental scale . We detected 1 . 9 million shared long genomic segments , and used the lengths of these to infer the distribution of shared ancestors across time and geography . We find that a pair of modern Europeans living in neighboring populations share around 2–12 genetic common ancestors from the last 1 , 500 years , and upwards of 100 genetic ancestors from the previous 1 , 000 years . These numbers drop off exponentially with geographic distance , but since these genetic ancestors are a tiny fraction of common genealogical ancestors , individuals from opposite ends of Europe are still expected to share millions of common genealogical ancestors over the last 1 , 000 years . There is also substantial regional variation in the number of shared genetic ancestors . For example , there are especially high numbers of common ancestors shared between many eastern populations that date roughly to the migration period ( which includes the Slavic and Hunnic expansions into that region ) . Some of the lowest levels of common ancestry are seen in the Italian and Iberian peninsulas , which may indicate different effects of historical population expansions in these areas and/or more stably structured populations . Population genomic datasets have considerable power to uncover recent demographic history , and will allow a much fuller picture of the close genealogical kinship of individuals across the world .
We can only hope to learn from genetic data about those common ancestors from whom two individuals have both inherited the same genomic region . If a pair of individuals have both inherited some genomic region from a common ancestor , that ancestor is called a “genetic common ancestor , ” and the genomic region is shared “identical by descent” ( IBD ) by the two . Here we define an “IBD block” to be a contiguous segment of genome inherited ( on at least one chromosome ) from a shared common ancestor without intervening recombination ( see Figure 1A ) . A more usual definition of IBD restricts to those segments inherited from some prespecified set of “founder” individuals ( e . g . , [8] , [27] , [28] ) , but we allow ancestors to be arbitrarily far back in time . Under our definition , everyone is IBD everywhere , but mostly on very short , old segments [29] . We measure lengths of IBD segments in units of Morgans ( M ) or centiMorgans ( cM ) , where 1 Morgan is defined to be the distance over which an average of one recombination ( i . e . , a crossover ) occurs per meiosis . Segments of IBD are broken up over time by recombination , which implies that older shared ancestry tends to result in shorter shared IBD blocks . Sufficiently long segments of IBD can be identified as long , contiguous regions over which the two individuals are identical ( or nearly identical ) at a set of single nucleotide polymorphisms ( SNPs ) that segregate in the population . Formal , model-based methods to infer IBD are only computationally feasible for very recent ancestry ( e . g . , [30] ) , but recently , fast heuristic algorithms have been developed that can be applied to thousands of samples typed on genotyping chips ( e . g . , [31] , [32] ) . The relationship between numbers of long , shared segments of genome , numbers of genetic common ancestors , and numbers of genealogical common ancestors can be difficult to envision . Since everyone has exactly two biological parents , every individual has exactly 2n paths of length n meioses leading back through their pedigree , each such path ending in a grandn–1parent . However , due to Mendelian segregation and limited recombination , genetic material will only be passed down along a small subset of these paths [8] . As n grows , these paths proliferate rapidly and so the genealogical paths of two individuals soon overlap significantly . ( These points are illustrated in Figure 1 . ) By observing the number of shared genomic blocks , we learn about the degree to which their genealogies overlap , or the number of common ancestors from which both individuals have inherited genetic material . At least one parent of each genetic common ancestor of two individuals is also a genetic common ancestor , so the number of genetic common ancestors at each point back in time is strictly increasing . A more relevant quantity is the rate of appearance of most recent common genetic ancestors . This quantity can be much more intuitive , and is closely related to the coalescent rate [33] , as we demonstrate later . For this reason , when we say “genetic common ancestor” or “rate of genetic common ancestry , ” we are referring to only the most recent genetic common ancestors from which the individuals in question inherited their shared segments of genome .
We should expect significant within-population variability , as modern countries are relatively recent constructions of diverse assemblages of languages and heritages . To assess the uniformity of ancestry within populations , we used a permutation test to measure , for each pair of populations x and y , the uniformity with which relationships with x are distributed across individuals from y . Most comparisons show statistically significant heterogeneity ( Figure S2 ) , which is probably due to population substructure ( as well as correlations introduced by the pedigree ) . A notable exception is that nearly all populations showed no significant heterogeneity of numbers of common ancestors with Italian samples , suggesting that most common ancestors shared with Italy lived longer ago than the time that structure within modern-day countries formed . Two of the more striking examples of substructure are illustrated in Figure 2 . Here , we see that variation within countries can be reflective of continuous variation in ancestry that spans a broader geographic region , crossing geographic , political , and linguistic boundaries . Figure 2A shows the distinctly bimodal distribution of numbers of IBD blocks that each Italian shares with both French-speaking Swiss and the United Kingdom , and that these numbers are strongly correlated . Furthermore , the amount that Italians share with these two populations varies continuously from values typical for Turkey and Cyprus , to values typical for France and Switzerland . Interestingly , the Greek samples ( EL ) place near the middle of the Italian gradient . It is natural to guess that there is a north-south gradient of recency of common ancestry along the length of Italy , and that southern Italy has been historically more closely connected to the eastern Mediterranean . In contrast , within samples from the United Kingdom and nearby regions , we see a negative correlation between numbers of blocks shared with Irish and numbers of blocks shared with Germans . From our data , we do not know if this substructure is also geographically arranged within the United Kingdom ( our sample of which may include individuals from Northern Ireland ) . However , an obvious explanation of this pattern is that individuals within the United Kingdom differ in the number of recent ancestors shared with Irish , and that individuals with less Irish ancestry have a larger portion of their recent ancestry shared with Germans . This suggests that there is variation across the United Kingdom—perhaps a geographic gradient—in terms of the amount of Celtic versus Germanic ancestry . The first two principal components of the matrix of genotypes , after suitable manipulations , can reproduce the geographic positions of European populations ( e . g . , [12]–[14] ) . Therefore , it is natural to compare the structure we see within populations in terms of IBD sharing to the positions on the principal components map . ( A PCA map of these populations , produced by EIGENSTRAT [38] , is shown in Figure S4 . ) It is not known what the geographic resolution of the principal components map is , but if relative positions within populations is meaningful , then comparison of IBD to PCA can stand in for comparison to geography . Indeed , as seen in Figures S5 and S6 , the substructure of Figure 2 correlates well with the position on certain principal components , further suggesting that the structure is geographically meaningful . Conversely , since the substructure we see is highly statistically significant , this demonstrates that the scatter of positions within populations on the European PCA map is at least in part signal , rather than noise . Individuals usually share the highest number of IBD blocks with others from the same population , with some exceptions . For example , individuals in the United Kingdom share more IBD blocks on average , and hence more close genetic ancestors , with individuals from Ireland than with other individuals from the United Kingdom ( 1 . 26 versus 1 . 09 blocks at least 1 cM per pair , Mann-Whitney p<10−10 ) , and Germans share similarly more with Polish than with other Germans ( 1 . 24 versus 1 . 05 , p = 5 . 7×10−6 ) , a pattern which could be due to recent asymmetric migration from a smaller population into a larger population . In Figure 3 we depict the geography of rates of IBD sharing between populations—that is , the average number of IBD blocks shared by a randomly chosen pair of individuals . Above , maps show the IBD rate relative to certain chosen populations , and below , all pairwise sharing rates are plotted against the geographic distance separating the populations . It is evident that geographic proximity is a major determinant of IBD sharing ( and hence recent relatedness ) , with the rate of pairwise IBD decreasing relatively smoothly as the geographic separation of the pair of populations increases . Note that even populations represented by only a single sample are included , as these showed a surprisingly consistent signal despite the small sample size . Superimposed on this geographic decay there is striking regional variation in rates of IBD . To further explore this variation , we divided the populations into the four groups listed in Table 1 , using geographic location and correlations in the pattern of IBD sharing with other populations ( shown in Figure S7 ) . These five groupings are defined as: Europe “E , ” lying to the east of Germany and Austria; Europe “N , ” lying to the north of Germany and Poland; Europe “W , ” to the west of Germany and Austria ( inclusive ) ; the Iberian and Italian peninsulas “I”; and Turkey/Cyprus “TC . ” Although the general pattern of regional IBD variation is strong , none of these groups have sharp boundaries—for instance , Germany , Austria , and Slovakia are intermediate between E and W . Furthermore , we suspect that the Italian and Iberian peninsulas likely do not group together because of higher shared ancestry with each other , but rather because of similarly low rates of IBD with other European populations . The overall mean IBD rates between these regions are shown in Table 2 , and comparisons between different groupings are colored differently in Figure 3G–I , showing that rates of IBD sharing between E populations and between N populations average a factor of about three higher than other comparisons at similar distances . Such a large difference in the rates of IBD sharing between regions cannot be explained by plausible differences in false positive rates or power between populations , since this pattern holds even at the longest length scales , where block identification is nearly perfect . To better understand IBD within these groupings , we show in Figure 3G–I how average numbers of IBD blocks shared , in three different length categories , depend on the geographic distance separating the two populations . Even without taking into account regional variation , mean numbers of shared IBD blocks decay exponentially with distance , and further structure is revealed by breaking out populations by the regional groupings described above . The exponential decays shown for each pair of groupings emphasize how the decay of IBD with distance becomes more rapid for longer blocks . This is expected under models where migration is mostly local , since as one looks further back in time , the distribution of each individual's ancestors is less concentrated around the individual's location ( recall Figure 1B ) . Therefore , the expected number of ancestors shared by a pair of individuals decreases as the geographic distance between the pair increases; and this decrease is faster for more recent ancestry . This wider spread of older blocks can also explain why the decay of IBD with distance varies significantly by region even if dispersal rates have been relatively constant . For instance , the gradual decay of sharing with the Iberian and Italian peninsulas could occur because these blocks are inherited from much longer ago than blocks of similar lengths shared by individuals in other populations . Conversely , there is a high level of sharing for “E–E” relationships over a broad range of distances . This is especially true for our shortest ( oldest ) blocks: individuals in our E grouping share on average more short blocks with individuals in distant E populations than do pairs of individuals in the same W population . We argue below that this is because modern individuals in these locations have a larger proportion of their ancestors in a relatively small population that subsequently expanded . Having seen the continent-wide patterns of IBD in Figure 3 , it is natural to wonder if similar information is contained in single-site summaries of relatedness , such as mean Identity by State ( IBS ) values across European populations . The mean IBS between populations and is defined as the probability that two randomly chosen alleles from x and y are identical ( “By State” ) , averaging over SNPs and individuals . In the analogous plot of IBS against geographic distance ( Figure S9 ) , some of the patterns seen in Figure 3 are present , and some are not . For instance , there is a continuous decay with geographic distance ( linear , not exponential ) , and comparisons to the southern “I” group and to Cyprus/Turkey are even more well-separated below the others . On the other hand , the “E–E” comparisons do not show higher IBS than the bulk of the remaining comparisons . Each block of genome shared IBD by a pair of individuals represents genetic material inherited from one of their genetic common ancestors . Since the distribution of lengths of IBD blocks differs depending on the age of the ancestors—for example , older blocks tend to be shorter—it is possible to use the distribution of lengths of IBD blocks to infer numbers of most recent pairwise genetic common ancestors back through time averaged across pairs of individuals . For this inference , we restricted to blocks longer than 2 cM , where we had good power to detect true IBD blocks . We obtain dates in units of generations in the past , and for ease of discussion convert these to years ago ( ya ) by taking the mean human generation time to be 30 years [39] .
As we have shown , patterns of IBD provide ample but noisy geographic and temporal signals , which can then be connected to historical events . Rigorously making such connections is difficult , due to the complex recent history of Europe , controversy about the demographic significance of many events , and uncertainties in inferring the ages of common ancestors . Nonetheless , our results can be plausibly connected to several historical and demographic events .
We used the two European subsets of the POPRES dataset—the CoLaus subset , collected in Lausanne , Switzerland , and the LOLIPOP subset , collected in London , England; the dataset is described in [34] . Those collected in Lausanne reported parental and grandparental country of origin; those collected in London did not . We followed [13] in assigning each sample to the common grandparental country of origin when available , and discarding samples whose parents or grandparents were reported as originating in different countries . We took further steps to restrict to individuals whose grandparents came from the same geographic region , first performing principal components analysis on the data using SMARTPCA [57] , and excluding 41 individuals who clustered with populations outside Europe ( the majority of such were already excluded by self-reported non-European grandparents ) . These individuals certainly represent an important part of the recent genetic ancestry of Europe , but are excluded because we aim to study events stemming from older patterns of gene flow , and because we do not model the whole-genome dependencies in recently admixed genomes . We then used PLINK's inference of the fraction of single-marker IBD ( Z0 , Z1 , and Z2; [58] ) to identify very close relatives , finding 25 pairs that are first cousins or closer ( including duplicated samples ) , and excluded one individual from each pair . We grouped samples into populations mostly by reported country , but also used reported language in a few cases . Because of the large Swiss sample , we split this group into three by language: French-speaking ( CHf ) , German-speaking ( CHd ) , or other ( CH ) . Many samples reported grandparents from Yugoslavia; when possible we assigned these to a modern-day country by language , and when this was ambiguous or missing , we assigned these to “Yugoslavia . ” Most samples from the United Kingdom reported this as their country of origin; however , the few that reported “England” or “Scotland” were assigned this label . This left us with 2 , 257 individuals from 40 populations; for sample sizes , see Table 1 . Table S1 further breaks this down , and unambiguously gives the composition of each population . Physical distances were converted to genetic distances using the hg36 map , and the average human generation time was taken to be 30 years [39] . All figures were produced in R [59] , with color palettes from packages colorspace [60] and RColorBrewer [61] . Code implementing all methods described below is provided in Text S2 , and is also distributed along with IBD block data sufficient to reproduce the historical analyses through http://www . github . com/petrelharp/euroibd and in the Dryad digital repository [62] . To find blocks of IBD , we used fastIBD ( implemented in BEAGLE; [31] ) , which records putative genomic segments shared IBD by pairs of individuals , along with a score indicating the strength of support . As suggested by the authors , in all cases we ran the algorithm 10 times with different random seeds , and postprocessed the results to obtain IBD blocks . Based on our power simulations described below , we modified the postprocessing procedure recommended by [31] to deal with spurious gaps or breaks introduced into long blocks of IBD by low marker density or switch error , as follows: We called IBD segments by first removing any segments not overlapping a segment seen in at least one other run ( as suggested by [31] , except with no score cutoff ) ; then merging any two segments separated by a gap shorter than at least one of the segments and no more than 5 cM long; and finally discarding any merged segments that did not contain a subsegment with score below 10−9 . As shown in Figure 6 , this resulted in a false positive rate of between 8–15% across length categories , and a power of at least 70% above 1 cM , reaching 95% by 4 cM . After postprocessing , we were left with 1 . 9 million IBD blocks , 1 million of which were at least 2 cM long ( at which length we estimate 85% power and a 10% false positive rate ) . All methods to identify haplotypic IBD rely on identifying long regions of near identical haplotypes between pairs of individuals ( referred to as identical by state , IBS ) . However , long IBS haplotypes could potentially also result from the concatenation of multiple shorter blocks of true IBD . While such runs can contain important information about deeper population history ( e . g . , [3] , [24] ) , we view them as a false positives as they do not represent single haplotypes shared without intervening recombination . The chance of such a false positive IBD segment decreases as the genetic length of shared haplotype increases . However , the density of informative markers also plays a role , because such markers are necessary to infer regions of IBS . To look for regions of unusual levels of IBD and to examine our assumption of uniformity , we compared the density of IBD tracts of different lengths along the genome , in Figure S1 . To do this , we first divided blocks up into nonoverlapping bins based on length , with cutpoints at 1 , 2 . 5 , 4 , 6 , 8 , and 10 cM . We then computed at each SNP the number of IBD blocks in each length bin that covered that site . To control for the effect of nearby SNP density on the ability to detect IBD , we then computed the residuals of a linear regression predicting number of overlapping IBD blocks using the density of SNPs within 3 cM . To compare between bins , we then normalized these residuals , subtracting the mean and dividing by the standard deviation; these “z-scores” for each SNP are shown in Figure S1 . We noted repeated patterns of IBD sharing across multiple populations ( seen in Figure S3 ) , in which certain sets of populations tended to show similar patterns of sharing . To quantify this , we computed correlations between mean numbers of IBD blocks; in Figure S7 , we show correlations in numbers of blocks of various lengths . Specifically , if I ( x , y ) is the mean number of IBD blocks of the given length shared by an individual from population x with a ( different ) individual from population y , there are n populations , and , then Figure S7 shows for each x and y: ( 3 ) the ( Pearson ) correlation between I ( x , z ) and I ( y , z ) ranging across . Other choices of block lengths are similar , although shorter blocks show higher overall correlations ( due in part to false positives ) and longer blocks show lower overall correlations ( as rates are noisier , and sharing is more restricted to nearby populations ) . The geographic groupings of Table 1 were then chosen by visual inspection . We assessed the overall degree of substructure within each population , by measuring , for each x and y , the degree of inhomogeneity across individuals of population x for shared ancestry with population y . We measured inhomogeneity by the standard deviation in number of blocks shared with population y , across individuals of population x . We assessed the significance by a permutation test , randomly reassigning each block shared between x and y to a individual chosen uniformly from population x , and recomputing the standard deviation , 1 , 000 times . ( If there are k blocks shared between x and y and m individuals in population x , this is equivalent to putting k balls in m boxes , tallying how many balls are in each box , and computing the sample standard deviation of the resulting list of numbers . ) Note that some degree of inhomogeneity of shared ancestry is expected even within randomly mating populations , due to randomness of the relationship between individuals in the pedigree . These effects are likely to be small if the relationships are suitably deep , but this is still an area of active research [50] , [65] . The resulting p values are shown in Figure S2 . We did not analyze these in detail , particularly as we had limited power to detect substructure in populations with few samples , but note that a large proportion ( 47% ) of the population pairs showed greater inhomogeneity than in all 1 , 000 permuted samples ( i . e . , p< . 001 ) . Some comparisons even with many samples in both populations ( where we have considerable power to detect even subtle inhomogeneity ) showed no structure whatsoever—in particular , the distribution of numbers of Italian IBD blocks shared by Swiss individuals is not distinguishable from Poisson , indicating a high degree of homogeneity of Italian ancestry across Switzerland . To assess the single marker measures of relatedness across the POPRES sample , we calculated pairwise identity by state , the probability that two alleles sampled at random from a pair of individuals are identical , averaged across SNPs . This was calculated for all pairs of individuals using the “–genome” option in PLINK v1 . 07 [58] , and is shown in Figure S9 with points colored as in Figure 3 . We also calculated principal components of the POPRES genotype data using the EIGENSOFT package v3 . 0 [57] , which were used in identifying outlying individuals and in producing Figures S4 , S5 , and S6 . Here , our aim is to use the distribution of IBD block lengths to infer how long ago the genetic common ancestors were alive from which these IBD blocks were inherited . A pair of individuals who share a block of IBD of genetic length at least x have each inherited contiguous regions of genome from a single common ancestor n generations ago that overlap for length at least x . If we start with the population pedigree , those ancestors from which the two individuals might have inherited IBD blocks are those that can be connected to both by paths through the pedigree . The distribution of possible IBD blocks is determined by the number of links ( i . e . , the number of meioses ) occurring along the two paths . Throughout the article we informally often refer to ancestors living a certain “number of generations in the past” as if humans were semelparous with a fixed lifetime . Keeping with this , it is natural to write the number of IBD blocks shared by a pair of individuals as the sum over past generations of the number of IBD blocks inherited from that generation . In other words , if N ( x ) is the number of IBD blocks of genetic length at least x shared by two individual chromosomes , and Nn ( x ) is the number of such IBD blocks inherited by the two along paths through the pedigree having a total of n meioses , then . Therefore , averaging over possible choices of pairs of individuals , the mean number of shared IBD blocks can be similarly partitioned as: ( 4 ) In each successive generation in the past , each chromosome is broken up into successively more pieces , each of which has been inherited along a different path through the pedigree , and any two such pieces of the two individual chromosomes that overlap and are inherited from the same ancestral chromosome contribute one block of IBD . Therefore , the mean number of IBD blocks coming from n/2 generations ago is the mean number of possible blocks multiplied by the probability that a particular block is actually inherited by both individuals from the same genealogical ancestor in generation n/2 . Allowing for overlapping generations , the first part we denote by K ( n , x ) , the mean number of pieces of length at least x obtained by cutting the chromosome at the recombination sites of n meioses , and the second part we denote by μ ( n ) , the probability that the two chromosomes have inherited at a particular site along a path of total length n meioses ( e . g . , their common ancestor at that site lived n/2 generations ago ) . Multiplying these and summing over possible paths , we have that: ( 5 ) that is , the mean rate of IBD is a linear function of the distribution of the time back to the most recent common ancestor averaged across sites . The distribution μ ( n ) is more precisely known as the coalescent time distribution [66] , [67] , in its obvious adaptation to population pedigrees . As a first application , note that the distribution of ages of IBD blocks above a given length x depends strongly on the demographic history—a fraction of these are from paths n meioses long . Furthermore , it is easy to calculate that for a chromosome of genetic length G: ( 6 ) assuming homogeneous Poisson recombination on the genetic map ( as well as constancy of the map and ignoring the effect of interference , which is reasonable for the range of we consider ) . The mean number of IBD blocks of length at least x shared by a pair of individuals across the entire genome is then obtained by summing equation ( 5 ) across all chromosomes , and multiplying by 4 ( for the four possible chromosome pairs ) . Equations ( 5 ) and ( 6 ) give the relationship between lengths of shared IBD blocks and how long ago the ancestor lived from whom these blocks are inherited . Our goal is to invert this relationship to learn about μ ( n ) , and hence the ages of the common ancestors underlying our observed distribution of IBD block lengths . To do this , we first need to account for sampling noise and estimation error . Suppose we are looking at IBD blocks shared between any of a set of np pairs of individuals , and assume that N ( y ) , the number of observed IBD blocks shared between any of those pairs of length at least y , is Poisson distributed with mean npM ( y ) , where: ( 7 ) ( 8 ) Here the false positive rate f ( z ) , power c ( x ) , and the components of the error kernel R ( x , z ) are estimated as above , with parametric forms given in equations ( 2 ) and ( 1 ) . The Poisson assumption has been examined elsewhere ( e . g . , [27] , [49] ) and is reasonable because there is a very small chance of having inherited a block from each pair of shared genealogical ancestors; there a great number of these , and if these events are sufficiently independent , the Poisson distribution will be a good approximation ( see , e . g . , [68] ) . If this holds for each pair of individuals , the total number of IBD blocks is also Poisson distributed , with M given by the mean of this number across all constituent pairs . ( Note that this does not assume that each pair of individuals has the same mean number , and therefore does not assume that our set of pairs are a homogeneous population . ) We have therefore a likelihood model for the data , with demographic history ( parametrized by ) as free parameters . Unfortunately , the problem of inferring μ is ill-conditioned ( unsurprising due to its similarity of the kernel ( 6 ) to the Laplace transform , see [69] ) , which in this context means that the likelihood surface is flat in certain directions ( “ridged” ) : for each IBD block distribution N ( x ) , there is a large set of coalescent time distributions μ ( n ) that fit the data equally well . A common problem in such problems is that the unconstrained maximum likelihood solution is wildly oscillatory; in our case , the unconstrained solution is not so obviously wrong , since we are helped considerably by the knowledge that μ≥0 . For reviews of approaches to such ill-conditioned inverse problems , see , for example , [40] or [70]; the problem is also known as “data unfolding” in particle physics [71] . If one is concerned with finding a point estimate of μ , most approaches add an additional penalty to the likelihood , which is known as “regularization” [72] or “ridge regression” [73] . However , our goal is parametric inference , and so we must describe the limits of the “ridge” in the likelihood surface in various directions ( which can be seen as maximum a posteriori estimates under priors of various strengths ) . To do this , we first discretize the data , so that Ni is the number of IBD blocks shared by any of a total of np distinct pairs of individuals with inferred genetic lengths falling between xi–1 and xi . We restrict to blocks having a minimum length of 2 cM long , so that x0 = 2 . To find a discretization so that each Ni has roughly equal variance , we choose xi by first dividing the range of block lengths into 100 bins with equal numbers of blocks falling in each , discard any bins longer than 1 cM , and divide the remainder of the range up into 1 cM chunks . To further reduce computational time , we also discretize time , effectively requiring μn to be constant on each interval , with , for 1≤j≤360—so the resolution is finest for recent times , and the maximum time depth considered is 6 , 660 meioses , or 99 , 900 years ago . ( The discretization and upper bound on time depth were found to not affect our results . ) We then compute by numerical integration ( using the function integrate in R ) the matrix L discretizing the kernel given in equation ( 7 ) , so that is the kernel that applied to μ gives the mean number of true IBD blocks per pair observed with lengths between xi–1 and xi , and is the mean number of false positives per pair with lengths in the same interval . We then sum across chromosomes , as before . The likelihood of the data is thus: ( 9 ) To the ( negative ) log likelihood we add a penalization γ , after rescaling by the number of pairs np ( which does not affect the result but makes penalization strengths comparable between pairs of populations ) , and use numerical optimization ( the L-BFGS-B method in optim; [59] ) to minimize the resulting functional ( which omits terms independent of μ ) : ( 10 ) Often we will fix the functional form of the penalization and vary its strength , so that γ ( μ ) = γ0z ( μ ) , in which case we will write for . For instance , the leftmost panels in Figure 4 show the minimizing solutions μ for γ ( μ ) = 0 ( no penalization ) and for ( “roughness” penalization ) . Because our aim is to describe extremal reasonable estimates μ , in this and in other cases , we have chosen the strength of penalization γ0 to be “as large as is reasonable , ” choosing the largest γ0 such that the minimizing μ has log likelihood differing by no more than two units from the unconstrained optimum . This choice of cutoff can be justified as in [74] , gave quite similar answers to other methods , and performed well on simulated population histories ( see Text S1 ) . This can be thought of as taking the strongest prior that still gives us “reasonable” maximum a posteriori answers . Note that the optimization is over nonnegative distributions μ also satisfying ( although the latter condition does not enter in practice ) . We would also like to determine bounds on total numbers of shared genetic ancestors who lived during particular time intervals , by determining , for example , the minimum and maximum numbers of such ancestors that are consistent with the data . Such bounds are shown in Figure 5 . To obtain a lower bound for the time period between n1 and n2 generations , we penalized the total amount of shared ancestry during this interval , using the penalizations , and choosing to give a drop of 2 log likelihood units , as described above . The lower bound is then the total amount of coalescence for minimizing . The upper bound is found by penalizing total shared ancestry outside this interval—that is , by applying the penalization . It is almost always the case that lower bounds are zero , since there is sufficient wiggle room in the likelihood surface to explain the observed block length distribution using peaks just below n1 and above n2 . Examples are shown in Figure S14 . On the other hand , upper bounds seem fairly reliable . In the above we have assumed that the minimizer of is unique , thus glossing over , for example , finding appropriate starting points for the optimization . In practice , we obtained good starting points by solving the natural approximating least-squares problem , using quadprog [75] in R . We then evaluated uniqueness of the minimizer by using different starting points , and found that if necessary , adding only a very small penalization term was enough to ensure convergence to a unique solution . Estimated numbers of genetic common ancestors can be found by simply solving for N ( 0 ) using an estimate of μ ( n ) in equations ( 5 ) and ( 6 ) ( still restricting to genetic ancestors on the autosomes ) . These tell us that given the distribution μ ( n ) , the mean number of genetic common ancestors coming from generation n/2—that is , the mean number of IBD blocks of any length inherited from such common ancestors—is , where Gk is the total sex-averaged genetic length of the kth human chromosome . Since the total sex-averaged map length of the human autosomes is about 32 Morgans , this is about . This procedure has been used in Figures 4 and 5 . Converting shared IBD blocks to numbers of shared genealogical common ancestors is more problematic . Suppose that modern-day individuals a and b both have c as a grandn–1parent . Using equation ( 6 ) at x = 0 , we know that the mean number of blocks that a and b both inherit from c is r ( 2n ) , with , since each block has chance 2−2n of being inherited across 2n meioses . First treat the endpoints of each distinct path of length n back through the pedigree as a grandn–1parent , so that everyone has exactly 2n grandn–1parents , and some ancestors will be grandn–1parents many times over . Then if a and b share m genetic grandn–1parents , a moment estimator for the number of genealogical grandn–1parents is m/r ( n ) . However , the geometric growth of r ( n ) means that small uncertainties in n have large effects on the estimated numbers of genealogical common ancestors—and we have large uncertainties in n . Despite these difficulties , we can still get some order-of-magnitude estimates . For instance , we estimate that someone from Hungary shares on average about five genetic common ancestors with someone from the United Kingdom between 18 and 50 generations ago . Since 1/r ( 36 ) = 5 . 8×107 , we would conservatively estimate that for every genetic common ancestor there are tens of millions of genealogical common ancestors . Most of these ancestors must be genealogical common ancestors many times over , but these must still represent at least thousands of distinct individuals . | Few of us know our family histories more than a few generations back . It is therefore easy to overlook the fact that we are all distant cousins , related to one another via a vast network of relationships . Here we use genome-wide data from European individuals to investigate these relationships over the past 3 , 000 years , by looking for long stretches of genome that are shared between pairs of individuals through their inheritance from common genetic ancestors . We quantify this ubiquitous recent common ancestry , showing for instance that even pairs of individuals from opposite ends of Europe share hundreds of genetic common ancestors over this time period . Despite this degree of commonality , there are also striking regional differences . Southeastern Europeans , for example , share large numbers of common ancestors that date roughly to the era of the Slavic and Hunnic expansions around 1 , 500 years ago , while most common ancestors that Italians share with other populations lived longer than 2 , 500 years ago . The study of long stretches of shared genetic material promises to uncover rich information about many aspects of recent population history . | [
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] | 2013 | The Geography of Recent Genetic Ancestry across Europe |
Human walking is a dynamic , partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control . The coordination of this process is a very difficult problem , and it has been suggested that it involves a hierarchy of levels , where the lower ones , e . g . , interactions between muscles and the spinal cord , are largely autonomous , and where higher level control ( e . g . , cortical ) arises only pointwise , as needed . This requires an architecture of several nested , sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems , which can be used to coordinate its movements . To complicate the situation , at a maximal walking speed of more than four leg-lengths per second , the cycle period available to coordinate all these loops is rather short . In this study we present a planar biped robot , which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control . Specifically , we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity . This robot can walk with a high speed ( >3 . 0 leg length/s ) , self-adapting to minor disturbances , and reacting in a robust way to abruptly induced gait changes . At the same time , it can learn walking on different terrains , requiring only few learning experiences . This study shows that the tight coupling of physical with neuronal control , guided by sensory feedback from the walking pattern itself , combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks .
When walking , humans can adapt quickly to terrain changes , and they can also learn to walk differently on different surfaces . This ability is known to us all when we quickly adapt our gait after having stumbled or more slowly devise different strategies for walking uphill , downhill , or on sand as compared with ice . Neurophysiological studies have revealed that these properties arise from a combination of biomechanics and neuronal control . For example , some walking animals ( e . g . , bears , dogs ) may be able to stand up and walk a few steps , but will not be able to develop a stable gait because their biomechanical design ( called here the biomechanical level ) is inappropriate for this . Neuronal control , on the other hand , assures that different gaits can first be learned and then be quickly applied , for instance to adapt to the terrain . In the 1930s the Russian physiologist Bernstein [1–3] pointed out that the coordination of the cooperation within and between the different functional levels of the motor system , including controlled forms of motor learning , is a very difficult problem , e . g . , due to the redundancy of effective movements ( “The Bernstein Problem , ” also discussed in [4] ) . Along this paradigm , Sporns and Edelman [5] proposed that a successful developmentally guided coordination between neuronal activity and the biomechanics of the musculoskeletal system can be achieved without determining a desired trajectory . Instead , it is based on variations of neuronal and biomechanical structures and is the result of somatic selection processes within brain circuits . The concept was applied to solve the arm-reaching problem , which was demonstrated with an artificial sensorimotor system . Mussa-Ivaldi and Bizzi [6] suggested a theoretical framework that combines some features of inverse dynamic computations with the equilibrium-point hypothesis for controlling a wide repertoire of motor behaviors also involving motor learning . They applied this to control the movement of a two-jointed robot arm with force fields as motor-primitives [6 , 7] . In the domain of dynamic legged locomotion control , Raibert [8] presented a series of successful hopping robots executing extremely dexterous and dynamic movements . The first of these robots is a single-legged running machine that works in two dimensions . It captures the feature of dynamic stability due to the carefully designed dynamics of the robot together with the use of simple feedback control . On the basis of these principles , Raibert and his collaborators extended their approach to a variety of machines using one , two , or four , legs , in two or three dimensions . Nakanishi et al . [9] reported one excellent example of biped locomotion control with motor learning . There , a central pattern generator ( CPG ) was employed to generate dynamical movement-primitives while the desired trajectories for walking behavior were learned by imitating demonstrated movement of humans . Nonetheless , some outstanding problems remain unsolved , in particular the problem of fast and adaptive biped walking based on self-stabilizing dynamic processes . Given that a biped has only one foot touching the ground during most of the time of a gait cycle , this poses huge difficulties for dynamic control , as the biped always tends to trip or fall . Thus , one particular objective of this article is to show that minimal adaptive neuronal control based on the reflexive mechanism [10] coupled with appropriate biomechanics can generate fast and adaptive biped walking gaits by a self-stabilizing process . As a result , our biped system can perform like a natural human walking ( as shown by similar Froude numbers , see Figure 1 ) where the maximum walking speed is comparable to that of humans . Neuronal walking control in general follows a hierarchical structure [11] . At the bottom level there are direct motor responses , often in form of a local , sometimes monosynaptic , reflex driven by afferent signals , which are elicited by sensors in the skin , tendons , and muscles—such as the knee tendon reflex . These sensor-driven circuits; which , following textbook conventions , we will call the spinal ( reflex ) level; can produce reproducible , albeit unstable gaits [12 , 13] and seem to play a more dominant role in nonprimate vertebrates [14] and especially in insects [15] . This level is often also augmented by CPGs in the spinal cord [14 , 16 , 17] . For example , Grillner [18 , 19] and others [20 , 21] have shown that generation of motor patterns as well as coordination of motor behavior in both vertebrates and invertebrates is basically achieved by CPGs which are in the central nervous system . Although CPGs provide the basis for generation of motor patterns , this does not mean that sensory inputs are unimportant in the patterning of locomotion . In fact , the sensory input is crucial for the refinement of CPG activity in response to external events . Especially in humans , CPG functions seem to be less important for walking , and they had been hard to unequivocally verify [22] because they can strongly be influenced and , thus , superseded by sensory influences and by the activity of higher motor centers [14 , 23 , 24] . In general , higher motor centers modulate the activity of the spinal level , and their influence leads to our flexibility and adaptivity when executing gaits under different conditions . For example , inputs from peripheral sensors ( e . g . , eye , vestibular organ ) can be used to adapt a gait to different terrains and also to change the posture of the walker , moving its body , to compensate for a disturbance . Reflexes also play a role at this level , called here the postural ( reflex ) level , but these long-loop reflexes [25 , 26] are always polysynaptic and can be much influenced by plasticity . Infants also use such peripheral sensor signals to learn the difficult task of adjusting and stabilizing their gaits [27 , 28] , which many times amounts to learning how to avoid reflexes from earlier compensatory motor actions . The cerebellum seems to play a fundamental role in this type of motor learning for reflex-avoidance or reflex-augmentation [29] . A more specific discussion of this is presented in Materials and Methods . Beyond postural reflexes , we find ourselves at the level of motor-planning , which involves basal ganglia , motor cortex , and thalamus , with which this study is not concerned . A suggested solution to the coordination problem ( Bernstein Problem ) invokes delegating control from higher to lower centers [4] . Central to this idea is the fact that the walking process itself leads to repetitive stimulation of the sensory inputs of the walker . As a consequence , at every step all neuro–mechanical components and their CPGs are retriggered [23] , which could be used to control coordination . While an appealing idea , whose importance has been discussed recently by Yang and Gorassini [30] , its applicability has so far not been demonstrated . In this study , we will try to show that sensor-driven control can be a powerful method to guide coordination of different levels in an artificial dynamic walker , and that this can also be combined with ( neuronal ) adaptivity mechanisms in a stable way . To this end and following from the introduction , we assume that there are three important requirements for basic walking control: 1 ) biomechanical level—the walker requires an appropriate biomechanical design , which may use some principles of passive walkers to assure stability [31] . 2 ) spinal reflex level—it needs a low-level neuronal structure , which creates dynamically stable gaits with some degree of self-stabilization to assure basic robustness . 3 ) postural reflex level—finally , it requires higher levels of neuronal control , which can learn using peripheral sensing to assure flexibility of the walker in different terrains . Fundamentally , these levels are coupled by feedback from the walking process itself , conveying its momentary status to different sensor organs locally in muscles and tendons and peripherally to the vestibular organ and the visual system as well as others as arising . At high walking speeds , cooperation of these three levels needs to take place very quickly and any learning also must happen fast . These demands for dynamic walking are currently impossible to fulfill with artificial ( robot ) walking systems , and the required tight interaction between levels embedded in a nested closed-loop architecture has not yet been achieved [31 , 32] .
RunBot has four active joints ( left and right hips and knees ) , each of which is driven by a modified RC servo motor . It has curved feet allowing for rolling action and a lightweight structure with proper distribution of mass at the limbs ( Figure 2D ) . The proper distribution of mass is calculated in the way that approximately 70% of the robot's weight is concentrated on its trunk where the parts of the trunk are assembled such that its center of mass is located forward of the hip axis . Furthermore , it has an upper body component ( UBC ) , which can be actively moved to shift the center of mass backward or forward . Central to its mechanical design is the proper positioning of the center of mass , the effect of which is shown in Figures 2D and 4 during walking on flat terrain where the UBC is kept stable in its rearward position . One walking step consists of two stages . During the first stage ( steps ( 1 ) and ( 2 ) shown in Figure 2D , compare with steps ( 1 ) – ( 3 ) shown in Figure 4 ) , the robot has to use its own momentum to rise up on the stance leg . When walking slowly , this momentum is small and , hence , the distance the center of mass has to cover in this stage should be as small as possible , which can be achieved by a low and slightly forward placed center of mass similar to humans [35] . In the second stage ( steps ( 2 ) and ( 3 ) shown in Figure 2D , compare with steps ( 3 ) – ( 6 ) shown in Figure 4 ) , the robot just falls forward naturally and catches itself on the next stance leg [36] . Hence , RunBot's design ( see Figure 2 ) relies quite strongly on the concepts of self-stabilization of gaits in passive walkers [31] . This property is emulated by the lowest loop ( Biomechanics [37] ) in Figure 3 . RunBot's passive properties are also reflected by the fact that during one quarter of its step cycle all motor voltages remain zero , as shown in Figure 5B ( gray areas ) . A detailed simulation analysis of the stability properties of RunBot is given in [33 , 34] . Figure 3 represents the basic neuronal control structure of RunBot . The right , uncolored side shows the general signal flow from sensors via motor neurons ( Mot . N . ) to the motors involving several closed loops . To reduce computational overhead , we designed the neuronal control network ( left side ) using only standard sigmoid , Hopfield-type neurons ( see Materials and Methods ) . The circuitry in general consists of an agonist–antagonist control structure for hips and knees with flexor and extensor components , a dichotomy which we have , for clarity , omitted in Figure 3 ( for details of the agonist–antagonist connectivity , see Figure 6 ) . Its motor neurons N are linear and can send their signals unaltered to the motors M . Furthermore , there are several local sensor neurons which by their conjoint reflex-like actions trigger the different walking gaits . We distinguish three local loops . Joint control arises from sensors S at each joint ( compare Figure 4 ) , which measure the joint angle and influence only their corresponding motor neurons ( Spinal1 ) . Interjoint control is achieved from sensors A , which measure the anterior extreme angle ( AEA , Figure 4 ) at the hip and trigger an extensor reflex at the corresponding knee ( Spinal2 ) . Leg control comes from ground contact sensors G ( compare Figure 4 ) , which influence the motor neurons of all joints in a mutually antagonistic way ( Spinal3 ) . In addition , there is the control circuit for the UBC ( Figure 3 ) . This circuit represents a long-loop reflex ( Postural1 ) , and its accelerometer sensor ( AS ) is also involved in controlling plasticity within the whole network . Here we first describe its pure reflex function prior to learning . The UBC is controlled by its flexor and extensor motor neurons NF , NE , driven by the activity of one AS neuron . ( Indexing of variables in this article follows this structure: body-level ( UBC = B , left-leg = L , right-leg = R ) ; leg level ( hip = H , knee = K ) ; joint level ( flexor = F , extensor = E ) . In general , indices are omitted below the last relevant level , i . e . , SL , H , E applies to the extensor of the hip of the left leg , whereas SL , H would apply to flexor and extensor of the hip of the left leg . On flat terrain , AS is inactive and the flexor is activated to lean the body backward while the extensor is inhibited . This situation is reverted when a strong signal from the AS exists , which happens only when RunBot falls backward ( see learning experiments in Figures 7 and 8 ) . This will trigger a leaning reflex of the UBC . This way , different loops are implemented , all of which are under sensory control , which assures stability of walking within wide parameter ranges . In Figure 9A , we show the stable domain for the two most sensitive parameters gH and . Within the blue area , a wide variety of different gaits can be obtained , two of which ( marked ) are shown in Figure 5 . To analyze the dynamical stability of RunBot , which follows a cyclic movement pattern , the Poincare-map method [38] is employed , because our reflexive controller exploits natural dynamics for the robot's motion generation , and not trajectory planning or tracking control . A simulation analysis of our robot system with Poincare maps has been shown in our previous study [34] . Here we present the stability analysis in a real walking experiment ( Figure 9 ) . In Figure 9B , we show a perturbed walking gait where the bulk of the trajectory represents the normal orbit of the walking gait , while the few outlying trajectories are caused by external disturbances induced by small obstacles such as thin books ( less than 4% of robot size ) obstructing the robot path . After a disturbance , the trajectory returns to its normal orbit soon , demonstrating that the walking gaits are stable and to some degree robust against external disturbances . Here , robustness is defined as rapid convergence to a steady-state behavior despite unexpected perturbations [39] . That is , the robot does not fall and continues walking . Furthermore , the intrinsic robustness of the RunBot system makes parameter fine-tuning unnecessary , which can be judged from Figure 5A . Here we show that it is possible to immediately switch manually from a slower walking speed of 39 cm/s ( ≈1 . 7 leg-length/s ) to a faster one of 73 cm/s ( ≈3 . 17 leg-length/s ) ( see Video S1 ) . This result has been achieved by abruptly and strongly changing two parameters: the threshold of the local extensor sensor neurons of hip joints ( see Figure 10 , ) and the gain gH of hip motor neurons . The dynamic properties of RunBot allow doing this without tripping it , and speed is almost doubled . Such quick and large changes in walking speed are no problem for humans but difficult if not impossible for existing biped robots . The self-stabilization against such a strong change demonstrates that RunBot's neuronal control parameters , analyzed in [33 , 34] , are not very sensitive and that a wide variety of stable gaits ( see Figure 9A ) can be obtained by changing them . The leg motor signals shown in Figure 5B demonstrate that during about one quarter of RunBot's step cycle all leg motors are inactive ( zero-voltage ) , making RunBot a passive walker during this time . To compare the walking speed of various biped robots whose sizes are quite different from each other , we use the relative speed , which is speed divided by the leg-length . Maximum relative speeds of RunBot and some other typical planar biped robots ( passive or powered ) are listed in Figure 1 . We know of no other biped robot attaining such a fast relative speed . Moreover , the world record for human walking is equivalent to about 4 . 0–5 . 0 leg-length/s . So , RunBot's highest walking speed is comparable to that of humans . In general , the Froude number Fr is used to describe the dynamical similarity of legged locomotion over a wide range of animal sizes and speeds on earth [40] . It can be determined by Fr = v2/gl where v is the walking speed , g gravity , and l leg-length . Figure 1 also gives Fr for different designs , where Fr of RunBot and humans are quite similar . For the postural level , we have implemented a long-loop body reflex at the UBC , triggered by a strong backward lean as described above . This reflex can be changed by learning , which will also influence several other network parameters to adapt the gait . The learning goal is to finally avoid the leaning reflex and at the same time to learn changing gait parameters in an appropriate way to prevent RunBot from falling . This requires an adaptive network of six more neurons ( Figure 11 ) which converge onto different target neurons at the spinal-level network , effectively changing their activation parameters ( see Materials and Methods ) . RunBot's task was to learn walking up a ramp and then continuing on a flat surface . Without gait and posture change , the robot can walk on slopes of only up to 2 . 5° [34] . Leaning the UBC forward and changing several gait parameters , RunBot manages about 8 . 0° . With a larger UBC mass , even steeper slopes ( up to 13 . 0° ) can be tackled , while walking down slopes can be also achieved in the reverse way with an appropriate gait . This is achieved by learning which is based on simulated plasticity . It is known that neurons can change their synaptic strength according to the temporal relation between their inputs and outputs . If the presynaptic signal arrives before the postsynaptic neuron fires , such a synapse gets strengthened , but it will get weakened if the order is reversed . Hence , this form of plasticity depends on the timing of correlated neuronal signals ( STDP , spike timing-dependent plasticity [41] ) . In neurons with multiple inputs , such a mechanism can be used to alter the synaptic strengths , through heterosynaptic interactions , according to the order of the arriving inputs . Formally , we have v = Σρiui as the neurons output driven by inputs ui , where synapses ρ get changed by differential Hebbian learning using the cross-correlation between both inputs u0 ( the AS ) and u1 ( the IR ( infrared ) sensor ) [42] ( see also Materials and Methods ) . As a consequence , if an early input signal is followed by a later input , where the later one drives the neuron into firing , then the early input will get strengthened . We make use of this type of sequence learning in adaptive walking experiments on different terrains , where RunBot was configured with a parameter set suitable for walking on a flat surface and learned to tackle an 8° ramp , which it manages after about three to five falls ( see Figure 7A and Video S2 ) . Its change in walking pattern after starting to climb the ramp is shown in Figure 7B . It takes about two steps on the slope for the machine to find its new equilibrium , which results in a slower stride up the slope as compared with flat terrain . The slowing down can be explained by the gravitational pull . Stride length , however , is shorter , and RunBot takes about seven steps on the slope , which is 80 cm long , while for the same distance it uses six steps on flat ground . Shortening the step size is similar to human behavior and is a result of the different parameters used for climbing together with the changed gravitational pull . Returning to the initial gait when reaching the top is faster and happens immediately . Note that RunBot's intrinsic stability can also be demonstrated by the fact that it will always succeed in walking up the slope , after having learned the new parameters , regardless of its starting point and independent of the positioning of the legs ( as long as this allows making the first step ) . A complete set of curves taken from RunBot similar to Figure 7A but from a different experiment is presented in Figure 8 . Every “spike” in the top panels ( Figure 8A–8D ) represents one step . Figure 8F and 8H show that the IR signal does indeed come earlier as compared with the AS signal . This is also visible in Figure 8E , where the leaning reaction first coincides with the AS signal and only after learning comes together with the IR signal . Figure 8G shows all synaptic weights ρ1 that grow with a different rate ( μ ) and stabilize at different values . Small glitches in the weights observed after the last fall ( see for example at about 22 s ) arise from the fact that the AS sensor will always produce a little bit of noise , which leads to a weak correlation with the IR signal and to minor weight changes . Note that weights will only change strongly again if the AS signal produces another strong response; hence , in the case that the robot falls again . Thus , learning is stable as soon as the AS-triggered reflex is being avoided , but will set in anew if the robot should fall again . As demonstrated in Figures 7 and 8 , on approaching the ramp , RunBot's IR sensor will sense the slope early , but initially the IR sensor signal converges with zero strength at the network and goes unnoticed . As a consequence , RunBot will begin walking up the ramp with a wrong set of gait parameters and will eventually fall , leading to a later signal at the AS . The AS signal triggers the leaning reflex of the UBC together with the gait adaptation , but too late . However , the early IR sensor signal and the later AS signal converge at the same neurons , and due to simulated plasticity the synapses from the early IR inputs will grow . As a consequence , after some learning , the postural control network ( see Figure 11 ) will receive nonzero input as soon as the IR sensor becomes active , RunBot will perform the leaning action earlier , and its gait will be changed in time . The used differential Hebbian learning rule has the property that learning will stop when the late input ( AS signal ) is zero [42] , which is the case as soon as the reflex has successfully been avoided and the robot does not fall anymore . Hence , we obtain behavioral and synaptic stability at the same time without any additional weight-control mechanisms . Recent studies on biped robots have emphasized the importance of the biomechanical design by focusing on so-called passive dynamic walkers , which are simple devices that can walk stably down a slope [43] . This is achieved only by their mechanical design . Adding actuators to their joints may allow these robots to walk also on a level surface or even uphill . The developed gaits are impressively human-like [31] , but these systems cannot easily adapt and/or change their speed . More traditionally , successful robot-walkers have been built based on precise joint-angle control , using mainstream control paradigms such as trajectory-based methods [44] , and some of the most advanced robots are constructed this way; e . g . , ASIMO [45] , HRP2 [46] , JOHNNIE [47] , and WABIAN [48] . It is , however , difficult to relate these machines to human walking , because closed-loop control requires highly precise actuators unlike muscles , tendons , and human joints , which do not operate with this precision . Furthermore , such systems require much energy , which is in conflict with measured human power consumption during walking or running [36 , 49 , 50] , and their control is non-neuronal . Neuronal control for biped walking in robots is usually achieved by employing CPGs [51 , 52] , which are implemented as a local oscillator under limited sensor control . Furthermore , if adaptive mechanisms are employed [32 , 53] , then conventional techniques from machine learning are used , which are not directly related to neuronal plasticity . The controller described in [54] is also based on the concept of CPGs where the trajectory of each joint is modeled by a specific oscillator . These are globally synchronized through sensory information ( e . g . , ground reaction force ) together with the robot dynamics , instead of being partially autonomous . The method does not start with generated limb patterns or a formal proof of stability as used in trajectory-based methods . By contrast , the model in Morimoto et al . has been designed and then tuned to obtain the desired effect . As a consequence of its simplicity , one can add more feedback in the control loop , or modify the generated trajectories without having to restart a global optimization process . The strategy pursued here is to some degree related—RunBot also relies on sensory feedback to synchronize its components , which are arranged in nested loops ( [8 , 55] , see Figure 3 ) , but without the help of CPGs . Instead , we achieved tight coupling of the different levels of physical and neuronal control via feedback from the walking processes itself , which conveys its momentary status to different sensors; locally at the joints/legs and peripherally to our very simple simulated “vestibular organ” ( AS ) and “visual system” ( IR ) . This structure made it possible to also implement a fast learning algorithm , which is driven by peripheral sensors but influences all levels of control; explicitly by augmenting neuronal parameters and implicitly at the biomechanical level by the resulting new walking equilibrium . The idea of downward-delegating coordination control , where local levels maintain a high degree of sensori-driven autonomy [3 , 4] , could thereby be implemented and tested . We believe that this demonstration is the major contribution of the current study . It shows that complex behavioral patterns result from a rather abstract model for locomotion and gait control consisting of a simple set of nested loops . Much of the biologically existing complexity has been left out . This especially should stimulate further biological investigations because little is known about how a possible Bernstein mechanism is actually implemented in humans for locomotion and gait control . The existing data in this field is plentiful and diverse , but often conflicting evidence exists for certain subfunctions . This may be due to neglect of context within which a certain dataset has been obtained . Thus , given the rich existing data , a better understanding of human locomotion would probably require a focus of new research on abstractions and synthesis trying to combine the different strands into a closed form picture and only carefully extending the existing datasets . This may also help to resolve the existing conflicts because synthesis will enforce context . Highly adaptive and flexible biped walking will certainly require additional mechanisms beyond those implemented here; for example , augmenting neuronal control via internal models of the expected movement outcome ( “efferent copies” [56] ) and/or adding intrinsic loops for CPG-like functions [14 , 22] . The results presented here , however , suggest that the employed nested-loop design remains open to such extensions bringing the goal of fully dynamic and adaptive biped walking in artificial agents a little bit closer .
RunBot is 23 cm high , with a foot-to-hip joint axis ( see Figure 2 ) . Its legs have four actuated joints: left hip , right hip , left knee , and right knee . Each joint is driven by a modified RC servo motor where the built-in Pulse Width Modulation ( PWM ) control circuit is disconnected , while its built-in potentiometer is used to measure the joint angles . A mechanical stopper is implemented on each knee joint to prevent it from going into hyperextension , similar to the function of human kneecaps . The motor of each hip joint is a HS-475HB from Hitec . It weighs 40 g and can produce a torque up to 5 . 5 kg·cm . Due to the use of the mechanical stopper , the motor of the knee joint bears a smaller torque than the hip joint in stance phases , but must rotate quickly during swing phases for foot clearance . Therefore , we use a PARK HPXF from Supertec on the knee joints , which has a light weight ( 19 g ) , but is fast with 21 rad/s . Thus , approximately 70% of the robot's weight is concentrated on its trunk , and the parts of the trunk are assembled in a way that its center of mass is located forward of the hip axis . RunBot has no actuated ankle joints , resulting in very light feet and efficiency for fast walking . Its feet were designed to have a small circular form ( 4 . 5 cm long ) , whose relative length , the ratio between the foot-length and the leg-length , is 0 . 20 , less than that of humans ( approximately 0 . 30 ) and that of other biped robots ( powered or passive , see discussion in [31] ) . Each foot is equipped with a switch sensor to detect ground contact events . The mechanical design of RunBot has some special features; for example , small curved feet and a properly positioned center of mass that allow the robot to perform natural dynamic walking during some stage of its step cycles . Hip and knee joints are driven by output signals of the leg controller ( running on a Linux PC ) through a DA/AD converter board ( USB-DUX ) . The USB-DUX provides eight input ( A/D ) and four output ( D/A ) channels , and it has the update frequency of 250 Hz . The signals of the joint angles and ground contact switches are also digitized through this board for the purpose of feeding them into the leg controller ( compare Figure 12 ) . To extend its walking capabilities for walking on different terrains , for example level floor versus up or down a ramp , one servo motor with a fixed mass , called the UBC , is implemented on top . The UBC has a total weight of 50 g . It leans backward ( see Figure 2A ) during walking on a level floor , and this position is also suitable for walking down a ramp [57] , and it will lean forward ( see Figure 2B ) when RunBot falls backward , and when it has successfully learned to walk up a ramp . The corresponding reflex is controlled by an AS , see Figure 2 . The AS is installed on top of the right hip joint . In addition , one IR sensor is implemented at the front part of RunBot ( see Figure 2 ) pointing downward to detect ramps ( see Figure 12 ) . Here , the IR sensor serves as a simple vision system , which can distinguish between a level floor with black color and a painted ramp with white color . This sensory signal is used for adaptive control . In our setup , the AS and IR signals are in parallel-feed to the USB–DUX for digitalization , providing them to the leg and body controllers afterward . The scheme of our setup is shown in Figure 12 . We constrain RunBot in the sagittal plane by a boom of one meter length . RunBot is attached to the boom via a freely rotating joint in the x-axis , while the boom is attached to the central column with freely rotating joints in the y and z axes ( see Figure 2A ) . With this configuration , the robot is in no way being held up or suspended by the boom , and its motions are only constrained on a circular path . Given that the length of the boom is more than four times the height of RunBot , the influence of the boom on RunBot's dynamics in the sagittal plane is negligible . In addition , by way of an appropriate mounting ( see Figure 2C ) , cabling also does not influence the dynamics of the walker . As shown here , the mechanical design of RunBot has the following special features that distinguish it from other powered biped robots and that facilitate high-speed walking and exploitation of natural dynamics: ( a ) small , curved feet allowing for rolling action; ( b ) unactuated , hence light , ankles; ( c ) lightweight structure; ( d ) light and fast motors; ( e ) proper mass distribution of the limbs; and ( f ) properly positioned mass center of the trunk . This is a common strategy toward fast walking which facilitates scalability and is , thus , also present in other large robots , as in the new design of LOLA , the followup to JOHNNIE ( [58] , personal communication ) . In general , scalability can be achieved by dynamic similarity [40 , 59]; for example , reflected in the same Froude number . Hence , by using similar design principles together with appropriate simulations ( see , for example , [34] ) , one can gradually upscale such designs . This justifies the cost-effective small RunBot architecture from which basic principles can be extracted . Clearly , difficulties are expected to arise when introducing more degrees of freedom , but this reflects a true change in the system , not just an upscaling . The reflexive neuronal controller of RunBot is composed of two neural modules: one is for leg control and the other for body control . The UBC and the peripheral sensors ( AS , IR ) are mounted on the rump of RunBot . Both controllers have a distributed implementation , but they are indirectly coupled through the biomechanical level; this way , the neural control network driven by the sensor signals will synchronize leg and body movements for stable locomotion . Leg control . Leg control of RunBot consists of the neuron modules local to the joints , including motor neurons N and angle sensor neurons S , as well as a neural network consisting of hip stretch receptors A and ground contact sensor neurons G ( see Figure 6 ) , which modulate the motor neurons . Neurons are modelled as nonspiking neurons ( Hopfield-type neurons ) simulated on a Linux PC with an update frequency of 250 Hz , and communicated to the robot via the USB–DUX ( see Figure 12 ) . Nonspiking neurons have been used to increase the speed of network operations . Connection structure and polarity are depicted in Figure 6 . The top part of Figure 6 shows the ground contact sensor neurons G , which are active when the foot is in contact with the ground ( see Figure 4 ) . Its output changes according to: Where ΔV equals VR − VL , computed by the output voltage signals from switch sensors of the right foot VR and left foot VL , respectively , used with a plus sign in Equation 1 for the left and with a minus sign for the right ground contact sensor . Furthermore , ΘG are thresholds and αG positive constants . Beneath the ground contact sensors , we find stretch receptor neurons A ( Figure 6 ) . Stretch receptors play a crucial role in animal locomotion control . For example , when the limb of an animal reaches an extreme position , its stretch receptor sends a signal to the controller , resetting the phase of the limbs [10] . There is also evidence that phasic feedback from stretch receptors is essential for maintaining the frequency and duration of normal locomotive movements in some insects [37] . Different from other designs [10 , 60] , our robot has only one stretch receptor on each leg to signal the AEA of its hip joint ( see Figure 4 ) . Furthermore , the function of the stretch receptor on our robot is only to trigger the extensor motor neuron on the knee joint of the same leg ( compare Figure 4 ) , rather than to implicitly reset the phase relations between different legs , as , for example , in the model of Cruse [10] . The outputs aA of the stretch receptor neurons A for the left and the right hip are: where I denotes the input signal of the neuron , which is the real time angular position of the hip joint φ , and αA is a positive constant . The hip anterior extreme angle ΘA depends on the walking pattern , for example ΘA = 105 . 0 deg for walking on a level floor , while it will be modified according to a learning rule for walking up a ramp described in the next section . This model is inspired by a sensor neuron model presented in [61] . At the joint level ( Figure 6 ) , the neuron module is composed of two angle sensor neurons ( SE , SF ) ( see Figure 4 ) and the motor neurons ( NE , NF ) they contact ( see Figure 6 ) . Whenever its threshold is exceeded , the angle sensor neuron S directly inhibits the corresponding motor neuron . This direct connection between angle sensor neurons and motor neurons is inspired by monosynaptic reflexes found in different animals [62] and also in humans [63] . The model of the angle sensor neurons S is similar to that of the stretch receptor neurons A described above . The angle sensor neurons change their output according to: where I is an input signal , which is the real time angular position φ obtained from the potentiometer of the joint . ΘS is the threshold of the motor neuron and αS a positive constant . The plus sign is for the extensor angle sensor neuron , and the minus sign is for the flexor angle sensor neuron . These three sensor signals ( G , A , S ) converge on the motor neurons N with different polarity , as shown in Figure 6 . Some signals connect between joints or between legs , which assures correct cross-synchronization . The motor neuron model is adapted from [60] . The state and output of each extensor and flexor motor neuron are governed by Equations 4 and 5 [64]: where y represents the mean membrane potential of the neuron . Equation 5 is a sigmoidal function that can be interpreted as the neuron's short-term average firing frequency , αN is a positive constant . ΘN is a bias constant that controls the firing threshold . τ is a time constant associated with the passive properties of the cell membrane [64] . ωZ represents the connection strength from the sensor neurons and stretch receptors to the motor neuron ( Figure 6 ) . The value of aZ represents the output of the sensor neurons and stretch receptors that contact this motor neuron ( e . g . , aS , aA , aG , etc . ) . The voltage of the motor U in each joint is determined by: where D represents the magnitude of the servo amplifier , which is predefined by the hardware with a value of 3 . 0 on RunBot and g stands for the software-settable output gain of the motor neurons in the joint . The variables ζE and ζF are the signs for the motor voltage of extensor and flexor in the joint , being +1 or −1 , depending on the hardware of the robot ( compare Figure 6 ) , and rE and rF are the outputs of the motor neurons . Parameters for leg control . RunBot is quite robust against changes in most of its parameters ( see details in [33] ) . Therefore , most parameters could be manually tuned by a few experiments supported by simulations ( see [33] ) . We set: , but αN = 1 . 0 , which assures a quick response of the corresponding neurons . The threshold of the sensor neurons for the extensor ( flexor ) in the neuron module roughly limits the movement range of the joint and effects stability of locomotion on the different terrains . For instance , for walking on a level floor , we choose , , , and ( compare Figure 10 ) , which is in accordance with observations of normal human gaits [65] . The movements of the knee joints are needed mainly for timely ground clearance . After some trials , we set the gain of the motor neurons in the knee joints to gK = 1 . 8 . Furthermore we set gH = 2 . 2 . The threshold of the stretch receptors is simply chosen to be the same as that of the sensor neurons for the hip extensor , . With these parameters , we obtain a walking speed of about 50 cm/s ( ≈2 . 17 leg-length/s ) . However , the walking speed of RunBot can be increased up to 80 cm/s ( ≈3 . 48 leg-length/s ) when gH is increased , while is decreased ( described more details in [33] ) . Note that for walking up a ramp , seven parameters ( , , , , , , and gH ) will be modified by the synaptic plasticity mechanism , which allows RunBot to autonomously learn by adapting its gait ( described later ) . The threshold ΘG of the ground contact sensor neurons is chosen to be 2 . 0 v following a test of the switch sensors , which showed that in a certain range the output voltage of the switch sensor is roughly proportional to the pressure on the foot bottom when touching the ground . The time constant of the motor neurons , τ ( see Equation 4 ) , is chosen as 10 . 0 ms , which is in the normal range of biological data . For the connection strengths wZ ( see Equation 4 ) as denoted in Figure 6 , we use: wNG ≥ ΘN , wNA − wNG ≥ ΘN , wNS − wNA − wNG ≥ ΘN , where wNG = weights of the synapses between the ground contact-sensor neurons and the motor neurons , wNA = weights of the synapses between the stretch receptors and the motor neurons , wNS = weights of the synapses between the angle sensor neurons and the motor neurons in the neuron modules of the joints , and ΘN = the threshold of the motor neurons ( see Equation 5 ) , which can be any positive value as long as the above conditions are satisfied . The function of these rules is to make sure that among all the neurons which contact the motor neurons , the angle sensor neurons have the first priority , while the stretch receptors have second priority , and the ground contact sensor neurons have lowest priority . So , we simply choose them as: ΘN = 5 . 0 , wNG = 10 . 0 , wNA = 15 . 0 , wNS = 30 . 0 ( compare Figure 6 ) . A more detailed description of the neuronal controller and a discussion of stability issues of all parameters can be found in [33] . Body control . Body control of RunBot consists of two motor neurons ( NE and NF ) and one AS providing a reflex signal ( see Figure 6 ) . These neuron models are similar to those for leg control . The synaptic strengths of the connection structure are shown in Figure 6 . This network is driven by the AS where its output aAS is modelled according to: where VAS is the output voltage signal from the AS . ΘAS and αAS are the threshold and a positive constant which are set to 4 . 0 and 2 . 0 , respectively . CAS is a positive amplification of the input signal set to 6 . 0 . The motor neurons ( NE , NF ) , which directly modulate the motions of the UBC , have the same characteristic as the leg motor neurons ( see Equations 4 and 5 ) but different parameters ΘN , αN , D , and g . We set ΘN of the extensor body–motor neuron to 0 . 75 and for the flexor to −0 . 75 and αN to 20 . 0 , while D and g are both set to 1 . 0 ( see Equation 6 ) . Usually , for example when walking on a level floor , NF is activated to lean the body backward ( see Figures 2A and 12 ) while NE is deactivated unless a strong signal from the AS drives its reflex ( leaning the UBC forward ) ; i . e . , this signal excites NE while it inhibits NF . This situation happens only when RunBot falls backward; e . g . , when RunBot tries to walk up a ramp . To create adaptive behavior for walking on different terrains , an effective way is to let RunBot learn adapting its gait and controlling the posture of its UBC by itself . To this end , we apply a learning technique , which will finally allow RunBot to walk up a ramp and then continue again on a level floor . To sense a ramp when RunBot is making an approach , we use an IR sensor ( see Figure 12 ) , which requires some preprocessing before it can be used by our learning algorithm . Thus , in the following sections , we will describe the sensory preprocessing , followed by the details of the learning network together with the learning algorithm . Sensory preprocessing . The raw infrared signals require preprocessing because they are too noisy due to RunBot's egomotion and because they arrive too early at the robot ( hence before it reaches or leaves the ramp ) . To address these issues , we construct the neural preprocessing of the raw IR signal as a hysteresis element [66 , 67] using a single neural unit with a “supercritical” self-connection ( wself > 4 ) . It is modelled as a discrete-time nonspiking neuron , and its activation function is given by: where VIR is the output voltage signal from the IR sensor , which is linearly mapped onto the interval [0 , 1] . ΘIR is the threshold , and CIR represents a positive amplification factor of the input signal . The output of the neuron is given by the standard sigmoidal transfer function . To get an appropriate hysteresis , we set ΘIR = −3 . 2 , CIR = 4 . 0 , and wself = 4 . 8 ( see Figure 13B ) . Note that the width of the hysteresis is proportional to the strength of the self-connection; i . e . , the stronger the self-connection , the wider the hysteresis . Learning network and its effect—reflex avoidance learning . In the following , we will describe our learning network , which enables RunBot to successfully perform the given task . To do so , its gait has to be changed as well as the posture of its UBC . The UBC is controlled by exciting or inhibiting NE , NF through sensory signals ( described above ) . We know from previous experiments [57] that a stable gait for upslope walking can be obtained by adjusting the following parameters . At the knee joints , the firing threshold of neurons SE , SF has to be decreased; while at the hip joints , the firing threshold of neurons SE , SF , which also affects the stretch receptor neurons A , has to be increased , but the gain g of neurons NE , NF , has to be decreased . This leads to smaller steps , also observed in humans when climbing . In our learning algorithm , the modification of all those parameters also common in human walking reflexes [68] will be controlled by two kinds of input signals: one is an early input ( called predictive signal ) and the other is a later input ( called reflex signal ) . Here , we use the preprocessed IR signal as a predictive signal , while the AS signal serves as a reflex signal . Both sensory signals are provided to the learner neurons as shown in Figure 13 . At the beginning , the connections ( ) between the predictive signal and learner neurons converge with zero strengths . In this situation , parameters of the target neurons will be altered only by the reflex signal; i . e . , the leaning reflex of the UBC together with the gait adaptation will be triggered by the AS signal as soon as RunBot falls . Hence , RunBot will begin walking up the ramp with a wrong set of gait parameters and an inappropriate posture of the UBC . Thus , it will eventually fall , leading to a signal at the AS , which will change RunBot's parameters—but too late ( when it already lies on the ground ) . Due to learning the modifiable synapses , ρ1 , which connects the predictive IR signal with the learner neurons , will grow . Consequently , after three to five falls during the learning phase , gait adaptation together with posture control of the UBC will finally be driven by the predictive IR-signal instead . Correspondingly , RunBot will adapt its gait together with leaning the UBC in time . The used learning algorithm has the property that learning will stop when the reflex signal is zero; i . e . , when RunBot does not fall anymore [42] . On returning to flat terrain , the IR output will get small again and RunBot will change its locomotion back to normal for walking on a level floor . Note that the same circuitry and mechanisms can be used to learn different gaits for other given tasks , for example walking down a ramp . Hence , the employed mechanism performs “reflex avoidance learning . ” Synapses stop growing as soon as the new anticipatory reaction has been learnt and the reflex to the later signal is not triggered anymore . As mentioned above , the principle of reflex avoidance learning appears to be emulated by cerebellar function [29] , albeit not by the same mechanisms as used here . The cerebellum rather seems to rely on an interplay between the mossy fiber to deep nucleus synapse and the parallel fiber to Purkinje cell synapse . The first seems to control the overall amplitude of a cerebellar response , the second the timing . The parallel fiber to Purkinje cell synapse does not seem to rely on STDP but rather it uses long-term depression to facilitate the reduction of Purkinje cell activity , leading to a release of the deep nucleus neurons to form inhibition and a rebound excitation . This possibly involves presynaptic mechanisms . This whole circuitry has been captured in a recent model by Hofstötter et al . [69] . Our learning rule operates at the single cell level using an STPD-like mechanism . This is necessary to achieve the required efficiency for real-time learning . Hence the same principle ( reflex avoidance ) is used here but with a different implementation , very much focusing on algorithmic efficiency . Learning algorithm . In general , each learner neuron Ln requires two input signals u0 and u1 with synaptic weights ρ0 , 1 . Here , we use the AS and the preprocessed IR signals as u0 and u1 , respectively . Furthermore , we initially set and . Only ρ1 is allowed to change through plasticity . The output activity v of Ln is given by: Note , since v is defined by weights and input strengths , we will—after learning—receive differently strong outputs for differently strong input signals IR ( signal u1 ) . Hence , after having learned a steep slope , less steep slopes will drive the output less , leading to smaller parameter changes and incomplete leaning of the body , which is the appropriate behavior , in this case preventing a fall ( not shown ) . We use a differential Hebbian learning rule ( ISO-learning , [42] ) for the weight change of given by: where v′ ( Ln ) is the temporal derivate and μn the learning rate . It is independently set for each learner neuron , which will define the desired equilibrium point ( μ1 = 10 , μ2 = 7 . 0 , μ3 = 10 . 5 , μ4 = 0 . 14 , μ5 = 3 . 0 , μ6 = 10 . 0 ) . One could consider μ as the susceptibility for a synaptic change , which in a biological agent will be defined by its evolutionary development , which determines the agent's ability to learn a certain task . How and if these values could also be influenced ( possibly by mechanisms of meta-plasticity ) , changing learning susceptibility , goes beyond the scope of this article . Our learning rule is based on differential Hebbian learning [70] , described in detail in [42] . Hence , this form of plasticity depends on the timing of correlated signals and thereby compares with STDP [41 , 71] . In neurons with multiple inputs , such a mechanism can be used to alter the synaptic strengths according to the order of the arriving inputs . Note that neuronal time scales for STDP do not match the much longer time scales required here . There are mechanisms discussed in the literature to address this problem [72] . In the context of the current study , we are , however , not concerned with this , and we are using Equations 9 and 10 directly . As a consequence of this rule , the modifiable synapses ρ1 will get strengthened if the predictive signal u1 is followed by the reflex input u0 , where the reflex drives the neuron into firing . This rule will lead to weight stabilization as soon as u0 = 0 [42]; hence , when the reflex has successfully been avoided . As a result , we obtain behavioral and synaptic stability at the same time without any additional weight-control mechanisms . The output of each learner neuron v ( Ln ) is directly fed to its target neuron in the network . The connection structure together with its synaptic polarity ζ is shown in Figure 13 . To control the UBC , we directly use the average firing rate of the learner neuron v ( L1 ) to drive the body motor neurons NE and NF . Once the learner neuron L1 gets active , it will inhibit NF , while NE will be activated . As a result , the UBC will lean forward . As described above , changing the gait of RunBot is achieved by controlling the values of the output gain of the leg motor neurons g and the firing threshold Θ of sensor neurons using the firing rate of learner neurons . To change a threshold , one can simply redefine the input signal I of the sensor neurons ( AL , AR , SE , SF ) presented in Equations 2 and 3 as: where I is the input summation of the real time angular position φ and the average firing rate of a learner neuron v ( Ln ) , and ζ is the connection polarity learner and target neuron ( see Figure 13 ) . To change the output gain of the hip motor neurons , we need to divide or multiply . Hence , the learner neuron L4 performs divisive ( shunting ) inhibition [73] , which in a real neuron is commonly generated by the influence of GABAA on chloride channels ( [74 , 75] , but see [76] ) . Thus , the gain of NE and NF is affected by divisive inhibition , defined by: where gmax is the maximum motor gain which is set to 2 . 2 for an optimal walking speed . Note that gmax is proportional to the walking speed and it can be set to up to 3 . 0 , beyond which the motors are damaged . | The problem of motor coordination of complex multi-joint movements has been recognized as very difficult in biological as well as in technical systems . The high degree of redundancy of such movements and the complexity of their dynamics make it hard to arrive at robust solutions . Biological systems , however , are able to move with elegance and efficiency , and they have solved this problem by a combination of appropriate biomechanics , neuronal control , and adaptivity . Human walking is a prominent example of this , combining dynamic control with the physics of the body and letting it interact with the terrain in a highly energy-efficient way during walking or running . The current study is the first to use a similar hybrid and adaptive , mechano–neuronal design strategy to build and control a small , fast biped walking robot and to make it learn to adapt to changes in the terrain to a certain degree . This study thus presents a proof of concept for a design principle suggested by physiological findings and may help us to better understand the interplay of these different components in human walking as well as in other complex movement patterns . | [
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] | 2007 | Adaptive, Fast Walking in a Biped Robot under Neuronal Control and Learning |
Listeria monocytogenes is a facultative intracellular bacterial pathogen that can infect the placenta , a chimeric organ made of maternal and fetal cells . Extravillous trophoblasts ( EVT ) are specialized fetal cells that invade the uterine implantation site , where they come into direct contact with maternal cells . We have shown previously that EVT are the preferred site of initial placental infection . In this report , we infected primary human EVT with L . monocytogenes . EVT eliminated ∼80% of intracellular bacteria over 24-hours . Bacteria were unable to escape into the cytoplasm and remained confined to vacuolar compartments that became acidified and co-localized with LAMP1 , consistent with bacterial degradation in lysosomes . In human placental organ cultures bacterial vacuolar escape rates differed between specific trophoblast subpopulations . The most invasive EVT—those that would be in direct contact with maternal cells in vivo—had lower escape rates than trophoblasts that were surrounded by fetal cells and tissues . Our results suggest that EVT present a bottleneck in the spread of L . monocytogenes from mother to fetus by inhibiting vacuolar escape , and thus intracellular bacterial growth . However , if L . monocytogenes is able to spread beyond EVT it can find a more hospitable environment . Our results elucidate a novel aspect of the maternal-fetal barrier .
L . monocytogenes is a ubiquitous , facultative intracellular , Gram-positive bacterium that causes food-borne disease in humans and other mammals [1] , [2] . Humans are exposed relatively frequently to L . monocytogenes: healthy adults in the United States are estimated to ingest 105 bacteria at least four times per year [3] . Ingestion of L . monocytogenes by an immunocompetent host is relatively innocuous , but in immunocompromised individuals and pregnant women listeriosis is a severe disease [1] , [4] . In the US there are ∼530 cases per year of listeriosis during pregnancy ( FDA , 2009 ) . The clinical manifestations depend on the gestational age . During the second trimester L . monocytogenes is the cause of ∼3% of spontaneous abortions [5] , [6] . Infection around term results in neonatal disease with mortality of up to 50% [7] . The mechanisms by which L . monocytogenes infects the placenta and crosses the maternal-fetal barrier are controversial and still poorly understood . The intracellular life cycle of L . monocytogenes has been characterized in a variety of different cell lines as well as primary murine bone marrow-derived macrophages [8] , [9] . L . monocytogenes is taken up either by phagocytosis or internalized via interaction of bacterial surface proteins , such as internalin A ( InlA ) , with host cell receptors , such as E-cadherin [10] , [11] . After internalization , the bacterium finds itself in an endocytic vacuole that develops into a late endosome and acidifies slightly [12] . Acidification activates the pore-forming toxin listeriolysin O ( LLO ) that is important for escape of the bacterium into the host cytosol , where L . monocytogenes replicates rapidly [13] , [14] . The listerial protein ActA nucleates actin and allows L . monocytogenes to spread from cell-to-cell without exposure to the extracellular milieu [15] . The placenta has to protect the fetus from vertical transmission of pathogens while also providing an environment of immunological tolerance for the fetal allograft [16] . How the placenta accomplishes these contradictory tasks is unknown . It has long been postulated that the placenta is an immune-privileged organ that has diminished adaptive immune defenses in order to establish tolerance . However , low placental infection rates in the face of frequent pathogen exposure suggest that the organ itself must have defense mechanisms against infection . What are the barriers of the placenta to infection and how is L . monocytogenes able to breach them ? Understanding the structure of the human placenta is critical for addressing these questions . The placenta is comprised of maternal and fetal cells ( Fig . 1 ) [17] . Prior to implantation , the maternal uterine lining transforms into the receptive decidua . Shortly after , specialized fetally derived cells called trophoblasts differentiate into several subpopulations that perform critical placental functions . Specifically , invasive extravillous trophoblasts ( EVT ) anchor the placenta in the uterus and invade the decidua and maternal spiral arteries . Consequently , maternal blood flows into the intervillous space , bathing the fetally derived villous trees . These villi are covered by a continuous layer of multinucleate syncytium ( SYN ) , a specialized trophoblast layer that mediates gas , nutrient and waste exchange between mother and fetus . The syncytium is underlaid by progenitor cells called subsyncytial cytotrophoblasts ( sCTB ) , which are separated by a basement membrane from the stroma of the villi where fetal capillaries are found . We and others have previously shown that primary human placental organ cultures are relatively resistant to infection with L . monocytogenes [18] , [19] . We utilized organ cultures from first trimester placentas to define where and how L . monocytogenes breaches the maternal-fetal barrier [19] . The syncytium that is in direct contact with maternal blood is highly resistant to infection . The other maternal-fetal interface is in the decidua where invasive EVT are in direct contact with maternal cells and tissues . Even though this interface has a much smaller surface area , it is the preferred site of initial placental infection . Indirect evidence suggested that EVT are capable of restricting intracellular growth and cell-to-cell spread of L . monocytogenes [19] . In this study , we further characterize the intracellular fate of L . monocytogenes in EVT . We used a cell culture model system of primary human EVT [20] to understand how these specialized cells are able to delay or inhibit the intracellular life cycle of L . monocytogenes . We found that isolated EVT were able to restrict intracellular bacterial growth and spread . Furthermore , EVT prevented vacuolar escape and steered vacuolated bacteria towards degradation in lysosomes . This phenotype was strongest in invasive EVT; cells that in vivo are in contact with maternal tissues . Our results suggest that EVT have effective defense mechanisms against intracellular pathogens and form a significant bottleneck in the transplacental transmission of pathogens .
In order to examine the role of EVT in placental infection , we characterized the intracellular fate of L . monocytogenes in isolated primary human EVT . We used a well-established model system that has been previously used to study differentiation of progenitor cytotrophoblasts along the invasive pathway [21] . In this cell-culture system , cytotrophoblasts are isolated from second trimester placentas and induced towards differentiation along the invasive phenotype by culture on extracellular matrix such as Matrigel [22] . Tissue from the second trimester is used because the yield of cytotrophoblasts is higher than from first and term placentas [20] . Cytotrophoblasts are isolated by a series of enzymatic digestions and ficoll gradients . In order to assure a pure population of cytotrophoblasts , we performed a CD45-based depletion to remove any remaining immune cells prior to culture and differentiation . Cytokeratin 7 , a cytotrophoblast marker [23] , was used to determine the purity of the cell population . Generally our cell preparations contained over 95% cytotrophoblasts , with the remaining <5% being predominantly placental fibroblasts from the villous stroma ( data not shown ) . EVT were infected with wild type L . monocytogenes at a multiplicity of infection ( MOI ) of 5 . Gentamicin was added to the culture medium at 1 hour post-inoculation ( p . i . ) to eliminate extracellular bacteria . We observed a ∼100-fold variation in susceptibility to infection across EVT from different placentas: 0 . 1–12% of EVT were infected , on average with one bacterium , at 2 hours p . i . This may be due to genetic differences between donors or heterogeneity in the condition of the placentas . We therefore normalized the growth of bacteria over time to the number of bacteria at the 2-hour time point for each placenta . Normalized data from 10 individual donor placentas was averaged to minimize the effects of individual differences . In contrast to almost all other previously studied cell types—which generally support L . monocytogenes growth [9]—intracellular bacteria in EVT decreased 2-fold between 2 and 5 hours p . i . and continued to decrease by 5-fold over the next 19 hours ( Fig . 2 ) . For comparison , the three commercially available human trophoblast-derived choriocarcinoma cell lines ( BeWo , Jeg3 , and Jar ) were also infected at an MOI of 5 , which resulted in significantly greater infection: ∼14% of cells at 2 hours p . i . in BeWo ( p = 0 . 004 by Student's T-test ) . Invasion of EVT and BeWo cells is InlA-dependent [18] , [19] , [24] . We observed lower levels of E-cadherin expression by immunofluorescence microscopy , the host cell receptor for InlA , in isolated EVT in comparison to BeWo cells ( data not shown ) , which most likely accounts for the difference in invasion between these two cell types . In all of the choriocarcinoma cell lines L . monocytogenes grew with a similar doubling time of about 77 minutes between 2 and 5 hours p . i . ( Fig . 2 and data not shown ) . This is 2-fold slower than intracellular growth rates in murine macrophage cell lines [25] , and in sharp contrast to the “halving time” of L . monocytogenes in EVT of about 310 min ( p<10−4 by Student's T-test ) . In addition , we tested the fate of L . monocytogenes in primary human placental fibroblasts . These fibroblasts were isolated from a first trimester placenta [26] and propagated in culture for at least 10 generations before infection to ensure purity . To compare with EVT , placental fibroblasts were infected at an MOI of 60 , which resulted in infection of ∼1% of cells at 2 hours p . i . Subsequently , L . monocytogenes grew with a doubling time of 40 minutes between 2 and 5 hours p . i . ( Fig . 2 ) . L . monocytogenes grows rapidly in the host cell cytosol [9] , while mutants that are unable to access the cytosol generally do not replicate [25] . Therefore , the lack of intracellular growth of L . monocytogenes in EVT could be due to an inability to escape from the primary vacuole . We thus determined whether L . monocytogenes can escape from the primary vacuole in EVT and whether bacteria can be found in the host cell cytosol . To determine vacuolar escape , we utilized a L . monocytogenes strain that expresses red fluorescent protein ( RFP ) under the actA promoter ( pactA-RFP ) . ActA nucleates actin , and its transcription is up regulated 200-fold in the host cell cytosol [27] , [28] . Therefore , the expression of RFP in this strain correlates with entry of bacteria into the cytoplasm [29] . In addition , polymerization of host actin filaments around bacteria indicates cytosolic localization of L . monocytogenes and can be visualized by staining fixed cells with fluorescently labeled phalloidin , a compound that binds F-actin [30] . First we analyzed vacuolar escape rates of L . monocytogenes in BeWo cells . We infected BeWo cells with pactA-RFP L . monocytogenes and fixed the cells for immunofluorescence microscopy at 2 , 5 , 8 and 24 hours . The preparation was counterstained with polyclonal anti-Listeria antibody to visualize the total number of bacteria per cell . Microscopic inspection of BeWo cells at 8 hours p . i . showed that the vast majority of bacteria expressed RFP ( Fig . 3A ) . The percentage of RFP-expressing bacteria increased from 17% to 95% between 2 and 8 hours p . i ( Fig . 3C ) . Consistent with high vacuolar escape rates in this cell line , the number of bacteria that co-localized with phalloidin steadily increased from 13% to 76% over the same time period ( Fig . 3C ) . Between 8 and 24 hours p . i . RFP expression remained at 95% , consistent with the long half-live of RFP [31] , which led to RFP persistence in all bacteria that had escaped the primary vacuole . In contrast , phalloidin co-localization decreased to 53% at 24 hours p . i . The significant difference between RFP expression and phalloidin co-localization at 8 hours p . i ( p = 0 . 04 by Student's T-test ) and the observed decrease in phalloidin co-localization at 24 hours p . i . is most likely due to the fact that phalloidin staining provides a snapshot of intracellular bacteria that are in the actin-nucleating stage of their life cycle . In contrast to RFP expression , phalloidin does not co-localize with bacteria that have spread to neighboring cells and are still in the secondary vacuole . It is unlikely that host cell death at 24 hours p . i . contributes substantially to the decrease in phalloidin co-localization , because host cell death would lead to a significant decrease in intracellular bacteria as well [32] , which we do not observe ( Fig . 2 ) . In contrast , infection of EVT with pactA-RFP L . monocytogenes counterstained with polyclonal anti-Listeria antibody revealed that the vast majority of bacteria did not express RFP at 8 hours p . i . ( Fig . 3B ) . Quantitation showed that less than 10% of L . monocytogenes expressed RFP over the 24-hour course of infection ( Fig . 3C ) , compared to nearly 100% in BeWo cells ( p<10−5 by Student's T-test ) . In addition , no co-localization of L . monocytogenes with phalloidin was observed in EVT ( data not shown ) . Our results suggest that bacteria are unable to grow in EVT because they are trapped in the primary vacuole . In the murine model of infection the virulence factor LLO , a cholesterol-dependent pore-forming cytolysin , is essential for vacuolar escape [33] . We evaluated the role of LLO in the intracellular fate of L . monocytogenes in EVT . We tested two bacterial strains: DP-L2161 which is deficient in LLO [34] and unable to grow in BeWo cells ( data not shown ) and DP-L4057 which has a mutation in LLO ( S44A ) that increases phagosomal escape in murine bone marrow derived macrophages [35] . The outcome of infection did not differ from wild type infection - both strains were eliminated in EVT over 24 hours - although with slightly different kinetics ( see Supplementary Fig . S1 ) . These results suggest the possibility that LLO function is impaired in EVT . The maturation of L . monocytogenes-containing vacuoles has been studied in detail in murine macrophage cell lines ( RAW 264 . 7 and J774A . 1 ) [12] . Wild type L . monocytogenes escapes from a vacuolar compartment that includes the late endosomal marker Rab7 . The early endosomal marker Rab5 does not associate with L . monocytogenes even at very early time points after phagocytosis . If the vacuole matures further and acquires the lysosomal marker Lamp1 , the rate of vacuolar escape is minimal . To characterize the vacuolar compartment that L . monocytogenes occupies in EVT , we examined these same markers . The early endosomal marker Rab5 was associated with less than 10% of bacteria at 2 and 5 hours p . i . ( Fig . 4A ) . The late endosomal marker Rab7 was found to co-localize with 55% of bacteria at 2 hours p . i . and with over 40% of bacteria at all other time points through 24 hours of infection ( Fig . 4B , C ) . Lamp1 co-localized with only 12% of bacteria at 2 hours p . i . and increased steadily to 40% at 24 hours p . i . ( Fig . 4B , D ) . To test whether the L . monocytogenes-containing vacuole in EVT becomes acidified , we used the acidotropic dye Lysotracker . Lysotracker staining followed a similar trend to Lamp1 staining: 17% of bacteria were found in an acidified compartment at 2 hours p . i . , increasing to 51% at 24 hours p . i . ( Fig . 4B , E ) . While it is generally believed that L . monocytogenes replicates in the cytoplasm and not in vacuoles , there have been a few reports suggesting the possibility of slow replication in vacuolar compartments . Bhardwaj et al . described the presence of multiple bacteria in membrane-bound vacuoles in mononuclear cells in the liver of SCID mice with chronic listeriosis [36] . Furthermore , Birmingham et al . found that 13% of bacteria in a murine macrophage cell line were replicating slowly in autophagosome-like vacuolar compartments ( LC3-positive , LAMP1-positive , non-acidified ) and named these structures SLAPS ( spacious Listeria-containing autophagosomes ) [37] . We therefore evaluated whether L . monocytogenes co-localizes with the autophagy marker LC3 , but found little to no co-localization in our system ( Fig . 4A ) . We concluded that bacteria in EVT are trapped in vacuoles that mature into acidified lysosomes , suggesting that L . monocytogenes is degraded in this compartment . To look more closely at the subcellular localization of L . monocytogenes in EVT , transmission electron microscopy was performed . EVT were infected with wild type L . monocytogenes at an MOI of 60 . This high inoculum was used to increase the number of infected cells and the number of bacteria/cell for better visualization . Because the most significant decrease in intracellular bacterial numbers occurred between 2 and 5 hours p . i . ( Fig . 2 ) , infected EVT at those time points were examined ( Fig . 5A , B ) . The number of vacuolar L . monocytogenes was enumerated: at both time points , 81–86% of bacteria were confined to vacuoles ( Fig . 5C ) . These escape rates ( 14–19% ) are slightly higher than those measured using the pactA-RFP strain above . This difference is significant ( p = 0 . 004 by Student's T-test ) and is likely due to differences in the infection ( MOI of 5 versus 60 ) and/or due to a more limited detection threshold of RFP fluorescence as compared to electron microscopy . Furthermore , we enumerated the number of bacteria that appeared intact versus degraded . Intact appearing bacteria decreased from 67% to 50% between 2 and 5 hours p . i . , and degraded bacteria increased from 33% to 50% during the same time interval ( Fig . 5D , E , F ) . A vacuolar compartment derived from the primary vacuole consists of a single lipid bilayer , whereas secondary vacuoles ( a result of infection via cell-to-cell spread ) and autophagosomes typically consist of two lipid bilayers [15] , [38] . With a membrane contrast-enhancing stain and at higher magnification the membranes of the L . monocytogenes-containing vacuoles were visualized and appeared to consist of a single lipid bilayer ( Fig . 5E ) . These ultrastructural results are consistent with bacterial entrapment in the primary vacuole and degradation in lysosomes . We previously found that EVT are the preferred initial site of infection for L . monocytogenes in first trimester placental organ cultures [19] . Furthermore , we observed that L . monocytogenes is able to spread beyond the EVT along sCTB in some placentas . Under the conditions Robbins et al . used , such spread occurs in 50% of placentas over a time period of 72 hours . The inability of L . monocytogenes to escape from the primary vacuole in EVT could explain the delay or lack of listerial dissemination in placental organ cultures . Thus , we analyzed the rates of vacuolar escape in first trimester placental organ cultures infected with pactA-RFP L . monocytogenes and counterstained with polyclonal anti-Listeria antibody as described above ( Fig . 6A , B ) . At 8 hours p . i . , only 14% of bacteria had escaped the vacuole , while 39% and 37% were in late endosomes and lysosomes respectively ( Fig . 6C ) . By 24 hours p . i . the percentage of RFP-expressing bacteria increased to 23% and the proportion of L . monocytogenes co-localizing with Rab7 and Lamp1 remained around 40% ( Fig . 6C ) . Vacuolar escape rates in placental organ cultures were somewhat higher than those observed in isolated second trimester EVT ( Fig . 3C; p = 0 . 19 by Student's T-test ) . We therefore decided to test whether vacuolar escape rates differ between distinct trophoblast subpopulations . When isolated cytotrophoblasts are grown on Matrigel they differentiate along the invasive pathway and therefore consist of a more homogeneous EVT population [20] , [21] . In contrast , there are several distinct subpopulations of trophoblasts in vivo and ex vivo that are in different stages of differentiation ranging from progenitor cytotrophoblasts near the stroma to invasive EVT at the outer villus margin . Therefore , infection of placental organ cultures leads to infection of a mixed population of trophoblasts . To test whether listerial escape rates differ in different trophoblast subpopulations , we compared escape rates in three distinct populations of trophoblasts: ( 1 ) trophoblasts that were in contact with Matrigel ( invasive border EVT ) , ( 2 ) trophoblasts that were surrounded by other trophoblasts on all sides ( middle EVT ) , and ( 3 ) those that were in contact with the basement membrane and its underlying stroma ( parastromal trophoblasts ) ( Fig . 7A–C ) . We increased the dose of L . monocytogenes to 2x107 bacteria/ml for 5 hrs before addition of gentamicin in order to achieve infection of all three subpopulations within one placenta at 24 hours p . i . , and compared escape rates between invasive border EVT , middle EVT and parastromal trophoblasts at 24 and 48 hours p . i . ( Fig . 7D , E ) . The average escape rate in invasive border EVT at 24 hours p . i . was 40% ( range 11% to 55% ) . Because of this large variability between placentas from different donors , we normalized the escape rates in middle EVT and parastromal trophoblasts to the escape rate in invasive border EVT from the same placenta . At 24 hours p . i . we determined the fold-difference in escape rates in middle EVT and parastromal trophoblasts in comparison to the escape rate in invasive border EVT from the same placenta . Vacuolar escape rates increased the closer the trophoblasts were to the core of the placental villus . The average increase in escape rates compared to invasive border EVT was 1 . 21-fold in middle EVT and 1 . 51-fold in parastromal trophoblasts ( Fig . 7E ) . At 48 hours p . i . we determined the fold difference in escape rates in all three subpopulations in comparison to the escape rate in invasive border EVT at 24 hours p . i . , and found similar results . The average increase in escape rates was 1 . 14-fold ( invasive EVT ) , 1 . 54-fold ( middle EVT ) , and 1 . 76-fold ( parastromal trophoblasts ) ( p = 0 . 02 by Student's T-test for combined 24- and 48-hour time points ) . We concluded that EVT at the invasive border—a cell type that is in direct contact with maternal cells in vivo—are especially prohibitive for listerial vacuolar escape . However , if L . monocytogenes is able to spread beyond the invasive EVT it can find a more hospitable environment .
Much of the pioneering work on the L . monocytogenes life cycle and intracellular growth kinetics has been performed in murine bone marrow derived macrophages as well as various murine and human cell lines [9] , [15] , [39] . In these cells , L . monocytogenes vacuolar escape rates are 80% or higher [29] , [40] , and bacteria grow rapidly ( generation time of ∼40 min ) in the nutrient-rich cytosol [25] . In contrast , the vacuolar escape rates in isolated primary EVT were less than 10% . It is possible that vacuolar escape and growth rates vary depending on the specific cell type , especially in cells that play a role in host defense against infection . For example , primary murine dendritic cells are less hospitable to L . monocytogenes than primary bone marrow-derived mouse macrophages [40] , [41] . Westcott et al . showed that bacterial doubling time is about 2-fold slower in primary murine dendritic cells ( ∼70 min ) , and only ∼40% of the bacteria escape into the cytosol . Specific endosomal maturation features in dendritic cells that are important for efficient processing and presentation of bacterial antigens to T cells are thought to be the underlying reason for these decreased vacuolar escape rates . Primary murine peritoneal macrophages are even more hostile to L . monocytogenes: Portnoy et al . has demonstrated that these cells kill ∼80% of L . monocytogenes during the first 2 hours p . i . and that surviving bacteria grow at a generation time of ∼120 min or longer [42] . Furthermore , if resident peritoneal macrophages are stimulated with IFNγ , bacterial growth is eliminated , and 95% of bacteria are found in vacuolar compartments [42] . While these activated professional immune cells are known to be critical in scavenging and containing infectious particles , it is more surprising that epithelial cells in the placenta , the EVT , would possess a similar bacteriocidal phenotype . In this context , it is interesting that IFNγ is crucial for a successful pregnancy and present at high levels at the maternal-fetal interface [43] . IFNγ is produced by uterine natural killer cells , which comprise approximately 20–40% of the leukocytes in the decidua [44] , [45] , and IFNγ receptors are expressed on human trophoblast cells throughout pregnancy [46] . It is possible that residual effects of in utero IFNγ exposure contribute to decreased vacuolar escape and increased bacterial degradation in EVT . Why are bacteria not able to escape the vacuole in EVT ? In the murine model of infection the virulence factor LLO is essential for vacuolar escape [33] . We found that lack of LLO or increased hemolytic activity of LLO did not alter the outcome of infection in EVT , suggesting that LLO function is impaired in this cell type . LLO-mediated pore formation is a pH dependent process , with a pH optimum of 5 . 5 [47] , [48] . Although the Listeria-containing vacuole in EVT acidifies , the kinetics or extent of acidification could present unfavorable conditions for LLO function . For example LLO loses its hemolytic activity at neutral pH in less than 10 min [49] . Another possibility is that the Listeria-containing vacuole has a different lipid composition that renders LLO non-functional . LLO is dependent on the presence of cholesterol , which is utilized by EVT for the synthesis of progesterone [50] . Specialized hormone synthesis in EVT could lead to differences in cholesterol metabolism and/or distribution in these cells , rendering it inaccessible to vacuolar LLO . Moreover , the active form of a host-derived thiol reductase ( GILT ) involved in antigen processing has been shown to be required for the activation of LLO [51] , and may not be present or accessible in the Listeria-containing vacuole in EVT . However , all of the above mentioned studies have been performed in the murine model of infection . In contrast , in many human cell types L . monocytogenes deficient in LLO is capable of vacuolar escape [25] , [52] , [53] . The mechanisms of LLO-independent vacuolar escape are poorly understood [54] , but the existence of these examples opens up a myriad of other pathways that may be different in EVT , that ultimately could lead to vacuolar entrapment of L . monocytogenes . Further studies will be needed to assess these possibilities . Work in several pregnant animal models of listeriosis supports our findings that L . monocytogenes has to pass several bottlenecks to infect the placenta and spread to the fetus . We have shown previously that the placenta in the pregnant guinea pig model is relatively protected from colonization , characterizing the kinetics of bacterial spread from maternal organs to the placenta and to the fetus [55] . The guinea pig placenta is colonized with 104-fold fewer bacteria than maternal liver and spleen after intravenous inoculation , and the bottleneck between placenta and fetus is again 1∶104 bacteria . Studies in the pregnant mouse and gerbil models also require high intravenous inoculums , >106 bacteria , to induce placental infection [56] , [57] . Interestingly , there are several lines of evidence that suggest EVT are a suboptimal niche for the growth of intracellular pathogens in general . Human CMV infection , for example , is inefficient in trophoblasts , progresses slowly , and releases only small amounts of progeny virus [58] . Likewise , placentas infected with CMV in utero show rare viral replication in EVT , with membrane-clustered virions [59] . Recent studies with HIV-1 indicate that EVT are also non-permissive to HIV-1 replication due to active degradation and/or passive inactivation of critical viral replication mechanisms [60] . Others have observed that the majority of HIV-1 virions are trapped within endosomal compartments [61] . The common thread in these studies is that vacuolar or endosomal trafficking is hindering the normal life cycles of pathogens and preventing growth and spread of the virus or bacterium . While little is known about EVT in general , ultrastructural studies of uninfected human placentas report many unidentified vesicles and vacuoles in EVT [62] , [63] . It is possible that the invasive role of EVT and their active degradation of extracellular matrix may require unique degradative and/or endosomal pathways that interfere with the life cycle of intracellular pathogens . As a result , EVT create a significant barrier to infection , and pathogens must get past the bacteriocidal EVT into more permissive cells in the placenta for the infection to progress . If the primary site of infection is an inhospitable cell-type , then how does placental infection progress to cause pregnancy complications and fetal infection ? One possibility is that even though EVT are the preferred site of initial infection with L . monocytogenes [19] and Toxoplasma gondii ( our unpublished observations ) , and can harbor CMV in utero [64] , they are a dead end for pathogens . This seems unlikely because we have not observed placental infection without infection of EVT , and L . monocytogenes can spread beyond EVT in some placentas [19] . In addition , other routes of crossing the trophoblast barrier appear even more difficult , since the syncytium is highly resistant to infection with L . monocytogenes [19] and T . gondii ( our unpublished observations ) . It is possible that EVT could differ in their resistance to infection due to host genotypic differences . This would mean that some people are simply more predisposed to placental infection and pregnancy complications than others . However , to our knowledge no genetic basis for differences in susceptibility to vertical transmission has ever been identified . We hypothesize that EVT can either contain or eliminate an infection until a certain threshold of cellular damage or placental inflammation is surpassed . For instance , non-infectious pregnancy complications that influence oxygen tension or pH in the placenta could alter the biochemical and/or physiological condition of EVT and decrease their resistance to infection . Co-infection with other pathogens could similarly escalate an immune imbalance at the maternal-fetal interface . If these imbalances threaten the healthy progression of pregnancy , spontaneous abortion or preterm labor are initiated to avoid continuation of pregnancy with a compromised placenta . The placenta has developed a marvelous defense against infection , most likely consisting of multiple layers of physical and biochemical barriers . Both subpopulations of trophoblasts—syncytium and EVT—that are in direct contact with maternal cells and tissues are effective barriers against infection . The syncytium is in direct contact with maternal blood and is highly resistant to infection . The EVT are in close contact to maternal cells and tissues in the implantation site , and are the preferred initial sites for infection , but are inhospitable to a variety of intracellular pathogens . Both barriers can probably be breached by additional damage , resulting in infection of subsyncytial cytotrophoblasts , which appear to be more hospitable to intracellular replication of pathogens . Nevertheless , L . monocytogenes still has to pass another physical barrier: the basement membrane [19] , to reach the villous stroma where the fetal capillaries are . L . monocytogenes will serve as an excellent model to characterize the precise molecular basis of the maternal-fetal barrier .
This study was conducted according to the principles expressed in the Declaration of Helsinki . The study was approved by the Institutional Review Board at the University of California , San Francisco , where all experiments were performed ( H497-00836-29 ) . All patients provided written informed consent for the collection of samples and subsequent analysis . All chemicals were purchased from Sigma-Aldrich unless otherwise stated . For human placental organ cultures , placentas from elective terminations of pregnancy ( gestational age 4 to 8 weeks ) were collected and prepared as previously described [65] . Briefly , fragments from the surface of the placenta were dissected into 1–3 mm tree-like villi , placed on Matrigel ( BD Biosciences , San Jose , CA ) -coated Transwell filters ( Millipore , Bedirica , MA , 30-mm diameter , 0 . 4 um pore size ) and cultured in Dulbecco's modified Eagle-F12 medium ( DMEM-F12; 1∶1 , vol/vol ) supplemented with 20% fetal bovine serum ( FBS , Fisher Scientific ) , 1% L-glutamine and 1% penicillin/streptomycin ( Invitrogen , Carlsbad , CA ) . For EVT isolation , placentas from elective terminations of pregnancy ( gestational age 14 to 24 weeks ) were collected and prepared as previously described [21] , [66] . Briefly , placentas from normal uncomplicated pregnancies were obtained immediately after aspiration and subjected to a series of enzymatic digestions followed by purification over a Percoll gradient . Remaining leukocytes were removed using a magnetic-bead-based EasySep CD-45 Depletion Kit with RoboSep device ( Stem Cell Technologies , Vancouver , Canada ) . For growth curves , purified cells were plated on Matrigel-coated Transwell filters ( Millipore , Bedirica , MA , 12-mm diameter , 0 . 4 um pore size ) in serum-free DMEM-high glucose , with 2% Nutridoma ( Roche Diagnostics , Indianapolis , IN ) , 1% L-glutamine , 1% sodium pyruvate , 1% 25 mM HEPES , 1% penicillin/streptomycin at a concentration of 1 . 25×105 cells/transwell . For immunofluorescence microscopy , purified cells were plated on Matrigel-coated 6-well plates at a concentration of 2×106 cells/well . Placental fibroblasts were isolated as described [26] from a placenta at gestational age 8 weeks , and were cultured in DMEM-high glucose with 10% FBS , 18% M-199 , 1% penicillin/streptomycin . For growth curves and immunofluorescence microscopy , cells were plated on glass coverslips in 24-well plates at 2 . 5×105 cells/well . The choriocarcinoma cell line BeWo ( ATCC CCL-98 ) was cultured in Ham's F12 medium with 10% FBS , 1% L-glutamine , 0 . 15% sodium bicarbonate , 1% penicillin/streptomycin . For growth curves and immunofluorescence microscopy , cells were plated on glass coverslips in 24-well plates at 2 . 5×105 cells/well . L . monocytogenes 10403S expressing green fluorescent protein ( GFP ) ( strain DH-L1252 ) was a gift from Darren Higgins [67] . The pactA-RFP strain ( PL512 ) was constructed as follows: The ORF encoding TagRFP from Entacmaea quadricolor [31] was codon optimized for expression in L . monocytogenes using Gene Designer software [68] and the gene was synthesized de novo ( DNA2 . 0 , Menlo Park , CA ) . The synthetic gene was cloned downstream of the actA promoter in the vector pPL2 and stably integrated at the tRNAArg locus of the bacterial chromosome in the wild type L . monocytogenes strain DP-L4056 as described previously [69] . Molecular constructs were confirmed by DNA sequencing . For infections , bacteria were grown overnight to stationary phase in BHI ( Brain Heart Infusion broth ) at 30°C and washed once with PBS before dilution and infection . Cells were incubated in antibiotic-free medium for 1 hr before infection . Bacteria were added for 30 minutes , followed by three washes with PBS and addition of antibiotic-free medium . For CFU ( colony forming units ) determination gentamicin ( 50 µg/ml ) was added at 60 minutes p . i . EVT were inoculated with 3×106 bacteria/ml ( MOI 5 ) , and placental fibroblasts with 4×107 bacteria/ml ( MOI 60 ) . At indicated times , cells were lysed with distilled water , aliquots were plated on BHI agar plates , and CFU were enumerated . Infection for immunofluorescence microscopy was performed as outlined above with following modification: at 60 minutes p . i . Matrigel was dissolved by incubation with BD Cell Recovery Solution ( BD Biosciences , San Jose , CA ) for 40 minutes , and cells were re-plated on fresh Matrigel on Transwell filters in media containing gentamicin ( 50 µg/ml ) . Therefore , gentamicin was added at 1 hour 45 minutes p . i . to infected cells that were analyzed by immunofluorescence microscopy . CFU after exposure to the enzymatic solution and gentamicin addition at 1 hour 45 min were not significantly different from those under standard CFU ( gentamicin at 1 hour p . i . ) conditions ( data not shown ) . For electron microscopy , EVT were infected as above with the following alteration: the infectious dose was 4×107 bacteria/ml ( MOI 60 ) . Infection of placental explants was performed as previously described [19] with the following alteration: the infectious dose was lowered to 3×106 bacteria/ml for 30 minutes . Explants were fixed in 3% paraformaldehyde , passed through a sucrose gradient and snap-frozen in OCT ( Ted Pella , Redding , CA ) . Histological slicing was performed on a Hacker-Slee cryostat . Glass slides with sections were incubated in acetone , soaked in blocking solution ( 1% bovine serum albumin ( BSA ) in PBS ) , then incubated with primary antibodies , rinsed in PBS , incubated with secondary antibodies , and affixed over Vectashield mounting medium with DAPI ( Vector Laboratories , Burlingame , CA ) . Cultured cell lines and EVT were fixed in 3% paraformaldehyde . For Lysotracker visualization , the dye was added to cells for 30 minutes at 5 µM and washed in PBS before fixation . For Rab7 staining , cells were rinsed in glutamate lysis buffer ( 25 mM HEPES , 25 mM potassium chloride , 2 . 5 mM magnesium acetate , 5 mM EGTA , 150 mM K-glutamate ) , dipped into liquid nitrogen , rinsed in lysis buffer , and fixed in paraformaldehyde . Transwell filters were cut out of wells , blocked and permeabilized in 1% BSA and 0 . 1% Triton-X100 , then stained as described above in BSA/TritonX-100/PBS solution . Primary antibodies: polyclonal rabbit Listeria O antiserum ( 1∶1000 BD Biosciences , San Jose , CA ) , mouse polyclonal Lamp1 antiserum ( 1∶100 DSHB at University of Iowa ) , mouse monoclonal Rab5 antibody ( 1∶100 , BD Biosciences , San Jose , CA ) , mouse monoclonal LC3 antibody ( 1∶100 , gift from Dr . Jay Debnath ) , rabbit monoclonal Rab7 antibody ( 1∶1000 , gift from Dr . Suzanne Pfeffer ) . Secondary antibodies: Alexa Fluor 594 goat anti-mouse IgG ( 1∶500 , Invitrogen ) , Alexa Fluor 488 and 594 goat anti-rabbit IgG ( 1∶1000 & 1∶500 , Invitrogen ) . Slides were viewed using an inverted TE2000-E microscope ( Nikon , Tokyo , Japan ) equipped with a 12-bit cooled CCD camera ( Q imaging , Surrey , Canada ) . Images were collected using Simple PCI software ( Hamamats , Sewickley , PA ) . For the 2-hour time point , cells were fixed overnight at 4°C in 3% glutaraldehyde , 1% paraformaldehyde in 0 . 1 M cacodylate buffer . Fixed cells were post-fixed with 2% osmium tetroxide , dehydrated in ethanol and embedded in Epon . Thin sections ( 70 nm ) were cut using a Leica Ultracut-UCT Microtome ( Leica Microsystems , USA ) . Observations were made under a Philips Tecnai 10 transmission electron microscope ( Department of Pathology , UCSF ) , and digital acquisition was performed with a CCD camera ( Maxim DL Software , Cyanogen , Canada ) . For the 5-hour time point , cells were fixed as above , and post-fixed with 1% osmium tetroxide and 1 . 6% potassium ferrocyanide , stained with 5% uranyl acetate solution , dehydrated with ethanol and embedded . Sections were cut using a microtome ( RMC MTX , Reichert Ultracut E , RMC MT6000 ) and observations made under a Philips Tectani 12 transmission electron microscope ( EM lab , UC Berkeley ) . For quantification , 100 bacteria at each time point were counted and categorized by cytoplasmic versus vacuolar localization and intact versus degraded bacteria . Images were prepared using ImageJ ( RSB , Bethesda , MD ) , Photoshop and Illustrator ( Adobe , San Jose , CA ) . RGB hues were linearly adjusted but no non-linear alterations were performed . | Infection of the placenta and fetus is an important cause of pregnancy complications and fetal and neonatal morbidity and mortality . Listeria monocytogenes is an intracellular bacterial pathogen that causes pregnancy-related infections in humans . The pathogenesis of listeriosis during pregnancy is poorly understood . We have previously shown that transmission of L . monocytogenes from maternal cells and tissues to fetal cells occurs in the uterine implantation site , and that a small subpopulation of specialized fetal cells called extravillous trophoblasts are the preferred initial site of infection . Here we use primary human placental organ and cell culture systems to characterize the intracellular fate of L . monocytogenes in extravillous trophoblasts . We found that these cells entrap bacteria in vacuolar compartments where they are degraded and therefore reduce bacterial dissemination into deeper structures of the placenta . Our study provides new insights into the nature of the maternal-fetal barrier . Extravillous trophoblasts that are accessible to infection with intracellular pathogens from infected maternal cells have host defense mechanisms that constitute a bottleneck in maternal-fetal transmission . | [
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"pediatrics",... | 2011 | Invasive Extravillous Trophoblasts Restrict Intracellular Growth and Spread of Listeria monocytogenes |
The Plasmodium falciparum parasite's ability to adapt to environmental pressures , such as the human immune system and antimalarial drugs , makes malaria an enduring burden to public health . Understanding the genetic basis of these adaptations is critical to intervening successfully against malaria . To that end , we created a high-density genotyping array that assays over 17 , 000 single nucleotide polymorphisms ( ∼1 SNP/kb ) , and applied it to 57 culture-adapted parasites from three continents . We characterized genome-wide genetic diversity within and between populations and identified numerous loci with signals of natural selection , suggesting their role in recent adaptation . In addition , we performed a genome-wide association study ( GWAS ) , searching for loci correlated with resistance to thirteen antimalarials; we detected both known and novel resistance loci , including a new halofantrine resistance locus , PF10_0355 . Through functional testing we demonstrated that PF10_0355 overexpression decreases sensitivity to halofantrine , mefloquine , and lumefantrine , but not to structurally unrelated antimalarials , and that increased gene copy number mediates resistance . Our GWAS and follow-on functional validation demonstrate the potential of genome-wide studies to elucidate functionally important loci in the malaria parasite genome .
Plasmodium falciparum malaria is a major public health challenge that contributes significantly to global morbidity and mortality . Efforts to control and eliminate malaria combine antimalarial drugs , bed nets and indoor residual spraying , with vaccine development a longer-term goal . Genetic variation in the parasite population threatens to undermine these efforts , as the parasite evolves rapidly to evade host immune systems , drugs and vaccines . Studying genetic variation in parasite populations will expand our understanding of basic parasite biology and its ability to adapt , and will allow us to track parasites as they respond to intervention efforts . Such understanding is increasingly important as countries move towards reducing disease burden and the ultimate elimination of malaria . Given the potential impact of rapid evolution of P . falciparum in response to control and eradication strategies , discovery and characterization of P . falciparum genetic diversity has accelerated in recent years . Since the first malaria genome was sequenced in 2002 [1] , over 60 , 000 unique SNPs have been identified by concerted sequencing efforts [2]–[4] , and several genomic tiling arrays [5]–[9] and low-density SNP arrays [10] , [11] have been developed to query this genetic variation . Recently the first malaria GWAS was published [11] , in which 189 drug-phenotyped parasites from Asia , Africa and the Americas were genotyped using a low-density array ( 3 , 257 SNPs ) ; that study identified loci under positive selection and found several novel drug resistance candidates . For our study , we adopted two strategies for identifying genes involved in the malaria parasite's adaptive response: searching for signals of recent or ongoing natural selection , and searching for loci associated with one important clinical adaptation—resistance to antimalarial drugs . To make these searches possible , we began by sequencing 9 geographically diverse strains of P . falciparum to identify novel variation , thereby doubling the number of publicly available SNPs to 111 , 536 ( all accessible at plasmodb . org ) , and used these SNPs to develop a high-density genotyping array assaying 17 , 582 validated markers . After characterizing linkage disequilibrium and population structure in our samples , we used the arrays to search for evidence of both ongoing balancing selection and recent positive selection , and to carry out a GWAS that sought loci associated with resistance to thirteen antimalarial agents . We then followed up one of the novel loci associated with drug resistance in order to verify that variation there was biologically involved in modulating drug response .
We identified global population structure among malaria parasites using principal components analysis ( PCA ) of 57 genotyped culture-adapted parasite samples ( Figure 1A , Table S1 , Figure S1 ) . African , American and Asian samples form three distinct clusters , reflecting the likely independent introduction of P . falciparum from Africa into Asia and the Americas . There was little evidence for structure within Africa , suggesting high gene flow throughout the region ( Figure S1 ) . Asian and American parasites however both show substantial internal structure . There are also dramatic differences in linkage disequilibrium ( LD ) between populations , with substantial LD extending less than 1 kb in Senegal , 10 kb in Thailand , and 100 kb in Brazil ( Figure S2 ) . These observations are consistent with previous findings , which showed that LD decays more rapidly in Africa , due either to founder effects in other continents [12] or to elevated outcrossing frequencies in Africa [12] , [13] , where higher transmission intensity leads to a greater likelihood of sexual outcrossing rather than selfing within the mid-gut of vector mosquitoes . The short LD in malaria , driven by high levels of recombination , means that a high density of markers is required to identify candidate loci in association studies , since causal variants not on the array can seldom be tagged by neighboring alleles ( Table S2 ) . On the other hand , short LD can aid in fine-mapping candidate associations and greatly accelerates the search for causal genes . Short LD also aids in identifying genomic regions under recent positive selection with recombination-based methods , since the increased LD in selected regions should stand out against the short-LD background . We expect that many parasite proteins that interact with the host immune system will be under balancing selection , because they will be under selective pressure to maintain high levels of diversity . Indeed , previous studies have shown that regions of the P . falciparum genome that are highly polymorphic and appear to be under balancing selection encode antigens that are recognized by the human immune system [4] . We examined evidence for balancing selection in our data by searching for regions with high nucleotide diversity ( as measured by SNP π ) and low population divergence ( as measured by FST ) ( Figure 1B ) . When we examined the loci lying in this region of the graph ( Figure S3 ) , we found a number of known antigens and vaccine candidates . Loci in the same region with unknown function are thus potential novel antigens that trigger human immune response to malaria , and may prove useful as biomarkers or as candidate vaccine molecules . We carried out a similar search for loci under positive selection by identifying regions with both low nucleotide diversity within Senegal and Thailand and high population divergence between the two populations ( Figure 1B ) . We observed throughout the genome that nucleotide diversity was lower for nonsynonymous SNPs than for intergenic SNPs ( Figure S4 ) , a characteristic result of widespread purifying selection . At the same time , nonsynonymous SNPs exhibited significantly greater divergence than intergenic SNPs in all pairwise population comparisons , suggesting the effect of positive selection in local P . falciparum populations . Nonsynonymous SNPs with low diversity within a population and high divergence between the two populations studied may represent polymorphisms responsible for adaptive evolution . We also carried out a genome-wide scan for recent positive selection using the long-range haplotype ( LRH ) test [14] , which identifies common variants that have recently spread to high prevalence using recombination as a clock . Approximately 15 genes were identified as having undergone recent positive selection by this approach , including known drug resistance loci ( pfcrt and dhfr ) as well as multiple members of the acyl-CoA synthetase ( ACS ) and ubiquitin protein ligase families ( Figures S5 and S6 ) ; these latter genes also exhibit high divergence between Senegal and Thailand ( Figure 1B ) , evidence for selection that is recent and population-specific . Taken as a group , the genes identified by the LRH test show a significant enrichment for higher than average population divergence ( as measured by FST , Mann-Whitney U = 1583 , P = 0 . 0071 ) . All of these loci ( Table S3 , Dataset S1 ) , which include genes in the folate metabolism , lipid biosynthesis and ubiquitin pathways , should be viewed as candidates for functional follow-up and further characterization . In order to directly assess the genetic basis for one important response to antimalarial intervention , we carried out a GWAS to identify loci associated with drug resistance in parasites . This same approach can potentially be applied to many other clinically relevant malaria phenotypes , e . g . host response , invasion , and gametocyte formation . Our first step was to measure drug resistance ( IC50 values ) to 13 antimalarial drugs ( amodiaquine , artemether , artesunate , artemisinin , atovaquone , chloroquine , dihydroartemisinin , halofuginone , halofantrine , lumefantrine , mefloquine , piperaquine and quinine ) in 50 culture-adapted parasites using a high-throughput assay ( Tables S4 and S5 , Text S1 , Dataset S1 ) . We performed the genome-wide association analysis using two statistical tests: efficient mixed-model association ( EMMA ) and a haplotype likelihood ratio ( HLR ) test ( Figures S7 , S8 , S9 , S10 , Methods ) . EMMA identifies quantitative trait associations in individuals with complex population structure and hidden relatedness; it has previously been shown to outperform both PCA-based and λGC-based correction approaches in highly inbred and structured mouse , maize , and Arabidopsis populations [15] . EMMA is particularly applicable for small and structured sample sets: one of its first applications was in a study of 24 diploid mouse strains [15] , essentially the same sample size as in our study ( 50 haploid strains ) . The HLR test is a multi-marker test designed to detect the association of a single haplotype with a phenotype , and is particularly powerful when the associated haplotype experienced recent strong selection ( and is therefore long ) and occurs on a low-LD background [16]; it is therefore particularly appropriate for this study . We addressed the effect of population structure in the HLR test using population-specific permutation ( Methods ) . When used together , these two complementary approaches provide a highly sensitive screen for association signals within the P . falciparum genome . The well-characterized chloroquine resistance locus , pfcrt , served as a positive control for our GWAS methods ( Figure 2A and 2C , Table S2 ) , an important test given our small sample size and the limited LD present in P . falciparum . As expected , we found evidence for association with resistance to chloroquine using both tests , consistent with previous studies [11]; EMMA yielded evidence for association with genome-wide signficance , while the signal from the HLR test fell just short of genome-wide significance ( Figure 2C ) . Applying the same tests to the other drug phenotypes , we detected numerous novel loci showing significant associations with drug resistance ( Figure 2A and 2D , Table 1 ) . Quantile-quantile plots for each test demonstrate that we were able to effectively control for population structure ( Figure 2B ) . Despite our small sample size and the low LD in P . falciparum , in total eleven loci achieved genome-wide significance for association with resistance to five different drugs: amodiaquine , artemisinin , atovaquone , chloroquine and halofantrine . In most cases , the short extent of LD allowed localization to individual genes . Among the loci identified were various transporters and membrane proteins , as well as five conserved genes with unknown function ( Table 1 , Dataset S1 ) . Demonstrating that a signal of association actually reflects a causal molecular process requires functional testing and validation of the candidate locus , both because of concerns about power and reproducibility of genetic association tests , and because even a robust statistical correlation need not imply biological causation . To confirm the ability of GWAS to identify functionally relevant candidates , we investigated one of our association findings , PF10_0355 , in greater depth . This gene contains multiple SNPs associated with halofantrine resistance ( Figure 2D ) , and encodes a putative erythrocyte membrane protein ( PlasmoDB . org ) characterized by high genetic diversity . We set out to determine the role of PF10_0355 in halofantrine resistance by transfecting halofantrine-sensitive Dd2 parasites with episomal plasmids containing the PF10_0355 gene from a halofantrine-resistant parasite ( SenP08 . 04 ) , a technique that is used routinely for stable transgene expression [17] . Two independent transfectants overexpressing the PF10_0355 gene from SenP08 . 04 both showed reduced susceptibility to halofantrine when compared with the Dd2 parent or a transfection control ( Figure 3A ) , suggesting that this gene is indeed involved in modulating parasite drug response . Two independent transfectants overexpressing the endogenous PF10_0355 gene from halofantrine-sensitive Dd2 also showed reduced susceptibility to halofantrine ( Figure 3A ) , however , pointing to a role of overexpression in the observed resistance . Because PF10_0355 is annotated as a putative erythrocyte membrane protein and belongs to the merozoite surface protein 3/6 family , we tested the hypothesis that the observed effect was the by-product of a growth or invasion-related process , rather than resistance due to a direct interaction with the antimalarial itself . To that end , we expanded our drug testing in the transfectant lines to include other antimalarials , some structurally related and some unrelated to halofantrine . Overexpression of PF10_0355 from either the Dd2 or the SenP08 . 04 parent caused increased resistance to the structurally related antimalarials mefloquine and lumefantrine ( Figure 3B and 3C ) , but had no effect on parasite susceptibility to the structurally unrelated antimalarials chloroquine , artemisinin or atovaquone ( Figure 3D and 3E ) . Indeed , we found evidence of cross-resistance between halofantrine and both mefloquine and lumefantrine ( Figure 4 ) . We also observed cross-resistance between halofantrine and artemisinin , which is expected as cross-resistance between aminoquinolines and artemisinin compounds has been previously demonstrated [11] , [18] and resistance to all these drugs has been shown to be mediated by changes in pfmdr1 copy number [19] , [20] . Overexpression of PF10_0355 , however , alters parasite susceptibility to the aminoquinolines but not to artemisinin , suggesting that this effect is specific for that set of structurally related compounds and distinct from the effect of pfmdr1 , which seems to exert a global effect of resistance to unrelated compounds ( i . e . both aminoquinolines and artemisinins ) . Using the Dd2 parasite line , which has amplified pfmdr1 copy number , as a background for PF10_0355 overexpression allowed us to distinguish between cross-resistance to a structurally related class of compounds ( mediated by PF10_0355 overexpression ) and pan-resistance to multiple classes of drugs . Given that overexpression of the PF10_0355 gene both from a halofantrine-resistant and from a sensitive parasite conferred resistance to halofantrine-related drugs , we investigated whether gene amplification might be driving the observed resistance , as it often does for antimalarial drugs [21]–[26] . We quantified PF10_0355 copy number in our transfectants and found that the transfectant with the highest IC50 for all three drugs ( Dd2+P08B ) also had the highest PF10_0355 copy number , as measured by quantitative PCR ( qPCR ) ( Figure 5A ) . Furthermore , when we examined the PF10_0355 gene on our SNP array , we detected a substantial increase in hybridization intensity at the PF10_0355 locus compared to the genome average , suggesting that this gene is amplified in some parasites ( Figure 5B ) . The amplified region appears only to contain the PF10_0355 gene itself and not surrounding loci . We observed a similar pattern at pfmdr1 on chromosome 5 , where copy number variation is well established ( Figure S11 ) . Follow-up qPCR analysis of 38 parasite lines confirmed that parasites with amplified PF10_0355 have a greater mean halofantrine IC50 . ( Figure 5C , Table S6 , Dataset S1 ) . Copy number variation was further confirmed in a number of parasites by quantitative Southern blotting ( Figure S12 ) .
In this study we used natural selection and genome-wide association methods to probe the genetic basis of adaptation in P . falciparum . These approaches are complementary: scanning for selected loci permits an unbiased search for unknown adaptive changes , but provides little information about the processes at work , while GWAS gives a focused look at one easily identified ( and clinically critical ) adaptive phenotype . Results from both approaches open up new avenues for study , as we seek to understand the biological significance of the findings . The specifics of our strategy were designed to cope with two potential limitations in applying genome-wide population genetic approaches to malaria: small sample sizes , due to the difficulty in adapting parasites to culture and assessing drug and other phenotypes; and a lack of correlation ( LD ) between nearby variants in the parasite genome , which limits our ability to infer untyped SNPs from genotyped markers . The second limitation we addressed by developing a high-density genotyping array ( based on new sequencing ) , to increase the fraction of genetic variation that we could directly interrogate , while the effect of the first was mitigated by the phenotype we targeted in our GWAS . Drug resistance is a phenotype well-suited for GWAS because it is expected to be caused by common alleles of large effect at few genomic loci [27] . If this is the case , associations will be much easier to detect than in a typical human GWAS , in which the phenotype is caused by alleles at many loci that are either rare or of small effect . Additionally , the haploid nature of the intra-erythrocytic stage of P . falciparum further heightens GWAS power by eliminating the issue of allelic dominance . Finally , the increased LD caused by recent selection for drug resistance counteracts the loss of power that comes from short LD , small sample size , and the temporal and geographic stratification of the parasite population that we examined . Thus , despite the potential limitations , we were able to detect a known drug resistance locus ( pfcrt ) , observed little P-value inflation in our GWAS data ( Figures S8 , S9 , S10 ) , and identified a number of genome-wide significant loci associated with drug resistance . Part of this success was likely due to specific tests we used to account for population structure . Going beyond these statistical tests , we went on to functionally validate one of these loci , demonstrating that increased PF10_0355 copy number confer resistance to three structurally related antimalarial drugs . This demonstrates the feasibility of coupling GWAS and functional testing in the malaria parasite for identifying and validating novel drug resistance loci and illustrates the power of GWAS to find functionally important alleles . Comparing our results to the recent GWAS described by Mu , et al . [11] , which was also directed at finding drug-resistance loci , we see that , beyond the well-known pfcrt locus , there was no overlap between the associations identified by each study . Differing sets of drugs tested and analytical methods explain much of the disagreement . Of the eleven candidate associations in Table 1 , one ( that with pfcrt ) was found by both studies , eight were associations with drugs not assayed in Mu , et al . ( atovaquone and halofantrine ) , and two were found only with a haplotype-based test , an approach not used by Mu , et al . Our candidate locus at PF10_0355 , in fact , would not have been detectable in the Mu study because it was identified only by the multi-marker HLR test , because it involved an association with halofantrine , and because the Mu , et al . genotyping array lacked markers within 4 kb of the gene ( plasmoDB . org ) . Different parasite populations and marker sets probably explain many of the dihydroartemisinin , mefloquine and quinine associations identified by Mu , et al . but not seen in our data set . The studies used different parasite population sets—theirs was weighted toward southeast Asian strains , and ours toward African strains—and selection pressures and selected alleles can both vary between populations . Our smaller sample size also means that we might lack power to identify some associations accessible to Mu , et al . These difficulties are reflected in human GWAS studies as well , where the ability to replicate associations using multiple tests and in different sample sets has also been challenging to achieve [28] . Ultimately , the disparities in loci identified point to the role of population analysis as a tool for candidate gene discovery and not as a definitive study . Even within each study , there is little overlap between the signals observed with different methods—our study detects only one gene ( pfcrt ) by both GWAS tests ( EMMA and HLR ) , while Mu , et al . detected only two genes ( unknowns , not pfcrt ) by both of their GWAS tests ( Eigensoft and PLINK ) . Even a well-designed GWAS serves only as a hypothesis-generating experiment , and it is vital to empirically validate candidate loci associated with a phenotype of interest . Especially given the small sample sizes and relatively sparse marker density used in both malaria GWAS studies to date , functional validation of candidates is necessary to address concerns about false positive results . Our functional result , that increased PF10_0355 copy number confers decreased susceptibility to halofantrine , mefloquine and lumefantrine , raises additional questions for study . Further work will be needed to determine the precise contributions of copy number variation and gene mutation to the parasite's response to these drugs . The biological function of this gene's product is unknown , but previous work indicates putative localization to the parasite surface [29] , as well as it being a potential target of host immunity and balancing selection [30] . While the protein itself does not appear to be a transporter , it is possible that it directly binds drug or perhaps couples with transport proteins to modulate drug susceptibility; interaction between membrane transporters and non-channel proteins has been demonstrated in cancer , plant and yeast systems [31]–[33] . Additional experiments are certainly required to determine the precise role of PF10_0355 in modulating parasite response to this class of compounds , including assessing its relevance to resistance in natural populations , but it is clear that alteration of this locus can mediate drug resistance in P . falciparum . Although halofantrine , mefloquine and lumefantrine are not commonly used as primary interventions , widespread halofantrine use has recently been documented in West Africa . Notably , halofantrine was used to treat nearly 18 million patients between 1988 and 2005 [34] , [35] , and it remains in production and use today . Use of halofantrine , mefloquine or lumefantrine as monotherapy may further explain how mutations and copy number variation in the PF10_0355 gene were selected . Lumefantrine is also currently used as a partner drug in the artemisinin-based combination therapy ( ACT ) Coartem . The shorter half-life of artemether allows lumefantrine to be present as monotherapy , making it vulnerable to selection of drug resistant mutants . As genetic loci associated with drug responses are identified and validated , these provide new molecular biomarkers to evaluate drug use and response in malaria endemic settings . Thus , our findings have implications for defining molecular biomarkers for monitoring partner drug responses as intervention strategies , such as ACTs , are applied . Beyond identifying a novel drug resistance locus , this study illustrates the general utility of a GWAS approach for the discovery of gene function in P . falciparum . Even with a small and geographically heterogeneous sample of parasites , we identified a number of new loci associated with drug response and validated one of them . Larger samples from a single population will have much greater power to detect additional loci , including those where multiple and low frequency alleles contribute to resistance . Future GWAS have the potential both to provide greater insights into basic parasite biology and to identify biomarkers for drug resistance and other clinically relevant phenotypes like acquired protection , pathogenesis , and placental malaria . Future GWAS will be able to counteract the loss of power caused by low LD , either by focusing on parasite populations with reduced outcrossing rates , or by studying cases of very strong selective pressure . This issue will soon become moot , however , as the declining cost of whole-genome sequencing makes it practical to assay every nucleotide in the genome on a routine basis . Culture-adapted parasites are amenable to robust and reproducible phenotypic characterization , but their limitations—the potential for artifactual mutations during adaptation and for a biased selection of clones within a given infection—mean that genetic changes identified using them require both functional validation and demonstration that the changes are important during natural infection . As direct sequencing of clinical isolates with demonstrable clinical phenotypes such as ex vivo drug response or invasion properties becomes increasingly feasible , sequencing will enable us to directly identify genetic changes in the parasite associated with clinically relevant phenotypes . In the years ahead , genome analysis of P . falciparum has the potential to identify genetic loci associated with many phenotypes , enhance our understanding of the biology of this important human pathogen , and inform the development of diagnostic and surveillance tools for malaria eradication .
Parasite samples and origins are detailed in Text S1 and Table S1 . Parasites were maintained by standard methods [36] and were tested for their response to amodiaquine , artemether , artesunate , artemisinin , atovaquone , chloroquine , dihydroartemisinin , halofuginone , halofantrine , lumefantrine , mefloquine , piperaquine and quinine according to the methods outlined by Baniecki , et al . [37] ( Table S4 , Figure S13 , Text S1 ) . Follow-up drug testing was done by measuring uptake of 3H-hypoxanthine [38] . Nucleic acids were obtained from parasite cultures using Qiagen genomic-tips ( Qiagen , USA ) . All DNA samples were evaluated by molecular barcode [39] . We sequenced nine geographically diverse parasite isolates to 1 . 25x coverage , nearly doubling the number of publicly available SNPs to 111 , 536 ( Text S1 ) . These parasites had been previously sequenced to 0 . 25x coverage [2] and the deeper sequencing allowed for more thorough SNP discovery . Using this combined marker set , we created a high-density Affymetrix-based SNP array for P . falciparum containing 74 , 656 markers . Arrays were hybridized to 57 independent parasite samples ( Table S1 ) , including 17 previously sequenced strains used as a validation set . Genotype calls were produced using the BRLMM-P algorithm [40] . Markers that did not demonstrate perfect concordance between sequence and array data for the 17 strains were removed ( Text S1 ) . The remaining 17 , 582 SNPs constituted the high-confidence marker set used throughout this study ( median marker spacing 444 bp , mean spacing 1 , 316 bp ) . All genomic positions and translation consequences are listed with respect to the PlasmoDB 5 . 0 assembly and annotation . SNP genotype data are publicly available on plasmodb . org ( release 6 . 0 , July 2009 ) and dbSNP ( Build B134 , May 2011 ) , accessible by searching for submission batches Pf_0002 ( sequencing of nine isolates ) and Pf_0003 ( genotyping of 57 isolates ) from submitter BROAD-GENOMEBIO . Genotype data is also available as Dataset S2 . Principal components analysis ( PCA ) was performed using the program SmartPCA [41] . All single-infection samples were used for the analysis in Figure 1 . Samples that tightly clustered with the wrong continental population ( A4 , Malayan Camp and T2_C6 ) represented likely cases of contamination and were thus omitted from all other analyses . We measured diversity using a statistic we term ‘SNP π , ’ which quantifies the average number of pair-wise differences among samples from a given population at assayed SNPs . Population divergence was measured using FST , calculated using the method of Hudson , et al . [42] . Statistical evaluation of the significance of differences in SNP π and FST among populations was performed using a bootstrapping approach , where the SNP set was re-sampled with replacement and each statistic recomputed 1000 times . The statistic r2 was calculated within each population for all pairs of SNPs sharing the same chromosome [43]; pairs were binned by distance and averaged within each bin . The level of LD between unlinked markers was estimated by calculating r2 between all pairs of SNPs on different chromosomes . To determine the bias caused by small sample size , the unlinked calculation was repeated , with the change that for each pair of SNPs , the genotype for one was taken from one strain while the genotype for the second was taken from another strain . This background value of r2 was calculated separately for the possible pairs of different strains and then averaged . Only single infections , as assessed by molecular barcode , were used . Because of the small number of samples , LRH results for individual continental populations had a high level of variance . Thus , we pooled together samples from Africa ( n = 26 ) and Asia ( n = 18 , excluding India ) , as suggested by our PCA analysis . SNPs included in the analysis had a minor allele frequency of at least 0 . 05 and a call rate of at least 0 . 8; missing genotypes were imputed using PHASE . LRH analysis was performed using Sweep . Each SNP defined two core alleles , one base pair in length . We calculated relative extended haplotype homozygosity ( REHH ) for each core allele , to its left and right [44] , yielding up to four REHH scores per SNP locus . We standardized the REHH scores as a function of core allele frequency , defined on a discrete grid from 0 . 05 to 0 . 95 with even spaces of 0 . 025 . This yielded a normally-distributed set of Z-scores for which we calculated corresponding P-values and Q-values . We performed a GWAS for drug resistance to thirteen antimalarials across 50 of our genotyped samples . 7 , 437 SNPs that had a minor allele count of five samples as well as an 80% call rate under every phenotype condition were used for GWAS . A Bonferroni significance threshold of –log10 ( P-value ) >5 . 17 was used for all tests . See Text S1 for more details on GWAS methods . The Efficient Mixed-Model Association ( EMMA ) test [15] models quantitative trait associations to a data set with complex population structure and hidden relatedness . It calculates a genotype similarity matrix instead of discrete categories and does not require a priori specification of populations . The resulting P-value distributions demonstrate little remaining effect from population structure ( Figure S8 ) while retaining power to find a number of associations at genome-wide significance ( Figure S8 , Figure 2A , Table 1 ) . The Haplotype Likelihood Ratio ( HLR ) test [16] models the likelihood that a single , resistant haplotype rose to dominance while all other haplotypes proportionally decreased . PLINK [45] is used to produce sliding window haplotypes across the genome and calculate haplotype frequencies for input to the HLR test . We produced input for all 2- , 4- and 6-marker windows . The LOD scores generated by the HLR test were converted to empirical pointwise P-values by performing approximately 370 , 000 permutations of the null model for each test condition , allowing us to calculate empirical P-values up to a significance of 10−5 . 6 . We preserved population-specific phenotype frequencies by permuting only within each of three populations defined by our PCA analysis ( Table S1 ) . Resulting P-value distributions fit expectations well for the vast majority of test conditions ( Figures S9 , S10 ) and the test demonstrates power to detect a number of loci at genome-wide significance ( Figure 2A , Table 1 ) . Copy number was assessed by evaluating the hybridization intensity at the PF10_0355 locus on the high-density SNP array ( Text S1 ) . Follow-up analyses were done by quantitative real-time PCR ( qPCR ) of the PF10_0355 locus using the Delta Delta Ct method [46] . PF10_0355 was compared to the reference locus PF07_0076 and 3D7 was used as a reference strain . A summary of PF10_0355 copy number for all parasite strains tested is provided in Table S6 . Select resistant strains that were found to have multiple copies of PF10_0355 were further analyzed by quantitative Southern blotting and PF10_0355 copy number was compared to the dhps gene from the 3D7 strain [47] . The full length ORF of PF10_0355 was amplified from either the Dd2 ( HFN sensitive ) or SenP08 . 04 ( HFN resistant ) parasite isolate and cloned into the pBIC009 plasmid under the expression of the Hsp86 promoter . Plasmid DNA was isolated , tranfected into the Dd2 parasite strain and stable transfectants were selected with 2 . 5 nM WR99210 [48] . Parasites from two independent experiments for each vector type ( Dd2+Dd2 and Dd2+SenP08 . 04 ) were isolated and successful transfection was confirmed by plasmid rescue as well as episome-specific PCR and sequencing . Additionally , a vector control strain was made by transfecting Dd2 parasites with the pBIC009 plasmid containing the firefly luciferase gene ( EC 1 . 13 . 12 . 7 ) . | Malaria infection with the human pathogen Plasmodium falciparum results in almost a million deaths each year , mostly in African children . Efforts to eliminate malaria are underway , but the parasite is adept at eluding both the human immune response and antimalarial treatments . Thus , it is important to understand how the parasite becomes resistant to drugs and to develop strategies to overcome resistance mechanisms . Toward this end , we used population genetic strategies to identify genetic loci that contribute to parasite adaptation and to identify candidate genes involved in drug resistance . We examined over 17 , 000 genetic variants across the parasite genome in over 50 strains in which we also measured responses to many known antimalarial compounds . We found a number of genetic loci showing signs of recent natural selection and a number of loci potentially involved in modulating the parasite's response to drugs . We further demonstrated that one of the novel candidate genes ( PF10_0355 ) modulates resistance to the antimalarial compounds halofantrine , mefloquine , and lumefantrine . Overall , this study confirms that we can use genome-wide approaches to identify clinically relevant genes and demonstrates through functional testing that at least one of these candidate genes is indeed involved in antimalarial drug resistance . | [
"Abstract",
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"genetics",
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] | 2011 | Identification and Functional Validation of the Novel Antimalarial
Resistance Locus PF10_0355 in Plasmodium
falciparum |
The endoplasmic reticulum ( ER ) Ca2+ sensors stromal interaction molecule 1 ( STIM1 ) and STIM2 , which connect ER Ca2+ depletion with extracellular Ca2+ influx , are crucial for the maintenance of Ca2+ homeostasis in mammalian cells . Despite the recent progress in unraveling the role of STIM2 in Ca2+ signaling , the mechanistic underpinnings of its activation remain underexplored . We use an engineering approach to direct ER-resident STIMs to the plasma membrane ( PM ) while maintaining their correct membrane topology , as well as Förster resonance energy transfer ( FRET ) sensors that enabled in cellulo real-time monitoring of STIM activities . This allowed us to determine the calcium affinities of STIM1 and STIM2 both in cellulo and in situ , explaining the current discrepancies in the literature . We also identified the key structural determinants , especially the corresponding G residue in STIM1 , which define the distinct activation dynamics of STIM2 . The chimeric E470G mutation could switch STIM2 from a slow and weak Orai channel activator into a fast and potent one like STIM1 and vice versa . The systemic dissection of STIM2 activation by protein engineering sets the stage for the elucidation of the regulation and function of STIM2-mediated signaling in mammals .
Store-operated Ca2+ entry ( SOCE ) is a major Ca2+ influx pathway that is crucial for many types of cellular functions [1–4] . SOCE is mediated by stromal interaction molecule 1 ( STIM1 ) and STIM2 , dynamic Ca2+ transducers localized at junctions between the endoplasmic reticulum ( ER ) and plasma membrane ( PM ) [5] . The ER luminal domains of STIMs contain a Ca2+-binding EF-hand motif , a hidden non-Ca2+–binding EF hand ( EF ) , and the sterile alpha motif ( SAM ) domain ( EF-SAM ) [6 , 7] , together serving as ER Ca2+ sensors . The cytosolic regions of STIMs comprise the STIM-Orai–activating region ( SOAR ) [8] or Ca2+-release–activated Ca2+ ( CRAC ) -activating domain ( CAD ) [9] and function as activators of Orai Ca2+ channels situated in the PM [1 , 10] . STIM2 and its splice variants play important roles in fine-tuning SOCE by diversifying functional Orai–STIM combinations [5 , 11–13] in the immune and nervous systems ( reviewed in [14 , 15] ) . Abnormalities in STIM2-mediated SOCE have been linked to diminished salivary fluid secretion , impaired sweat secretion , Alzheimer’s disease , and Huntington's disease in mouse models [13 , 16–21] . Although considerable progress has been made in understanding STIM1-mediated SOCE [1 , 5 , 10 , 22] , the role of the STIM2 molecule has been largely ignored and is consequently less well known . Two crucial issues regarding STIM2 activation remain to be addressed immediately . First , the Ca2+ binding affinity of the EF-SAM of STIM2 should be determined in cellulo . The applicability of in vitro determinations and in situ estimations to addressing this issue is limited [6 , 23] . In vitro measurements are carried out in a non-membrane–like environment using purified recombinant proteins under nonphysiological conditions and hence may not reflect the true behavior of STIMs embedded in the ER membrane [24] . The in situ estimations are based on calculated values of resting Ca2+ levels within the ER lumen , a subcellular compartment that is difficult to access and with the measurements subject to large variations depending on which Ca2+ indicators are used [25–29] . As a result , previous in vitro determinations and in situ estimations showed discrepancies in the cooperativity of Ca2+ binding [6 , 23 , 30] . In vitro measurements showed that the Ca2+ binding behavior of STIM1 is temperature dependent [31] and that the Ca2+ affinities measured under room temperature showed no difference between STIM1 and STIM2 [24] , which is inconsistent with results from in situ measurements [23 , 26 , 30] . Thus , a better approach is needed to determine the Ca2+ affinities of STIMs , ideally enabling in cellulo measurements to reconcile the discrepancies in the reported values . Second , it is unclear how STIM2 , a slow Orai activator [32–34] , gets activated and mediates Ca2+ influx to compensate for small fluctuations in the Ca2+ levels within the ER lumen [5 , 23] . The formation of puncta and the development of ICRAC current or SOCE through STIM1-activated Orai1 channels have been used as indicators of the activation status of STIM1 [5] . However , these approaches are not appropriate for the dissection of STIM2 activation . As a weak Orai1 activator [35–37] , STIM2 induces only small Ca2+ influx through Orai1 , and the ICRAC or SOCE measurements are not sufficiently sensitive to describe its activation status . Further , STIM2 is always partially active , forming constitutive puncta and constantly inducing Ca2+ flux through Orai1 channels at rest [23 , 32 , 33] . This makes it difficult to use puncta or current measurements to gauge its degree of activation following store depletion . A new tool is thus clearly required to uncover the mechanistic details of STIM2 function . To tackle these challenges , we designed a set of molecular tools to report the activation status of STIM2 in situ in real time based on a two-component FRET biosensor recently developed by us [38] . We bypassed the accessibility issue of the ER luminal Ca2+-binding EF-SAM region by redirecting engineered STIM constructs to the PM so that the luminal domain faced toward the extracellular space . This allowed us , for the first time , to determine the apparent Ca2+ affinities of STIM constructs in cellulo by simply changing the Ca2+ levels in the extracellular medium . We also engineered a series of ER-resident STIM1/STIM2 chimeric constructs that stayed quiescent at rest , which enabled an accurate dissection of the contribution of the individual key structural elements of STIM2 to the protein’s activation kinetics and dynamics . With these FRET-based probes , we identified E470 in SOAR2 as a critical residue that rendered SOAR/CAD more activated at rest and accounted for a narrower dynamic range for STIM2 activation compared with STIM1 . These novel findings well explained a long-standing puzzle in the field: how STIM2 is able to efficiently respond to minor changes in ER luminal Ca2+ levels . Overall , our systematic analysis provided new insights into STIM2 activation dynamics and kinetics .
To circumvent the difficulties of accessing the ER lumen and determining the ER Ca2+ levels , we first engineered STIM1 and STIM2 proteins to relocate them to the PM , with their luminal region facing the extracellular space . We replaced the original signal peptide ( SP ) of STIM with the SP derived from CD8A1-21 and introduced a PM-trafficking target peptide ( TP ) ( Kir2 . 1233−252 ) and an ER-exporting TP ( Kir2 . 1374−380 ) at the C terminus of STIM ( Figs 1A–1C and S1 and S2A ) . Through several rounds of optimization ( S2B Fig ) , the resulting PM-Myc-STIM21-CC1 or PM-STIM1-CC1 construct tagged with yellow fluorescent protein ( YFP ) ( see S1E and S1F Fig for nomenclature details of the engineered constructs ) showed PM-like distribution in HeLa cells ( Fig 1D , middle ) . The coiled-coil 1 ( CC1 ) of PM-SC2222-YFP faced the cytosol because the YFP tag could only be recognized by an engineered nanobody that binds to green fluorescent protein ( GFP ) or YFP ( i . e . , mCherry [mCh]-tagged LAG9 ) in live cells ( Fig 1D , bottom row ) . By contrast , the N terminus of the engineered STIM faced toward the extracellular space because the N-terminal Myc tag was detected by immunostaining of live cells without PM permeabilization ( Fig 1D , top row; S2A and S2B Fig ) . Moreover , the cytosolic mCh-CAD migrated toward PM in HeLa cells co-transfected with PM-SC2222-YFP in the presence of 2 mM Ca2+ ( S2C Fig ) , while lowering the extracellular Ca2+ concentration caused cytosolic dispersion of mCh-CAD . This strongly implied that PM-SC2222 retained its native structure even after translocation to the PM . Hence , this approach constitutes a convenient engineering strategy of forcing the trafficking of transmembrane proteins originally embedded in the ER membrane to the PM . After relocation to the PM , the proteins retained their proper membrane topology and exposed the otherwise inaccessible luminal domains to the extracellular space . Effectively , this overcame the major impediment to the biophysical and electrophysiological studies of ER-resident ion channels and transducers . Specifically , the engineered ER-to-PM trafficking constructs allowed , for the first time , a precise and facile manipulation of the extracellular Ca2+ levels in the vicinity of the extracellular EF-SAM . We then coexpressed mCh-CAD and PM-SC1111-CFP in HeLa cells and examined whether the association of CAD and CC1 of PM-SC1111 depended on the extracellular Ca2+ levels ( Fig 1E ) . With the extracellular Ca2+ levels in a millimolar range , the cytosolic mCh-CAD displayed a PM-like decoration ( Fig 1E , leftmost image ) , indicating its specific interaction with PM-SC1111 ( Fig 1C , left ) . The latter adopts a resting inactive conformation in its Ca2+-bound form [5] . After switching to a nominally Ca2+-free extracellular solution , PM-docked CAD molecules rapidly dissipated into the cytosol ( Fig 1E , second image from the right ) , indicating dissociation from PM-SC1111 . These observations clearly established that the EF-SAM of PM-SC1111 sensed the fluctuation of extracellular Ca2+ levels , similarly to sensing Ca2+ depletion within the ER lumen ( “store depletion” ) , and could faithfully phenocopy the Ca2+-dependent switch between active and inactive conformations ( Fig 1C ) . This process was fully reversible because the cytosolic CAD immediately redecorated the PM when extracellular Ca2+ was replenished to the mM range ( Fig 1E , right panel ) . Following this , we used a FRET assay to precisely determine the Ca2+-binding affinities of STIM in cellulo . In the assay , YFP-SOAR was the acceptor and PM-SC1111-CFP was the donor , which allowed the characterization of the CC1–SOAR interactions in response to alterations in extracellular Ca2+ levels . We used a slightly modified SOAR1 variant , SOAR1L ( STIM1343-491 ) , instead of SOAR1 ( STIM1344-442 ) ( S1G Fig ) because of its superior performance in FRET experiments with PM-anchoring SCs . To avoid artifacts induced by endogenous STIM1 or STIM2 molecules and the filling status of the ER Ca2+ stores , the FRET experiments were performed in STIM1 and STIM2 double knockout ( SK ) HeLa cells ( Fig 1F ) . The obtained apparent Kd value of STIM2 was lower than that of STIM1 . Interestingly , both values were in the mM range ( STIM1: 0 . 97 ± 0 . 02 mM and STIM2: 1 . 48 ± 0 . 02 mM; Fig 1F ) , much higher than previous reports ( 1 . 0 versus 0 . 2 mM for STIM1; 1 . 5 versus 0 . 4 mM for STIM2 ) [23 , 24] . Furthermore , these results would predict both STIM1 and STIM2 being constitutively active at rest . Such a prediction contradicts current knowledge about STIM [5 , 33] and is not consistent with our own observations showing that only STIM constructs with STIM2-EF-SAM were constitutively active ( S3A Fig ) . Since other engineered PM-anchoring SCs ( S3B Fig ) showed a similar trend , we thus checked whether these “abnormal” values were artifacts caused by protein engineering . A minor portion of cells ( approximately 20%–30% ) expressing unengineered SCs with the STIM2-CC1 domain showed some PM-like distribution , and results from these cells showed that even unengineered SCs with PM-like distribution also bear similarly high Kd values ( S1C and S1D and S3C Figs ) . We also performed in cellulo measurements with bath solutions that contained high K+ ( 140 mM ) and low Na+ ( 10 mM ) to mimic the ER-like ionic and electric environment and obtained values no different from those done with regular extracellular solution . These results indicate that the high Kd values we obtained were not artifacts caused by low K+ concentration or negative membrane potentials induced by regular extracellular solutions . Thus , our in cellulo results showed that STIM constructs with their EF-SAM facing extracellular space do have much lower affinities for Ca2+ than previous in vitro and in situ measurements [23 , 33] . This prompted us to perform in situ calibrations using the ER-localized SC constructs and R-CEPIA1er ( an ER Ca2+ indicator that is more sensitive than D1ER [25] ) [26] , as previously described ( Figs 1G and S3D and S3E ) [23 , 26] . Using the STIM1CC1-SOAR1L FRET as a readout for STIM activation [38] , the in situ approach done in HeLa SK cells showed that SC constructs with STIM1-EF-SAM or STIM2-EF-SAM both bind Ca2+ with a high cooperativity ( Hilln: 3 . 5 ± 0 . 1 for SC1111 , 6 . 5 ± 0 . 1 for SC1211 ) , similar to previous in situ results ( Hilln around 4 ) [23 , 25 , 30] . The results also showed that the Ca2+ dissociation constants ( Kd ) of STIM1EF-SAM and STIM2EF-SAM were 0 . 31 ± 0 . 04 mM and 0 . 42 ± 0 . 06 mM , respectively . Using STIM1 puncta as readouts , we also found a similar Ca2+ affinity for full-length STIM1 ( 0 . 33 ± 0 . 2 mM ) . All these in situ values were similar to the previously reported ones . Thus , our in situ results again validated the robustness of the STIMCC1 and SOAR1L FRET signals as readouts for STIM activation . Overall , obtained with the same FRET-based readout ( Fig 1B ) , results from our in cellulo and in situ measurements both agree that the Ca2+ affinity of STIM2 is lower than that of STIM1 ( Fig 1F and 1G ) . Our in cellulo data and in situ results have important implications . The discrepancy between our in cellulo results and previous in vitro data indicates that the EF-SAM , associated with the membrane under physiological conditions , may behave differently than isolated recombinant proteins in an aqueous solution in vitro [24] . Indeed , STIM EF-SAM fragments bind Ca2+ with a stoichiometry of 1 in vitro [13 , 19] , indicating that isolated EF-SAM has no cooperativity in Ca2+ binding . While the in cellulo data showed Hill numbers around 2 ( Hilln: 2 . 7 ± 0 . 3 for STIM1; 2 . 3 ± 0 . 03 for STIM2; n = 4 ) , indicating that membrane-associated STIM-EF-SAM has some cooperativity . The in cellulo Hill numbers were consistent with the notion that STIMs function as dimers ( reviewed in [35] ) . When measured in situ , the Hill numbers significantly increased to around 4 ( Fig 1F versus Fig 1G ) ( P < 0 . 001 , n = 4 , Student t test ) [23 , 25 , 30] , indicating that STIM proteins on ER membrane have a much higher cooperativity in Ca2+ binding . Since the in cellulo data and in situ results were obtained with the same FRET readout in the same type of cells , the observed differences in Ca2+ bindings thus clearly indicated the existence of possible additional modulators or post-translational modifications of STIM within the ER lumen , providing an explanation for current discrepancies between in situ and in vitro results in the literature . Recently , it was shown that STIM1 glycosylation at residues N131 and N171 substantially reduced its Ca2+ affinity [39] . However , no other protein regulators affecting the Ca2+ sensitivity of STIMs within the ER lumen have been reported to date . Further follow-up studies are needed to identify factors that alter the Ca2+-binding behavior of STIM proteins . Collectively , the differences between our in cellulo and in situ results indicate the existence of possible modulators for STIM within the ER lumen , explaining why previous in situ results differ from in vitro ones . In the meantime , our in cellulo and in situ results both reveal differences in the Ca2+ binding affinities of STIM1 and STIM2 . After determining the Ca2+ affinity of the luminal STIM2-EF-SAM that defined its partial activation status at rest , we then addressed the kinetics of its activation . We first examined the applicability of FRET-based nanoprobes [38] for monitoring the STIM activation kinetics in situ . When the N terminus of SC1111 was replaced with that of STIM2 ( SC2111 ) , the rate of ionomycin-induced decreases in apparent FRET efficiency ( Eapp ) signals between SC1111 and SOAR1 was significantly reduced ( SC1111: –0 . 0020 ± 0 . 0001 ΔEapp/s versus SC2111: –0 . 0009 ± 0 . 0001 ΔEapp/s , n = 3 , t test , P < 0 . 0001 ) . This was consistent with previous findings from whole-cell patch clamping [32] and confirmed that the FRET-based nanoprobe could indeed be used to determine the STIM activation kinetics . We subsequently examined the effect of swapping the STIM2 transmembrane region ( STIM2-TM ) for SC1111 on its activation kinetics ( Fig 2A and 2B ) . Replacing STIM1-TM with STIM2-TM domain ( SC1121 ) did not affect the basal Eapp between SC constructs and SOAR1 , while the ionomycin-induced rate of changes in Eapp ( ΔEapp ) ( –0 . 0033 ± 0 . 0011 versus –0 . 0022 ± 0 . 0009 ΔEapp/s ) and extent of Eapp decrease was significantly reduced ( –90 . 47% ± 6 . 66% versus –66 . 13% ± 11 . 93% ) ( Fig 2B ) . This indicated that following Ca2+ store depletion , STIM2-TM transduced the depletion signal across the ER membrane to the cytosolic portion of STIMs more slowly and less efficiently , resulting in slower and reduced release of SOAR molecules from the STIM CC1 . We then confirmed the findings in the context of full-length STIMs with or without the PM anchoring lysine-rich ( K ) domain [8 , 9 , 40] . PM tethering by the K region was recently shown to accelerate the activation of STIM1 molecules [41] . To avoid possible complications caused by PM tethering , we thus first examined the effects of TM domain swapping with STIM-ΔK constructs . The coupling of STIM1 harboring STIM2-TM ( STIM1121-ΔK ) with Orai1 was reduced ( Fig 2C ) and , functionally , its ability to induce SOCE was significantly impaired ( Fig 2D ) . We then directly measured the activation kinetics with whole-cell current recordings . ICRAC mediated by STIM1121-ΔK indeed developed significantly more slowly than that mediated by STIM1-ΔK ( Fig 2E , traces in the left panel ) . Consistent with FRET and SOCE measurements ( Fig 2B and 2D ) , the magnitude of the peak current density was also significantly reduced in STIM1121-ΔK–expressing cells ( Fig 2E , traces in the right panel ) . Thus , exchanging STIM2-TM for STIM1-ΔK rendered the activation of STIM1-ΔK slow and inefficient . Conversely , STIM2 harboring STIM1-TM ( STIM2212 ) activated Orai1 and induced Ca2+ influx more efficiently both at rest and after store depletion than STIM2 ( Fig 2F ) . Among the three TM residues that are different between STIM ( STIM1: M215 , V217 , I231 versus STIM2: I306 , T308 , T324 ) , we do not know which of them are more important for defining their activation kinetics . Since the two hydrophobic residues ( STIM1 V217 and I231 ) that are involved in the packing of STIM1-TM [42] are changed to two polar threonines in STIM2 , we speculate that these two polar residues might compromise the packing of STIM2-TM and subsequent activation events . Further research is needed to clarify this . Nevertheless , these observations suggested that STIM-TM plays a role in defining the kinetics of STIM activation and that STIM2-TM is a weak transducer of the ER Ca2+ signals . Collectively , we conclude that the luminal domains or EF-SAMs of STIM define its Ca2+ sensitivity , which determines whether there will be constitutive Ca2+ influx at rest ( S4A and S4B Fig versus S4C and S4D and S3A Figs ) , and that the entire luminal and TM domain of STIM controls the kinetics of its activation upon store depletion . Once the conformational changes of STIM1 trigged by Ca2+ store depletion propagate across the ER membrane , the cytosolic region of STIM1 adopts a more activated configuration by overcoming intramolecular clamping mediated by the CC1–SOAR interaction [5 , 38 , 43–45] , moving the SOAR region of STIM1 from the vicinity of the ER membrane [38 , 46 , 47] across the ER–PM junctions to activate Orai1 channels on the PM to evoke Ca2+ influx [5] . However , it remains unclear whether the cytosolic region of STIM2 undergoes similar dynamic changes after store depletion . We addressed this question by generating a series of chimeric STIM nanoprobes to map the critical protein regions involved in the intramolecular conformational switch ( S1 Fig ) . We first asked whether the cytosolic region of STIM2 was autoinhibited by an intramolecular clamp ( Fig 3A ) . Consistent with the existence of constitutive puncta formed by chimeric full-length STIM1122 ( S4A Fig versus S4B Fig ) , the basal FRET signals between sensors containing STIM2-CC1 and SOAR2 were considerably lower than those of STIM1 ( top traces , S4E Fig versus S4F Fig [n = 3 , ****P < 0 . 0001 , t test]; S4G Fig versus S4H Fig [n = 3 , ****P < 0 . 0001 , t test] ) , indicating that the STIM2-CC1 failed to retain SOAR2 near the ER membrane as STIM1 did . This suggested that CC1 of STIM2 partially lost the capability to interact with SOAR2 . We validated this observation by generating an STIM1 chimera in which the cytosolic region was swapped for that of STIM2 ( STIM1122 ) . At rest , even though cells expressing STIM1122 had no constitutive Ca2+ influx , the STIM1122 construct showed discernible punctate distribution in the absence of store depletion ( S4B Fig ) , indicating that the cytosolic region of STIM2 adopts a distinct partial activated configuration . This is consistent with a recent report showing that the cytosolic fragments of STIM2 are less well folded [48] . It is well established that the location of the SOAR1 region in STIM1 is critically defined by the folding status of the STIM1 cytosolic region . At rest , the cytosolic region of STIM1 is well folded , keeping SOAR1 close to the ER . Once activated , the cytosolic region becomes extended , bringing SOAR1 close to the PM [49] . We reasoned if the location of SOAR2 in STIM2 were determined by the same principles as those for SOAR1 in STIM1 , then the less-well–folded cytosolic region of STIM2 would indicate that its SOAR2 domain was located farther away from ER and closer to the PM . We then proceeded to identify the structural elements responsible for the weak CC1–SOAR interaction within STIM2 . To do that , we analyzed the effect of subdomain swapping on the resting FRET signals of chimeric nanosensors ( Fig 3A–3C ) . The result indicated that the CC1 domain of STIM2 restricted and slowed down the release of SOAR1 from CC1 after store depletion ( –0 . 0032 ± 0 . 0005 versus –0 . 0012 ± 0 . 0006 ΔEapp/s ) ( Fig 3B ) . Further experiments on more chimeric constructs failed to detect this trend , thus indicating the CC1 domain has a minor role in determining the kinetics of STIM activation . Similarly , swapping of the CC1 domain had minimal effect on the resting FRET signals between SC constructs and SOAR molecules ( Fig 3C ) . These observations ruled out the possibility that STIM-CC1 is a major determinant of STIM activation . In contrast , swapping SOAR1 for SOAR2 significantly and consistently reduced the resting FRET signals ( Fig 3C ) , thereby unequivocally establishing that the SOAR region shaped the initial configuration of the cytosolic region of STIM2 molecules at rest . The lower basal FRET signals between STIM-CC1 and SOAR2 indicate that the SOAR2 region is farther away from CC1 in full-length STIM2 ( Fig 3C ) . And since the STIM-CC1 domain directly anchors on the ER membrane via the STIM-TM domain , these results thus indicate that the SOAR2 region is farther away from the ER at rest . As domains localized between the ER and PM membranes , SOAR2 being farther away from the ER membrane would indicate that it is closer to the PM . Hence , unlike STIM1 , the SOAR2 domain in STIM2 docks near the ER membrane less well , enabling for STIM2 a distinct partial activated configuration at rest . We mapped the domains within SOAR2 responsible for its weak interaction with CC1 by examining the effect of swapping the SOAR subdomains on the resting FRET signals of the chimeric nanosensors [37 , 38] . The results showed that swapping the α4 helix of SOAR had no effect on basal FRET signals between SC constructs and SOAR molecules ( Fig 3D ) . On the contrary , swapping the α1 helix from SOAR2 into SOAR1 resulted in greatly diminished basal FRET with SC1111 , similar to that of SOAR2 ( Fig 3E ) and much lower than that of SOAR1 ( Fig 3D ) . Further results also confirmed that only the swapping of α1 helix would change basal FRET ( Fig 3F ) . Together , these results revealed that the α1 helix of SOAR is the region determining the basal FRET between chimeric SOAR and SC constructs ( Fig 3D–3F ) . Following this , we used site-directed mutagenesis to identify crucial residues within the SOAR α1 helix . The α1 helices of SOAR1 and SOAR2 differ by nine residues ( Fig 4A , left ) . Mutagenesis studies revealed four residues that were crucial for CC1–SOAR interactions ( Fig 4B ) . When these four residues in SOAR1 ( K371 , G379 , N388 , L390; KGNL ) were substituted with those of STIM2 ( M462 , E470 , S479 , V481; MESV ) , the ability of the resulting SOAR1-K371M-G379E-N388S-L390V ( SOAR1-MESV ) variant to colocalize with a coexpressed SC1112 was greatly diminished , resulting in an even cytosolic distribution ( Fig 4B , top left image versus bottom left image ) , with the construct behaving like SOAR2 ( Fig 4B , top right image ) . Conversely , the introduction of the corresponding SOAR1 residues into SOAR2 ( SOAR2- M462K-E470G-S479N-V481L , SOAR2-KGNL ) rendered the SOAR2 variant SOAR1-like , with appreciable docking to the ER membrane and pronounced colocalization with the coexpressed SC1112 ( Fig 4B , bottom right image versus bottom left image ) . This trend was further quantitatively confirmed by FRET assays: the basal FRET signals between SC1112 and SOAR constructs bearing MESV residues ( Fig 4C , top two traces ) were lower than those containing KGNL residues ( Fig 4C , bottom two traces ) . Together , these experiments identified the four critical residues within SOAR that largely determine the relative strength of CC1–SOAR interaction locking the SOAR molecules in the vicinity of the ER membrane . To pinpoint the residue that was most critical for the differential CC1–SOAR interactions of STIM1 and STIM2 , we generated chimeric SOAR variants with single STIM1/STIM-swapping substitutions . When E470G was introduced into SOAR2 , its FRET signal with SC1112 was substantially enhanced to a level that was comparable with the SC1112–SOAR1 interaction ( Fig 4D ) . Conversely , introduction of G379E ( a residue equivalent to STIM2-E470 within STIM1 ) into SOAR1 resulted in a significant reduction of the FRET signal ( S5A Fig , right panel ) . To investigate the possible causes for this disruptive effect , we monitored the effect of SOAR2-E470 or SOAR1-G379 substitutions with different types of amino acid residues . As demonstrated , residues with side chains larger than that of alanine tended to result in a reduced basal FRET signal ( S5A Fig ) . Collectively , these experiments indicated that the spatial constraints imposed by SOAR2-E470 might weaken the CC1–SOAR interaction . Next , we addressed the question of why a single amino acid substitution had such a dramatic effect on STIM activation . We calculated the principal axis of each α1 helix within monomeric SOAR subunit and obtained the angle between the two α1 helixes ( Fig 4E ) . This revealed that the angle of the SOAR2-E470G dimer was wider than that of SOAR2 ( 43 . 9° ± 0 . 1 ° versus 42 . 8° ± 0 . 1° , P < 0 . 0001 , t test , n = 3 ) , indicating that such a wider angle may facilitate the interactions between SOAR2 and the STIM2-CC1 domain . Consistent with this , the angle of the SOAR1-G379E dimer , which only weakly interacted with STIM1-CC1 , was narrower than that of wild-type ( WT ) SOAR1 ( 36 . 6° ± 0 . 1° versus 38 . 9° ± 0 . 1° , P < 0 . 0001 , t test , n = 3 ) . Although the potential conformational change induced by these chimeric mutations was moderate , the shift in the size of angles after mutagenesis clearly demonstrated that the proper alignment of SOAR monomers is critical for the function of the SOAR dimer . We then examined the effect of the weak CC1–SOAR interaction associated with SOAR2-E470 or SOAR1-G379E on the full-length STIM at rest . Since the low Ca2+ affinity of STIM2-EF-SAM rendered it partially active at rest and masked the actual basal cytosolic configuration of STIM2 , we investigated the effect of the chimeric G379E substitution on the resting configuration of STIM1 cytosolic region . We reasoned that if the cytosolic region of STIM1-G379E was more extended than the WT , its C terminus would be closer to the PM , resulting in a higher basal FRET with PM-localized Orai1 . The data agreed with this prediction ( Fig 4F , bar graph on the left ) . Even after deletion of the PM-tethering K-rich region , the resulting STIM1-G379E-ΔK still has higher basal FRET signal with Orai1 ( Fig 4F , bar graph on the right ) , again indicating a more extended configuration of the cytosolic region of STIM1-G379E . Moreover , STIM1-G379E , but not STIM1-G379E-ΔK , could form constitutive puncta under resting condition . This result thus revealed that the SOAR-bearing cytosolic region of STIM1-G379E variant was open enough to expose the membrane-anchored K-rich region to form constitutive puncta ( Fig 4F , top images ) [43 , 50 , 51] . We then checked whether the SOAR region in STIM1-G379E is close enough to the PM to engage and activate Orai1 channels . Confocal imaging results showed that neither STIM1-G379E nor STIM1-G379E-ΔK would colocalize with Orai1 channels at rest ( Fig 3F , top two images ) . Thus , the cytosolic region of STIM1-G379E mutants were not extended enough to interact with Orai1 channels on PM . Consistent with this notion , STIM1-G379E WT or ΔK mutants did not induce constitutive Ca2+ entry ( 6 . 6 ± 1 . 8 nM and 3 . 5 ± 0 . 6 nM , respectively; P > 0 . 14 as compared with blank controls , n = 3 ) . Overall , these findings clearly indicated that the chimeric STIM1-G379E adopted a more activated cytosolic configuration than WT STIM1 , presenting the SOAR1 region closer to the PM at rest than WT STIM1 ( Fig 4F ) . Taken together , the data indicated that critical residues within SOAR2 define the start point of this transition , with SOAR2 located further away from the ER membrane and closer to the PM . Upon activation , the cytosolic region of STIM overcomes the CC1-SOAR–mediated autoinhibition to expose SOAR to the PM , engage , and activate Orai1 channels [38 , 44 , 45] . We previously identified a residue within the SOAR α2 region , STIM1-F394 or STIM2-L485 , that defines the distinct Orai1-activating ability of STIM1 and STIM2 [37] . Even though the SOAR1-F394H mutant lost its ability to bind with Orai1 [37 , 52] , the chimeric mutations , SOAR1-F394L or SOAR2-L485F , still retain the same Orai1-binding abilities as the corresponding WT SOAR molecules , indicating a minimal role of SOAR1-F394 or SOAR2-L485 for Orai1 binding . What defines the distinct Orai1-binding behavior of SOAR1 and SOAR2 still remains elusive . We asked whether the identified four amino acid residues also impacted the SOAR–Orai1 interaction , i . e . , the late step of the intermolecular switch , since they are located in close proximity to five positively charged residues ( 382KIKKKR387 ) that are essential for the binding of SOAR to Orai1 [44 , 53] . Upon coexpression with Orai1 , the SOAR1-MESV variant exhibited a SOAR2-like cytosolic distribution ( Fig 5A , bottom left image ) , indicating a weak interaction with Orai1 . By comparison , the SOAR2-KGNL variant was SOAR1-like , with clear PM decoration , suggesting a strong interaction with Orai1 ( Fig 5A , top right image ) . This tendency was quantitively confirmed by FRET imaging ( Fig 5B ) . Functionally , MESV substitutions in SOAR1 almost abolished its ability to induce constitutive Ca2+ influx , while the SOAR2-KGNL variant acquired an Orai1-activating ability that was similar to SOAR1 ( Fig 5C ) . Pharmacologically , all these constitutively Ca2+ influxes have corresponding typical 2-APB responses mediated by SOAR2-Orai1 or SOAR1-Orai1 [37] , indicating that these Ca2+ entries are mostly mediated by Orai1 channels . Together , these findings indicated that the four critical residues determined not only the SOAR’s distinct capability to interact with CC1 at rest but also the ability to engage and activate the Orai1 channel . We next examined whether SOAR2-E470 was crucial for the interaction with Orai1 . In cells stably expressing Orai1-CFP , the basal FRET signal between Orai1 and SOAR2-E470G was significantly stronger than that of SOAR2 and similar to that of SOAR1 ( Fig 5D ) . Accordingly , the chimeric E470G substitution resulted in an altered cellular distribution of SOAR2 , from dispersed cytosolic to mostly PM associated , echoing the behavior of SOAR1 ( Fig 5E ) . This indicated that the SOAR2-E470G protein was able to couple with Orai1 with a similar efficacy as SOAR1 . Indeed , the SOAR2-E470G variant induced constitutive Ca2+ entry on a scale close to that of SOAR1 ( Fig 5F ) . After the introduction of an additional substitution—L485F , previously identified to enhance the gating of Orai1 [37]—the resulting double-variant SOAR2-E470G-L485F generated an even larger constitutive Ca2+ influx , fully recapitulating the function of SOAR1 ( Fig 5F ) . The behavior of the corresponding SOAR1 chimeric variants was consistent with these observations ( S5B Fig , right panel ) . Taken together , our findings demonstrated that the E470 residue of SOAR2 defines the protein’s distinct efficacy of Orai1 binding , leading to weaker activation of Orai1 channels upon STIM2 activation . We then examined the effect of the chimeric substitutions in STIM-ΔK or full-length STIM on the coupling and subsequent activation of Orai1 . Similar to results obtained from SOAR fragments , G379E mutation significantly decreased the maximal FRET signals between store-activated STIM1-ΔK and Orai1 ( Fig 6A ) . Conversely , STIM2-E470G-ΔK and Orai1 produced larger FRET signals than STIM2 ( Fig 6B ) . Both results revealed that E470/G379 critically defines the coupling of Orai1 with STIM lacking its PM-anchoring K domain , or STIM-ΔK . Consequently , as anticipated based on the observations with SOAR variants ( Figs 5F and S5B ) , the amplitude of ICRAC and SOCE of the chimeric STIM1-G379E-ΔK variant were smaller than those of STIM1-ΔK in HEK293-Orai1 stable cells ( Fig 6C ) . In contrast , the chimeric STIM2-E470G-ΔK variant induced a substantially larger SOCE than WT STIM2-ΔK ( Fig 6D ) , reaching a level comparable to STIM1-ΔK . Chimeric substitution in full-length STIM expressed similar effects in HEK293-Orai1 stable cells: the STIM1-G379E variant behaved like STIM2 , inducing smaller SOCE than STIM1 ( Fig 6E ) ; and STIM2-E470G functioned similarly to STIM1 , mediating larger SOCE than STIM2 ( Fig 6F ) . Taken together , these results showed that E470 of STIM2 defines its weaker capability to couple and activate Orai1 . Overall , our results indicate that STIM2-E470 critically narrows down the dynamic rearrangement of the SOAR2 region during STIM2 activation by defining the lesser efficacy of SOAR2 to interact with CC1 at rest ( Fig 5D ) and to couple with Orai1 once activated ( Figs 6A and 6B and S5C; Fig 6 , diagrams on the right ) . Thus , the corresponding G379E substitution would present the SOAR1 region in STIM1 closer to the PM at rest than WT STIM1-ΔK ( Fig 4F ) , then STIM1-G379E-ΔK would undergo a relatively smaller rearrangement to engage and activate Orai1 after activation . Indeed , compared with WT STIM1-ΔK , Ca2+ store depletion after the addition of ionomycin induced a reduced FRET increase between STIM1-G379E mutant and Orai1 , indicating impaired dynamics of its cytosolic region ( Fig 6A ) . In contrast , compared with WT STIM2 , the cytosolic region of STIM2-E470G-ΔK mutant underwent more pronounced dynamic changes and induced greater FRET changes ( Fig 6B ) . Collectively , the data indicated that the E470 residue of STIM2-ΔK enabled a more open cytosolic confirmation at rest , resulting in smaller activation dynamics ( diagrams in Figs 6 and S5C ) . In summary , we used FRET-based biosensor to systemically dissect the mechanism of STIM2 activation . Together with previously reported findings , observations made in the current study enable us to propose a model that could reconcile the paradox between the distinct mode of STIM2 activation and the previously proposed STIM2 physiological functions . Briefly , its low Ca2+ affinity renders STIM2 partially active at rest , constitutively inducing Ca2+ entry via STIM2-activated Orai1 channels . The protein regions upstream of the SOAR2 domain ensure a slow activating kinetics , while the E470 and L485 residues within SOAR2 are responsible for the weak engagement and activation of Orai1 by STIM2 . These two factors constrain STIM2-mediated Ca2+ entry , thus preventing potential Ca2+ overload associated with the constitutively active STIM2 . The low Ca2+ affinity also renders STIM2 sensitive to small fluctuations in the ER Ca2+ levels and enables a partially activated configuration of the STIM2 cytosolic region . The weak docking of SOAR2 to CC1 further unfolds the cytosolic STIM2 region , placing the Orai1-activating SOAR2 region in the vicinity of Orai1 at rest . Once further activated by a small reduction of the ER Ca2+ levels , SOAR2 only has to move a relatively short distance to engage with and activate Orai1 channels . Thus , the small dynamic range of STIM2 activation compensates for the slow activating kinetics of STIM2 and might ensure a rapid response to the fluctuations in the ER Ca2+ levels , rendering it an efficient regulator for the maintenance of ER Ca2+ homeostasis [23] .
To generate full-length STIM1 and STIM2 constructs in pECFP-N1 and pEYFP-N1 vectors ( Clontech , Mountain View , CA , USA ) , STIM1 and STIM2 were amplified from STIM1-YFP in MO91 vector [54] and STIM2-YFP in pIRESneo vector [32] , respectively; they were inserted into pECFP-N1 and pEYFP-N1 , respectively , between the Xhol and BamHI sites . To generate chimeric STIM constructs in pECFP-N1 or pEYFP-N1 vectors ( shown in S1 Fig ) , various STIM fragments and vector fragments were amplified from STIM-YFP or STIM-CFP ( in pECFP-N1 or pEYFP-N1 ) and ligated using the NEBuilder HiFi DNA assembly enzyme ( New England BioLabs , Ipswich , MA , USA ) . To construct chimeric STIMN-EF-SAM-TM-CC1-CFP capable of translocation from the ER to the PM , the Myc tag was first inserted between SP and EF-SAM to track chimeric STIM location . Then , the ER-targeting SP of STIM was replaced by the extracellularly targeting SP from CD8A1-21 . Next , the trafficking SP from Kir2 . 1233−252 and ER-exporting SP from Kir2 . 1374−380 were inserted upstream and downstream of the CFP tag , respectively , using standard PCR and T4 ligation . mCh-CAD was purchased from Addgene ( #73566; Cambridge , MA , USA ) . YFP-SOAR was generated as previously described [8] . YFP-SOAR1L ( STIM1343-491 ) and YFP-SOAR2 ( STIM2435-533 ) were generated by replacing the SOAR1-coding sequence in YFP-SOAR with that of SOAR1L or SOAR2 . To generate a chimeric YFP-SOAR construct from SOAR1 and SOAR2 , the SOAR fragments and vector fragments were PCR amplified and then ligated by using the NEBuilder HiFi DNA assembly enzyme ( New England BioLabs ) . The corresponding variants of all STIM constructs were generated using the QuikChange Lightning multisite-directed mutagenesis kit ( Agilent , Santa Clara , CA , USA ) . HEK293 and HeLa cells were cultured in DMEM ( HyClone , Chicago , IL , USA ) containing 10% FBS ( cat: 900–108 , Gemini Bio-Products , West Sacramento , CA , USA ) and 5% penicillin and streptomycin ( Thermo Scientific , Waltham , MA , USA ) at 37°C with 5% CO2 [55] . Transfections were performed by electroporation using the Bio-Rad Gene Pulser Xcell system ( Bio-Rad , Hercules , CA , USA ) in 4 mm cuvettes and OPTI-MEM medium [55 , 56] . For HEK293 cells , a voltage step pulse ( 180 V , 25 ms , in 0 . 4 ml of the medium ) was used; for HeLa cells , an exponential pulse ( 260 V , 525 μF , in 0 . 5 ml medium ) was used . After electroporation , the cells were seeded on round coverslips and cultured in OPTI-MEM medium for another 45 min before FBS was added to a final concentration of 7% . All experiments were carried out 24 h after transfection . Fluorescence imaging of time-series experiments was conducted using a ZEISS observer Z1 microscope equipped with X-Cite 120-Q ( Lumen Dynamics , Waltham , MA , USA ) light source , 40× oil objective ( NA 1 . 3 ) , and Axiocam 506 mono Camera ( Zeiss , Oberkochen , Germany ) . The imaging system was controlled with the Zen software . All filters or filter sets were purchased from Semrock ( BrightLine; Semrock , Rochester , NY , USA ) . Both FRET and Ca2+ imaging were performed using this system at room temperature ( 20°C ) , as previously described [47 , 55] . Data were acquired from cells that had been cultured on round coverslips placed in the imaging solution . The imaging solution contained 107 mM NaCl , 7 . 2 mM KCl , 1 . 2 mM MgCl2 , 11 . 5 mM glucose , and 20 mM HEPES-NaOH ( pH 7 . 2 ) . For single-cell intracellular cytosolic Ca2+ measurements , the cells were first bathed in the imaging solution containing Fura-2 AM for 1 h to get Fura-2 AM loaded into cells and then de-esterified . The cytosolic Ca2+ signals were then acquired using a FURA2-C-000 filter set . Emission fluorescence signal at 510 ± 42 nm generated by light at the 340 ± 12 . 5 nm excitation wavelength ( F340 ) and at 387 ± 5 . 5 nm ( F380 ) was acquired every 2 s; the intracellular Ca2+ levels are collected as F340/F380 ratio [57] . For FRET measurements , CFP ( 438 ± 12 nmEx/483 ± 16 nmEm ) , YFP ( 500 ± 12 nmEx/542 ± 13 . 5 nmEm ) , and FRETraw ( 438 ± 12 nmEx/542 ± 13 . 5 nmEm ) filters were used for image acquisition ( FCFP , FYFP , and Fraw , respectively ) every 10 s at room temperature . The corresponding fluorescence readings from regions of interest were exported from the Zen software and imported into Matlab 2014a ( The MathWorks , Natick , MA , USA ) to calculate the F340/F380 ratio , or the system-independent apparent FRET efficiency , Eapp [55 , 58 , 59] . The parameters and calculation methods used to generated Eapp values from raw fluorescent signals were the same as those previously described [47] . The calibration of Fura-2 signals was done with a modified protocol based on a previously described one [60] . Fura-2 traces in 10 mM EGTA or 30 mM Ca2+ were fitted with corresponding exponential equations to achieve more accurate maximal or minimal Fura-2 signals . The resulting traces or values of Ca2+ concentrations or Eapp were plotted using the Prism 7 software . Representative traces from at least three independent experiments are shown as mean ± SEM . In situ Ca2+ titration of R-CEPIA1er and measurements of ER Ca2+ levels with R-CEPIA1er were performed as the following . HeLa WT or SK cells transiently coexpressing R-CEPIA1er and a Ca2+-insensitive ER marker , CFP-Sec61β , were used for measurements . CFP ( 438 ± 12 nmEx/483 ± 16 nmEm ) or a TxRed-A-Basic-000 filter was used to collect the corresponding CFP or R-CEPIA1er fluorescence every 2 s . In situ calibration of CEPIA1er signals was performed in a calibration solution containing 10 mM NaCl , 140 mM KCl , 1 mM MgCl2 , 20 mM HEPES , 0 . 025 mM digitonin , and 0 . 01 mM ionomycin ( pH 7 . 2 ) [26 , 61] . To avoid artifacts caused by cell movements and leaking of R-CEPIA1er after cell permeabilization , the fluorescence ratio between R-CEPIA1er and CFP-sec61β ( R ) were used for calibration . During calibration , cells were first permeabilized with the above solution containing various amounts of Ca2+ for 4 min to obtain the response ratio ( R ) , and the response to 30 mM CaCl2 was taken as the maximum response ( Rmax ) . Then , Ca2+ was removed and 1 mM EGTA was added in the bathing solution to obtain the minimum response ( Rmin ) . The in situ apparent Ca2+ affinity ( Kd = 467 ± 20 μM ) and Hill coefficient ( Hilln = 1 . 46 ± 0 . 08 ) of R-CEPIA1er were then calculated through curve fittings with the following equation [26]: [Ca2+]free=Kd⋅[ ( R–Rmin ) / ( Rmax−R ) ]1/n Afterwards , the ER Ca2+ levels were calculated using the following equation [26]: [Ca2+]ER=Kd⋅[ ( R–Rmin ) / ( Rmax−R ) ]1/n The obtained in situ values of CEPIA1er indicators were used in further calculations [26] . All experiments were carried out at room temperature . Traces shown are representative of at least three independent repeats , with 15–60 single cells analyzed per each repeat . The FRET signals between the YFP-SOAR1L-SC1111/1121 pairs and R-CEPIA1er signals were acquired simultaneously from HEK293 cells or HeLa SK cells transiently coexpressing R-CEPIA1er , YFP-SOARL , and SC1111-CFP or SC1211-CFP . The same filters as described in the above sections were used . For measurements of Ca2+ affinities of full-length STIM1 using STIM1 puncta as readouts , confocal microscopy ( described below ) was used to collect corresponding YFP and R-CEPIA1er signals . ER Ca2+ stores were gradually depleted with 1 μM thapsigargin ( TG ) ; the resulting decrease in FRET signal or increases in punctate area were then plotted against the corresponding ER Ca2+ levels indicated by R-CEPIA1er fluorescence . Similar to previous reports [23 , 30] , either densities of STIM puncta or the FRET signals between YFP-SOAR1L and SC1111-CFP or SC1211-CFP functioned as an indicator of STIM activation in the current study . Ca2+ affinities of the STIM constructs were then calculated by fitting the obtained puncta/FRET-Ca2+ relationship to the Hill equation using Prism 7 software . Subcellular distribution of fluorescently tagged STIM and Orai1 constructs was monitored using a Zeiss LSM 880 confocal system equipped with a 100× oil lens ( NA 1 . 45; Zeiss ) . The acquired raw images were analyzed using Image J software ( NIH ) . Studies of the PM targeting of STIM- and STIM-CC1–binding SOAR were conducted using the Nikon Eclipse Ti-E microscope ( Nikon Instruments , Tokyo , Japan ) equipped with an A1R-A1 confocal module with LU-N4 laser sources and CFI Plan Apochromat VC series Objective Lenses ( 60× or 40× ) . All acquired confocal images were analyzed by using the NIS-Elements AR microscope imaging software ( Nikon , NIS-element AR version 4 . 0 ) . To confirm the orientation of either end of the engineered PM-STIM protein , live-cell immunofluorescence staining without cell permeabilization was performed . HeLa cells were grown on 35 mm glass-bottomed dishes ( MatTek , Ashland , MA , USA ) and transfected with PM-STIM constructs using Lipofectamine 3000 [62] . Next , 18 h post-transfection , cells were incubated with c-Myc antibody ( 1:400 dilution; Santa Cruz Biotechnology Cat#: sc-41; Santa Cruz Biotechnology , Dallas , TX , USA ) in regular DMEM medium at 37°C incubator and under 5% CO2 for 1 h . The cells were then washed twice with fresh culture medium . Secondary goat anti-mouse IgG Alexa Fluor 568 antibody ( 1:500 dilution; Z25106 , Thermo Fisher ) was then incubated with the cells in the growth medium for 1 h . After extensive washing , images were acquired using a Nikon A1R confocal microscope at 40× magnification . Data were collected using an HEKA EPC 10 USB double patch amplifier controlled by Patchmaster software ( HEKA Elektronik , Lambrecht/Pfalz , Germany ) . The ICRAC in HEK293 Orai1-CFP stable cells transiently expressing STIM1-ΔK-YFP or corresponding 1121 or G379E variants was measured with conventional whole-cell recordings [38] . After the establishment of the whole-cell configuration , a holding potential of 0 mV was applied . A 50 ms step to –100 mV followed by a 50 ms ramp from –100 to +100 mV was delivered every 2 s . Currents were low-pass–filtered at 2 . 3 kHz ( four-pole Bessell ) and acquired at a sampling rate of 10 kHz . HEKA Fitmaster and Matlab 2014b software were used for offline data analysis , and the currents were further low-pass–filtered at 500 Hz . The intracellular or pipette solution contained 135 mM Cs-aspartate , 8 mM MgCl2 , 10 mM EGTA , and 10 mM Cs-HEPES ( pH 7 . 2 ) . The extracellular solution contained 130 mM NaCl , 4 . 5 mM KCl , 20 mM CaCl2 , 10 mM TEA-Cl , 10 mM d-glucose , and 5 mM Na-HEPES ( pH 7 . 4 ) . A 10 mV junction potential compensation was applied to correct the liquid junction potential of the pipette solution relative to the extracellular solution . Currents from at least six cells for each condition were collected and averaged . To determine the dynamics of SOAR1 , its initial dimeric structure was taken from the X-ray structure ( PDB: 3TEQ ) . The other structures ( SOAR2 , SOAR1-G379E , SOAR1-MESV , SOAR2-E470G , and SOAR2-KGNL ) were built by homology modeling with SOAR1 as the template; the modeling was conducted using the Swiss-model server [63 , 64] . For each construct , the protein molecule was solvated in a cubic water box . To mimic the physiological environment , 150 mM NaCl was introduced; extra Cl- ions were introduced to neutralize the system . The total number of atoms was approximately 84 , 000 . The CHARMM 36 force field [65] was used for protein and ions , and the TIP3P model was used for water [66] . Molecular dynamics ( MD ) simulations were performed using the NAMD package suite [67] . The simulations were run at constant temperature ( 300 K ) and constant pressure ( 1 bar ) . The particle mesh Ewald method [68] was used to treat long-range electrostatic interactions , with a cutoff of 12 Å . Integration time step was set at 2 fs , with all bonds containing hydrogen held rigid . Langevin dynamics were used to control the temperature , while the pressure of the system was controlled by the Nosé-Hoover-Langevin piston [69] . Each system was accumulated over a 400 ns trajectory , and data from the last 200 ns were used for analysis . | Calcium ions play a major regulatory role in the physiology and biochemistry of the cell , and thus their levels and activities should be tightly regulated . The stromal interaction molecules ( STIMs ) are sensors of the calcium levels within the endoplasmic reticulum ( ER ) —which serves as a major intracellular calcium store—to mediate communication between the ER and the plasma membrane and are regarded as ubiquitous central players of calcium signaling in mammalian cells . STIM2 acts as a slow and weak activator of Orai1 calcium channels on the plasma membrane by direct binding; however , the affinity of STIMs for calcium or how Orai1 channels are activated remain unclear . In this study , we systematically analyzed the molecular determinants that govern the activation of STIM proteins . Adopting protein engineering approaches that enable the relocation of ER-resident STIM proteins at the plasma membrane , we determined the calcium affinities of STIMs under physiological conditions in mammalian cells . We identified a critical position within STIMs , which defines their distinct resting states and activation kinetics , as well as the efficacy to activate Orai1 channels . These findings shed new light on how STIM2 can efficiently respond to small changes within the ER lumen to regulate calcium homeostasis and signaling in mammalian cells . | [
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"fluoresce... | 2018 | Identification of molecular determinants that govern distinct STIM2 activation dynamics |
During meiotic prophase I , double-strand breaks ( DSBs ) initiate homologous recombination leading to non-crossovers ( NCOs ) and crossovers ( COs ) . In mouse , 10% of DSBs are designated to become COs , primarily through a pathway dependent on the MLH1-MLH3 heterodimer ( MutLγ ) . Mlh3 contains an endonuclease domain that is critical for resolving COs in yeast . We generated a mouse ( Mlh3DN/DN ) harboring a mutation within this conserved domain that is predicted to generate a protein that is catalytically inert . Mlh3DN/DN males , like fully null Mlh3-/- males , have no spermatozoa and are infertile , yet spermatocytes have grossly normal DSBs and synapsis events in early prophase I . Unlike Mlh3-/- males , mutation of the endonuclease domain within MLH3 permits normal loading and frequency of MutLγ in pachynema . However , key DSB repair factors ( RAD51 ) and mediators of CO pathway choice ( BLM helicase ) persist into pachynema in Mlh3DN/DN males , indicating a temporal delay in repair events and revealing a mechanism by which alternative DSB repair pathways may be selected . While Mlh3DN/DN spermatocytes retain only 22% of wildtype chiasmata counts , this frequency is greater than observed in Mlh3-/- males ( 10% ) , suggesting that the allele may permit partial endonuclease activity , or that other pathways can generate COs from these MutLγ-defined repair intermediates in Mlh3DN/DN males . Double mutant mice homozygous for the Mlh3DN/DN and Mus81-/- mutations show losses in chiasmata close to those observed in Mlh3-/- males , indicating that the MUS81-EME1-regulated crossover pathway can only partially account for the increased residual chiasmata in Mlh3DN/DN spermatocytes . Our data demonstrate that mouse spermatocytes bearing the MLH1-MLH3DN/DN complex display the proper loading of factors essential for CO resolution ( MutSγ , CDK2 , HEI10 , MutLγ ) . Despite these functions , mice bearing the Mlh3DN/DN allele show defects in the repair of meiotic recombination intermediates and a loss of most chiasmata .
Meiosis is a specialized cell division process in which a diploid parental cell undergoes one round of DNA replication followed by two rounds of division , resulting in up to four haploid gametes . Successful halving of the genome during meiosis I depends on the tethering of maternal and paternal homologous chromosomes during meiotic prophase I , and their subsequent release at the first meiotic division . This tethering is ensured by homologous recombination , leading to the formation of crossovers; by synapsis , the formation of a tripartite proteinaceous structure , the synaptonemal complex , or SC between homologous chromosomes; and by cohesion between replicated sister chromatids that ensures appropriate tension on the metaphase I spindle [1 , 2] . Thus , recombination and synapsis are hallmarks of prophase I , and are both essential for ensuring homolog interactions leading to the formation of at least one crossover event per chromosome pair . Moreover , the correct placement , frequency , and distribution of crossovers is critical for ensuring appropriate disjunction at metaphase I and for maintaining genomic stability [1 , 3] . Meiotic recombination begins with the introduction of a large number of programmed double-strand breaks ( DSBs ) , which are repaired as non-crossovers ( NCOs ) or crossovers ( COs ) . Evidence for distinct NCO versus CO pathways was obtained in S . cerevisiae , where it was shown that the former occur earlier in meiotic prophase I , and subsequent work suggested that they appeared primarily through synthesis-dependent strand annealing ( SDSA ) [4 , 5] . In M . musculus , only 10% of DSBs are repaired as COs , while the majority are mostly repaired as NCOs , presumably via SDSA or other pathways , [6–9] . COs can form via one of at least two distinct mechanisms ( referred to as class I and class II ) , each of which is used in varying degrees in different eukaryotic organisms [6 , 10 , 11] . The class I CO pathway is also known as the ZMM pathway , named after the major genes discovered in yeast that regulate this mechanism [12–18] . Class II COs , on the other hand , do not involve the ZMM proteins , but instead appear to rely on the structure-specific endonuclease ( SSN ) , MUS81/EME1 ( Mus81/Mms4 in S . cerevisiae ) [6 , 10 , 11] . Class I COs also differ from class II COs in that the former are regulated by interference , the process by which placement of one CO prevents the nearby localization of a second CO , thus resulting in CO events spaced further apart than expected by chance [19] . In the class I CO pathway , DSBs are processed and resected to form single-end invasion ( SEI ) intermediates . This is followed by displacement of single strand DNA ( ssDNA ) from the recipient homolog to produce a double Holliday junction ( dHJ ) . The yeast ZMM proteins Msh4 and Msh5 form a complex known as MutSγ that associates with a subset of these intermediate structures [20–22] . At least in yeast , this recruitment may be dependent on the STR complex , consisting of Sgs1 ( BLM in mammals ) , Top3 and Rmi1 [23 , 24] . STR is proposed to act by disassembling the early recombination intermediates that would otherwise be processed through SSN-directed recombination pathways , thereby promoting either early NCO formation via SDSA , or CO formation through the capture of these recombination intermediates by the ZMM proteins , including MutSγ [23] . MutSγ is then thought to stabilize the dHJs , leading to the recruitment of a second MMR complex , MutLγ , consisting of the MutL homologs , Mlh1 and Mlh3 [25 , 26] . The mouse MutSγ complex associates with chromosome cores in zygonema [27] , recruiting the MutLγ complex in pachynema . However , MutLγ associates with only a subset of MutSγ sites ( ~24–26 and 150 foci/nucleus , respectively ) , designating these events as class I COs [28 , 29] . Though not formally considered to be ZMM proteins , MLH1 and MLH3 are critical for most , if not all , class I CO events in numerous organisms [26 , 29–36] . In fact , the M . musculus MLH1-MLH3 heterodimer localizes to sites that are destined to become class I COs and the absence of either subunit in male spermatocytes leads to a dramatic decrease , but not complete absence , of chiasmata ( the physical manifestation of a CO ) [28 , 29 , 37–40] . While MutLγ is known to be recruited to sites that are preloaded with MutSγ , recent studies have shown that S . cerevisiae MutLγ can bind to single and double-stranded DNA ( ssDNA , dsDNA ) , as well as a variety of branched DNA structures [33 , 41–43] . How such binding properties relate to the in vivo functions of MutLγ remains unclear . Class I CO formation in M . musculus is dependent on MLH3 , and on its heterodimeric interaction with MLH1 [8 , 29 , 37] . Interestingly , MLH3 recruitment precedes that of MLH1 [28] . Further analysis of MutLγ has shown that MLH3 contains a conserved metal binding motif , DQHA ( X ) 2E ( X ) 4E , originally discovered in the human MutL homolog , PMS2 , and found to be required for human MutLα ( hMLH1/hPMS2 ) endonuclease function [44] . This putative endonuclease motif is highly conserved in eukaryotic homologs of human PMS2 and MLH3 , but not in homologs of human MLH1 and PMS1 . The expectation for MLH3 is that this endonuclease function might represent a “resolvase”activity for class I COs . Studies in S . cerevisiae have shown that a single point mutation in the endonuclease motif of yeast Mlh3 ( mlh3-D523N ) disrupts its endonucleolytic activity and results in meiotic crossover defects similar to full mlh3 ( mlh3Δ ) null mutants , yet does not affect the protein stability of Mlh3 or its interaction with Mlh1 [32] . Further analysis of the entire endonuclease domain in S . cerevisiae revealed that mutation of any conserved residue results in a null or near-null phenotype with respect to crossing over [34] . Biochemical analysis reveals that the Mlh1-mlh3D523N protein lacks the ability to nick closed circular double stranded DNA , indicating loss of endonuclease activity [33 , 43] . Collectively , these studies in S . cerevisiae suggest that MutLγ plays a direct role in resolving dHJs to generate COs through its endonuclease activity . To investigate the function of the putative endonuclease domain of MLH3 in mammalian meiotic recombination , we generated a point mutant mouse ( termed Mlh3DN ) in which the endonuclease domain was disrupted at the orthologous residue to the D523N mutation in yeast , allowing the overall structure of MLH3 to remain intact , as determined by the ability to form a stable complex with MLH1 . By mutating the catalytic domain of MLH3 , we hypothesized that the mutant MutLγ complex would remain structurally intact and thus might reveal a functional interplay with other meiotic CO functions . We demonstrate that normal function of the MLH3 endonuclease domain is required for resolution of DSB repair intermediates towards CO formation and thus for late meiotic recombination events . Mlh3DN/DN spermatocytes exhibit grossly normal DSB formation and early processing events , and normal timing of synapsis through early prophase I . Mlh3DN/DN spermatocytes exhibit appropriate localization of MLH3 and MLH1 to the synaptonemal complex during pachynema , along with pro-crossover factors HEI10 and CDK2 , phenotypes that are clearly different from that observed in Mlh3-/- males . However , Mlh3DN/DN diakinesis-staged spermatocytes show significantly fewer chiasmata compared to wild-type mice ( WT ) , but significantly more when compared to Mlh3-/- males , suggesting either that the MLH3DN protein retains partial endonuclease activity , or that the presence of the MutLγ complex , albeit altered in its endonuclease capacity , can invoke MLH3-independent repair pathways to become active by interfering with normal resolution of recombination intermediates . In line with these suggestions , we find that the RecQ helicase , BLM , is upregulated throughout prophase I in Mlh3DN/DN spermatocytes , perhaps aiding the recruitment of other repair proteins . To explore the increase in residual chiasmata observed at diakinesis in Mlh3DN/DN males relative to that of Mlh3-/- males , we demonstrate that co-incident loss of the class II CO pathway in Mlh3DN/DNMus81-/- double mutant males results in altered distribution of MutLγ , with an increased proportion of synapsed autosomes bearing no MutLγ foci . Furthermore , the proportion of chiasmata remaining in these double mutants is between that of Mlh3DN/DN and Mlh3-/- males , suggesting that MUS81-EME1 may account for only a proportion of these additional chiasmata , the mutant MutLγ retains residual resolvase activity , and/or mutant MutLγ can recruit other proteins to perform this resolvase activity at a subset of recombination intermediate sites . Collectively , our data show that the endonuclease activity of MLH3 is important for normal processing of DSB repair intermediates through the Class I pathway .
To investigate the meiotic requirement for the presence of a functional endonuclease domain in mammalian MLH3 , we generated a mouse line with a point mutation in a conserved endonuclease motif located in the M . musculus protein: DQHAAHERIRLE [44 , 45] . Specifically , we replaced the aspartic acid "D" in amino acid position 1185 , with an asparagine "N" by changing GAC to AAC in the genomic sequence , termed MLH3DN throughout . Extrapolating from an analogous mutation in the S . cerevisiae gene , this D-to-N replacement is predicted to disrupt the endonuclease function of MLH3 while maintaining its ability to interact with MLH1 ( [32] S1 Fig ) . Mice were maintained on a C57Bl/6J background throughout the study . Male Mlh3+/DN mice were phenotypically similar to WT littermates and displayed full fertility . Mlh3DN/DN males are also grossly normal when compared to WT littermates , survive into adulthood , and live normal lifespans . Mlh3DN/DN males also exhibit normal mating behaviors as determined by observing a vaginal plug in WT females the morning after mating . However , breeding between multiple sets of Mlh3DN/DN males and WT females never resulted in offspring over a four-year period . Similar to the situation seen for Mlh3-/- males [29] , Mlh3DN/DN males show complete infertility , accompanied by significantly reduced testes size when compared to WT ( Fig 1A and 1B; p < 0 . 0001 ) and the absence of spermatozoa in the epididymides ( Fig 1C; p < 0 . 0001 ) . Whereas histological cross-sections of testes stained with hemotoxylin and eosin from WT males showed the presence of meiotic and post-meiotic cells within the seminiferous epithelium , testis sections from Mlh3DN/DN males were devoid of spermatids , but showed the presence of spermatogonia and spermatocytes ( Fig 1D–1G ) . In addition , metaphase I spermatocytes were observed in the tubular lumen of Mlh3DN/DN mice ( Fig 1G , black arrows ) . Thus , mutation of the endonuclease domain of Mlh3 in the mouse results in a sterility phenotype grossly similar to that seen in Mlh3-/- mice . To investigate the progression of meiotic recombination , prophase I chromosome spreads were prepared from WT , Mlh3DN/DN , and Mlh3-/- adult males and stained for a variety of markers involved in synapsis and recombination . Chromosome spreads were stained with antibodies against γH2AX , the phosphorylated form of histone H2AX , as a marker of DSBs [46 , 47] . In spermatocyte preparations from WT males , γH2AX signal is abundant throughout the nucleus at leptonema , coincident with the induction of several hundred DSBs [1 , 47] . The γH2AX signal declines in zygonema as DSBs are processed for repair [47 , 48] . In pachynema and diplonema , γH2AX signal is absent from the autosomes , but emerges throughout the sex body due to meiotic sex chromosome inactivation ( MSCI ) ( [49]; S2A–S2D Fig ) . Spermatocytes from both Mlh3DN/DN and Mlh3-/- males exhibit the same γH2AX signal and temporal dynamics as observed in WT spermatocytes , with abundant staining in leptonema , slightly reduced signaling in zygonema , followed by the absence of γH2AX signal on the autosomes of pachytene and diplotene spermatocytes , except at the sex body ( S2F–S2I and S2K–S2N Fig ) . We do not see specific persistent γH2AX signal on the autosomes at pachynema in Mlh3-/- spermatocytes [50] , unless we markedly increase our imaging exposure time γH2AX ( S2E , S2J and S2O Fig; white arrows ) . Under these conditions , we see persistent foci of γH2AX in spermatocytes from WT and from Mlh3DN/DN spermatocytes also . Thus , in our hands , we see no specific persistence in autosomal γH2AX signal through pachynema in mice lacking MLH3 or harboring a mutation within the endonuclease domain of MLH3 . Spermatocyte chromosome spreads from WT and Mlh3DN/DN males were stained with antibodies against synaptonemal complex ( SC ) components , SYCP3 and SYCP1 , marking the axial/lateral elements and the transverse filaments , respectively . Prophase I progression in WT spreads is characterized by the initial accumulation of SYCP3 signal in discrete dots along chromosomes at leptonema , and these dots gradually coalesce into continuous filaments along the chromosome cores in zygonema ( S2Q Fig ) . At this time , SYCP1 appears in patches along the SYCP3 signal , indicating that synapsis is occurring . By late zygonema , most of the chromosome core is now labeled with SYCP1 , and by pachynema synapsis is complete , as demonstrated by complete overlap of the SYCP3/SYCP1 signals on the autosomes . For the sex chromosomes , synapsis only occurs at the pseudoautosomal region ( PAR ) . After meiotic recombination occurs , the SC begins to degrade in diplonema , and the homologs are no longer tethered to one another except at CO sites ( S2P–S2S Fig ) . Synapsis appears normal in Mlh3DN/DN spermatocytes with discrete accumulation of SYCP3 on the chromosomes in leptonema , followed by continued accumulation of SYCP3 along the chromosomes as SYCP1 appears in patches in zygonema ( S2T and S2U Fig ) . Complete synapsis of the autosomes and the PAR is observed in pachynema with co-localization of SYCP1 and SYCP3 ( S2V Fig ) . Desynapsis is then observed in diplonema with the degradation of the SC ( S2W Fig ) . Thus , synapsis in Mlh3DN/DN spermatocytes appears unaffected by loss of the endonuclease activity of MLH3 , a result similar to that seen for complete loss of MLH3 protein . Early DSB repair events were monitored by examining localization of the RecA strand exchange protein , RAD51 , on chromosome cores of the autosomes throughout prophase I [51 , 52] . In WT mice , RAD51 localizes to chromosome cores of early and late zygotene cells as discrete foci at a high frequency ( EZ and LZ , respectively; Fig 2A and 2G ) . Compared to WT littermates in early and late zygonema , RAD51 counts in spermatocytes from Mlh3DN/DN males were significantly elevated ( Fig 2C and 2G; p<0 . 001 and p<0 . 01 , respectively , by unpaired t-test with Welch’s correction ) . However , while early zygotene RAD51 counts were indistinguishable in Mlh3-/- spermatocytes compared to WT ( Fig 2E and 2G ) , they were significantly lower than that seen at the equivalent stage in Mlh3DN/DN males ( p<0 . 001 by unpaired t-test with Welch’s correction ) . By late zygonema , the RAD51 counts were significantly lower in Mlh3-/- spermatocytes compared to WT and Mlh3DN/DN animals ( p<0 . 001 by unpaired t-test with Welch’s correction ) . By pachynema , RAD51 foci frequency in spermatocytes from WT mice decreased to very low numbers , as did that of Mlh3-/- males ( Fig 2B , 2F and 2H; p = 0 . 55 unpaired t-test ) . In contrast , focus counts in pachytene spermatocytes from Mlh3DN/DN males remained significantly elevated following the pattern first seen in zygonema ( Fig 2D and 2H; p<0 . 0001 ) . The localization and accumulation of single strand DNA binding protein RPA , which associates with chromosomes from zygonema through until early pachynema , was also explored . ( S3 Fig ) . In leptonema and zygonema , RPA focus counts on chromosome cores were significantly elevated in Mlh3DN/DN animals compared to WT ( p<0 . 01 and p<0 . 001 , respectively , unpaired t-test with Welch’s correction ) , similar to the increased focus frequency observed for RAD51 . However , unlike RAD51 , the RPA focus counts were not significantly different between genotypes at pachynema and diplonema ( S3 Fig ) . These observations suggest either that there is a prolonged period of DSB induction in Mlh3DN/DN animals , or that there is a lag time in the turnover of DSB repair intermediates in early prophase I . Taken together with the persistent RAD51 localization , these observations suggest that , in Mlh3DN/DN spermatocytes , there is a persistence of DSB repair intermediates loaded with RPA in zygonema , and that these intermediates continue to persist as they accumulate RecA homolog proteins , with RAD51 remaining on chromosome cores of Mlh3DN/DN spermatocytes through pachynema . We hypothesize , based on these observations , that RPA accumulation in leptonema and RAD51 accumulation in zygonema are affected by loss or mutation of MLH3 protein , suggesting an early function for MutLγ in establishing appropriate DSB repair intermediates that is not confined to CO pathway fate [42 , 53] . We hypothesize that early DSB repair events occur within the normal timeframe in mice lacking MLH3 protein entirely , but at an even faster rate than in WT spermatocytes , because in late zygotene RAD51 counts in Mlh3-/- mutants have declined to levels seen in pachytene . The repair of these DSBs in Mlh3-/- mutants may occur through repair pathways that differ from those utilized in WT-derived spermatocytes . We hypothesize that in Mlh3DN/DN mutants DSBs are not repaired efficiently , or there is an extended period of DSB induction resulting from feedback mechanisms that lead to a persistence of RPA and RAD51 foci . Bloom's syndrome mutated ( BLM ) is a mammalian RecQ DNA helicase whose S . cerevisiae ortholog , Sgs1 , was shown to promote the resolution of complex multi-chromatid joint molecule intermediates , that may result from SEI events , into both NCOs and COs [23 , 24] . During prophase I in WT male spermatocytes , BLM localizes to the chromosomal cores at a high frequency in zygonema and diminishes to a few foci in pachynema [54–56] . Recently , we showed that loss of MLH3 results in up-regulated BLM localization during prophase I , along with persistence of BLM on chromosome cores through late pachynema [56] . To determine if the disruption of the MLH3 endonuclease domain affects the localization of BLM in a similar fashion , to Mlh3-/- , we stained prophase I chromosome spreads with an antibody against BLM . In zygonema , as previously reported , WT cells show the accumulation of BLM foci on the cores in high numbers , and this frequency is elevated in spermatocytes from both Mlh3DN/DN and Mlh3-/- spermatocytes ( Fig 3A , 3B , 3E , 3F , 3I , 3J and 3M; p<0 . 0001 unpaired t-test ) . This is similar to that reported previously for Mlh3-/- spermatocytes [56] . In early to mid-pachynema , BLM localization on chromosome cores persists in a small percentage of WT spermatocytes , but the number of foci is very much reduced at this stage ( Fig 3C , 3D and 3M ) . In contrast , all spermatocytes from Mlh3DN/DN and Mlh3-/- spermatocytes show persistent BLM focus localization along chromosome cores ( Fig 3G , 3H , 3K and 3L ) at a frequency that is elevated above that of WT spermatocytes ( Fig 3M , p<0 . 0001 unpaired t-test ) . Moreover , the number of BLM foci at pachynema in Mlh3DN/DN spermatocytes is significantly elevated relative to that seen in Mlh3-/- spermatocytes ( Fig 3M , p<0 . 05 unpaired t-test ) . By diplonema this difference appears even greater , with BLM localization in Mlh3DN/DN spermatocytes persisting in stretches along the cores , and being lost entirely in Mlh3-/- spermatocytes ( Fig 3D , 3H and 3L ) . Thus , altered MLH3 endonuclease function , like complete loss of MLH3 , leads to persistence of BLM helicase on chromosome cores in late prophase I , but at an elevated frequency in Mlh3DN/DN spermatocytes relative to Mlh3-/- spermatocytes . “Crossover designation” is defined as the process by which class I COs are selected from an excess pool of DSB repair intermediates . In mouse , the 250+ DSBs are processed through zygonema into various repair pathways , and only a subset of these will proceed towards a class I CO fate [1] . These sites become “licensed” for crossing over through the accumulation of the MutS homolog heterodimer , MutSγ ( MSH4 and MSH5; [27 , 57] ) . The MutSγ complex then serves as an early pro-crossover factor by recruiting the MutLγ complex to a select subset of sites and it is these sites that will become “designated” as class I CO events . Notably , all of the 150+ MutSγ sites must be repaired , either as a CO or an NCO , which means that approximately ~125 MutSγ sites must leave the class I CO pathway and undergo repair through an alternate CO pathway or via an NCO pathway , a situation that is unlike that seen in S . cerevisiae where the number of MutSγ sites appear to correspond more closely to the number of CO events [20] . While the mechanism by which only a subset of MutSγ foci are retained through pachynema remains unclear , studies from a number of groups have implicated the Zip3-like protein , RNF212 , in this process [58 , 59] . RNF212 has been shown to co-localize with the majority of MutSγ foci in spermatocytes from WT males and is thought to act as a pro-crossover factor by stabilizing these MutSγ-loaded events [59] . As such , the number of RNF212 foci on chromosome cores is pared down through pachynema in a similar fashion to that of MutSγ [59 , 60] . Moreover , in mouse mutants that disrupt this paring down process , both RNF212 and MutSγ focus counts remain elevated , but equivalent , throughout prophase I [50 , 61] . To investigate how loss of MLH3 endonuclease function could affect this paring down process , we analyzed RNF212 and MSH4 focus dynamics on chromosome spreads throughout prophase I from WT , Mlh3DN/DN , and Mlh3-/- adult male mice ( S4 Fig ) . For both RNF212 ( S4A–S4M Fig ) and MSH4 ( S4N–S4Z Fig ) , we find the expected paring down of focus counts from early pachynema ( EP ) to late pachynema ( LP ) in spermatocytes from WT , Mlh3DN/DN , and Mlh3-/- adult males . In all three cases , RNF212 and MSH4 foci appear on chromosome cores in zygonema ( S4B , S4F , S4J , S4O , S4S and S4W Fig ) , persist at high levels in early pachynema ( S4C , S4G , S4K , S4P , S4T and S4X Fig ) , and then are reduced to approximately 1–2 foci per chromosome in late pachynema ( S4D , S4H , S4L , S4Q , S4U and S4Y Fig ) . Quantification of RNF212 and MSH4 focus numbers in early and late pachytene spermatocytes from WT , Mlh3DN/DN , and Mlh3-/- adult male reveals the expected statistically significant decline in these foci through pachynema ( S4M and S4Z Fig; p<0 . 0001 Mann-Whitney U Test for all ) . However , the levels of RNF212 foci in both early and late pachynema are significantly higher in spermatocytes from Mlh3DN/DN and Mlh3-/- adult males compared to that seen in WT spermatocytes ( S4M Fig; p<0 . 001 Mann-Whitney U test for all ) . Thus , while the dynamics of RNF212 ( high in early and low in late pachynema ) are evident in Mlh3DN/DN and Mlh3-/- adult males , their focus counts at each of these stages are significantly elevated compared to equivalently-staged WT RNF212 counts . By contrast , at both early and late pachynema , MSH4 counts did not differ between spermatocytes from WT , Mlh3DN/DN and Mlh3-/- adult males at both early and late pachynema ( S4Z Fig ) . Thus , mice bearing no MLH3 or catalytically defective MLH3 show a phenotypic divergence in RNF212 and MSH4 focus counts in pachytene spermatocytes compared to WT . MutLγ represents the ultimate marker of DSB repair events that have adopted a class I CO fate , and has been used as a CO proxy marker in many organisms [29 , 62–64] . We anticipated that the D1185N endonuclease mutation in MLH3 would not affect localization of this complex . In WT spermatocytes , MLH3 localizes on the chromosomes during early pachynema , remaining associated with SYCP3 signal through to diplonema ( Fig 4A ) . In pachytene spermatocyte preparations from Mlh3DN/DN mice , MLH3 signal remains associated with the autosomal chromosome cores from early pachynema at a focus frequency that is statistically indistinguishable from that of WT cells ( Fig 4A–4C , p = 0 . 36 by unpaired t-test ) . MLH3 association with the PAR of the synapsed X and Y chromosomes was similarly unaffected in Mlh3DN/DN pachytene spermatocytes . In addition , the timing of MLH3 appearance , in early pachynema and prior to that of MLH1 , was normal in Mlh3DN/DN pachytene spermatocytes . Localization of MLH1 was similarly explored in spermatocytes from Mlh3DN/DN mice . As with MLH3 , there was no difference in the timing of MLH1 accumulation on chromosome cores between WT and Mlh3DN/DN mice ( Fig 4D–4F ) . Moreover , when autosomal MLH1 foci were quantified , no statistical difference was observed in MLH1 focus frequency between WT and Mlh3DN/DN pachytene cells ( Fig 4D–4F , p = 0 . 2 by unpaired t-test ) . These data suggest that disruption of the endonuclease domain of MLH3 does not alter recruitment of MutLγ to chromosomes in pachynema . To observe class I CO events in pachynema , we employed two well characterized markers of these sites: the putative ubiquitin E3 ligase , Human Enhancer of Invasion-10 ( HEI10 ) , and cyclin-dependent kinase-2 ( CDK2 ) [50 , 65 , 66] . In WT prophase I cells , CDK2 localizes to the telomeres ( Fig 5A , yellow arrows ) as well as on the chromosome cores ( Fig 5A , white arrows ) during mid to late pachynema and remains associated with SYCP3 signal through to diplonema [66] . The localization of CDK2 along chromosome cores parallels the localization of MLH1 and MLH3 , both temporally and quantitatively ( Fig 5A and 5D ) , and is associated with nascent class I CO events . In pachytene spermatocyte preparations from Mlh3DN/DN mice , CDK2 signal remains associated with both the telomeres and chromosome cores at a frequency and intensity that is reminiscent of that seen in WT spermatocyte spreads ( Fig 5B and 5D ) . This is in contrast to the situation in spermatocyte preparations from Mlh3-/- males , in which CDK2 association with the telomere persists , but is lost from nascent CO sites ( Fig 5C and 5D ) . HEI10 was recently shown to co-localize with MutLγ at sites of class I CO , and its localization is dependent on Cyclin N-terminal Domain-containing-1 ( CNTD1 ) [50 , 61] . HEI10 is thought to play a key role in CO designation/maturation [50] . As previously reported for WT cells at pachynema , HEI10 localizes with similar frequency to that of CDK2 and MutLγ ( Fig 5E [pink arrows] , 5H ) . Similar localization patterns and frequency were observed for Mlh3DN/DN mice , with a frequency of one to two foci per chromosome ( Fig 5F [pink arrows] and H ) , indicating normal recruitment of HEI10 on pachytene chromosome cores in Mlh3DN/DN males . This is in contrast to the pattern of HEI10 staining in spermatocytes from Mlh3-/- mice , where there is an increased accumulation of HEI10 foci ( Fig 5G [pink arrows] , 5H ) , as previously reported [50] . For both CDK2 and HEI10 , the altered frequencies of foci observed in spermatocytes from Mlh3-/- mice were significantly different from that of WT or Mlh3DN/DN males ( Welch’s T-test , p<0 . 0001 ) . CDK2 and HEI10 focus counts for WT or Mlh3DN/DN males were not statistically different from each other . Taken together , these observations demonstrate that loading of HEI10 and CDK2 on class I CO designated sites is affected differently by mutation of Mlh3: complete loss of MLH3 results in failure to load CDK2 and hyper-accumulation of HEI10 , while altered endonuclease activity of MLH3 results in normal loading of both CDK2 and HEI10 . Thus , the physical accumulation of MutLγ is required for normal loading of associated pro-crossover maturation factors . Mouse Mlh1 and Mlh3 were amplified from cDNA and cloned into pFastBac1 vectors as described in the Methods . The MLH1-MLH3 and MLH1-MLH3-D1185N complexes were expressed from Sf9 cells infected with baculoviruses containing MBP-Mlh1 and His10-Mlh3 or His10-Mlh3-D1185N constructs ( Fig 4G ) . Extracts from these cells were applied to a Ni-NTA column . Fractions containing induced proteins were pooled and then applied to an amylose column . Two major bands of molecular weights predicted for an MBP-MLH1-His10-MLH3 complex were detected on SDS-PAGE after amylose chromatography ( Fig 4H ) . These bands were further analyzed by mass spectrometry , and the results from this analysis confirmed their identity ( Fig 4I ) . Importantly , MLH1-MLH3 and MLH1-MLH3-D1185N eluted with an apparent 1:1 stoichiometry in both chromatography steps , indicating that the heterodimers were stable , and the protein yields of the two complexes after amylose chromatography were similar ( Fig 4H ) . Chiasmata are the physical manifestations of crossing over and , as such , can inform the process of DSB repair via all pathways . Diakinesis-staged spermatocytes from WT and Mlh3DN/DN males were used to quantify chiasmata . WT cells exhibited a chiasmata frequency of 23 . 5 ±1 . 3 per nucleus ( Fig 6A and 6D ) whereas Mlh3DN/DN spermatocytes exhibited a dramatically reduced chiasmata count of 5 . 2 ± 1 . 7 chiasmata per nucleus ( Fig 6B and 6D; p < 0 . 0001 by unpaired t-test ) . Chiasmata counts for Mlh3-/- males were even more dramatically reduced at 2 . 8 ± 1 . 1 chiasmata per nucleus , a value that is significantly lower than both WT and Mlh3DN/DN spermatocytes ( Fig 6C and 6D; p <0 . 0001 by unpaired t-test ) . Thus , complete loss of MLH3 protein leads to the loss of approximately 88% of chiasmata , while loss of endonuclease activity , but retention of MutLγ heterodimer results in only a 78% loss . Thus , the number of residual chiasmata observed in Mlh3DN/DN spermatocytes is higher than the expected number of chiasmata achieved through the MUS81-EME1-driven class II CO pathway ( ~2–3 , assessed both cytologically and genetically; [8 , 28] ) . The increased residual chiasmata observed in Mlh3DN/DN males compared to Mlh3-/- animals prompted us to ask whether some or all of these crossovers were dependent on the activity of the MUS81-EME1 heterodimer . Previous studies in our lab showed that Mus81-/- animals show increased accumulation of MutLγ , resulting in normal chiasmata counts , suggesting that class I CO events are up-regulated in the absence of the class II machinery [6] . Co-incident mutation of one or both Mlh3 alleles to the Mlh3DN variant on the Mus81-/- mutant background yielded MLH1 focus counts that were significantly reduced compared to those observed in Mlh3+/+Mus81-/- mice ( Fig 6E , p<0 . 005 by unpaired t-test with Welch’s correction ) , and instead resembled MLH1 focus counts observed in spermatocytes from Mlh3+/+ mice ( Fig 4F ) . Thus , the upregulation of MLH1 foci at pachynema requires both a defective MUS81-EME1 dimer and the presence of only functional MutLγ heterodimer . Interestingly , pachytene spermatocytes from Mlh3+/DN Mus81-/- and Mlh3DN/DN Mus81-/- males show an abnormal distribution of MLH1 foci across all autosomal pairs ( Fig 6F ) . In Mlh3+/+Mus81-/- males , 17% ( 5/29 ) cells showed synapsed chromosomes without any MLH1 foci in pachynema ( so-called “no exchange” or “E0” chromosomes ) , while in Mlh3+/DN Mus81-/- males , this proportion increased to 67% ( 8/12 ) , with up to 3 E0 chromosomes per cell ( Fig 6F; examples shown in S5 Fig ) . In Mlh3DN/DNMus81-/- males , 94% of cells had E0 chromosomes ( 29/31 ) , with as many as 6 E0 bivalents being observed ( Figs 6F and S5 ) . The higher proportion of MLH1-devoid autosomes in Mlh3+/DN and Mlh3DN/DN males on the Mus81 null background was statistically significant in all pairwise comparisons ( Fig 6F , unpaired t-test with Welch’s correction with Bonferroni adjustment ) , indicating that the placement of an obligate crossover is perturbed in mice having one or two copies of the Mlh3DN allele on a Mus81 null background , and suggesting that mechanisms that ensure correct CO placement require a function MutLγ complex . In yeast , and also probably in mice , CO interference requires a functional MutSγ complex [67] . However , MutSγ alone is not sufficient to ensure appropriate CO placement since Mus81-/- males exhibit disrupted interference despite appropriate MutSγ loading [6] . Assessment of chiasmata counts in single ( Fig 6D ) and double mutants ( Fig 6G ) revealed the expected normal chiasmata frequency in spermatocytes from Mus81-/-Mlh3+/+ and Mus81-/-Mlh3+/DN males , and the loss of most chiasmata in spermatocytes from Mus81-/-Mlh3DN/DN males . However , whereas the loss of chiasmata structures in Mlh3-/- and Mlh3DN/DN single mutants was observed to be 88% and 78% , respectively ( Fig 6D ) , the loss of chiasmata in Mus81-/-Mlh3DN/DN males was 83% . The frequency of chiasmata in cells from these double mutants was statistically different from both Mlh3 single homozygous mutant animals ( p<0 . 05 , unpaired t-test with Welch’s correction ) . Thus , loss of the class II pathway in addition to the mutation of the endonuclease domain of Mlh3 only partially reduces residual chiasmata counts to the levels observed in Mlh3-/- animals . Interestingly , despite reduced chiasmata , the overall number of bivalent structures observed in diakinesis preparations from Mus81-/-Mlh3DN/DN males is the same as that seen in Mlh3DN/DN males , and is significantly elevated above that seen in Mlh3-/- single mutant males ( Fig 6H ) . Taken together , these observations suggest two important features regarding class I/II interactions: ( 1 ) that a fully functional class I and class II machinery is required for appropriate distribution of MutLγ foci across the genome; and 2 ) that MUS81-EME1 activity cannot fully account for the residual chiasmata count observed in Mlh3DN/DN males .
Studies in S . cerevisiae and M . musculus have implicated MutLγ as the major resolvase of dHJs in the class I CO pathway [29 , 32–35 , 41 , 43 , 44 , 68] . The current study examines the importance of an intact endonuclease domain for the proper functioning of MLH3 during prophase I of mammalian meiosis , and is the first exploration of a point mutation for MutLγ in the mouse . We generated a mouse with a mutation in the MLH3 endonuclease domain that affects its catalytic activity while allowing for heterodimer assembly . We found that , as in Mlh3-/- mutant males , Mlh3DN/DN males are infertile , exhibit significantly smaller testes than their WT litter mates , and have no epididymal spermatozoa . Beyond this , our data reveal important similarities and differences in the meiotic phenotypes to that observed with a nullizygous Mlh3-/- allele , as articulated below . Importantly , the phenotypic consequences of loss of a functional MutLγ complex occur despite normal accumulation of both MutSγ and MutLγ indicating that the physical presence of these complexes is not sufficient to ensure complete CO resolution . However , CO licensing ( defined by MutSγ deposition ) and designation ( defined by MutSγ and MutLγ deposition ) are both normal in Mlh3DN/DN males , indicating that a functional MutLγ complex is not required for these CO-defining processes . Similarly , although normal accumulation of pro-crossover factors , CDK2 and HEI10 , is observed in Mlh3DN/DN males ( unlike the situation in Mlh3-/- males ) , this is not sufficient to drive CO resolution along the class I CO pathway . We demonstrate that an intact endonuclease domain within MLH3 is not required for DSB or synaptonemal complex formation in early prophase I , similar to that seen in Mlh3-/- males [29] . However , there are distinct differences in RAD51 accumulation and persistence in spermatocytes from Mlh3-/- and Mlh3DN/DN males , suggesting that the effect of MutLγ loss on DSB repair processing is quite different from the presence of a defective MutLγ complex . Most importantly , while RAD51 is recruited in elevated numbers to chromosome cores of Mlh3DN/DN males , it fails to be cleared effectively in pachynema , perhaps because the defective MutLγ complex blocks subsequent processing of DSB repair intermediates . Intriguingly , the significantly altered RAD51 accumulation in leptonema in both Mlh3 mutants indicates a role for MutLγ prior to pachynema , far earlier than has been defined thus far . Indeed , analogous early pairing roles for MutLγ have been proposed for M . sordaria and S . cerevisiae [42 , 53] . In yeast , Al-Sweel et al . constructed whole genome recombination maps for wildtype , endonuclease defective , and null mlh3 yeast mutants . Both the endonuclease defective and null yeast mutants for mlh3 showed increases in the number of NCO events , consistent with recombination intermediates being resolved through alternative recombination pathways [34] . Thus , in the case of yeast , loss of Mlh3 protein , or the production of an endonuclease defective protein , increases the frequency of other recombination outcomes , most notably including earlier NCO events . The absence of either component of MutLγ results in the loss of 90–95% of chiasmata , consistent with the established dogma that class I COs account for the majority , but not all , chiasmata in mammalian meiosis [6 , 8 , 29 , 39 , 68] . By contrast , loss of MUS81 , the major class II CO regulator , results in normal chiasmata levels as a result of up-regulation of class I events , as evidenced by a ~10% increase in MutLγ localization during pachynema [6] , suggesting that loss of the class II pathway leads to a compensatory increase in class I events . Furthermore , our previous analysis of Mlh3-/-Mus81-/- double mutant animals revealed a very small ( <1 on average ) , but consistent , number of residual chiasmata , indicating the existence of other resolvase complexes [6] , as has been demonstrated for yeast and plants [10 , 11] . Taken together , these observations have two important implications for crossing over in the mouse: first , additional class I CO events can be achieved through recruitment of additional MutSγ-designated precursor sites in the absence of the class II pathway ( and possibly under other circumstances too ) , and second , a few crossovers can be achieved without implementing either MutLγ or MUS81-EME1 . In the current study , we show that spermatocytes from Mlh3DN/DN males show normal accumulation of both MLH1 and MLH3 at pachynema , but this results in an increase in the residual chiasmata count at diakinesis relative to that seen in Mlh3-/- mice: approximately 78% of COs are lost in Mlh3DN/DN spermatocytes , leading to 22% residual chiasmata . This suggests several possibilities: either that the endonucleolytic function of MLH3 does not account for the resolution of all class I COs under wildtype situations , and/or that other resolvases can be recruited under certain circumstances once MutLγ loads , irrespective of whether this complex is endonucleolytically competent . Alternatively , the point mutation in the endonuclease domain does not completely eliminate endonucleolytic activity in the mouse , resulting in partial class I resolvase activity . We find this latter possibility unlikely due to the severity of the defect in endonuclease activity in the S . cerevisiae Mlh1-mlh3-D523N complex [32 , 33] , but we were unable to test this in the current analysis . Another explanation for the difference in chiasmata counts between Mlh3DN/DN and Mlh3-/- mice is that , in the former , the existence of a defective MutLγ prevents most class I-type COs , but facilitates the resolution of recombination intermediates through alternative pathways . Thus , despite the presence of the MLH3DN protein , some class I CO events can be processed by other CO machinery under conditions of normal accumulation of pro-crossover factors , MutSγ , HEI10 and CDK2 . While we cannot assess the recruitment of the class II machinery to sites of DSB repair in prophase I in the mouse ( due to the lack of available reagents ) , there is evidence to support the idea that MUS81-EME1 might participate in this CO resolution crosstalk . Specifically , double mutants lacking Mus81 and bearing a homozygous Mlh3DN allele show reduced chiasmata relative to Mlh3DN/DN males , indicating that MUS81 may account for at least some of the increase in chiasmata above that of Mlh3-/- males . Arguing against this idea is the observation that the loss of Mus81 on the Mlh3-/- background results in a similar drop in residual chiasmata from Mlh3-/- single mutant males alone to that observed in Mlh3DN/DNMus81-/- double mutant animals compared to Mlh3DN/DN single mutant animals . Previous analysis of Mus81-/- single mutant males revealed a compensatory increase in MutLγ in pachynema that resulted in normal crossover numbers as a result of upregulation of class I CO events [6] . The persistence of RAD51 at several foci in late pachynema in these Mus81-/- males suggested that the class II-destined DSBs were not repaired , but that additional COs were derived from increased CO designation from the larger pool of MutSγ-loaded DSB repair intermediates , suggesting some crosstalk between CO pathways to achieve CO homeostasis . Further evidence for crosstalk between the two major crossover pathways is provided in the current study . However , this elevated MLH1 focus frequency is not observed in Mlh3DN/DNMus81-/- and Mlh3+/DNMus81-/- males , suggesting that a functional MutLγ is required for this crosstalk . Intriguingly , loss of Mus81 on the Mlh3DN/DN and Mlh3+/DN backgrounds results in altered distribution of MLH1 across the genome , resulting in elevated numbers of chromosomes lacking an MLH1 focus entirely . This increase in “E0” chromosomes does not occur in either Mus81-/- or Mlh3DN/DN single null males , indicating that CO distribution is dependent on the functionality ( or partial functionality ) of both pathways . While we cannot fully explain the reason for altered MLH1 distribution in both Mlh3DN/DNMus81-/- and Mlh3+/DNMus81-/- males , these observations point to complex interplay between crossover pathways in achieving normal distribution of crossover events in mammalian meiosis . Indeed , our recent studies involving a mouse model harboring a point mutation within Msh5 indicates that altered MutSγ function affects both crossover pathways [69] . Our previous studies , along with the current one , indicate a role for BLM helicase in modulating the pathway choice in DSB repair during mouse meiosis . In Mlh3-/- [56] and Mlh3DN/DN ( current work ) males , prophase I spermatocytes show increased and persistent accumulation of BLM helicase through until late pachynema . A similar increase in BLM localization was also noted in Mus81-/- spermatocytes [6] . In S . cerevisiae , loss of class I CO pathway components ( for example , in msh4/5 or mlh1/3 mutants ) is suppressed by mutation of the BLM ortholog , Sgs1 , highlighting the role of Sgs1 as an anti-crossover factor . However , the additional CO events that arise in these double mutant yeast strains are presumed to be processed via Mus81-dependent resolution , and thus via class II CO events [13 , 36 , 70] . In msh4/5 sgs1 double mutant strains , the restoration of COs occurs without any concomitant decrease in NCO events , suggesting either that other CO pathways account for the non-class I COs , or that these DSBs are repaired via inter-sister repair processes . In this sense , Sgs1 has been proposed to be master orchestrator of recombination pathway choice [36] , while the Sgs1-Top3-Rmi1 complex as a whole can regulate CO formation both positively and negatively in yeast [23 , 24] . The situation we observe in Mlh3-/- and Mlh3DN/DN males with respect to BLM persistence may be similar to that seen in yeast for Sgs1 , in that up-regulation of BLM foci is observed in both Mlh3 mutant lines from zygonema onwards , but is significantly higher in Mlh3DN/DN males at pachynema . Thus , residual chiasmata counts in Mlh3-/- and Mlh3DN/DN males are proportional to pachytene BLM focus counts ( lower in full nulls , higher in Mlh3DN/DN males ) . Thus , we can postulate that the loss of MLH3 protein entirely in Mlh3-/- males results in a compensatory , but ineffective , increase in BLM that cannot overcome the failure to process class I COs sufficiently ( a situation that is different to yeast ) . In the presence of intact , but catalytically inert MutLγ , on the other hand , the availability of additional BLM foci can then direct DSB repair in favor of other CO pathways in a similar fashion to the situation in yeast , where the engagement of Sgs1 promotes alternative repair mechanisms , primarily through the recruitment of structure specific nucleases , and the resolution of some dHJs through a class II ( or other ) CO pathway [13 , 36 , 70–72] . We provide evidence that COs are achieved in Mlh3DN/DN spermatocytes in a manner that may be dependent on the MUS81-EME1 endonuclease , or on other resolvase complexes that have yet to be determined in mammalian meiosis . Indeed , our previous analysis of Mlh3-/-Mus81-/- males indicated the existence of additional CO events that were independent of the class I and class II pathways [6] . Additional resolvases in yeast include SLX1-SLX4 and YEN1/GEN1[35 , 73 , 74] . The persistence of DSB repair intermediates into pachynema , along with the upregulated and persistent BLM might suggest that the defective MutLγ complex prevents accumulation of other such resolvase complexes . This might , in turn delay CO maturation until later in prophase I when , for example , GEN1 can be invoked to resolve the CO [75] . Thus , we propose that the timing of MutLγ activity , and its clearance from nascent COs is an important factor in the recruitment of alternative CO processing machineries , but in a manner that is not dependent on its endonuclease activity .
Work performed in this manuscript was approved by the Cornell Institutional Animal Care and Use Committee , under protocol 2004–0063 . A PL253 targeting vector containing the Mlh3-D1185N point mutation in the potential endonuclease domain and a loxP-neo-loxP cassette in intron 5–6 of Mlh3 was incorporated into an embryonic stem cell line . Mlh3DN transgenic mice were crossed with a Spo11-Cre mouse line to remove the neo cassette [76] , and then maintained on an inbred background through backcrossing on to the C57Bl/6J line ( Jackson Laboratory , Bar Harbor , ME ) . Genotyping of WT , Mlh3+/DN , and Mlh3DN/DN mice was performed using the following PCR primer pairs: forward ( 5’-AAGCCAAGTCTGCATGAGTA-3’ ) and reverse ( 5’-TAAATGTGCCACTGACTAAAT-3’ ) followed by a restriction enzyme digestion with Sau96I ( New England Biolabs ) at 37°C for 2–3 hours , which results in 439-bp and 263-bp fragments from the WT allele and a 702-bp fragment from the mutant allele . Fertility tests were performed by breeding Mlh3DN/DN adult males with WT females . At least 3 males of each genotyped were evaluated . Presence of a copulation plug the following morning counted as a successful mating event . Pregnancy was confirmed by gentle palpation of the abdomen after gestation day 11 or on delivery date of litters . Mus81-/- animals were generated from our breeding stock of such mice , as previously described [6] . Mice were housed and utilized under the guidance and approval of the Cornell University Institutional Animal Care and Use Committee . Testes from adult mice were fixed in Bouin’s solution overnight at room temperature and then washed 3 x 10 min with 70% ethanol at room temperature with agitation . Fixed and paraffin-embedded testes were section at 5 μm . H&E staining was performed on Bouin’s fixed testes using standard methods . At least 6 males of each genotyped were evaluated . Caudal epididymides were removed from adult males and placed in pre-warmed 1X PBS containing 4% bovine serum albumin . Sperm were released into solution by squeezing epididymis with tweezers and incubated for 20 min at 32°C/5% CO2 . After incubation , 20 μL of sperm suspension was re-suspended in 480 μL of 10% formalin . Sperm counts were performed with a hemocytometer . At least 10 males of each genotype were evaluated for sperm counts and testis weights . Prophase I chromosome spreads from adult testes were prepared as previously described [28 , 61] . For all experiments , at least 6 males of each genotyped were evaluated . Chromosome slides were then washed in 0 . 4% Kodak Photo-Flo 200/1X PBS for 2 x 5 min , 0 . 4% Kodak Photo-Flo 200/dH2O for 2 x 5 min , then air-dried for approximately 10 min and stored in -80°C or used immediately for staining . Primary antibodies used were: anti-γH2AX ( Millipore , NY , #05–636 1:10 , 000 ) , anti-SYCP3 ( Abcam , MA , #97672 , 1:5000 ) , anti-SYCP1 ( Abcam , MA , #15087 , 1:1000 ) , anti-RAD51 ( Calbiochem , #PC130 , 1:500 ) , anti-BLM ( generous gift from Dr . Ramundo Freire; 1:100; ) , anti-CDK2 ( Santa Cruz , TX , sc-163; 1:250 ) , anti-MLH3 ( [61]; 1:1000 ) , anti-RNF212 ( generous gift from Dr . Neil Hunter ) , anti-RPA ( generous gift from Dr . Jeremy Wang; 1:500 ) , anti-MSH4 ( Abcam , MA , #58666; 1:500 ) , anti-HEI10 ( Anti-CCNB1IP1 , Abcam , MA # 71977 ) and anti-MLH1 ( BD Biosciences Pharmingen , CA , #550838 , 1:100 ) . Secondary antibodies used were: goat anti-mouse Alexa Fluor 488 ( #62–6511 ) , goat anti-mouse Alexa Fluor 555 ( #A-10521 ) , goat anti-rabbit Alexa Fluor 488 ( #65–6111 ) , goat anti-rabbit Alexa Fluor 555 ( #A-10520; all Invitrogen , 1:2000 ) . Diakinesis chromosome spreads were prepared as previously with slight modifications [61 , 77] . Slides were stained with 10% Giemsa for 10 mins , washed , air-dried and mounted with Permount . All chromosome spread slides were visualized using the Zeiss Imager Z1 microscope ( Carl Zeiss , Inc . ) . Images were captured with a high-resolution microscopy camera AxioCam MRM ( Carl Zeiss , Inc . ) and processed with ZEN Software ( version 2 . 0 . 0 . 0; Carl Zeiss , Inc . ) . Focus counts were performed manually by at least two people , and the results averaged before analysis . For RPA , we employed and ImageJ algorithm for automated counting , as described [69] , and compared this automated calculation to manual counts for consistency . Manual and automated counts were not statistically significantly different to each other . cDNA was synthesized from total testis RNA from wildtype C57B/6J adult males using the SuperScript III Reverse Transcriptase Kit from ThermoFisher . Mlh1 and Mlh3 open reading frames were PCR amplified from cDNA using Expand High Fidelity DNA polymerase using primer pairs AO3365 ( 5’GCTAGCAGCTGATGCATATGGCGTTTGTAGCAGGAG ) and AO3366 ( 5’TACCGCATGCTATGCATTAACACCGCTCAAAGACTTTG ) for Mlh1 , and AO3367 ( 5’ACGTCGACGAGCTCATATGCATCACCATCACCATCACCATCACCATCACATCAGGTGTCTATCAGATGAC ) and AO3368 ( 5’CGAAAGCGGCCGCGATCATGGAGGCTCACAAGG ) for His10-Mlh3 . Each fragment was cloned into the Spe1 site of pFastBac1 ( ThermoFisher ) using Gibson assembly PCR ( NEB ) to create pEAE393 ( Mlh1 ) and pEAE397 ( His10-Mlh3 ) . Constructs were verified by DNA sequencing with NCBI reference sequences NM_026810 . 2 and NM_175337 . 2 for Mlh1 and Mlh3 , respectively . These constructs were then modified as follows: Sf9 cells were transfected with pEAE397 ( His10-Mlh3 ) , pEAE413 ( His10-Mlh3-D1185N ) and pEAE395 ( MBP-Mlh1 ) using the Bac-to-Bac baculovirus infection system ( Invitrogen ) . Fresh Sf9 cells were co-infected with both viruses ( containing Mlh1 and Mlh3 or Mlh3-D1185N ) . Cells were harvested 60 hours post infection , washed with phosphate buffered saline , and kept at -80°C until use . Cell pellets from 250 ml of cells as thawed , resuspended in 60 ml hypotonic lysis buffer ( 20 mM HEPES-KOH pH 7 . 5 , 5 mM NaCl , 1 mM MgCl2 , 1 mM PMSF and EDTA free protease inhibitor mixture from Roche and Thermo Scientific ) and incubated for 15 min on ice . The suspension was adjusted to 250 mM NaCl , 15 mM imidazole , 10% glycerol , 2 mM ß-mercaptoethanol ( BME ) , and clarified by centrifugation at 17 , 000 g for 20 min at 4°C . The supernatant was mixed with 6 ml of 50% nickel-nitrolotriaceticacid-agarose ( Ni-NTA ) resin and allowed to bind for 2 hours or overnight followed by centrifugation to remove the unbound fraction . The resin was packed onto a column and washed with 7–10 column volumes of wash buffer ( 50 mM HEPES-KOH pH 7 . 5 , 250 mM NaCl , 40 mM imidazole , 10% glycerol , 2 mM BME , 1 mM PMSF ) . Protein was eluted with 15 ml of 300 mM imidazole in 50 mM HEPES-KOH pH 7 . 5 , 250 mM NaCl , 40 mM imidazole , 10% glycerol , 2 mM BME and 1 mM PMSF . Elution fractions containing MLH1-MLH3 , determined by SDS-PAGE , were pooled and loaded onto 1 ml 100% amylose resin ( NEB ) . The resin was washed with 10 column volumes of wash buffer ( 50 mM HEPES-KOH pH 7 . 5 , 250 mM NaCl , 10% glycerol , 2 mM BME , 1 mM PMSF ) and eluted with 6 ml wash buffer containing 10 mM maltose . Fractions containing MLH1-MLH3 were pooled and aliquots were flash frozen and stored in -80°C . The protein yield , following amylose chromatography , was similar for wild-type and mutant complexes ( approximately 120–150 μg per 250 ml cells ) . It is important to note that we were unable to detect a specific endonuclease activity for the mouse MBP-MLH1-MLH3 complex , suggesting that the MBP tag interferes with MLH1-MLH3 functions . We were unable to test this directly because , despite numerous attempts , we were unable to efficiently remove the MBP tag from MLH1 by treating MBP-MLH1-MLH3 with TEV protease . SDS-PAGE bands following amylose chromatography predicted to contain MBP-MLH1 and His10-MLH3 were excised and analyzed by the Cornell University Proteomics facility using a Thermo LTQ Orbitrap Velos Mass Spectrometer . The majority of comparisons involved with unpaired parametric t-test with Welch's correction or nonparametric Mann-Whitney U-test , depending on the data distribution . Where necessary , Bonferroni’s adjustment was used for multiple comparisons . All statistical analysis was performed with GraphPad Prism Version 7 . 00 for Mac , Graphpad Software , La Jolla California USA , www . graphpad . com . P-values less than 0 . 05 were considered statistically significant . | Meiosis is a specialized cell division whereby a diploid cell undergoes one round of DNA replication followed by two rounds of division , yielding up to four haploid gametes . This process depends on tethering of maternal and paternal homologous chromosomes , and by the formation of crossovers ( COs ) between homologs during prophase I . COs arise from programmed double-strand breaks ( DSBs ) , and can form via one of at least two mechanisms ( class I , class II ) . In mouse , class I represents the major CO pathway , with the MLH1-MLH3 ( MutLγ ) complex being critical . MLH3 contains a conserved metal binding motif , DQHA ( X ) 2E ( X ) 4E , required for its endonuclease function , and this activity is postulated to represent a “resolvase”activity for class I COs . We generated a point mutant ( Mlh3DN ) in the endonuclease domain without altering the overall structure of MutLγ . Mlh3DN/DN males have no spermatozoa and are infertile , yet spermatocytes have grossly normal DSBs and chromosome pairing . The MLH3DN mutation permits normal loading of MutLγ , but key DSB repair factors persist in Mlh3DN/DN males , indicating a temporal delay in repair and suggesting a mechanism by which alternative DSB repair pathways may be selected . Thus , the endonuclease domain of MLH3 is important for normal processing of DSB repair intermediates . | [
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"re... | 2019 | A mutation in the endonuclease domain of mouse MLH3 reveals novel roles for MutLγ during crossover formation in meiotic prophase I |
Immune responses are regulated by diffusible mediators , the cytokines , which act at sub-nanomolar concentrations . The spatial range of cytokine communication is a crucial , yet poorly understood , functional property . Both containment of cytokine action in narrow junctions between immune cells ( immunological synapses ) and global signaling throughout entire lymph nodes have been proposed , but the conditions under which they might occur are not clear . Here we analyze spatially three-dimensional reaction-diffusion models for the dynamics of cytokine signaling at two successive scales: in immunological synapses and in dense multicellular environments . For realistic parameter values , we observe local spatial gradients , with the cytokine concentration around secreting cells decaying sharply across only a few cell diameters . Focusing on the well-characterized T-cell cytokine interleukin-2 , we show how cytokine secretion and competitive uptake determine this signaling range . Uptake is shaped locally by the geometry of the immunological synapse . However , even for narrow synapses , which favor intrasynaptic cytokine consumption , escape fluxes into the extrasynaptic space are expected to be substantial ( ≥20% of secretion ) . Hence paracrine signaling will generally extend beyond the synapse but can be limited to cellular microenvironments through uptake by target cells or strong competitors , such as regulatory T cells . By contrast , long-range cytokine signaling requires a high density of cytokine producers or weak consumption ( e . g . , by sparsely distributed target cells ) . Thus in a physiological setting , cytokine gradients between cells , and not bulk-phase concentrations , are crucial for cell-to-cell communication , emphasizing the need for spatially resolved data on cytokine signaling .
Cell-to-cell communication is a defining property of multicellular organisms . In particular , the release , sensing and uptake of cytokines , small signaling proteins , by cells is essential for the regulation of the mammalian immune system [1] . Prominent quantitative characteristics of cytokine signaling are high receptor specificity ( with Kd ≈ 10−10 nM ) and low free cytokine concentrations in the picomolar range [2 , 3] . The physiological cytokine milieu regulates critical processes like the type and strength of the immune response . Quantitative understanding of such cytokine-driven cellular decisions is beginning to emerge [4–8] , yet the underlying spatio-temporal cytokine dynamics remain poorly understood . Cytokines act in a heterogeneous environment , typically with high cell-densities . It is not known how they diffuse under such conditions and , in turn , regulate immune responses . Specifically , how far cytokines can signal away from the producing cell is not clear . Perona-Wright et al . [9] have found that interleukin ( IL ) -4 is seen by most T cells in the lymph node upon parasite infection , including non-specific ‘bystander’ cells . In this case , many T cells throughout the lymph node could be IL-4 producers . By contrast , several observations suggest more localized cytokine communication [4 , 10–13] . Given the low measured cytokine concentrations , which are often below 10 pM , the question arises whether and how effective paracrine signals are possible at all , in a situation where only a certain fraction ( ~25% ) of the cells secrete cytokine molecules . Of note , 1 pM is about 1 molecule in 1700 μm3 , compared to ~500 μm3 volume of a typical lymphocyte . Higher , systemically elevated cytokine levels arise only in certain immunopathologies , so-called ‘cytokine storms’ , where they cause severe damage [14] . However , it has been demonstrated that cytokine concentrations are not always well mixed , and locally higher cytokine concentrations can occur also in ex vivo T cell cultures [12] . Therefore , we asked how and under which conditions such cytokine gradients arise , and if they are able to explain effective paracrine signals . One possibility to enrich cytokine concentrations would be localized signaling to specific target cells by an immunological synapse [15–18] . Immunological synapses are formed between immune cells by surface proteins after antigen recognition [15 , 16 , 19] . They have been observed between various cell types of the immune system , including immunological synapses between T cells and antigen presenting cells ( APC , e . g . B cells [10] and dendritic cells [20] ) , and immunological synapses between T cells and T cells [12] . Many cytokines are secreted preferentially into the immunological synapse [10 , 21–23] , and a range of high-affinity cytokine receptors have been found to be specifically located in the immunological synapse , too [11] . Therefore , it is likely that the synapse has an important function for cytokine signaling , beyond its role for T cell receptor signaling on which theoretical studies have focused [24 , 25] . Cytokine signaling through immunological synapses might also explain the pleiotropic effects observed for most cytokines , as it would provide specificity of cytokine signaling by restriction of their action . Nevertheless , it is unlikely that paracrine cytokine signals are only possible between cells that are directly connected by an immunological synapse . For instance , Sanderson et al . [26] found that interferon-γ can be seen by bystander cells other than the target cells to which the synapses are formed . To understand which parameters govern autocrine versus paracrine cytokine signaling , we analyzed in this study reaction-diffusion models of cytokine signaling at two scales: through the immunological synapse between two cells and in three-dimensional arrays of many ( >100 ) cells . For this purpose , we chose the cytokine interleukin ( IL ) -2 as a model system , a cytokine showing polarized secretion and corresponding receptor expression [10 , 11 , 22] . IL-2 was first identified as a T cell growth factor [27] , but , paradoxically , is a critical mediator of immune tolerance [28–31] . It is secreted by T helper ( Th ) cells early after antigenic stimulation and taken up by high-affinity IL-2 receptor ( IL-2R ) on Th cells and regulatory T ( Treg ) cells [28–31] . Treg cells mediate immune tolerance and are critical for the prevention of autoimmune reactions [32 , 33] . IL-2 secretion is digital , i . e . upon receiving an antigen stimulus , only about one quarter of a Th cell population releases IL-2 molecules [34–36] . It is an open question if IL-2 and other cytokine signals act in an autocrine or paracrine manner [31 , 37] . In response to IL-2 uptake , Th cells and Treg cells upregulate CD25 , the α-subunit of the IL-2R . CD25 is often used as an activation marker of T cells , because it precedes proliferation of Th cells and subsequent recruitment of effector immune cells [30 , 38] . Although IL-2 secreting Th cells upregulate CD25 , Long and Adler [37] reported that they lack phosphorylated STAT5 , a key intermediate in the IL-2R signal transduction cascade . In the same experiment , other Th cells not secreting IL-2 also upregulate CD25 in response to IL-2 , and in addition show fully functional signal transduction [5 , 37] . These data suggest that the dominant mode of IL-2 signaling is paracrine , in contrast to the presumed function of the immunological synapse in containing secreted cytokines [16 , 17] . However , unlike T cells , most APC do not express functional IL-2 receptor ( IL-2R ) [39] . Thus , both the study by Sanderson et al . [26] and the properties of IL-2 signaling suggest a role of the immunological synapse for cytokine signals that goes beyond signal amplification between the two cells associated by a synapse . In this study , we addressed the question of how and under which conditions paracrine cytokine signals occur despite the measured low bulk concentrations in the picomolar range , and we aimed to define the parameters that control the range of cytokine signaling . To this end , we considered the two key spatial scales , the sub-μm scale of the immunological synapse and the supra-μm scale of cell-to-cell communication . We investigated reaction-diffusion models on these two scales by analytical techniques and advanced finite-element computations in three spatial dimensions [40–44] . To be specific , we utilized a simple , experiment-based mathematical model for IL-2 signaling and gained more general insight through systematic variation of parameters . Our results show that paracrine cytokine signaling is possible in the presence of local concentration gradients combined with nonlinear signal amplification . The spatial range of cytokine signaling can be tuned from purely autocrine via intrasynaptic and short-range paracrine to long-range paracrine . For a wide array of parameters , we found that cytokine gradients in dense multicellular environments range over one to few cell diameters . These computational findings can inform novel experiments probing the spatio-temporal dynamics of cytokine signaling [45] .
The binding of cytokines to their high-affinity receptors is followed by receptor internalization and intracellular cytokine degradation , so that cytokine molecules are removed from the medium ( Fig 1A ) . Thus , regulating the strength of cytokine signaling by cytokine receptor expression might also affect the extracellular cytokine concentration and hence , indirectly , signaling . To gain quantitative insight , we first studied a simple reaction-diffusion model , where a cytokine-secreting cell is surrounded by cells that can take up the cytokine . To allow for an analytical solution , we assume the surrounding cells to be placed on a spherical shell with the secreting cell in the center ( Fig 1B , see Materials and Methods ) . For convenience , parameter values are summarized in Table 1 . If the target cells are located far away ( i . e . , their density is low ) , the cytokine concentration experienced by the target cells is nearly independent of the level of receptor expression ( Fig 1C ) because the dilution of the cytokine occurs primarily by diffusion in the three-dimensional tissue . On the other and , if the density of target cells is so high that they immediately surround the cytokine secreting cell , the cytokine concentration is practically homogeneous in the small intervening space , as the timescale of diffusion over such a short distance is fast compared to the timescale of cytokine uptake ( Fig 1D and S1 Text ) . As a consequence , the cytokine concentration experienced by proximal target cells is set by the balance of secretion rate by the cytokine-producing cell and uptake rate . The autocrine and paracrine uptake rates Jauto and Jpara depend on the level of cytokine receptor expression on the target cells ( Fig 1E ) , and are practically independent of the cell-to-cell distance even at high cell density ( Fig 1F; the low cell-density scenario is independent of the cell-to-cell distance by construction ) . Interestingly , cytokine concentration ( Fig 1C ) and uptake rates ( Fig 1D ) are sensitive to receptor expression on proximal targets cells in the physiologic range of 100 to several 1000 receptor molecules per cell [5] . Thus , this simple model indicates that with a high density of target cells , cytokine receptor expression controls the amount of paracrine cytokine signal . The model of the previous section assumed homogeneous secretion of the cytokine over the cell surface . However , T cells release IL-2 and other cytokines in a polarized fashion into the immunological synapse [10 , 21–23] . Therefore , we analyzed a model of cytokine secretion and uptake in the immunological synapse , represented by a small cylindrical region between a Th cell and an opposed APC or second T cell ( Fig 2A , see Materials and Methods ) , extending previous work [46] . The distance between Th cell and opposed cell , in the following referred to as synaptic distance , is in the range of 10 to 40 nm [19 , 47] . This close contact between Th cell and opposed cell causes a cytokine concentration profile which is almost homogeneous between the two cells in the center of the synapse , and sharply falls off towards the outer boundaries through which cytokine molecules are lost practically irreversibly ( Fig 2B , top ) . In the case of low receptor expression ( Fig 2B , top left ) , the cytokine concentration reaches values in the nM range . Thus , the synaptic cytokine secretion results in locally much higher concentrations than homogeneous secretion ( see Fig 1C ) , in line with experimental data [12] . For comparison , consider cytokine secretion into a cylindrical region with length 2 μm , a typical value for nearby cells but much larger than the immunological synapse ( Fig 2B , bottom ) . In this case , the cytokine concentration falls to less than 20 pM at the surface of the opposed cell . Hence , the very small synaptic distance in a fully formed immunological synapse is crucial for the establishment of high local cytokine concentrations . The immunological synapse causes two conceptually different types of paracrine signals: Cytokine molecules may bind to cytokine receptors at the opposed cell ( Jsynapse ) or escape into the extracellular space ( Jescape ) , potentially reaching other nearby cells . Cytokine molecules may also induce autocrine signals by binding to receptors at the secretory cell ( Jauto ) . Fig 2C shows the fractions of Jauto , Jsynapse and Jescape , choosing IL-2 receptor densities that are characteristic of naïve ( IL-2Rlow ) or preactivated ( IL-2Rhigh ) IL-2 secreting T cells , and for opposed cells with different IL-2R expression . IL-2Rhigh cells recapture most of the secreted IL-2 molecules , irrespective of the type of opposed cell and the synaptic distance . Naïve , IL-2Rlow cells show a strong dependence on the synaptic distance ( Fig 2C , left ) . If the synaptic space is sufficiently narrow , Jescape is small; the escape flux could be even further reduced by adhesion molecules sealing off much of the synapse from the extracellular space . If the opposed cell is a second T cell , then the secretory cell and the opposed cell compete for the cytokine molecules . Treg cells outcompete Th cells due to their large receptor number . On the other hand , APCs do not express IL-2R and the IL-2 signals would be purely autocrine . For synapses with somewhat larger synaptic distances , a considerable amount of cytokine molecules can escape ( enhanced Jescape ) and provide paracrine signals to surrounding cells . Interestingly , the ratio of Jauto and Jescape is most sensitive to the synaptic distance in the physiologic range between 10 nm in the close-contact zone and 40 nm in the outer region of the immunological synapse [19 , 47] . In all cases , the fraction Jescape of cytokine molecules that escape for paracrine signaling is considerably smaller than in the case of homogeneously distributed cytokine secretion and receptor expression ( Figs 2C and 1D ) . However , even if the cytokine secreting cell is pre-activated and hence autocrine IL-2 uptake is high ( IL-2Rhigh ) , a sizeable fraction of cytokine molecules still diffuses out of the synapse ( Jescape ~20% ) . In summary , our model reveals two main implications of an immunological synapse for cytokine signaling: A tight synapse causes highly localized cytokine distributions , and it enhances the probability of autocrine recapture . Both properties result from the high aspect ratio of the radius of the cell contact area and the synaptic distance ( r and z in Fig 2A ) . The two properties have opposing effects on the strength of paracrine cytokine signals: While localized cytokine concentrations increase the likelihood of a local paracrine signal , the reduction in effective cytokine secretion reduces the potential of paracrine signals . The analytically tractable models gave insight into the qualitative properties of paracrine cytokine signaling , and they made quantitative predictions on the consequences of the various time and length scales in the system . For example , the high aspect ratio of the immunological synapse evokes highly localized cytokine concentrations in the vicinity of cytokine secreting cells resembling secretion from a point source ( see Fig 2B ) , and the high diffusion constant in relation to the receptor dynamics makes the system largely independent of the cell-to-cell distance ( Fig 1E and 1F ) . However , the simple models studied above cannot answer the question if effective paracrine signals are possible despite the low bulk cytokine concentrations . To illustrate this problem , consider a classical formula from Berg and Purcell for the timescale of ligand diffusion towards a receptor [48] ( Materials and Methods ) . Measured cytokine concentrations in serum or in supernatants of ex-vivo T cell cultures are typically in the picomolar range [2 , 3] . Assuming a spatially uniform cytokine concentration of 10 pM and a receptor number of 100 per cell , as is typical for the high-affinity IL-2R on naïve T cells , that calculation reveals that on average , every 7 min a receptor becomes bound by a cytokine molecule . Under these conditions it would take hours to induce a reliable signal , indicating that bulk cytokine concentrations might just be capable of , or even be too low for , stimulating signal transduction . However , it has been reported that IL-2 is subject to appreciable spatial gradients , with much higher concentrations at the surfaces of T cells [12 , 49] . To investigate the origins and consequences of spatially inhomogeneous dynamics of cytokine signaling , we performed extensive three-dimensional simulations of a T cell population ( Fig 3A and 3B ) . As before , we focus on the cytokine IL-2 , for which many parameters , including secretion and receptor expression rates , have been estimated from experiments [5 , 35 , 50] , and experimentally tested models for the IL-2R dynamics are available [4 , 5 , 7] . To account for polarized secretion at the immunological synapse , we do not explicitly model synapse formation but consider the effect of discrete IL-2 sources from which IL-2 escapes into the extra-synaptic space ( with rate qeff , corresponding to Jescape in the simplified model of the previous section ) . The position of the IL-2 source of a producing cell is a randomly chosen point at the cell surface . IL-2 secretion is all-or-nothing [34 , 36]: only about one quarter of antigen-stimulated T cells release IL-2 molecules , and among these cells , the IL-2 secretion rate is in the range of 10 molecules per second [35] . In accordance with experimental data , already activated IL-2 secreting cells have high IL-2R expression which , for simplicity , we take as constant [37] . Non-secreting cells are assumed to upregulate IL-2R expression in response to IL-2 homogeneously at their cell surface . Consistent with experimental data [39] , APC themselves do not express IL-2R but constitute simply ‘excluded volumes’ with respect to the IL-2 dynamics . To focus on the role of IL-2 uptake by T cells , we do not consider the APC explicitly , but only its consequences for polarized secretion and uptake ( see above ) ( Table 1 ) . Despite this simplification , our simulations consider realistic extracellular volumes ( as determined by the cell distances ) between the cells as a basis for determining the extracellular concentrations of the secreted cytokines . Based on these assumptions , we simulated the IL-2 dynamics for a large number of T cells ( 216 cells in a volume of ~1 nl , Fig 3 ) . Stimulating IL-2 secretion in a fraction of T cells ( Fig 3A and 3B ) , the IL-2 concentration increases rapidly and nearly homogeneously for several hours after stimulation ( Fig 3C and 3D and S1 Fig ) . Then , in response to the high IL-2 concentration resulting from paracrine signaling , IL-2R expression is upregulated in non-secreting cells ( Fig 3E ) and causes fast IL-2 uptake from the medium . As a result , concentration gradients occur: In large parts of the simulated region , the IL-2 concentration reaches a steady-state at around 10 pM while locally it is more than twice as large ( Fig 3C , red regions at 30 h ) . This inhomogeneity in IL-2 concentration corresponds to receptor upregulation ( activation ) of non-secreting Th cells: IL-2Rhigh cells are found near the regions with high IL-2 concentration . Analysis of the time course ( Fig 3E ) shows that all cells upregulate IL-2R levels in response to the increased IL-2 concentration in the first hours after antigenic stimulation and IL-2 secretion . However , as the high-affinity IL-2R is being upregulated , IL-2 becomes increasingly depleted in the medium . As a result , only a fraction of the cells receive a sufficient IL-2 stimulus to sustain high IL-2R expression ( IL-2Rhigh cells in Fig 3E ) , whereas the remaining cells downregulate IL-2R expression ( IL-2Rlow cells ) . Interestingly , the time courses of IL-2 concentrations at the surfaces of the cells show only small differences between IL-2Rhigh and IL-2Rlow cells ( Fig 3F ) : In the beginning , IL-2 equally rises near IL-2Rhigh and IL-2Rlow cells ( see Fig 3D ) , but as IL-2 depletion sets in , the cells that eventually become IL-2Rlow cells receive slightly less IL-2 . Later , at steady-state , the IL-2 concentration is somewhat higher in the microenvironment of IL-2Rlow cells , because they do not consume as many IL-2 molecules . This form of local bistability , which occurs in the expression of IL-2R on Th cells , was observed already in Ref . [4]: Based on a quasi-stationary state assumption , Busse et al . showed that in the model without Treg cells , the IL-2R expression rate responds to the increase of the secretion rate in a digital way and the cells are activated only after a certain threshold is exceeded . A small bistable region around the threshold is observed . These findings were supported by experimental data from primary T cells cultured ex vivo [4] . Thus our present model with the immunological synapse and 3D diffusion matches the bistable system behavior seen in the simpler analytical model . Taken together , our simulations indicate that the amount of IL-2 escaping from the immunological synapse is sufficient to sustain paracrine signaling in at least a fraction of surrounding cells . However , competition for the cytokine can cause heterogeneity in the response of a cell population and result in bulk IL-2 levels that are much lower than local concentration peaks and in agreement with concentration levels measured by ELISA ( see Discussion ) . Regulatory T cells constitutively express high levels of high-affinity IL-2R but do not secrete IL-2 [29 , 30] . To study the effect of Treg cells on the IL-2 dynamics after activation of conventional Th cells , we simulated a T-cell population consisting of antigen-stimulated IL-2 secreting and non-secreting Th cells as well as Treg cells ( Fig 4A and 4B ) . Compared to the situation in the absence of Treg cells ( cf . Fig 3 ) , the IL-2 concentration attains a spatially inhomogeneous steady state more rapidly , with the overall IL-2 concentration being lower ( Fig 4C and 4D and S2A Fig ) . Importantly , the non-secreting Th cells do not permanently upregulate IL-2R in the presence of Treg cells because the Treg cells suppress the paracrine IL-2 signal . The comparison with the simulations without Treg cells ( Fig 3 ) imply that Th cells require for sustained IL-2 signaling both a transient strong and a stable weak IL-2 stimulus . The finding that Th cells can sustain IL-2 signaling at low cytokine concentration , but only after initial stimulation with high cytokine concentration , is a spatio-temporal phenomenon similar to hysteresis: Active cells express more cytokine receptors , which bind more cytokine molecules even at lower concentration and thus stabilize the active state once it is achieved . Treg cells can suppress prolonged IL-2 signaling in Th cells by inhibiting the strong initial IL-2 signal and the resulting upregulation of the high-affinity IL-2R . Having established that an effective paracrine IL-2 signal is possible in our model , and that it can be suppressed by Treg cells , we analyzed to which extent key parameters shape the spatio-temporal dynamics: IL-2 secretion rate , cell-to-cell distance , and fraction of IL-2 secreting cells . Without Treg cells , the number of activated Th cells increases linearly with the effective IL-2 secretion rate qeff until , eventually , all cells in the simulated region become active ( Fig 4E , left panel ) . By contrast , the presence of Treg cells creates a threshold at an effective secretion rate of qeff ~ 20000 molecules/h ( about 5 molecules/s ) , below which there is no paracrine IL-2 signaling between Th cells . The same pattern is observed if we vary the fraction of cytokine secreting cells instead of the effective secretion rate ( Fig 4E , middle panel ) , which reflects digital IL-2 secretion [34 , 36] . Hence the presence of Treg cells changes the paracrine IL-2 dynamics from a gradual to an all-or-none response: Either the paracrine signal is completely suppressed by competitive uptake , or suppression is overrun and all cells are activated . Note that qeff measures only the IL-2 molecules that escape from the immunological synapse; assuming a tight synapse , this would only be 20% of the total secretion ( see Fig 2 ) . However , measured IL-2 secretion rates are ~10 molecules/s [35 , 50] , which is likely to be too small to titrate out the Treg cells in a physiological setting where IL-2 is secreted into the synapse . Within the range from 2 to 20 μm [12] , the cell-to-cell distance ( measured between cell surfaces of neighbored cells ) does not influence the amount of Th cells that become activated by the paracrine IL-2 stimulus ( Fig 4E , right panel ) . This is because , as anticipated by the analytically treatable model ( see Fig 1F ) , cytokine molecules can reach nearby cells rapidly by diffusion compared to the slower time scales of changes in IL-2R expression and IL-2 internalization . Thus , the exact cell-to-cell distance is unimportant in the physiological range . Our simulations yielded global elevations in IL-2 concentration only transiently before the target cells expressed high levels of IL-2R; beyond this point , only short-range IL-2 gradients were observed , with local concentrations governing IL-2 signaling . Generally , we expect that the balance between cytokine secretion , dilution through diffusion in the three-dimensional extracellular space and cellular consumption will determine the signaling range . To understand the interplay of these three factors , we performed large-scale simulations of an area containing ~2000 cells , with a single IL-2-secreting Th cell surrounded by non-secreting Th cells which all are potential responders to the IL-2 ( Fig 5A ) . Although we use the specific parameters for IL-2 here , this model is of more general interest and applies to other situations with few signaling cells and many responder cells ( e . g . , IL-4 secreting Th cells in a B cell population [9] ) , or can be thought of as representing a cluster of several cytokine secreting cells in a population with a small density of cytokine secreting cells elsewhere . We found that for the secretion rates estimated for IL-2 [35 , 50] , high IL-2 concentrations are restricted to the microenvironment of the cytokine secreting cell ( Fig 5B ) . Remarkably , although secretion is assumed to be polarized through the synapse , the cytokine concentration is higher along the entire surface of the secreting cell , including the pole opposite to the synapse , than at nearby cells . This is due to the absence of IL-2R on the surface of secreting cells ( except for the synaptic space ) . For larger secretion rates ( of the order to 106 molecules/h or 280 molecules/s ) , the IL-2 signal reaches hundreds of cells . However , with the experimental estimate for the IL-2 secretion rate ( 10 molecules/s [35 , 50] ) , of the order of a 100 secreting cells would be needed to realize such a high rate ( assuming an effective secretion rate of 10-20% of the total rate , see Figure 2 ) . Therefore , IL-2 from an individual producer will act locally whereas only large clusters of activated cells could cause long-range signals . The occurrence of two distinct spatial signaling regimes as a function of secretion rate is expected because the cellular uptake rate can be saturated by high cytokine concentrations ( akin to an enzymatic Michaelis-Menten rate law where the cytokine receptors function as the enzyme ) . Below saturation the cytokine signal remains local . Interestingly , the spatial range scales linearly with the logarithm of the secretion rate ( Fig 5C ) . Hence the signaling range exhibits a fold-change response to the effective secretion rate ( see Discussion ) . To further analyze the properties of cytokine diffusion , we computed the traveling distance of cytokine molecules , i . e . the distance from the cytokine secreting cell at which a ligand is taken up by a receptor . For this purpose , we simulated a pulsed , homogeneous stimulation ( see Methods ) in a region covering ~5000 cells . We found that despite the apparent short-range induction of effective paracrine signaling , the traveling distance has a broad distribution peaking around four cells away from the cytokine secreting cell in each direction ( Fig 5D ) . Thus , in our reaction-diffusion system , the chemical reactions on the cell surface dominate the diffusion and determine the IL-2 gradient formation . We further compared the distribution of traveling distances with earlier analytical expressions obtained from a reaction-diffusion model of morphogen gradient formation [51] . Despite some differences in the model architecture ( see Methods ) , and despite numerical limitations in simulating an ‘infinite domain’ as assumed by the analytical methods of Ref . [51] , our simulations are in good agreement with those analytical results ( S3 Fig ) . Taken together , our simulations of long-range cytokine diffusion and uptake show that long-range paracrine signals are possible in principle , but require exceptional circumstances ( extremely high rates of cytokine production or large clusters of cytokine producing cells ) that might not readily occur in vivo .
The spatial regulation of cytokine signaling in the immune system has spurred much interest , particularly in relation to the specificity of cytokine action [4 , 9–12 , 22 , 23 , 26] . Experimentally , however , cytokine signaling has not been probed directly at fine spatial resolution , although recent advances in synthetic biology could provide new tools in the near future [45] . Here , we used a computational approach to study cytokine signaling in realistic three-dimensional geometries . To this end , we considered two distinct spatial scales . First , we analyzed polarized signaling across narrow junctions – immunological synapses – between immune cells ( nm scale ) . We find that synapses enhance autocrine signaling and signaling towards the cell connected by the synapse , but , importantly , cannot prevent substantial cytokine escape for paracrine communication by mere geometry . Second , we employed advanced simulation tools for partial differential equations to dissect the dynamics of this ‘spill-over’ paracrine signal in dense ensembles of hundreds of communicating cells ( μm scale ) . Using experimentally established parameters for the T-cell cytokine IL-2 , we find that cytokine signals emanating from producing cells are short-range ( one to few cell-to-cell distances ) because of uptake by target cells or competitors . Long-range communication requires coherent secretion by tens to hundreds of producers or/and sparse uptake . Thus we predict that gradients at the cellular length scale are a key property of cell-to-cell communication by cytokines . We note that the spatial range of diffusible signals is also of relevance for morphogen action [52 , 53] . In contrast to immune cell signaling with a typical time scale of many hours during which diffusive gradients reach steady state , the transient behavior on shorter time scales is of particular interest for morphogen gradients [51 , 54] . Cytokine concentrations as measured by ELISA studies in cell supernatants are typically very low , in the picomolar range [3 , 5 , 49] . As low cytokine concentrations would imply long signaling times ( see Eq 1 below ) , we hypothesized that paracrine cytokine signals rely on much higher cytokine concentrations in the microenvironment of target cells , which have indeed been detected by live cell imaging [12] . However , it is generally believed that cytokine signaling occurs in the regime of fast diffusion , which is reflected by our parameter values—the typical spatial range of fast diffusion , D/kd ( see e . g . [55] ) , spans 40 cells away from the cytokine secreting cell for our values ( see Table 1 ) . Therefore , we analyzed the spatiotemporal dynamics of cytokine signals by more detailed mathematical modeling and simulations . We found that spatial gradients do occur due to nonlinear receptor dynamics and polarized IL-2 secretion at the immunological synapse , despite fast diffusion . This was also quantified , e . g . in terms of the traveling distance of cytokine molecules ( Fig 5D ) . We showed by extensive simulations in three spatial dimensions that such cytokine gradients can mediate paracrine signals targeting cells other than those connected by the immunological synapse , as previously suggested for interferon-γ [26] . Moreover , we analyzed the parameters that control paracrine signaling on the different spatial scales . It is a long-standing question if cytokine signals are predominantly autocrine or paracrine . IL-2 has initially been thought of as a prototypical autocrine signal facilitating self-activation of Th cells [27 , 30 , 56] . More recently , paracrine IL-2 signaling towards Treg cells was identified as essential to prevent autoimmune diseases [29 , 31] , possibly due to competition with autocrine self-activation [4 , 5 , 28 , 57] . Recent experimental observations suggest that also paracrine IL-2 signals towards other Th cells are important for regulation of immune responses , while true autocrine IL-2 signals are suppressed by the intracellular signal transduction pathway [5 , 37] . A plausible explanation would be that IL-2 secreting cells are constitutively activated , i . e . prone to proliferation and differentiation , due to a strong signal from the T cell receptor , and do not rely on signals via the IL-2R . Th cells not secreting IL-2 may have received a weaker T cell receptor signal , and are only fully activated if they receive additional stimulation from the IL-2R . In this theoretical study , we cannot address the question to what extent such a mechanism is responsible for the activation of T cell populations in vivo . However , our simulations show that the need for paracrine cytokine signals provides several checkpoints for the induction of immune responses downstream of the T cell receptor . We identified three major control points which are likely important for the fine-tuned regulation of paracrine cytokine signals . First , cytokine receptors have high affinity and are internalized after binding of cytokine molecules . That allows for control of paracrine cytokine signals by expression of cytokine receptors ( Fig 1 ) . Second , the effective rate of cytokine secretion , i . e . the paracrine cytokine signal escaping the immunological synapse , sensitively depends on the configuration of the immunological synapse , in terms of the exact synaptic distance ( Fig 2 ) . Therefore , we propose that regulation of cytokine signals is an important function of the immunological synapse ( see also Refs . [15–18] ) , along with regulation of the strength of T cell receptor activation [47] and the exchange of microvesicles between T cell and APC [58] . Note that using the synaptic distance is an idealization; in reality the influence of the immunological synapse on cytokine diffusion is more complex , due to its structure consisting of several layers with different types of surface proteins [16 , 24] . Third , in our simulations , Treg cells efficiently suppress paracrine IL-2 signals , because they express high basal levels of IL-2R , preventing the strong transient cytokine signal . In line with earlier work from us and others [4 , 5 , 29 , 57] , this suggests that suppression of IL-2 signals is an important mechanism contributing to immune tolerance mediated by Treg cells . Of note , Treg cells most likely interfere with T cell activation in several other ways , e . g . by release of anti-inflammatory cytokines like IL-10 , by forming immunological synapses with T cells , and by other mechanisms yet to be discovered [12 , 33] . Interestingly , a fourth system property one might expect to have a large influence on the dynamics of the system , the cell density or cell-to-cell distance , is unimportant for the results of our simulations ( Fig 4E ) . This property results from the timescale separation between cytokine diffusion and cytokine uptake ( see Fig 1E and S1 Text ) , and explains recent experimental data [7] . In our model simulations , paracrine cytokine signals are not only characterized by stable cytokine gradients , but also by a rapid and transient cytokine boost occurring in the first hours after stimulation . Such a transient cytokine signal has been observed by single-cell IL-2 capture assays [49 , 59] , and recently also by ELISA in cell supernatants [7] , although with conflicting time-scales: IL-2 capture assays evoked a peak in the number of IL-2 secreting cells at 1–6 hr after antigen stimulation [49 , 59] , while Tkach et al . report a peak in the IL-2 concentration measured in vitro after ~50 hr [7] . Our simulations point to an IL-2 peak in the first 10 hr after stimulation , and thus support the earlier suggestion [49] that ELISA studies have limitations in reflecting the time-course of in vivo cytokine signals , although the study of Tkach et al . provides valuable quantitative insight into the dose-response characteristics of IL-2 signals . A reason might be that in culture , cells form thin layers on the bottom of the well , and therefore cytokine molecules are detected by ELISA in the supernatant after a certain delay . The large-scale simulations resembling a cluster of highly active T cells in the center of a lymphoid organ ( Fig 5 ) reveals a logarithmic , or fold-change response of the spatial signal range with respect to the effective secretion rate . That means , the cell population recognizes relative rather than absolute increases in the stimulus strength ( here , the amount of secreted cytokine molecules per time ) . Fold-changes in sensory biological systems are a classical phenomenon referred to as Weber’s law , and were recently observed in various intracellular signal transduction pathways [60–63] . As a consequence of the fold-change response , sensory systems can act over a broad range of stimulus intensities , from nearly detectable to very intense stimulations . Our computer simulations suggest a similar mechanism for paracrine cytokine signals: Moderate effective secretion by a small fraction of cells allows for short-range signals inside an immunological synapse , larger effective secretion rates may evoke paracrine signals that reach bystander cells in close vicinity but not connected by a synapse , and very high secretion rates or large clusters of secreting cells may evoke an organ-wide cytokine signal or ‘cytokine storm’ [14] . Adaptive immune responses must be rapid and effective in the case of strong infection , but also carefully controlled to avoid autoimmune diseases . In our simulations , the spatial distribution of cytokine secretion and uptake within a population of immune cells had a huge impact on the cellular response , generating multiple layers of plasticity that can be exploited for appropriate regulation of immune responses .
For the simulations of the three-dimensional in silico T cell population ( Figs 3–5 ) , a problem specific software was developed in the Heidelberg Numerical Methods Group , based on the open source C++ library deal . II [41] . The system was discretized in time by the damped Crank-Nicolson method . The intercellular area was discretized with an unstructured adaptive mesh , which describes each cell with at least 342 degrees of freedom ( 64 in the long range simulations in Fig 5 ) by a Galerkin approach using continuous finite elements ( Q1 ) . The discretized system was solved efficiently by controlling the error with adaptive space and time grids by means of the Dual Weighted Residual ( DWR ) method[42 , 44] . To allow for larger time steps , the equations were solved in a fully coupled fashion and not with the commonly applied iterative segregating approach . We linearized the nonlinear equations with Newton's method and applied Krylov-Space methods ( GMRES ) with a geometric multilevel preconditioner [40 , 43] to solve the resulting linear equations . In the simulations , the secreting Th cells and the Treg cells and the synapse on the cell surface of secreting cells were positioned randomly . We checked the influence of this cell positioning on the simulations with different randomly chosen positions and found that the variations between simulations were negligible . Our discretized high-resolution numerical data were visualized in cooperation with the Visualization and Numerical Geometry Group from the Interdisciplinary Center of Scientific Computing ( IWR ) in Heidelberg . For the graphical representation of the three-dimesional scalar data , here the IL-2 distribution in space , two methods were applied , the visualization of isosurfaces using topological methods [64 , 65] and volume rendering [64] . With the first method specific isosurfaces are visualized by varying the transparency for different isovalues to get an impression of the 3D data set ( Figs 5A , S1 and S2 ) . To choose these specific isosurfaces with important features , topological information ( Morse complex , persistent homology classes and Betti numbers ) is computed . The rendering was performed by using the Visualization Toolkit VTK ( http://www . vtk . org ) which allows rotation in real time . The second method , volume rendering , produces the image directly from the data without an intermediate geometrical representation . A play with transparency of the whole data set makes the inner structures visible ( Figs 3D and 4D ) . With flexible mapping of the data on colors and opacity , different structures can be visualized efficiently and a realistic representation is obtained ( Figs 3C and 3D and 4C and 4D ) . Difficulties in the data-representation were the wide range of the values over several orders of magnitude and the porous domain ( extracellular domain ) . The simulations for pulsed stimulation ( Fig 5D and S3 Fig ) were realized by homogeneous secretion by the cell in the center of the region for a very short time ( 7 sec ) with a qeff such that a concentration corresponding to a single cytokine molecule is released . The simulation is then run until the concentration reaches zero in the whole area . The fraction of the released IL-2 concentration bound by a certain responder cell is equivalent to the probability that the ‘secreted molecule’ was bound . This probability was calculated for the successive layers of responder cells surrounding the secretory cell , in order to obtain the distribution of the traveling distance . Analytical calculations were supported by Wolfram’s Mathematica . Matlab from Mathworks was used to generate plots and to calculate the special functions applied in Fig 2 . A classical formula derived by Berg and Purcell approximates the characteristic time τ of a ligand diffusing towards a receptor [48]: τ=14πDρc+14DdRRc=6 . 9min ( 1 ) Here , we suppose a cytokine concentration of c = 10 pM , a receptor diameter of dR = 0 . 1nm , a receptor number of R = 100 per cell , and diffusion constant D and cell radius ρ as in Table 1 . Note that in Eq 1 and in the following , cytokine concentrations ( nM ) are implicitly converted to molecules/μm3 by Avogadro’s constant NA , wherever necessary , as follows: nM = 10-9mol/l = 10-9NA molecules/ ( 1015μm3 ) = 6/10 molecules/μm3 . Note that the time to diffuse towards a T cell ( first term in Eq 1 ) is less than a second , but the mean time to reach a receptor at the cell surface ( second term in Eq 1 ) is in the order of minutes due to the small number of receptors on naïve T cells . One cytokine secreting cell is either surrounded by a layer of responder cells ( ‘high cell-density’ , see Fig 1B ) or placed in a cell-free medium ( ‘low cell-density’ ) . The cytokine secreting cell has R cytokine receptors , and responder cells have Rresp cytokine receptors , both binding cytokine molecules in their immediate vicinity with rate kon . We assume homogeneous cytokine secretion and uptake , so that the system has radial symmetry . As diffusion is fast ( D = 10μm2/s , see Table 1 ) , it reaches a steady state after about L2/D = 0 . 5 s , where L is the cell-to-cell distance in the case of high cell-density . Thus , it is sufficient to consider the diffusion equation in steady state in the extracellular domain with flux boundary condition at the cell surface: DΔc ( r ) =0 , r∈[ρ , ∞]−4πρ2D∂c∂r|r=ρ=q−konc ( ρ ) R ( 2 ) c ( r ) is the cytokine concentration at distance r from the center of the cell , Δ is the Laplace operator in spherical coordinates , ρ is the cell radius , and q is the cytokine secretion rate . Note that cytokine concentrations are implicitly converted from unit nM to unit molecules/μm3 , as above . The boundary condition on the outer boundary is either ( low cell-density limit ) c ( r→∞ ) =0 ( 3 ) or ( high cell-density limit ) −4π ( L+ρ ) 2D∂c∂r|r=L+ρ=konc ( L+ρ ) NRresp , ( 4 ) where N is the number of IL-2 consuming responder cells . In both cases , the problem can be solved analytically for the cytokine concentration c ( r ) and eventually for the uptake rates Jauto = konc ( ρ ) R , Jpara = q − Jauto ( see S1 Text ) . We consider stationary cytokine diffusion in a cylindrical region between a cytokine secreting Th cell and a responder cell , both potentially expressing cytokine receptors ( see Fig 2A ) . This leads to the following boundary conditions at the cytokine secreting cell ( z = 0 ) and the responder cell ( z = l ) : DΔc ( r , z ) =0 , r∈[0 , a] , z∈[0 , l]−πa2D∂c∂z|z=0=q−konRc|z=0−πa2D∂c∂z|z=l=konRrespc|z=l ( 5 ) The synaptic distance is l = 20 nm , and the radius of the contact area is a = 2 μm ( see Table 1 ) , corresponding to the region where localized IL-2R expression is reported [11] . At the outer boundary of the synapse , we assume c ( a , z ) = 0 , which means that cytokine molecules which escape the cylindrical region do not return to it . The cytokine concentration , and the uptake rates Jauto , Jescape and Jsynapse resulting from this model , can be calculated analytically using Bessel functions ( see S1 Text ) . We performed simulations in three spatial dimensions ( see section ‘software’ above ) of our earlier model [4] , with some modifications: We consider polarized IL-2 secretion and autocrine uptake at the immunological synapse , by assuming an effective secretion rate at one grid point at the surface of IL-2 secreting cells . Moreover , due to recent experimental observations [5 , 35] , we discard the previously assumed positive feedback from IL-2 uptake to IL-2 secretion , and we set the IL-2 secretion rate to 10 molecules/s and the fraction of IL-2 secreting cells to about 25% ( see Table 1 ) . In brief , the model [4] considers interactions of three kinds of cells: Secretory Th cells , responder Th cells and Treg cells . All three cell types express IL-2R molecules on the cell surface . Responder Th cells and Treg cells express IL-2R homogeneously at the cell surface , Treg cells at higher levels than responder Th cells . IL-2 signaling leads to the expression of the α subunit of the IL-2 receptor that is required for high-affinity IL-2 binding in both responder Th cells and Treg cells . Hence both cell types enhance their rate of IL-2R expression ( v ) upon IL-2 uptake , which we model , following Busse et al . [4] , by a Hill equation with a moderate Hill coefficient of 3: v ( t ) =v0+v1C ( t ) 3K3+C ( t ) 3 ( 6 ) Here , v0 and v1 are the basal and the IL-2 induced rates of IL-2R expression , K is the half-saturation constant , and C ( t ) is the number of IL-2/IL-2R complexes , which is a dynamic variable of the model ( Table 1 ) . For details and the full model see S1 Text . | The adaptive immune system fights pathogens through the activation of immune cell clones that specifically recognize a particular pathogen . Tight contacts , so-called immunological synapses , of immune cells with cells that present ‘digested’ pathogen molecules are pivotal for ensuring specificity . The discovery that immune responses are regulated by small diffusible proteins – the cytokines – has been surprising because cytokine diffusion to ‘bystander’ cells might compromise specificity . It has therefore been argued that cytokines are trapped in immunological synapses , whereas other authors have found that cytokines act on a larger scale through entire lymph nodes . Measurements of cytokine concentrations with fine spatial resolution have not been achieved . Here , we study the spatio-temporal dynamics of cytokines through mathematical analysis and three-dimensional numerical simulation and identify key parameters that control signaling range . We predict that even tight immunological synapses leak a substantial portion of the secreted cytokines . Nevertheless , rapid cellular uptake will render cytokine signals short-range and thus incidental activation of bystander cells can be limited . Long-range signals will only occur with multiple secreting cells or/and slow consumption by sparse target cells . Thus our study identifies key determinants of the spatial range of cytokine communication in realistic multicellular geometries . | [
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] | [] | 2015 | Three-Dimensional Gradients of Cytokine Signaling between T Cells |
Dimethylation of histone H3 lysine 9 ( H3K9m2 ) and trimethylation of histone H3 lysine 27 ( H3K27m3 ) are two hallmarks of transcriptional repression in many organisms . In Arabidopsis thaliana , H3K27m3 is targeted by Polycomb Group ( PcG ) proteins and is associated with silent protein-coding genes , while H3K9m2 is correlated with DNA methylation and is associated with transposons and repetitive sequences . Recently , ectopic genic DNA methylation in the CHG context ( where H is any base except G ) has been observed in globally DNA hypomethylated mutants such as met1 , but neither the nature of the hypermethylated loci nor the biological significance of this epigenetic phenomenon have been investigated . Here , we generated high-resolution , genome-wide maps of both H3K9m2 and H3K27m3 in wild-type and met1 plants , which we integrated with transcriptional data , to explore the relationships between these two marks . We found that ectopic H3K9m2 observed in met1 can be due to defects in IBM1-mediated H3K9m2 demethylation at some sites , but most importantly targets H3K27m3-marked genes , suggesting an interplay between these two silencing marks . Furthermore , H3K9m2/DNA-hypermethylation at these PcG targets in met1 is coupled with a decrease in H3K27m3 marks , whereas CG/H3K9m2 hypomethylated transposons become ectopically H3K27m3 hypermethylated . Our results bear interesting similarities with cancer cells , which show global losses of DNA methylation but ectopic hypermethylation of genes previously marked by H3K27m3 .
Post-transcriptional modifications of histone tails—and combinations thereof—are thought to define specific chromatin structures and transcriptional states across eukaryotes [1] , [2] . In both animals and plants , trimethylation of histone 3 lysine 27 ( H3K27 ) and dimethylation of histone 3 lysine 9 ( H3K9 ) ( and/or trimethylation in metazoa ) are two , generally alternative , hallmarks of transcriptional repression . In Arabidopsis thaliana , H3K27m3 is deposited by Polycomb group ( PcG ) proteins in euchromatic domains containing protein-coding genes—in particular , transcription factors and genes involved in developmental transitions [3] , [4] , [5] . H3K27m3 marks are largely non-overlapping with H3K9m2 and cytosine DNA methylation , which are targeted to repeated sequences throughout the genome and associated with silent , constitutive heterochromatin [4] . However , H3K27m3 was found to mark selected transposons and repeated sequences in some particular contexts when they are DNA hypomethylated such as in the met1 mutants [6] or in endosperms [7] . In mammals DNA methylation is usually observed exclusively in the CG-dinucleotide context , while in Arabidopsis thaliana cytosines are methylated in every context . At least three DNA methyltransferases control DNA methylation in Arabidopsis , each with its own sequence preference: CG , CHG , or CHH ( where H is any base except G ) . Establishment of cytosine methylation in all sequence contexts is catalyzed by DOMAINS REARRANGED METHYLTRANSFERASE 2 ( DRM2 ) , the plant homolog of mammalian DNMT3a and DNMT3b . DRM2 is guided to chromatin by siRNAs in a pathway known as RNA-directed DNA methylation ( RdDM ) . This pathway also maintains DNA methylation in the asymmetric CHH context . CG methylation is catalyzed by DNA METHYLTRANSFERASE 1 ( MET1 ) , the plant homolog of mammalian DNMT1 , and this mark is passively maintained during replication . Finally , CHG methylation is mostly catalyzed by CHROMOMETHYLASE 3 ( CMT3 ) —a plant-specific DNA methyltransferase that contains a chromodomain that recognizes dimethylated histone tails at lysine 9 ( H3K9m2 ) [8] . In turn , CHG methylation is recognized by the SRA domain within the KRYPTONITE ( KYP ) H3K9m2 methyltransferase . Therefore , CHG methylation is largely maintained through a reinforcing loop of DNA and H3K9 methylation . This is consistent with genome-wide studies showing that CHG methylation and H3K9m2 are highly co-incidental [9] , [10] . At some heterochromatic loci , H3K9m2 is also dependent on CG methylation , potentially through other SRA-domain containing proteins [11] , [12] . Primary targets of DNA methylation in Arabidopsis include transposons , repetitive sequences , and occasionally genes when they contain repeats in their promoter [13] , [14] . In these cases , DNA methylation is present in all three cytosine contexts and is associated with H3K9m2 marks and transcriptional silencing [9] , [15] . However , at least 30% of expressed genes [15] , [16] , [17] , [18] , [19] show a significant amount of DNA methylation in their transcribed regions ( or gene bodies ) . In this case , DNA methylation is dependent on MET1 , is restricted to CG sites , is not associated with H3K9m2 , and does not result in gene silencing [17] , [18] . The function of this methylation remains unknown , although a recent study gave some insights into its regulation: hundreds of genes in Arabidopsis were shown to gain non-CG methylation ( mainly at CHG sites ) in plants mutated in the increase in bonsai methylation 1 ( ibm1 ) gene [20] . IBM1 encodes a Jumonji C-domain protein with H3K9m2 demethylase activity [21] , [22] , and was initially identified in a genetic screen for mutants showing ectopic cytosine methylation of the BONSAI ( BNS ) gene . This discovery raised the idea that genes are actively being protected from acquiring H3K9m2 methylation . Interestingly , ectopic CHG methylation and associated H3K9m2 have been previously reported in the met1 background [16] , [23] , [24] , [25] . However , neither the mechanism nor the biological significance of the ectopic DNA/H3K9m2 methylation in met1 is currently understood . In order to gain a better understanding of the ectopic DNA methylation in met1 , and to test the hypothesis that CHG hypermethylation in met1 , like in ibm1 , could be the result of crippled IBM1-mediated control of H3K9m2 at genes , we generated high-resolution , genome-wide maps of H3K9m2 methylation in met1 and ibm1 mutants . Our results revealed that hundreds of genes become H3K9m2 hypermethylated in both met1 and ibm1 backgrounds . However , the sets of genes most H3K9m2 hypermethylated in each mutant were largely non-overlapping , suggesting that MET1 and IBM1 regulate H3K9m2 at different subsets of genes . The genes most H3K9m2 hypermethylated in met1 tended to be either genes already marked with less extensive levels of H3K9m2 or , more surprisingly , genes marked with H3K27m3 . To explore the relationship between the repressive H3K9m2 and H3K27m3 marks further , we mapped H3K27m3 levels using wild type and met1 plants . We observed a significant loss of H3K27m3 at PcG-targeted genes in met1 , in particular at the ones that became DNA and H3K9m2 hypermethylated in met1 . This phenomenon was accompanied by a massive redistribution of H3K27m3 marks to many H3K9m2 and/or CG hypomethylated loci in met1 such as transposons . Finally , to determine the effect of these changes in the epigenetic landscape on transcription , we conducted RNA-seq experiments using wild type and met1 plants . These analyses showed that the PcG targets remain relatively unexpressed upon replacement of H3K27m3 marks by H3K9m2 marks . Our results suggest that H3K27m3 and H3K9m2/DNA methylation are mutually exclusive , and can replace one another in a locus specific manner . In addition , these data bring important new insight into the biology of met1 mutants by showing an important role for MET1 in maintaining H3K27m3 patterning at PcG targets . Finally , our observations draw a striking parallel between the epigenetic phenomena displayed in the met1 mutant and the local DNA hypermethylation observed in cancer cells .
To examine the relationship between MET1 and IBM1 in negatively controlling H3K9m2 deposition throughout the genome , we generated high-resolution genome-wide maps of H3K9m2 in the two first inbred generations of met1 and ibm1 rosette-stage mutants by performing chromatin immunoprecipitation experiments coupled with whole-genome Roche Nimblegen microarray analyses ( ChIP-chip ) . We observed hundreds of regions that became H3K9m2 hypermethylated in each of the mutants . However , while significant H3K9 hypermethylation was observed in the first generation of ibm1 mutants ( ibm1-1st ) , this phenomenon only become clearly apparent in the second generation of met1 mutant ( met1-2nd ) . These results are in contrast with previous immunofluorescence analyses showing appearance of H3K9m2 in the euchromatic , gene-rich regions only after three generations of the absence of a functional MET1 allele [25] , but are consistent with genome-wide DNA methylation analyses where CHG ectopic methylation ( and presumably H3K9 dimethylation ) were evident in the flowers of met1 first generation homozygous mutants [16] . This suggests that immunofluorescence experiments may not be sensitive enough to detect de novo H3K9m2 patterns in the second inbred generation . Regions of H3K9m2 hypermethylation were defined ( using the BLOC algorithm [26] ) and all the analyses performed in the first generation for ibm1 and the second generation for met1 . The most hypermethylated genes were defined as genes that overlap with defined hypermethylated regions by at least 150 bases ( the approximate length of DNA wrapped around one nucleosome ) . By this method , 1833 genes ( 6 . 49% of all genes , Table S1 ) were found to be H3K9m2 hypermethylated in met1 . This set of genes included SUPERMAN ( SUP ) and AGAMOUS ( AG ) , which have previously been reported to be DNA hypermethylated in met1 [23] , [24] , [27] ( Figure S1 ) . In addition , we examined published whole genome bisulfite sequencing data obtained from flowers of first generation met1 homozygous mutants , in which genic hypermethylation was readily detected [16] . We found that the set of genes that are H3K9 hypermethylated in met1 in our experiment also displayed increased levels of non-CG methylation , particularly in the CHG context , at individual loci and in a genome-wide manner ( Figure 1A , Figure 1B , Figure S1 ) . This indicates that the feed-forward loop between H3K9 and CHG methylation is also active at ectopically methylated loci in met1 . However , we note that the comparison between the patterns of DNA and H3K9 methylation is limited by the use of different developmental stages in the two studies . In ibm1 mutants , 1682 genes ( 5 . 96% of all genes , Table S2 ) were found to be H3K9m2-hypermethylated . Interestingly , this set of genes was largely distinct from the set observed for met1 , with only 113 genes being significantly hypermethylated in both backgrounds ( Figure 1C , 1D ) . These results imply that there are at least two mechanisms at play in the protection of genes from ectopic DNA and H3K9m2 methylation , one that depends on IBM1 and another that depends on MET1 . One possibility is that the small overlap between the two sets is due to the reduced IBM1 mRNA levels in met1 mutant ( Figure 1E ) . Consistent with this notion , we found that met1 usually had a smaller effect on H3K9m2 hypermethylation at these sites than ibm1 ( Figure S2 ) . A recent study reported that the re-establishment of IBM1 expression in met1 mutants restored the wild-type H3K9m2 patterns at selected loci and suggested that down-regulation of IBM1 could account for most of H3K9m2 relocation at genes [28] . However , the use of stringent thresholds to define H3K9m2 hypermethylated regions revealed that the most hypermethylated genes in met1 are usually not targets of IBM1 . One difference between the characteristics of genes most hypermethylated in ibm1 versus met1 is that most of the H3K9m2 hypermethylated genes in ibm1 are moderately expressed in wild-type , while many of the hypermethylated genes in met1 are lowly expressed ( Figure 1F ) or silent ( Figure 1G ) . By examination of individual loci ( Figure 2A , Figure S3A ) and genome-wide analysis ( Figure 2B and 2C ) , we observed that many hypermethylated genes in met1 were enriched for genes pre-marked with H3K9m2 in wild-type ( “Class I” genes ) , presumably due to the presence of repeats within their coding-region ( Figure 2A ) that do not necessarily correspond to transposable elements ( Figure S3B ) . 531 such “Class I” genes were identified ( 28 . 9% of all H3K9m2 hypermethylated genes in met1 ) . At these genes , siRNAs levels were increased in met1 ( Figure 2D , Figure S3C ) , indicating that loss of CG DNA methylation at these genes , either upstream or in the coding-region , results in the stimulation of de novo methylation by the RdDM pathway , which in turn likely leads to increased H3K9m2 levels via the maintenance of CHG methylation which involves the H3K9m2 HMTase KRYPTONITE ( KYP ) . We also observed a second class of H3K9m2 hypermethylated genes in met1 , including SUPERMAN or AGAMOUS , which consistently display H3K27m3 marks in wild type plants at this same developmental stage ( “Class II” genes ) ( Figure 3A and 3C , Figure S4A and S4B ) . With our stringent parameters , this class comprised 515 genes ( 28 . 1% of all H3K9m2 hypermethylated genes ) . However , this number is an under-estimation as we noted a large number of genes that had the characteristics of Class II genes but were not retrieved by our conservative cutoffs for defining H3K27m3-marked genes in wild type ( Figure 3D , Figure S5A ) . These genes seemed to largely account for the H3K9m2 hypermethylated genes in met1 that were not included in Class I or II ( data not shown ) . In addition , genome-wide analyses revealed that genes H3K9m2 hypermethylated in met1 are significantly enriched in H3K27m3 marks relative to all genes ( Figure 3B ) . Together , these observations indicate that the ectopic H3K9m2/DNA methylation in met1 is preferentially targeted to regions enriched in H3K27m3 in wild type . These observations are strikingly reminiscent of the phenomena displayed by human cancers cells where ectopic de novo DNA methylation occurs predominantly at genes specifically marked with H3K27m3 in either the corresponding normal adult cells or in the progenitor cells [29] , [30] , [31] . While regions that gained H3K9m2 in met1 were enriched for sites marked with H3K27m3 in wild type , only 7 . 8% of PcG-targeted genes ( 515 genes out of 6592 ) gain H3K9m2 in met1 ( Figure S5B ) . While this number is likely an under-estimation ( due to our conservative cutoffs ) , this nonetheless indicates that additional factors must contribute to the observed phenomenon . To identify such factors , we looked for additional features of H3K9m2 hypermethylated genes in the mutant background and found that they were enriched in sequences annotated as “dispersed repeats” , but were not annotated as transposable elements but rather as regions of homology between gene families ( Figure 3E , Figure S4 ) . Specifically , genes H3K9m2 hypermethylated in met1 were often found in tandem and had similar gene ontologies indicating recent gene duplication . In other cases , some H3K9m2 hypermethylated genes with paralogous domains were located on different chromosomes such as the transcriptions factors ( At3g58780 , At2g42830 ) related to AGAMOUS ( At4g18960 ) by their MADS-box domain . Interestingly , tandemly repeated genes were also shown to be represented among H3K27m3-marked genes [3] . The homologous nature of the genes H3K9m2 hypermethylated in met1 suggests that a sequence-specific process—such as RdDM—may be involved in the formation of these ectopic methylation patterns . The presence of SUP DNA hypermethylated alleles ( also known as clark kent or clk alleles ) in a globally hypomethylated background was previously shown to depend on both the CMT3 and RdDM pathway [32] . Interestingly , in wild-type plants , we detected SUP-hybridizing small interfering RNAs , that did not originate from the SUP locus ( since they were still detected in a strain with a deletion of the SUP gene ) ( Figure 3F ) . This shows that SUP-hybridizing siRNAs produced by another locus might potentially target SUP in trans . Consistent with this idea , the gene families H3K9m2 hypermethylated in met1 that we examined tended to match with at least one potential siRNA-generating locus ( Figure S4A ) . Thus , our observations suggest that some paralogous genes might be cryptic targets of RdDM , and become methylated only in met1 . This does not seem to be due to an increase of small RNAs at these sites in met1 ( Figure S5D ) . One possibility is that a decrease in H3K27m3 marks could contribute to the onset of siRNA-directed DNA methylation ( RdDM ) , after which methylation would then be maintained by H3K9m2-CMT3 feed-forward loop . Since ectopic H3K9m2 and DNA hypermethylation in met1 occurs at PcG targets , and PcG targets are usually non-overlapping with siRNAs and H3K9m2 [4] , we sought to test whether H3K27m3 levels are reduced in met1 at these loci . To this end , we generated high-resolution genome-wide maps of H3K27m3 in rosette-stage met1 mutant plants and observed a massive redistribution of H3K27m3 levels throughout the genome . H3K27m3 levels were significantly decreased at ectopically H3K9m2 hypermethylated genes ( Figure 4 , Figure S9 ) and were significantly increased at H3K9m2 hypomethylated regions ( Figure 5A and 5B , Figure S9 ) , namely transposons and other repetitive DNA elements . The extent of the H3K27m3 decrease at H3K9m2 hypermethylated regions was correlated with the extent of H3K9m2 ectopic methylation at the genes tested by quantitative PCR ( Figure 4C ) . However , we also observed that many PcG-targeted genes with limited or no ectopic H3K9m2 were also partially depleted of H3K27m3 marks in met1 , but on average the decreases in H3K27m3 at these genes seemed smaller than observed for PcG-targeted genes that gained H3K9m2 and DNA methylation ( Figure 4D , Figure S6A ) . A possibility is that the smaller decreases in H3K27m3 at these loci could result from the partial relocation of H3K27m3 marks and/or PcG complexes to transposable elements and heterochromatic genes ( Figure 5A , Figure 5B , Figure S8 ) . In addition , the massive increase of H3K27m3 marks at transposons is likely contributed by a global increase of H3K27m3 marks ( Figure S6B ) and of PcG gene expressions . According to our RNA-seq data , in met1 , FIE , CLF and SWN expression levels are increased by 33% ( P = 0 . 005 ) , 13% ( P = 0 . 33 ) , and 32% ( P<0 . 001 ) , respectively ) . An increase in H3K27m3 marks was previously observed at discrete heterochromatic loci and at chromocenters in met1 [6] and our data show that this phenomenon can now be extended to hundreds of sites throughout the genome ( Table S3 ) . These findings suggest a model in which H3K9m2 and/or associated DNA methylation excludes H3K27m3 from heterochromatic loci in wild-type plants . To better understand the contributions of DNA and H3K9m2 methylation on H3K27m3 exclusion , we compared the pattern of H3K27m3 marks at several well-characterized transposable elements in various mutant backgrounds . Transposable elements such as ROMANIAT5 , AtCOPIA28 lost H3K9m2 marks in the triple suvh4 suvh5 suvh6 ( suvh456 ) mutant ( in which both H3K9m2 and CHG methylation are reduced drastically , but not CG methylation ) but H3K9m2 was not lost at these sites in the met1 mutant ( Figure 5C ) . However , we observed that the met1 mutant exhibited a stronger increase in H3K27m3 marks at these sites compared to suvh456 ( Figure 5C ) . In addition , at AtCOPIA4 ( in its 3′half ) , H3K9m2 levels were reduced to the same extent in both suvh456 and met1 , yet only met1 gained H3K27m3 at this locus ( Figure 5C ) . Together , these results were consistent with previous data in the suvh4 mutant [6] and suggest that the loss of DNA methylation at heterochromatic loci in met1 , rather than the loss of H3K9m2 marks , is associated with an increase in H3K27m3 . This idea is further supported by the chromosomal distributions of H3K9m2 and H3K27m3 in met1 which show that H3K27m3 is targeted to centromeric sites that are free of CG methylation in this background but still contain similar levels of H3K9m2 or even increased levels of H3K9m2 ( for example transposons behaving like Class I genes ) ( Figure S8 ) . Notably , there was no consistent gain of H3K27m3 at AtMU1 and at AtCOPIA4 ( in its 5′half ) ( Figure S7 ) suggesting that loss of CG methylation and associated H3K9m2 alone was not sufficient to induce H3K27m3 deposition . A recent study suggested that a high density of unmethylated CpG sites could be sufficient for vertebrate Polycomb recruitment [33] . Consistent with this idea , we found that the density of CG sites was higher at transposable elements that gained H3K27m3 in met1 ( Figure 5D ) ( including AtCOPIA28 , ROMANIAT5 , AtCOPIA4-3′half ) than the ones that did not ( including AtMu1 and AtCOPIA-5′half ) . Thus , CG density may contribute to the differential recruitment of PcG complexes to transposons in met1 . To gain insight into the biological significance of the relocation of H3K27m3 to heterochromatic loci and of H3K9m2/DNA methylation to PcG genes , we performed RNA-seq in wild type and met1 plants . Consistent with previous locus-specific analyses [6] , transposable elements were usually reactivated in met1 , despite the presence of ectopic H3K27m3 ( Figure 6A ) . Therefore , H3K27m3 is not as competent as CG methylation and associated H3K9m2 in the silencing of transposons . Notably , the transposable elements targeted by H3K27m3 in met1 tend to be lowly expressed in wild type ( Figure 6A , Figure 6B ) . Further analyses of transposon expression in a fie-met1 double mutant will be required to determine whether H3K27m3 marks can at least partially compensate for the loss of CG and H3K9m2 methylation in met1 and whether the increase of H3K27m3 at these sites is a back-up mechanism deployed by the plant cell to avoid massive transposon expression and transposition . We found that H3K9m2 hypermethylated loci generally did not alter expression levels ( Figure 6C ) . This was also true at PcG-genes despite a significant loss of H3K27m3 marks ( Figure 6D ) . This finding suggests a functional redundancy between H3K9m2 and H3K27m3 at PcG-targets in leaves , even though the dynamics of these two silencing marks are thought to be quite distinct , with H3K27m3 marks acting in a reversible manner during the course of development to allow developmental switches [5] and H3K9m2 acting in a permanent manner to lock a gene or transposon into a silent heterochromatic state . With these differences in epigenetic plasticity in mind , we propose that the replacement of H3K27m3 by H3K9m2 at PcG-targets may contribute to array of developmental defects , including many floral defects , which are observed in the met1 mutant by locking PcG-target genes into a stably silent state , which is then unresponsive to developmental cues . Alternatively , the reactivation of key PcG-targets that lose H3K27m3 and become reactivated in met1 could also contribute the developmental defects , reminiscent of those displayed in the lhp1 mutant where H3K27m3-mediated silencing is impaired .
In Arabidopsis , loss of the maintenance CG DNA methyltransferase , MET1 , results in DNA and H3K9 hypermethylation at some specific loci and in hypomethylation at other regions . Despite the strong parallels between this epigenetic state and those described in numerous cancers , how these patterns are established in met1 and their biological significance has remained unknown . In this study , we analyzed the genome-wide patterns of ectopic H3K9m2 methylation ( which mirrors DNA methylation ) in the otherwise globally DNA hypomethylated met1 mutant and provide significant insights into these questions . Based on our findings , we propose a model that accounts for the ectopic DNA and H3K9m2 methylation observed in met1 mutant . First , genes that gain DNA and H3K9 methylation in met1 fall into three categories: ( 1 ) a small class of genes that are affected by both met1 and ibm1 that are presumably IBM1 targets sensitive to the reduced levels of IBM1 expression in the met1 background , ( 2 ) genes that possess low levels of H3K9m2 in wild type plants and become H3K9 hypermethylated in met1 and ( 3 ) PcG-target genes ( i . e . H3K27m3 pre-marked genes ) that lose H3K27m3 marks in met1 , but gain H3K9m2 marks . Interestingly , concomitant with the decrease in H3K27m3 at PcG targets , H3K27m3 levels increase at transposons and other heterochromatic loci where unmethylated CG sites may facilitate the recruitment of PcG complexes . The replacement of CG DNA methylation by H3K27m3 in met1 and vice-versa suggests that these two marks are mutually exclusive in Arabidopsis , as previously demonstrated in mammals at some imprinted loci [34] as well a in cancer cells [35] . While the exclusion of H3K27m3 by DNA methylation was previously proposed in Arabidopsis [6] , [7] , our findings add strength to this assertion and further suggest that it is the loss of H3K27m3 at PcG targets that contributes to the occurrence of H3K9m2 and DNA hypermethylation . This causative relationship is supported by the observation that the H3K27m3 decrease is not specific to H3K9m2 and DNA hypermethylated genes although it is stronger at these loci , consistent with the notion of mutual exclusion . Interestingly , the PcG-target genes that become H3K9m2 and DNA hypermethylated may also represent cryptic targets of RdDM as they tend to have higher than average levels of sequence homology with other regions in the genome , many of which are known to generate siRNAs . Many genes such as transcription factors are part of large families and the presence of H3K27m3 at these loci may have the dual role of mediating transient , reversible repression and excluding RdDM and associated H3K9m2 methylation . How Polycomb-complexes are recruited to deposit H3K27m3 marks is still unknown in Arabidopsis [5] . However , at the FLC locus , there are cis-sequences that have been shown to be important for the recruitment of PRC2 ( Polycomb Repressive Complex 2 ) through a long-coding RNA [36] , [37] . Our data suggest that a high density of unmethylated CG sites , as previously observed in vertebrates , may be another factor facilitating PcG recruitment ( Figure 5D ) . Further analyses may identify cis sequences in the transposons targeted by PRC2 in met1 and/or show a general role for non-coding RNAs in PRC2 recruitment . Alternatively , heavily methylated CG sites such as those seen in transposons , could recruit a H3K27m3 demethylase which would be inactive in met1 . Future mechanistic exploration of these new epigenetic phenomena in met1 will likely bring insight into the recruitment of PcG complexes as well as RdDM components . Our observations also provide a possible explanation for the drastic developmental phenotypes displayed by the met1 mutant: genes targeted by ectopic DNA and H3K9m2 methylation in met1 are PcG-targets in wild type plants , which are enriched in genes involved in transcriptional regulation and development . At specific developmental stages , for example during the vegetative phase where our analyses were performed , it appears that H3K9m2 marks are functionally redundant with H3K27m3 marks since the vast majority of genes with either mark in this study remained silent . However , the replacement of a transient repressive mark such as H3K27m3 by a stable silencing mark such as H3K9m2 may affect gene transcription during specific developmental windows where H3K27m3 marks are removed , thus impairing critical developmental switches and contributing to the myriad of developmental phenotypes observed in met1 mutants . Furthermore , the finding that hypomethylated regions of the genome induced by loss of MET1 in vegetative tissue can become targets of the Polycomb silencing machinery raises the question of whether hypomethylation of the genome caused by other processes also leads to PcG-targeting and gene silencing . Several examples of naturally occurring global hypomethylation have recently been described . These include the endosperm ( plant extra-embryonic tissues ) , which is globally hypomethylated , due to the activity of the DNA demethylase DEMETER but also possibly due to down-regulation of MET1 in this context [38] . Interestingly , H3K27m3 was found at DNA hypomethylated transposable elements and genes that had less CG methylation than in the vegetative tissues [7] . In this respect , the strong endosperm phenotype observed after loss of polycomb function ( proliferation and eventually seed abortion [39][40] ) could indicate the crucial role of H3K27m3 marks at DNA hypomethylated sites . In addition , other studies revealed DNA hypermethylation ( presumably associated with H3K9m2 hypermethylation ) of specific sites in the endosperm [41] . However , the nature of these sites has not been investigated and it is possible that PcG-targets in the endosperm are similarly affected as in the vegetative tissues of met1 mutants . Finally , our work in an Arabidopsis globally DNA hypomethylated mutant has uncovered striking similarities with epigenetic phenomena occurring in human cancer cells . First , H3K9m2 and DNA hypermethylated promoters in human cancer cells tend to be marked with H3K27m3 in the corresponding adult cells or in the progenitor cells they are derived from . Another point of convergence is the decrease of H3K27m3 marks associated with the ectopic gain of H3K9m2 in both contexts [42] . Finally , repressive chromatin formation , mediated in particular by H3K27m3 , was observed at DNA hypomethylated regions in breast cancer cells [35] . The same study demonstrated genome-wide , mutual exclusivity of these two marks , which had been previously shown at one imprinted locus in mouse [34] . The striking similarities between the epigenetic landscapes of a globally hypomethylated mutant , the globally hypomethylated endosperm and human cancer cells suggest common underlying mechanisms , and suggests the potential of future Arabidopsis research as a framework for understanding developmental and cancer biology .
http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=dvotficqwuacufg&acc=gse37075 | In plants and animals , repetitive DNA sequences and transposable elements are marked with DNA methylation , which is associated with methylation on lysine 9 of histone 3 ( H3K9 ) and silencing . On the other hand , protein-coding genes , in particular the ones involved in differentiation processes , are targeted by Polycomb Group ( PcG ) proteins , which results in trimethylation of H3K27—another hallmark of transcriptional repression . These two systems of silencing are thought to be independent , but in this study we reveal an interplay between them . In the model plant Arabidopsis we show that , in a globally DNA–hypomethylated mutant , H3K27m3 marks can now be found at repeats and transposons; this is associated with a decrease of H3K27m3 at PcG targets , with some of them becoming targets of DNA and H3K9 methylation . Our data suggest that H3K27m3 prevents ectopic DNA/H3K9 methylation at cryptic DNA methylation targets , which could provide a novel significance for this mark with regard to genome integrity . In addition , this study reveals interesting similarities with cancer cells , which show global losses of DNA methylation but ectopic hypermethylation of genes previously marked by H3K27m3 , and suggests the potential of Arabidopsis as a system for understanding mammalian developmental and cancer biology . | [
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] | 2012 | Loss of the DNA Methyltransferase MET1 Induces H3K9 Hypermethylation at PcG Target Genes and Redistribution of H3K27 Trimethylation to Transposons in Arabidopsis thaliana |
Many protein functions can be directly linked to conformational changes . Inside cells , the equilibria and transition rates between different conformations may be affected by macromolecular crowding . We have recently developed a new approach for modeling crowding effects , which enables an atomistic representation of “test” proteins . Here this approach is applied to study how crowding affects the equilibria and transition rates between open and closed conformations of seven proteins: yeast protein disulfide isomerase ( yPDI ) , adenylate kinase ( AdK ) , orotidine phosphate decarboxylase ( ODCase ) , Trp repressor ( TrpR ) , hemoglobin , DNA β-glucosyltransferase , and Ap4A hydrolase . For each protein , molecular dynamics simulations of the open and closed states are separately run . Representative open and closed conformations are then used to calculate the crowding-induced changes in chemical potential for the two states . The difference in chemical-potential change between the two states finally predicts the effects of crowding on the population ratio of the two states . Crowding is found to reduce the open population to various extents . In the presence of crowders with a 15 Å radius and occupying 35% of volume , the open-to-closed population ratios of yPDI , AdK , ODCase and TrpR are reduced by 79% , 78% , 62% and 55% , respectively . The reductions for the remaining three proteins are 20–44% . As expected , the four proteins experiencing the stronger crowding effects are those with larger conformational changes between open and closed states ( e . g . , as measured by the change in radius of gyration ) . Larger proteins also tend to experience stronger crowding effects than smaller ones [e . g . , comparing yPDI ( 480 residues ) and TrpR ( 98 residues ) ] . The potentials of mean force along the open-closed reaction coordinate of apo and ligand-bound ODCase are altered by crowding , suggesting that transition rates are also affected . These quantitative results and qualitative trends will serve as valuable guides for expected crowding effects on protein conformation changes inside cells .
It is increasingly recognized that protein dynamics serves the critical link between structure and function [1]–[6] . An important manifestation of protein dynamics is the sampling of alternative conformations . These conformational changes can be triggered by substrate ( or ligand ) binding [7] and post-translational modifications such as phosphorylation [8] . Increasingly , structures of the same proteins at different functional states are becoming available . These structures provide atomistic details of conformational changes . For example , adenylate kinase ( AdK ) , an enzyme that catalyzes the phosphoryl transfer from ATP to AMP , undergoes a significant conformational transition , from an “open” conformation in the apo form and to a “closed” conformation in the ligand-bound form [9]–[11] . The main differences between these two conformations occur in the ATP- and AMP-binding domains , with the CORE domain relatively rigid ( Figure 1 ) . Other examples with well-characterized conformational transitions include proteins responsible for signal transduction across cell membranes [12]–[14] and ion channels [15] . Biophysical characterizations of protein conformational changes have mostly been carried out under dilute conditions . However , the environments where proteins perform their biological functions , i . e . , extracellular space , cell membrane , and cytoplasm , are crowded with macromolecules . For example , the cytoplasm of Escherichia coli contains about 300–400 g/l of macromolecules [16] , which are estimated to occupy over 30% of the total volume . In cell membranes , membrane proteins occupy a similar level of the total surface area [17] . How the crowded cellular environments affect the equilibria and transition rates between different conformations of proteins is still poorly understood . Qualitatively , one expects that macromolecular crowding will significantly modify the energy landscapes of conformational changes , favoring more compact structures over more open ones [18] . Such effects of crowding have been scrutinized experimentally ( see [18] for a recent review ) . Molecular dynamics simulations have also been carried out to investigate the energy landscapes of a number of proteins under crowding , in the context of either conformational change [19] or folding-unfolding transition [20]–[22] . To speed up conformational sampling , the proteins in these studies were represented at a coarse-grained level . Recently we have developed an approach [23] , [24] , referred to as postprocessing , which opens the door to atomistic modeling of proteins under crowding . In this approach , the motions of a test protein are simulated in the absence of crowders . Conformations from this simulation are then used to calculate the change in chemical potential if they are transferred to a crowded solution . The dependence of the change in chemical potential on reaction coordinates then captures the influence of crowding on the energy landscape . The postprocessing approach has been applied to study effects of crowding on protein folding and binding stability [23] , [25] , [26] and on the open-closed equilibrium of the HIV-1 protease dimer [24] . In the latter application it has been shown that the postprocessing approach yields results identical to those obtained from direct simulations of the protein in the presence of crowders [19] , [24] . Here we apply the postprocessing approach to investigate the impact of macromolecular crowding on the open-closed equilibria of seven proteins: AdK [9] , [10] , yeast protein disulfide isomerase ( yPDI ) [27] , [28] , orotidine phosphate decarboxylase ( ODCase; functioning as a dimer ) [29] , Trp repressor ( TrpR ) [30] , hemoglobin ( Hb ) [31] , DNA β-glucosyltransferase ( BGT ) [32] , and Ap4A hydrolase ( Ap4Aase ) [33] , [34] . The biological functions and subcellular locations of these proteins are listed in Table 1 . We find crowding to reduce the open-to-closed population ratios to various extents; the potentials of mean force ( PMFs ) along the open-closed reaction coordinate of apo and ligand-bound ODCase , and hence the transition rates , are similarly affected . The biological implications of these results are discussed below .
Relatively strong effects of crowding are found on the open-to-closed population ratios of four proteins: AdK , yPDI , ODCase , and TrpR . In Figure 2 we show the relative changes in the open-to-closed population ratios of the four proteins by 15-Å crowders at various volume fractions . Corresponding results for 30-Å crowders are shown in Figure S2 . We describe the calculation results on AdK in some details . Previous computational and experimental studies have determined the pathways and rates of the open-closed transitions for this protein [36]–[39] . In dilute solutions , the open-to-closed population ratio is ∼3 in the ligand-free form and ∼1/5 in the ligand-bound form [36] , [39] . In the presence of 15-Å crowders occupying 35% of volume , the open-to-closed population ratio is reduced by 4 . 6-fold , or , equivalently , 78% , making the open state less stable than the closed one even in the ligand-free form . The reduction in the open-to-closed population ratio comes about because it is harder to accommodate the open conformations than the closed conformations in the crowded solution . The probabilities of successful placement for the open and closed states are 2 . 96×10−8 and 1 . 36×10−7 , respectively ( see Figure S3 ) . At a given volume fraction , the effects of crowding are larger for smaller crowders than for larger crowders . For example , at 35% volume fraction , crowding reduces the open-to-closed population ratio of AdK by 78% , 59% , 35% , and 17% , respectively , when the crowder radii are 15 , 20 , 30 , and 50 Å . Comparison of the results for 15-Å crowders in Figure 2 and the counterparts for 30-Å crowders in Figure S2 shows the same trend . This dependence on crowder size has been seen previously in studies of protein folding , binding , and conformational change [23]–[26] . As we noted [23] , only a small number of large crowders are needed to occupy the same volume as a large number of small crowders . Even though the “obstacles” amount to the same total volume , the former arrangement is more compact than the latter , and hence easier to accommodate a test protein . In other words , the latter arrangement is more discriminating between the open conformations and closed conformations of a protein , and therefore produces a stronger effect on the open-to-closed population ratio . Qualitatively similar effects of crowding are found for the other three proteins . In the presence of 15-Å crowders occupying 35% of volume , the open-to-closed population ratios of yPDI , ODCase and TrpR are reduced by 79% , 62% and 55% , respectively . These are to be compared with the 78% reduction reported above . Clearly , the open-closed transitions of all these four proteins are prone to crowding effects expected of their intracellular environments . As shown in Figure 2 , the effects of crowding on the open-to-closed ratios of the other three proteins , Hb , BGT , and Ap4Aase , are modest . In the presence of 15-Å crowders occupying 35% of volume , the open-to-closed population ratios of these proteins are reduced by 44% , 39% , and 20% , respectively . It should be noted that the modest effects on the open-to-closed population ratios come about not because crowding does not exert significant effects on the open or closed state , but rather because the effects exerted on the open and closed states are similar . For example , at Rc = 15 Å and φ = 35% , the probabilities of successful placements for the open and closed states of Hb are 3 . 26×10−15 and 5 . 83×10−15 , respectively . These values are seven orders of magnitude smaller than the corresponding quantities for AdK reported above ( due to the much larger size of Hb ) . However , here the values for the open and closed states are very similar , leading to a modest effect on the open-to-closed population ratio . What explains the large variations in crowding effects reported above ? For a given protein , what matters is the difference in the effects exerted on the open state and the closed state . Therefore we expect the extent of conformational changes between the two states to be a main determinant . We use two quantities to measure the conformational changes: the solvent accessible surface area ( SASA ) and the radius of gyration , Rg . In Table 2 we list the relative differences in SASA and in Rg for the seven proteins . It is clear that the four proteins that experience significant crowding effects ( AdK , yPDI , ODCase , and TrpR ) have large differences in SASA and Rg between the open and closed states . On the other hand , relatively small differences in SASA and Rg are found for the remaining three proteins ( Hb , BGT , and Ap4Aase ) , which experiences more modest crowding effects . In one extreme , in the case of AdK , both measures differ by over 10% between the open and the closed states , whereas in the other extreme , in the case of Hb , both measures differ by less than 2% . We may thus conclude that the extent of conformational changes is the main determinant for the magnitudes of crowding effects . We have emphasized the fact that , for a given protein , the effect of crowding depends on the size of the crowders ( when the volume fraction is kept constant ) . Conversely , for the same crowder size and crowder volume fraction , it can be expected that larger proteins will experience stronger crowding effects . This expectation is borne out by our calculation results . In particular , yPDI and TrpR show similar differences in SASA and Rg between their open and closed states but differ in size . yPDI has ∼5 times more residues than TrpR . Correspondingly the crowding effects experienced by yPDI is ∼2-fold higher than those by TrpR ( Figure 2 ) . Similarly the difference in crowding effects between Hb and Ap4Aase can be partly attributed to their size difference ( 574 vs . 165 residues ) . Crowding not only affects the equilibria of open and closed states but is also expected to affect their transition rates . We study the latter problem on ODCase . The open and closed structures of this protein were solved in the absence and presence of a transition-state analogue , 6-hydroxyuridine 5′-phosphate ( BMP ) , respectively [29] . The two structures differ significantly in the distance between two loops around the active site ( Figure S1C ) , which is ∼20 and 12 . 5 Å , respectively . The PMFs of the apo and BMP-bound forms along the loop distance are calculated in the absence of crowding and shown in Figure 3 . In the apo form , the transition from the open state ( at loop distance ∼20 . 5 Å ) to the closed state ( at loop distance ∼12 . 5 Å ) encounters an energy barrier ∼6 . 7 kBT , where kB is Boltzmann's constant and T is the absolute temperature . In the BMP-bound form , the transition from the closed state to the open state encounters a series of barriers , the most significant of which is at ∼1 . 5 kBT . Using the postprocessing approach we calculate the PMFs in the presence of crowding . The results for the crowding condition of Rc = 15 Å and φ = 35% are also shown in Figure 3 . It can be seen that the energy barrier for the open-to-closed transition in the apo form is lowered , by ∼0 . 5 kBT , which corresponds to a 65% increase in transition rate . On the other hand , the most significant energy barrier for the closed-to-open transition is raised , by ∼0 . 2 kBT , corresponding to a 22% decrease in transition rate . These effects of crowding on the energy barriers , while relatively moderate , are nevertheless statistically significant ( see error bars in Figure 3 ) . Proteins dynamics provides the critical link between structure and function . In present study , we have investigated how the crowded environments inside cells might affect protein dynamics . Our results have important implications for the functions of the proteins studied here and for in vivo biological functions in general . In catalyzing the phosphoryl transfer from ATP to AMP , AdK undergoes large conformational changes . In dilute solutions , the open conformation is favored in the apo form whereas the closed conformation is favored when the substrates are bound . However , open-closed transitions occur in both the apo form and the substrate-bound form [36] , [39] . Moreover , the closed-to-open transition in the substrate-bound form is thought to limit the overall catalytic rate ( under substrate saturation ) [36] . Our calculations show that crowding will significantly shift the open-closed equilibrium toward the closed state , such that the closed state may become favored even in the apo form . Qualitatively it can be expected that , under crowding , the rate of the closed-to-open transition in the substrate-bound form , i . e . , the putative rate-limiting step of the catalytic reaction , will be reduced . Intracellular crowding will thus have a significant impact on the catalysis activity of AdK . Future work on the transition state of the open-closed transitions will allow us to quantitatively assess the effects of crowding on the transition rates . The case of ODCase is similar . Conformational changes , especially closure and opening of the loops around the active site , are essential for substrate binding and product dissociation . A recent kinetic study by Wood et al . [40] indicates that loop closure and opening are partially rate-limiting for kcat/KM and kcat , respectively , in dilute solutions . Our calculations show that crowding stabilizes the closed state . Furthermore , calculations of the PMFs along the loop distance show that crowding increases the rate of loop closure in the apo form and hence kcat/KM but decreases the rate of loop opening in the ligand-bound form and hence kcat . Interestingly , these predicted trends were actually observed by Wood et al . when the kinetic study was done in the presence of Ficoll , a common crowding agent . The considerable conformational flexibility of yPDI , which is essential for the formation of the correct pattern of disulfide bonds in substrate proteins , has been demonstrated by two structures determined from crystals grown at two different temperatures , 4°C and 22°C [27] , [28] . The protein consists of four domains , labeled as a , b , b′ , and a′ ( see Figure S1B ) . Both the a and a′ domains harbor an active site . The 4-°C structure is more compact , forming a twisted U shape with the b and b′ as the base and the a and a′ domains as the arms . The 22-°C structure is more open , with the a and a′ domains showing significant rearrangement relative to the b and b′ domains , resulting in a boat shape . The conformational flexibility may be important in affording yPDI the ability to accommodate substrate proteins with different sizes . It may also be important in promoting the activity of yPDI on each substrate . Indeed , with ether reduced or scrambled ribonuclease as substrate in dilute solutions , the activity of yPDI is reduced when the arrangement of the a and a′ domains relative to the b and b′ domains is locked in that of the twisted U shape by new disulfide bonds [28] . Moreover , the same restriction on domain rearrangement also reduces the activity of yPDI in vivo . Our calculations show that crowding favors the compact U conformation over the open boat conformation , thereby reducing the conformational flexibility . In addition , crowding promotes protein oligomerization [23] , [25] . yPDI forms a weaker dimer in dilute solutions [28] , but the propensity to dimerize may be significantly enhanced in the crowded environment of its cellular location , the lumen of the endoplasmic reticulum . ( In a model for the dimer as present in the 22°C crystal structure , the a′ domain active site becomes buried whereas the a domain active site is still solvent exposed [28] . ) Dimer formation has indeed been detected in the endoplasmic reticulum [28] . yPDI exhibits a difference between in vitro and in vivo activities: the a′ domain active site is more potent than the a domain active site for a native substrate in vitro , but the reserve is true in vivo [41] . In vivo dimer formation suggests a plausible explanation for this difference . Intrinsically the a′ domain active site may be more potent , but in the endoplasmic reticulum yPDI may most exist as dimer , in which the a′ domain active site is buried and hence inaccessible to the substrate whereas the a domain active site is accessible to the substrate . The transition between the R state and the T state is essential for Hb to carry out its function of transporting oxygen from the lungs to the tissues . The R state is slightly more “open” than the T state and has high oxygen affinity; the T state has low oxygen affinity . In red blood cells , Hb has concentrations ∼300 g/l , amounting to ∼35% of the cell volume . Modeling Hb molecules as spheres with a radius ∼30 Å , our calculations show that intracellular crowding leads to a modest 10% increase in the T-to-R population ratio and correspondingly a small decrease in the oxygen affinity of Hb . Given that the oxygen affinity of Hb is strongly regulated by pH , CO2 , Cl− , and D-2 , 3-bisphosphoglycerate [42] , the small decrease in oxygen affinity expected of intracellular crowding is probably not of physiological significance . We note in passing that a variant of normal Hb , sickle Hb , can polymerize when deoxygenated , leading to deformation of red blood cells , vascular occlusion , and anemia . It has been demonstrated that macromolecular crowding has a dramatic effect on sickle Hb polymerization [43] . Our calculations show that the extent of conformational changes is the main determinant for the magnitudes of crowding effects . The study here has focused on structured proteins . We note that intrinsically disordered proteins should undergo even greater conformational changes , and are thus expected to experience even stronger effects of macromolecular crowding . Indeed , the intrinsically disordered α-synuclein undergoes a temperature-induced collapsed-to-expanded transition in dilute solutions , but such a transition is prevented in a solution crowded by a bystander protein and in living Escherichia coli cells [44] . α-synuclein has a tendency to aggregate into fibrils , which is the underlying cause for Parkinson's disease; the delay time before fibrillation is considerably shortened by macromolecular crowding [45] , [46] . Our calculations also show that protein size plays an important role in the magnitudes of crowding effects . Larger proteins are expected to experience stronger crowding effects . One of the largest systems in the Protein Data Bank ( PDB ) is that of the ribosome , which is the machinery for protein translation . During translation , the ribosome undergoes a series of conformational changes; the structures of the ribosome in many of these functional states have now been determined [47] . Our preliminary calculation indicates that the relative stability between these states may be significantly changed by intracellular crowding . The largest change exerted by crowding in the relative stability between the open and closed states of the seven proteins studied here is 1 . 6 kBT . The same crowding condition favors the conformational change of the ribosome upon binding of a release factor by ∼5 kBT . The particularly strong crowding effects predicted for the conformational transitions of the ribosome perhaps partly explain why protein translation in an in vitro setting [48] is not as efficient as in vivo [49] . In conclusion , the preceding discussion makes it clear that intracellular crowding will significantly affect conformational changes and biological functions of proteins and molecular machines . Consequently , deduction of intracellular behaviors from in vitro experiments requires explicit consideration of crowding effects .
We briefly summarize our approach for atomistic modeling of crowding effects [23] . The aim here is to calculate the crowding-induced change in the free-energy difference , ΔΔG , between the open and closed states of a protein . If the change in chemical potential when the protein in the open state is transferred from a dilute solution to a crowded solution is Δμo and the corresponding quantity in the closed state is Δμc , thenAs the measure of the effect of crowding , we report the relative change , κ , in the open-to-closed population ratio by crowding , given by Here , like in previous molecular-dynamics studies of macromolecular crowding [19]–[22] , we model the interactions between the test protein and crowders as hard-core repulsion . In that case , Δμ , the change in chemical potential in a given state due to crowding , is related to the probability , f , that the protein in that state can be successfully placed into a box of randomly distributed crowders:Following previous studies [19]–[22] , we further model the crowders as spherical particles . We have developed an algorithm for calculating f [23] , hereafter referred to as the particle-insertion algorithm , since it is similar in spirit to Widom's particle-insertion method [50] . In our previous study of crowding effects on the open-closed equilibrium of the HIV-1 protease dimer [24] , the aim was to demonstrate that the postprocessing approach yields results identical to those obtained from direct simulations of the protein in the presence of crowders [19] . The open and closed conformations in the absence of crowding , used for postprocessing , were obtained from a single simulation; we were able to simulate transitions between the two states because the protein was represented at a coarse-grained level . In the present study , we simulate the open state and closed state separately and thus avoid the slow transitions between them . ( The separate simulations of end states follow our previous studies of protein folding and binding [23] , [25] , [26] . ) Postprocessing the conformations from the two separate simulations allows us to determine the effects of crowding on the open-to-closed population ratio . The particle-insertion algorithm has been used to implement the postprocessing approach in all our previous studies [23]–[26] . More recently we have discovered that Δμ can be predicted theoretically [51] , thus significantly speeding up the postprocessing approach . The gain in computational speed is especially important when the size of protein increases , whereupon the values of f become exceedingly small and hence difficult to obtain using the particle-insertion algorithm . In brief , the prediction of Δμ is based on generalizing the fundamental measure theory [52] , which is designed for convex test particles and crowders , to atomistic proteins interacting with spherical crowders . The theory predicts Δμ as a linear function of the volume vp , surface area sp , and linear size lp of the test protein:where ρ is the number density of the crowders . In the generalized fundamental measure theory ( GFMT ) [51] , vp , sp , and lp are defined according to the so-called crowder-exclusion surface . This surface is similar to Richard's molecular surface [53] , but with the probe radius set to the radius of the crowders . The average values of vp , sp , and lp calculated on the open and closed conformational ensembles of the seven proteins are listed in Table S1 . We do not use these average values to predict the final Δμ result . Instead , we use the particular vp , sp , and lp for each conformation to calculate a Δμ value and then Boltzmann-average the individual Δμ values over the conformational ensemble to yield the final Δμ result . In Figure S3 , we show that the GFMT predictions for Δμo and Δμc of AdK agree very well with the results obtained by the particle-insertion algorithm . Note that , for the crowding condition of Rc = 15 Å and φ = 35% , convergent results could not be obtained by the particle-insertion algorithm . All the results reported in the main text are calculated by the GFMT . For each protein , molecular dynamics simulations of the open and closed states are separately run using the Amber program . In each simulation , the protein molecule starts from the X-ray or NMR structure ( with PDB code given in Table 1 ) and is solvated in TIP3P water molecules . The simulations are run at constant temperature ( 300 K ) and constant pressure ( 1 bar ) , with the particle mesh Ewald method used to treat long-range electrostatic interactions . The total time of each simulation is 10 ns; 100 conformations are evenly selected from the last 8 ns as representatives of the conformational ensemble . Root-mean-square-deviations ( RMSDs; measured on Cα atoms ) of these representative conformations from the starting X-ray or NMR structure are typically ∼1 . 5 Å , but the open states of AdK and yPDI and the closed state of TrpR show higher RMSDs ( see Figure S4 ) . To investigate how representative the conformational ensemble from a single trajectory is of a protein in a given state , four independent trajectories each of ODCase in the open state and the closed state are run . Crowding effects calculated on the four independent ensembles are similar . For example , the probabilities of successful placement for ODCase in the closed state calculated on the four ensembles are ( 1 . 89±0 . 34 ) ×10−14 under the crowding condition of Rc = 15 Å and φ = 35% . For some proteins , there are small differences in the residues present in the starting structures of the open and closed states . Such differences would make an artificial contribution to ΔΔG; we eliminate this artifact by keeping only the residues that are present in both the open and closed conformations in calculations of Δμ . Solvent accessible surface area is calculated by using the NACCESS program [54] , with a 1 . 4-Å probe radius . The PMFs along the open-closed reaction coordinate are calculated by umbrella sampling . The reaction coordinate , x , for ODCase is taken as the distance between the centers of mass of two loops , consisting of residues 151–161 and residues 203–218 , respectively ( Figure S1C ) . The umbrella sampling consists of 32 windows covering x values from 11 . 5 Å to 20 . 8 Å ( with 0 . 3 Å increment ) . Harmonic restraints with force constants of 5 and 10 kcal/mol/Å2 , respectively , are used for the apo form and the BMP-bound form , respectively . For the apo form , simulations in different restraint windows are independent; the simulation in each window consists of 0 . 2 ns of equilibration and 1 . 3 ns of production , with 1625 conformations used for PMF calculations . For the BMP-bound form , the simulations are carried out sequentially , with the conformation after 0 . 2 ns of equilibration in one window saved for the starting structure in the next window; 3500 subsequent conformations in the next 2 . 8 ns of simulation are used for PMF calculations . The weighted histogram analysis method ( WHAM ) [55] , [56] was used to obtain the PMFs . A PMF , W ( x ) , is related to the probability density in x , P ( x ) , via P ( x ) = exp[−W ( x ) /kBT]/C , where C is a normalization constant . In the standard WHAM , the probability density at discrete x values , xj , is obtained by iterating the following two equations to convergence:where i is the index for restraint windows; Nwindow is the total number of such windows; Ubias , i ( x ) is the bias potential in window i; Ni is the total number of sampled conformations from the window i simulation; Ci is a normalization constant for window i; Nbin is the total number of x values for which P ( x ) is calculated; and ni ( xj ) is the number of conformations from the window i simulation which have x values belong to the xj bin . Errors are calculated by bootstrapping ( http://membrane . urmc . rochester . edu ) . From the conformations sampled in the absence of crowding , we use the postprocessing approach to obtain the PMF in the presence of crowding . Specifically , we assign each sampled conformation a statistical weight due to crowding; this statistical weight is f , the probability of successful placement into a box of crowders . Note that Ni in the above equation can be viewed as the sum over a statistical weight of 1 for each conformation from the window i simulation; similarly ni ( xj ) can be viewed as a sum over a statistical weight of 1 for each conformation from the window i simulation with an x value in bin xj . For the PMF in the presence of crowding , Ni is replaced by the sum over the f values of the conformations from the window i simulation , and ni ( xj ) is replaced by the sum over the f values of the conformations from the window i simulation which have x values in bin xj . ODCase functions as a homodimer but the two active sites are catalytically independent [57] . In our umbrella sampling , for each window the loop distances in the two subunits are restrained to the same value . For each sampled conformation , the actual loop distances of the two subunits were first averaged and then the result was used to find the corresponding bin xj . In addition , two independent sets of umbrella sampling simulations are carried out for both the apo form and the BMP-bound form; their averages are reported in Figure 3 . | The biophysical properties of proteins inside cells can be expected to be quite different from those typically measured by in vitro experiments in dilute solutions . In particular , intracellular macromolecular crowding may significantly affect the equilibria and transition rates between different conformations of a protein , and hence its functions . What are the trends and magnitudes of such crowding effects ? We address this question here by applying a recently developed approach for modeling crowding . Seven proteins , each with structures for both an open state and a closed state , are studied . Crowding exerts significant effects on the open-closed equilibria of four proteins and more modest effects on the remaining three . Potentials of mean force along the open-closed reaction coordinate , and hence transition rates , are similarly affected . The extent of conformational changes is the main determinant for the magnitudes of crowding effects , but the protein size also plays an important role . The effects of crowding become stronger as the protein size increases . Conformational transitions of the ribosome , an extremely large complex , during translation are predicted to experience particularly strong effects of intracellular crowding . We conclude that deduction of intracellular behaviors from in vitro experiments requires explicit consideration of crowding effects . | [
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] | 2010 | Effects of Macromolecular Crowding on Protein Conformational Changes |
Insulators can block the action of enhancers on promoters and the spreading of repressive chromatin , as well as facilitating specific enhancer-promoter interactions . However , recent studies have called into question whether the activities ascribed to insulators in model transgene assays actually reflect their functions in the genome . The Drosophila even skipped ( eve ) gene is a Polycomb ( Pc ) domain with a Pc-group response element ( PRE ) at one end , flanked by an insulator , an arrangement also seen in other genes . Here , we show that this insulator has three major functions . It blocks the spreading of the eve Pc domain , preventing repression of the adjacent gene , TER94 . It prevents activation of TER94 by eve regulatory DNA . It also facilitates normal eve expression . When Homie is deleted in the context of a large transgene that mimics both eve and TER94 regulation , TER94 is repressed . This repression depends on the eve PRE . Ubiquitous TER94 expression is “replaced” by expression in an eve pattern when Homie is deleted , and this effect is reversed when the PRE is also removed . Repression of TER94 is attributable to spreading of the eve Pc domain into the TER94 locus , accompanied by an increase in histone H3 trimethylation at lysine 27 . Other PREs can functionally replace the eve PRE , and other insulators can block PRE-dependent repression in this context . The full activity of the eve promoter is also dependent on Homie , and other insulators can promote normal eve enhancer-promoter communication . Our data suggest that this is not due to preventing promoter competition , but is likely the result of the insulator organizing a chromosomal conformation favorable to normal enhancer-promoter interactions . Thus , insulator activities in a native context include enhancer blocking and enhancer-promoter facilitation , as well as preventing the spread of repressive chromatin .
A variety of regulatory elements have evolved in higher eukaryotes to regulate gene expression . Cis-regulatory modules ( CRMs , or enhancers ) are bound by DNA-binding transcription factors that coordinately recruit coactivators and corepressors . Enhancers communicate with basal promoters at least in part through a looping out of intervening DNA , allowing them to act over large distances along a chromosome , or even in trans , with a promoter on another chromosome [1]–[4] . Enhancer activities are regulated by the chromatin environment , which is “managed” by both the enhancers themselves and other DNA elements such as Polycomb-group response elements ( PREs ) [5]–[8] . Further coordination of these activities is provided by elements such as insulators that affect chromosomal organization and conformation . Insulators harbor activities that can limit the range of action of enhancers and repressive chromatin , as well as facilitate long-range enhancer-promoter communication , depending on context [9]–[12] . Insulators typically show “barrier” function that prevents the spread of heterochromatin , as well as enhancer blocking activity , in model transgene assays [9]–[12] . Pairs of insulators can interact with each other to generate chromosomal loops between them . This has been postulated to create distinct functional domains that somehow prevent enhancer-promoter cross-talk between domains . Repressive chromatin structures include heterochromatin and Polycomb ( Pc ) chromatin , which constitutes a form of epigenetic transcriptional memory , stabilizing developmental fate choices , among other functions . Pc chromatin is maintained through the recruitment of Pc-group ( PcG ) gene products to PREs [5]–[8] . PREs can extend their influence outward to produce Polycomb domains that encompass multiple regulatory regions within a gene or a gene complex [13]–[17] . PREs can also synergize with each other in trans [18] , and in some cases facilitate long-range enhancer-promoter communication [19] . Both Pc domains and mammalian X-inactivation involve the histone modification H3K27me3 , catalyzed by Pc-repressive complex 2 ( PRC2 ) [20]–[22] . The functions of PREs and insulators have been studied within Drosophila Hox genes [23]–[25] . There , functional chromatin domains are flanked by insulators , so that all the enhancers and PREs within a domain are coordinately regulated . Enhancers acting early in development ( “initiators” ) are spatially regulated to determine whether a domain will be active or not throughout the rest of development . They do this by inactivating PREs , so that where initiators are active , later-acting enhancers can also be active . The main effect of deleting insulators in this context is to extend the influence of initiators to inactivate PREs in the adjacent domain , which allows its later-acting enhancers to be inappropriately active . However , phenotypic details suggest that in some cells , repressive chromatin may spread instead [26] . Genome-wide chromatin immunoprecipitation ( ChIP ) analysis of the locations of insulator binding proteins show a wide range of binding patterns [27]–[40] . In Drosophila , insulator proteins include dCTCF ( CCCTC-binding factor ) , Mod ( mdg4 ) 67 . 2 , Su ( Hw ) ( Suppressor of hairy wing ) , CP190 ( centrosomal protein 190 ) , BEAF32 ( boundary element-associated factor 32 ) , and Zw5 ( Zeste-white-5 ) . Recent genome-wide studies also implicate the mitotic spindle protein Chromator [41] and the nuclear lamina [30] , [37] in insulator function . In mammals , CTCF is associated with most known insulators [10] , [12] , [42]–[44] . CTCF functions in the regulation of β-globin [45] , [46] and the imprinted Igf2 and H19 loci [47]–[49] . Based on recent genome-wide studies , it has been suggested that insulator proteins bind at many sites that do not function as predicted by model transgene assays [34] , [36] , [38] . Transgenic dissection in a native context can help to determine their normal functions . The even skipped ( eve ) locus is a well-defined Pc domain based on genome-wide analysis [13]–[17] , and is regulated by PcG genes [50]–[54] . An insulator flanks its well-characterized regulatory region , which includes the eve PRE at its 3′ end [51] , [55] . Thus , this insulator is in a position to separate both positive and negative eve regulatory elements from the constitutively expressed neighboring gene TER94 , and/or to prevent ectopic activation of eve by TER94 enhancers . This insulator was shown to have 3 distinct activities in model transgene assays . In addition to enhancer blocking , it causes homing of P-element transgenes to the endogenous eve neighborhood , for which it was nicknamed Homie ( Homing insulator at eve ) . Furthermore , from within a several megabase region flanking endogenous eve , it causes long-range interactions of transgenic promoters with endogenous eve enhancers [55] . Genome-wide analysis showed that most known insulator proteins bind to the Homie region [27] , [33] . Homie shares properties with other insulators based on model transgene assays and , like many other putative insulators , is situated close to both a transcription start site ( TSS ) and a PRE . Thus , understanding Homie's function in its native context can illuminate many of the mysteries that surround this enigmatic group of regulatory elements . In order to investigate its native function , we constructed a transgenic eve-TER94 locus that mimics the normal regulation of both genes . Using this artificial locus , we show that Homie functions as a PRE blocker to protect TER94 from repression due to spreading of the eve Pc domain . Heterologous insulators and PREs can substitute for Homie and the eve PRE , suggesting that limiting the range of PRE action is an important function of insulators generally . Homie also prevents the eve enhancers from activating TER94 in specific tissues . Furthermore , Homie facilitates normal eve expression by augmenting communication between the eve promoter and its 3′ enhancers , likely through a chromosomal looping mechanism .
In order to analyze the function of the eve 3′ insulator Homie , we employed a pseudo-locus that contains all the regulatory DNA necessary for normal expression of both eve [56]–[59] and the 3′ adjacent gene TER94 [60]–[62] . This transgene extends from −6 . 4 to +11 . 3 kb relative to the eve TSS , from the 5′-most enhancer of eve to the 3rd exon of TER94 . In addition to all of the eve enhancers , this region contains a characterized PRE [51] located just upstream ( on the eve side ) of Homie [55] . On the other side of Homie is the TER94 promoter and TSS , which are sufficient for ubiquitous expression , augmented by enhancers in the TER94 introns ( data not shown ) . The eve coding region was replaced with lacZ coding DNA , and the 3rd exon of TER94 was fused with the EGFP coding region ( Figure 1A ) . In this study , we make repeated use of a version of recombinase-mediated cassette exchange ( RMCE ) [63] that allows modified transgenes to be inserted in either orientation at pre-defined chromosomal landing sites . All aspects of transgene expression were consistent for both orientations and at multiple landing sites , with a few minor exceptions ( as noted below ) . In embryos , TER94 RNA is present ubiquitously at early blastoderm , and begins to fade around stage 10 . Most of this RNA is maternally derived , but there is a ubiquitous zygotic contribution as well ( see below ) . At stage 10 and later , strong expression is also observed throughout the brain and central nervous system ( CNS ) [55] . TER94-GFP expression from our transgene simulates endogenous TER94 expression ( Figure 1B , “intact t'gene” ) . Although the level of expression varies somewhat with chromosomal location , the relative behavior of modified transgenes was consistent at each chromosomal location ( compare Figure 1B and Figure S1 ) . Deletion of Homie caused a severe loss of early , ubiquitous expression driven by the TER94 promoter ( Figure 1B “ΔHomie” , Figure S1 ) . When the eve PRE was deleted in addition to Homie , the ubiquitous expression in embryos returned ( Figure 1B “ΔHomie ΔPRE” , Figure S1 ) . Deletion of the eve PRE alone did not affect the expression pattern ( Figure 1B “ΔPRE” ) . These results show that the loss of ubiquitous expression from the TER94 promoter caused by deletion of Homie depends on the presence of the PRE . So , one function of Homie is to protect TER94 from PRE-dependent repression . We also note that when Homie is removed , expression in an eve-like pattern is seen ( Figure 1B “ΔHomie” , Figure S1A ) . This indicates that without Homie , eve enhancers can access the TER94 promoter . We investigate this effect further below . Early , ubiquitous expression of TER94 comes from maternally deposited RNA , based on its early appearance and the fact that TER94 is expressed strongly in developing oocytes [60]–[62] . This was confirmed by staining for transgene expression in the absence of a maternal contribution , which is much weaker at early stages than the maternally derived signal ( Figure S2 “intact t'gene”; compare to Figure 1B , Figure S1 ) . Since TER94-GFP RNA is deposited maternally , we examined expression in ovaries . TER94 mRNA is present in both the germline , including nurse cells , and somatic epithelial follicle cells [60]–[62] . No eve expression in ovaries has been reported . In our transgenic lines , strong TER94-GFP expression was seen at all stages of oogenesis ( Figure 2 “intact t'gene” , Figure S4 ) in both germline and somatic epithelial cells ( Figure S3 “intact t'gene” ) . However , the level depended to some extent on chromosomal location ( compare Figure 2 and Figure S4 ) . In each case , expression was severely repressed when Homie was deleted ( Figure 2 “ΔHomie” , Figure S4 ) . As was seen in embryos , it was restored when the PRE was also deleted ( Figure 2 “ΔHomie ΔPRE” , Figure S4 ) . These data confirm that in ovaries , Homie is required for TER94 promoter activity , due to its blocking of PRE-dependent repression . Since most of the ubiquitous TER94-GFP RNA seen in early embryos is maternally derived , we tested whether zygotic expression from a paternally-derived transgene is affected by Homie deletion . In this assay , non-transgene-carrying ( yw ) female flies are crossed with transgene-carrying males , so that there is no maternal GFP RNA in the progeny . Two chromosomal locations were analyzed . In both cases , GFP expression was reduced when Homie was deleted ( Figure S2 ) . Because of the relatively low level of expression , we quantified GFP RNA using RT-PCR . Embryos from three timed collections were analyzed: 2–3 hr . ( stages 5–6 ) and 4–6 hr . ( stages 9–11 ) after egg deposition , and stages 13–15 . The effect of Homie deletion paralleled those described above for both ovaries and embryos , in that expression was repressed . However , unlike in ovaries , when both Homie and the PRE were deleted , TER94-GFP expression remained repressed at all stages examined ( Figure S2 and data not shown ) . This is consistent with the idea , confirmed below , that another PRE in the eve locus substitutes in embryos ( but not in ovaries ) for the eve 3′ PRE . In fact , the eve promoter-proximal region has PRE-like properties [51] ( see Discussion ) . Are the functions of Homie seen in our assays unique , or are they shared among insulators ? In order to test this , we replaced Homie with other known insulators . As a negative control , a >500 bp stretch of λ phage DNA was tested . It had no effect on repression of the TER94 promoter by the eve PRE ( Figure 3A “ΔHomie” ) . In contrast , other characterized Drosophila insulators can substitute for Homie to block repression . gypsy ( Figure 3A , B ) , Fab-7 , scs' ( Figure 3A ) , and Fab-8 ( Figure 3B ) each prevented TER94 promoter repression . Although in one orientation , scs did not work ( Figure 3A , “+ scs” ) , it did work in the opposite orientation ( Figure 3A , “+ scs ( inv ) ” ) . Fab-8 and gypsy showed a minor directionality in their effectiveness ( not shown ) . Restoration of GFP expression is somewhat weaker for scs' and scs ( inv ) than for gypsy and Fab-7 , indicating that they only partially block eve PRE action . Despite differences in efficiency , blocking of PRE action in this context is a shared property of insulators . In order to test whether the repression of TER94 by the eve PRE is due to some unusual property associated with this PRE , we replaced it with other known PREs . We tested both the bxd PRE and an en PRE for the ability to substitute for the eve PRE in ovaries , in the context of a Homie-deleted transgene . In both cases , repression was seen at a comparable level to that seen with the eve PRE ( Figure 3C , compare to Figure 3A , B “ΔHomie” ) , indicating that TER94 repression is due to a property shared by PREs . We also tested whether Homie can prevent repression by heterologous PREs . To so this , we replaced the eve PRE with either the bxd PRE or the en PRE . In both cases , Homie blocked their action on the TER94 promoter , and the resulting GFP expression was like that of the wild-type transgene ( Figure 3D ) . This shows that Homie can block repression by a variety of PREs . Taken together , these results suggest that insulators block PRE-dependent repression generally . Thus , the commonly occurring arrangement of PREs flanked on one side by insulators [31] is likely to function to provide a sharp transition in chromatin structure . The eve locus is a Pc domain , associated with both Polycomb and the characteristic histone modification H3K27me3 [13]–[17] . We asked whether the repression of TER94-GFP in the ΔHomie transgene is accompanied by spreading of this Pc domain over the TER94-GFP promoter . Indeed , in ovaries , we found that H3K27me3 was increased in the TER94-GFP region when Homie was deleted ( Figure 4A , B ) . Additionally removing the PRE reversed this effect almost completely ( Figure 4A , B ) , indicating that spreading of H3K27me3 depends on the eve PRE . Thus , when TER94-GFP is repressed , H3K27me3 is increased , and when this repression is reversed , H3K27me3 levels return to normal . This suggests that Pc domain spreading is likely to be responsible for the repression . In embryos , as in ovaries , H3K27me3 spreads into the TER94-GFP region when Homie is deleted ( Figure 4C , D ) . However , in contrast to ovaries , additionally removing the PRE does not reverse the effect ( Figure 4C , D ) , suggesting that there is redundancy between this PRE and other PREs in embryos . This redundant activity may be provided by the eve upstream promoter region [51] , or by uncharacterized PREs within the eve locus . Again , recalling that the eve PRE is redundant in embryos for repression of TER94-GFP in the absence of Homie ( Figure S2 ) , there is a striking correlation between spreading of the Pc domain and repression of the TER94 promoter . Intriguingly , when Homie is deleted , the loss of ubiquitous TER94-GFP expression is accompanied by weak expression in an eve pattern ( Figure 1B “ΔHomie” , Figure S1A ) . With the intact transgene , early stripe expression of TER94-GFP driven by eve enhancers might be obscured by early ubiquitous expression , so we cannot rule out that eve enhancers are working on the TER94 promoter at early stages . In fact , eve-like stripe expression from the transgenic TER94 promoter is seen at one chromosomal landing site when the intact transgene is heterozygous and paternally derived , so that there is no maternal contribution ( Figure S2B “intact t'gene” ) . However , eve-like mesodermal , CNS , and anal plate ring ( APR ) expression seems clearly to be caused by deletion of Homie ( Figure 1B “ΔHomie” , stages 11 and 15; Figure S1A “ΔHomie” , stage 13 ) , because with the intact transgene , ubiquitous expression in these tissues is low , yet no such eve-like expression is seen . Furthermore , these later-stage aspects of eve expression are not seen with a paternally derived , intact transgene ( Figure S2 ) . Therefore , the data suggest that one of Homie's functions is to prevent interaction between the TER94 promoter and eve enhancers . Accompanying the recovered ubiquitous expression when the PRE is also deleted , expression in an eve pattern is lost ( Figure 1B “ΔHomie ΔPRE” , stages 11 and 15; Figure S1A “ΔHomie ΔPRE” , stage 13 ) . This loss of mesodermal , CNS , and APR expression of TER94-GFP caused by additional deletion of the PRE indicates that the PRE not only represses ubiquitous TER94 promoter activity , but also facilitates communication between the eve enhancers and the TER94 promoter in the absence of Homie ( see Discussion ) . We then tested whether Homie affects eve promoter activity . To do this , we monitored transgenic lacZ expression , which is driven by the eve promoter ( Figure 5 , Figure S5A , B ) . When Homie is removed , there is a reduction in expression driven by enhancers located 3′ of the eve coding region . Interestingly , these are the eve enhancers located between Homie and the eve TSS . Comparing “ΔHomie” with the intact transgene at stage 5 ( Figure 5 left column ) , we see that stripes 1 , 4 , 5 , and 6 are weakened relative to stripes 2 , 3 , and 7 . A similar reduction of expression is seen at later stages , where mesodermal , CNS , and APR expression are weakened by deletion of Homie ( Figure 5 middle and right columns ) . This effect is seen at all transgene landing sites tested ( Figure 5A , Figure S5A , B ) , although it varies in strength with the direction of transgene insertion ( data not shown ) . Despite these differences , we consistently see significant disruptions of normal eve expression when Homie is removed . It seemed possible that the effects of removing Homie on eve promoter activity were caused by the relief of enhancer blocking , which then might allow eve enhancers access to the TER94 promoter . The resulting promoter competition might reduce eve expression . Alternatively , removing Homie might cause the loss of a chromosome conformation that favors eve enhancer-promoter interactions . This possibility is suggested by the ability of Homie to promote the activation by endogenous eve enhancers of a transgenic eve promoter located up to several megabases away [55] . To distinguish between these possibilities , we performed two sets of experiments . First , we tested whether expression from the TER94 promoter occurs in a pattern that matches the loss of expression from the eve promoter when Homie is deleted . We found that this is not the case . Rather , TER94 expression in an eve striped pattern does not show a difference among the stripes ( Figure 1B ΔHomie , compare to Figure 5 ) . Second , we directly tested the promoter competition hypothesis by deleting the TER94 promoter in addition to Homie . Removing the potentially competing promoter did not restore normal eve expression ( Figure 5B ) . While we cannot rule out competition with endogenous promoters , promoter competition seems unlikely to be the primary cause of the eve pattern disruptions that result from removal of Homie . The facilitation of eve promoter activity by Homie may therefore be due to its ability to organize specific chromosomal loops , possibly with the eve promoter ( see Discussion ) . Interestingly , heterologous insulators are able to restore normal eve enhancer-promoter interactions to different degrees ( Figure S5C and data not shown ) , roughly in parallel to their abilities to restore PRE blocking ( Figure 3A , B ) . For example , gypsy restores normal eve promoter activity , while scs does not ( Figure S5C ) . The abilities of heterologous insulators to perform this function may be due to interactions between them and a region of the eve locus that normally interacts with Homie .
The eve 3′ insulator , Homie , was shown previously to have three activities: P-element transgene homing , enhancer blocking , and facilitation of long-range enhancer-promoter communication between endogenous eve enhancers and a transgenic promoter [55] . We sought to address how these activities relate to Homie's normal function . Both eve and TER94 are essential genes , and eve is highly dose-dependent , making it problematic to manipulate the endogenous locus . Therefore , we constructed a transgene that contains these genes in their normal configuration . Both the eve and TER94 coding regions were replaced with reporter genes to monitor promoter activity . This transgene simulates the expression pattern of both genes , when inserted at several different chromosomal sites . We used this system to manipulate both Homie and the nearby PRE , to assess their normal functions . A major finding of this study is that Homie is required to prevent PRE-dependent repression of the TER94 promoter . Removal of Homie causes a near-complete loss of the normally ubiquitous TER94 promoter activity . Although Homie is close to the TER94 promoter , its removal does not affect the promoter directly . Rather , removing Homie allows eve enhancers to drive the TER94 promoter in an eve pattern ( Figure 1 ) . Furthermore , additional removal of the nearby PRE restores ubiquitous expression . This restoration is complete in some instances ( e . g . , Figures 1 , 2 ) , although it is incomplete in others ( e . g . , with a paternally-derived transgene in embryos , Figure S2 ) . A simple explanation for the lack of complete restoration in some circumstances is that PRE activity varies in different tissues , and the eve 3′ PRE is partially redundant with other PREs at some times in development . Ubiquitous expression of TER94 in early embryos , as well as some of the later ubiquitous CNS expression , is due to maternally loaded RNA . Consistent with this , expression in ovaries is robust , and , like early embryonic expression , is strongly repressed without Homie ( Figures 2 , S4 ) . Accompanying repression in both ovaries and embryos , trimethylation of H3K27 at TER94-GFP is strongly increased when Homie is removed ( Figure 4 ) . Thus , without Homie , the eve Pc domain spreads into the adjacent gene , apparently shutting down expression . Homie is bound in vivo by most known insulator binding proteins , including Su ( Hw ) , CP190 , Mod ( mdg4 ) 67 . 2 , BEAF32 , CTCF , and GAF [27] , [33] . In a previous study , depletion of CTCF by RNAi in a cultured cell line caused a reduction in H3K27me3 levels throughout the eve locus [36] . The authors suggested that depleting CTCF altered the activity of insulators flanking eve , which led to a decrease in H3K27me3 . In contrast , we found that deletion of Homie did not cause a significant reduction in H3K27me3 levels in the eve-lacZ region of our pseudo-locus , either in embryos or in ovaries ( Figure 4 ) . There could be several possible reasons for this discrepancy , including the cell types assayed , and indirect effects of depleting CTCF . With removal of Homie , the spreading of H3K27me3 in ovaries is reversed by deletion of the PRE ( Figure 4A , B ) . However , in embryos , this spreading is only partially reversed ( Figure 4C , D ) . A simple explanation for this is that additional PRE activity within the eve locus comes into play in embryos . Consistent with this , the eve promoter-proximal region has PRE-like properties . It causes pairing-sensitive silencing of mini-white in transgenes that carry it [51] , a property associated with most known PREs . Furthermore , it has consensus binding sites for several PRE-associated DNA binding proteins [8] , [51] , and it shares with the eve 3′ PRE the ability to support positive epigenetic maintenance of enhancer activity from embryos to larvae within eve-positive neurons [51] . Perhaps the clearest evidence for redundant PRE activity within the eve locus is that the level of H3K27me3 at the eve-lacZ coding region is not significantly reduced when the 3′ PRE is deleted . This is true in both embryos and ovaries . In contrast , spreading of the Pc domain into TER94 in ovaries requires the 3′ PRE ( Figure 4 ) . Our data are consistent with the idea that PREs are the nucleation point for spreading of the H3K27me3 mark , and that PRE activity is regulated , so that PREs are differentially active in different tissues . Furthermore , because there may be a dynamic balance between active and repressive chromatin , maintaining a boundary between them may have different requirements at different chromosomal locations , and at different times in development . Insulators that are not required to maintain a boundary in one cell type may be required for that function in other cells , or at specific times in development , as previously suggested [34] . One reason for such differences may be regulated PRE activity . In some cases , spreading of repressive chromatin can be stopped by an active promoter [11] , [64] . In the case of the TER94 promoter , although it is robustly expressed , particularly in ovaries , this is not sufficient to stop the spreading of H3K27me3 in the absence of an insulator . This contrasts with the suggestion from recent genome-wide studies in both cultured cells and Drosophila that insulator protein function is generally not required to prevent spreading of H3K27me3 into active genes , or to maintain most normal gene expression [34] , [36] , [38] . Because many insulator proteins bind to overlapping sets of sites , it is likely that there is considerable redundancy in their function . Thus , knocking out any one of them may not reveal the full function of a majority of their binding sites . It is intriguing that the eve locus is a Pc domain with well-defined boundaries that flank its extensive regulatory regions . Within chromosomal domains of the Drosophila bithorax complex ( BX-C ) , active enhancers prevent the establishment of repressive Pc-dependent chromatin in early embryos . Conversely , in tissues where such repressive chromatin has been established , such as in parts of the CNS and imaginal discs , later-acting enhancers are repressed [25] . Do similar mechanisms operate within the eve locus ? Extensive dissection of eve regulatory DNA has not identified enhancers that can drive expression outside the normal eve pattern , arguing against such a close analogy with the BX-C . However , in PcG mutants , eve is ectopically expressed throughout the late-stage embryonic CNS [50] , [54] , showing that PcG genes do negatively regulate eve , as they do the Hox complexes . In our previous studies of eve PRE activity , we found that in a transgenic context , both the 3′ PRE and the PRE-like eve promoter region could facilitate positive maintenance of an eve CNS enhancer from embryonic to larval stages , as well as prevent ectopic expression in cells that normally do not express eve [51] . Unlike maintenance elements [65] in the BX-C , the eve 3′ PRE was found to require the DNA binding PcG protein Pleiohomeotic rather than Trithorax-group members for positive maintenance [51] . In this study , we also see evidence of a positive effect of the eve 3′ PRE on enhancer activity . In this case , it facilitates TER94-GFP expression in an eve pattern when Homie is removed ( Figure 1B: eve-like mesodermal and CNS expression are seen when Homie is removed , but are not seen when both Homie and the PRE are removed ) . One possible explanation for this is that eve enhancers have evolved to function within a Pc domain , and they may be better able to activate the TER94 promoter when the Pc domain spreads over it . In this view , PREs facilitate both the on state and the off state , yet the chromatin may be differently modified in the two cases . This model is similar to the “integration model” proposed for how heterochromatin can have a positive effect on the expression of genes that normally reside within it [66] . Homie sits adjacent to the eve 3′ PRE , an arrangement that is reminiscent of some boundaries in the BX-C . The mammalian homologs of eve , evx1 and evx2 , are located at the 3′ end of the HOX-A and HOX-D clusters , respectively , suggesting that the ancestral eve locus was part of a Hox cluster [67] . Consistent with conservation of the eve insulator-PRE relationship , recent studies identified an enhancer-blocking activity between evx2 and Hoxd13 [68] , and a PRE in the HOX-D cluster [69] . The presence of a PRE near an insulator , with a promoter on the other side , may indicate a functionally important boundary between active and repressive chromatin domains . Previous studies showed that within the BX-C , neither scs nor gypsy could functionally replace Fab-7 [70] , indicating that there are different classes or strengths of insulators . In these cases , the primary effects of insulator deletion was ectopic activation , due to early acting enhancers ( “initiators” ) “turning off” PREs throughout a chromatin domain delineated by insulators [26] , [71] . In our system , the major effect of insulator deletion is the spreading of the eve Pc domain , reminiscent of the shielding of transgenic reporter genes from repressive effects at some insertion sites [18] , [72]–[75] . Despite the differences in normal function , BX-C insulators can replace Homie in our assay , indicating some degree of universality in insulator function as a PRE blocker . However , our assays did reveal differences in effectiveness in carrying out this function . Specifically , scs' showed slightly weaker activity than either gypsy , Fab-7 , or Fab-8 , while the activity of scs was highly orientation-dependent ( Figure 3 ) . Deletion of Homie results in expression of TER94-GFP in an eve pattern . In fact , the eve early embryonic stripe enhancers may access the TER94 promoter even when Homie is present , because with a paternal-only transgene , we sometimes see eve-like stripe expression from TER94-GFP ( Figure S2B “intact t'gene” ) . However , at later stages of embryogenesis , we do not see eve-like expression in either the mesoderm , CNS , or APR unless Homie is deleted . Therefore , one of Homie's functions is to prevent communication between the TER94 promoter and eve enhancers . Deletion of Homie , but not deletion of the PRE , also reduced eve-lacZ expression driven by the eve 3′ enhancers ( Figures 5 , S5 ) . We considered the possibility that because the TER94 promoter has access to eve enhancers in the absence of Homie , the resulting promoter competition might reduce eve promoter activity . However , in ΔHomie lines where we see TER94 expressed in eve stripes , there is no apparent bias in expression toward the 3′ enhancers ( Figures 1B , S1A ) , arguing against this possibility . Furthermore , at later embryonic stages , eve promoter activity is reduced when both Homie and the PRE are removed ( in mesoderm , CNS , and APR , which are all the tissues where eve is expressed at these stages , Figure 5A ) , but this is not accompanied by TER94-GFP expression in an eve pattern ( Figures 1B , S1A ) . Finally , when the TER94 promoter is removed along with Homie , pattern disruptions persist ( Figure 5B ) . While we cannot rule out competition with other promoters in the genome , these lines of evidence together suggest that promoter competition is unlikely to be responsible for this effect . A second possible explanation for the reduction in eve 3′ enhancer-promoter communication when Homie is deleted is that a 3-dimensional ( 3-D ) conformation that allows the eve promoter to better access the 3′ enhancers is stabilized by the presence of Homie . One possible conformation is a loop between the eve promoter region and Homie ( Figure 6 ) . Although we have not tested this directly , evidence consistent with this model is that activation of promoters , including the eve promoter , by downstream Gal4 binding sites can be facilitated by heterologous insulators in a model transgene assay [76] . This possible pairing of Homie with the eve promoter region would result in a loop that would bring the 3′ enhancers in closer proximity to the promoter . Such a model is similar to that proposed for the 3-D organization of regulatory regions upstream of the Abd-B gene [25] . If such loops are anchored to large clusters of insulator proteins , perhaps within insulator bodies , this may serve as a 3-D barrier that separates distinct chromatin domains , and occludes interactions between regulatory elements located on opposite sides of the insulator . At the same time , otherwise distant elements can be brought closer together , facilitating specific enhancer-promoter contacts , particularly if those elements are brought to the same side of the 3-D barrier . The activities of Homie and the eve PRE are largely interchangeable with those of other insulators and PREs , respectively , in our assay system . Previous studies showed that Homie and the eve PRE have the canonical properties of insulators and PREs when tested in other contexts [51] , [55] . Thus , our results are likely to be applicable to many such elements throughout the genome . In particular , a common function of insulators is likely to be to limit the action of PRE-dependent repressive chromatin . Genome-wide studies using RNAi to knock down specific insulator proteins suggested that insulators may not typically be required in their normal context either to block enhancer-promoter cross-talk or to prevent the spread of repressive chromatin [34] , [36] . Our results suggest that Homie is critically important in its normal context for just such activities , functionally separating the loci on either side . Importantly , other insulators function in place of Homie . This suggests that the activities of insulators defined in model transgene assays do in fact correspond to their normal functions . In particular , as with Homie and the TER94 promoter , the tendency of insulator proteins to cluster just upstream of promoters suggests that one of their typical functions is to shield basal promoters from the effects of upstream CRMs , especially PREs . Further , our finding that insulators facilitate enhancer-promoter communication in this context suggests that their ability to organize chromosomal conformations that augment appropriate transcription is also likely to be a common mode of endogenous insulator function .
The eve-TER94 locus construct ( “intact t'gene” in figures ) was created as follows ( detailed sequence coordinates are given in Figure S6 ) . DNA from −6 . 4 kb to +166 bp relative to the eve TSS was fused to the lacZ coding region . The 3′ end of the lacZ coding region was fused to DNA from +1 . 3 to +11 . 4 kb , which includes the eve poly-A signal , and extends into the 3rd exon of TER94 . This was joined with the EGFP coding region , followed by the poly-A signal of α–tubulin . The entire construct was placed between two inverted attB sequences [63] , [77] . The following deletions were then made in this construct: from +8 . 4 to +9 . 2 kb for ΔPRE , from +8 . 4 to +9 . 7 kb for ΔHomie ΔPRE , and from +9 . 2 to +9 . 7 kb for ΔHomie . To test promoter competition between eve and TER94 , DNA from −7 . 4 to +8 . 6 kb relative to the eve TSS was used , with the eve coding region replaced by that of lacZ , as described above . This construct does not contain the TER94 promoter . Replacements of Homie with either heterologous insulators or phage λ DNA were created using the ΔHomie construct , and adding DNA fragments corresponding to gypsy [78] , Fab-7 [79]–[82] , Fab-8 [83] , [84] , scs [85] , [86] , scs' [85] , [86] , or λ DNA ( see Figure S6 for details ) . For testing repression activity of heterologous PREs , either the engrailed 181PRE [87] or the bxd PRE [88] were inserted into the ΔHomie ΔPRE construct at the site of deletion . For testing Homie activity against these PREs , either the en PRE or the bxd PRE were inserted into the ΔPRE construct at the site of deletion . All transgenic lines were made using φC31 recombinase-mediated cassette exchange ( RMCE ) [63] . Three alternative attP target sites were used , at cytological locations 95E5 , 74A2 , and 30B5 . The direction of each insertion was determined by PCR . Both directions were analyzed if obtained . Some variations with insertion site were found , as described in Results . Embryos were collected at time points described in figure legends , and subjected to in situ hybridization using DIG-labeled anti-sense RNA probes against either lacZ or GFP . Expression patterns were visualized by alkaline phosphatase-conjugated anti-DIG with BCIP and NBT as substrates ( Roche Applied Science ) . GFP expression was detected by fluorescence microscopy in ovaries dissected from 1–2 day-old females . In some cases , expression was also detected using anti-GFP antibody staining ( Roche Applied Science ) , analyzed by confocal microscopy ( Zeiss ) of material in DAPI-containing mounting medium . Ovaries were dissected from 2–3 day-old females . Fifty ovaries were cross-linked in 1 . 8% formaldehyde in PBS for 10 min . After sonication so as to produce a peak near 500 bp in the DNA fragment size distribution , isolated chromatin was immunoprecipitated with anti-H3K27me3 ( EMD Millipore ) , and with rabbit IgG ( Jackson ImmunoResearch ) as a negative control . Precipitated chromatin samples were collected using ProteinG magnetic beads ( EMD Millipore ) . Immunoprecipitated DNA samples were dissolved in 20–50 µl TE , and 1 µl was used for each PCR reaction . Either duplicate or triplicate samples were analyzed by real-time PCR ( Life Technologies , StepOnePlus ) , using SYBR Green Master Mix with ROX dye ( Roche Applied Science ) . Data were analyzed with StepOne software ( Life Technologies ) , using the standard curve method . Standard deviations were calculated using Excel software ( Microsoft ) . Embryo ChIP analysis was described previously [51] , except that results were quantified by real-time PCR , as described above for ovary analysis . Specific ChIP signals were determined by subtracting the average non-specific IgG signal from the average α-H3K27me3 signal , with standard deviations combined by adding . Errors bars for specific signals relative to that of endogenous eve were determined by adding the relative errors in quadrature; that is , by taking the sum of the squares of the relative standard deviations ( the standard deviations divided by their respective averages ) to give the square of the relative standard deviation of the ratio . The following primers were used: TCCAGTCCGGATAACTCCTTGAAC and TGTAGAACTCCTTCTCCAAGCGAC for the endogenous eve coding region , TGAAGCCACCGCGTGGTATTCTTA and TTTGGACATGATCTCCGGTCCGTT for the endogenous TER94 coding region , GCTGTGCCGAAATGGTCCATCAAA and TACTGACGAAACGCCTGCCAGTAT for the transgenic eve-lacZ coding region , and GGGCACAAGCTGGAGTACAACTACAA and TGGCGGATCTTGAAGTTCACCTTG for the transgenic TER94-GFP coding region . Total RNA was purified from either five pairs of ovaries from 2–3 day-old females or 10–20 µl of dechorionated embryos for each data point , using an RNA purification kit ( Roche Applied Science ) . RNA was eluted in 50–100 µl elution buffer and stored at −80°C . cDNA was synthesized using the Transcriptor first strand cDNA synthesis kit ( Roche Applied Science ) , and quantified by real-time PCR as described above . A constitutively expressed RNA , RpL32 ( a . k . a . RP49 ) , was used to normalize GFP RNA levels . The primers listed above for TER94-GFP were used for GFP , and AAGCCCAAGGGTATCGACAACAGA and TGCACCAGGAACTTCTTGAATCCG were used for RpL32 . | Insulators are specialized DNA elements that can separate the genome into functional units . Most of the current thinking about these elements comes from studies done with model transgenes . Studies of insulators within the specialized Hox gene complexes have suggested that model transgenes can reflect the normal functions of these elements in their native context . However , recent genome-wide studies have called this into question . This work analyzes the native function of an insulator that resides between the Drosophila genes eve and TER94 , which are expressed in very different patterns . Also , the eve gene is a Polycomb ( Pc ) domain , a specialized type of chromatin that is found in many places throughout the genome . We show that this insulator has three major functions . It blocks the spreading of the eve Pc domain , preventing repression of TER94 . It prevents activation of TER94 by eve regulatory DNA . It also facilitates normal eve expression . Each of these activities are consistent with those seen with model transgenes , and other known insulators can provide these functions in this context . This work provides a novel and convincing example of the normal role of insulators in regulating the eukaryotic genome , as well as providing insights into their mechanisms of action . | [
"Abstract",
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"Methods"
] | [] | 2013 | The Drosophila eve Insulator Homie Promotes eve Expression and Protects the Adjacent Gene from Repression by Polycomb Spreading |
Many infectious Gram-negative bacteria , including Salmonella typhimurium , require a Type Three Secretion System ( T3SS ) to translocate virulence factors into host cells . The T3SS consists of a membrane protein complex and an extracellular needle together that form a continuous channel . Regulated secretion of virulence factors requires the presence of SipD at the T3SS needle tip in S . typhimurium . Here we report three-dimensional structures of individual SipD , SipD in fusion with the needle subunit PrgI , and of SipD:PrgI in complex with the bile salt , deoxycholate . Assembly of the complex involves major conformational changes in both SipD and PrgI . This rearrangement is mediated via a π bulge in the central SipD helix and is stabilized by conserved amino acids that may allow for specificity in the assembly and composition of the tip proteins . Five copies each of the needle subunit PrgI and SipD form the T3SS needle tip complex . Using surface plasmon resonance spectroscopy and crystal structure analysis we found that the T3SS needle tip complex binds deoxycholate with micromolar affinity via a cleft formed at the SipD:PrgI interface . In the structure-based three-dimensional model of the T3SS needle tip , the bound deoxycholate faces the host membrane . Recently , binding of SipD with bile salts present in the gut was shown to impede bacterial infection . Binding of bile salts to the SipD:PrgI interface in this particular arrangement may thus inhibit the T3SS function . The structures presented in this study provide insight into the open state of the T3SS needle tip . Our findings present the atomic details of the T3SS arrangement occurring at the pathogen-host interface .
Bacterial infections including Salmonellosis and Shigellosis affect millions of people every year . These bacteria use a T3SS to secrete virulence factors to manipulate host cells . The T3SS is a multi-component system that forms a continuous protein transport channel through the two bacterial membranes and the periplasmatic space that extends into the surrounding medium by a needle structure [1]–[3] . Spatiotemporal control of secretion is essential for effective host invasion [4] . Tip proteins , which bind to the distal end of the T3SS needle , are thought to play an important role in this process [4]–[6] . SipD from S . typhimurium , IpaD from Shigella flexneri and BipD from Burkholderia mallei are tip proteins that are thought to interact with their corresponding needle subunits PrgI , MxiH and BsaL , respectively , to make the needle tip complex [5] . Although the mechanism is unclear , tip proteins were shown to influence secretion and invasion of bacteria [7]–[9] . Sterols like cholesterol or cholic acid derivatives found in the bile are amphipathic compounds that play important roles in cellular communication and metabolic processes . Bile salts influence the T3SS of intestinal bacteria . For instance , the presence of deoxycholate either impedes ( S . typhimurium ) or facilitates ( S . flexneri ) host invasion[10]–[13] . Noteworthy , it was recently shown that SipD and IpaD bind deoxycholate and some of its derivatives [14] , [15] . To understand how T3SS are regulated it is required to analyze the structure and mechanism of proteins gating the transport channel . Here , we address the questions of how the Salmonella SipD interacts with PrgI and deoxycholate and the mechanistic consequences of the assembly of the T3SS needle tip complex .
We solved the X-ray crystal structure of SipD , the needle tip protein of S . typhimurium at 3 . 0 Å resolution ( Figure 1A and Table 1 ) . The crystal contained 4 copies of SipD in the asymmetric unit with structural information for 306 of 343 amino acids . In SipD crystals , as in the crystal structures of IpaD [16] and BipD [17] , the N-terminal 31 , 38 or 29 amino acids , respectively , were not defined . Here we report structural features of SipD chain A , which , compared with chains B to D , showed continuous electron density for most amino acids . SipD is predominantly α-helical folded and can be divided in three structurally different domains ( Figure 1B ) . The central domain , domain 2 , ( green in Figure 1 ) adopted a coiled coil structure with two helices of 47 and 52 amino acids length , respectively . The bending of the coiled coil of domain 2 allowed us to distinguish between a concave and a convex surface . Domain 2 is joined to domain 3 , an α/β-structure ( yellow in Figure 1 ) . Domain 3 is in contact with the convex surface provided by the coiled coil of the central domain ( Figure 1A ) . Domain 2 and 3 of SipD share high sequence homology and similar three dimensional structure to the orthologs from S . flexneri ( Figure S1 , r . m . s . deviation 1 . 3 Å with IpaD ) [16] or B . mallei ( Figure S1 , r . m . s . deviation 2 . 2 Å with BipD ) [17] . Notably , amino acids involved in intramolecular contacts are identical or highly conserved in all three orthologs , suggesting functional relevance in T3SS tip proteins . In contrast to domain 2 and 3 , the amino acid sequence of domain 1 in SipD ( grey in Figure 1 ) show almost no sequence conservation with IpaD or BipD . However , α-helices of domain 1 adopted a similar structure in both SipD and IpaD ( Figure S2 ) , encompassing the central coiled coil of domain 2 ( Figure 1A ) . In the S . flexneri cytosol , IpaD domain 1 was suggested to function as a self-chaperone avoiding either spontaneous self oligomerization or its interaction with the needle forming protein MxiH before secretion [16] . We tested the oligomerization state of purified SipD by static light scattering and found that SipD is a monomer in solution ( Mw ∼37 kDa , Figure S3 ) . Deletion of domain 1 ( SipDΔD1 ) resulted in a mixture of SipD dimers and trimers ( Figure S4 ) . Though oligomerization of the needle tip protein changed depending on the presence of domain 1 , deletion of this domain did not favour spontaneous protein polymerization as was found for PrgI and its orthologues [18] . SipD did not bind to PrgI* , a soluble and functional PrgI mutant [18] , as tested by isothermic titration calorimetry ( ITC , Figure 2A ) . In contrast , SipDΔD1 bound with a Kd of 88±3 µM to PrgI* ( Figure 2B ) . Deletion of domain 1 did not destabilize SipD , as demonstrated by the comparative analysis of X-ray crystal structures of SipDΔD1 ( Figure 2C and Table 1 ) and SipD ( Figure 1A ) . Superposition of SipDΔD1 and SipD showed almost identical 3-dimensional structure reflected in an r . m . s . deviation of 0 . 9 Å for amino acids 149 to 328 . ITC results showed that domains 2 and 3 of SipD mediate binding to PrgI* , while domain 1 impedes the interaction . In fact , prior to binding to PrgI* , the self-chaperoning domain 1 of SipD may unfold independently from the rest of the molecule as recently suggested [19] , [20] . In order to decipher the 3-dimensional structure of the entire T3SS needle tip , we generated a fusion protein of N-terminal truncated SipD with PrgI ( PrgI-SipDΔD1 ) . Crystals of PrgI-SipDΔD1 contained two similar copies ( chain A and chain B ) in the asymmetric unit . We describe here the structure of chain A , which is more complete than chain B . The X-ray crystal structure of PrgI-SipDΔD1 solved at 2 . 4 Å resolution showed noteworthy features ( Figure 3A and Table 1 ) . Comparing structures of individual SipD ( brown ) and PrgI* molecules ( grey ) with the PrgI-SipDΔD1 fusion protein ( SipDΔD1 in green and PrgI in blue ) revealed conformational changes in both proteins ( Figure 3B and Figure S5 ) . Two of the five helices providing contact between SipD and PrgI changed conformation during complex formation . The central helix of domain 2 in SipD is kinked at Asn141 in the complex ( Figure 3A–B ) . We observed also a kink in the C-terminal helix of PrgI at Asn63 ( Figure 3A and Figure S5 ) . Both kinked helices , together with two additional helices from SipD and PrgI , adopted a new four helix bundle in the complex . Recent studies [9] suggest that the needle proteins could replace the two helices of domain 1 in the corresponding T3SS tip protein that are in contact with the concave side of the central coiled coil ( Figure 1A ) . This hypothesis is in agreement with our ITC data showing that PrgI* may replace domain 1 during SipD binding ( Figure 2A–B ) . However , the crystal structure of the complex presented here revealed PrgI binding to the convex surface of the central coiled coil in SipD ( Figure 3A ) . Thus , in contrast to the proposed model , a single PrgI molecule may replace the helix-loop motif of SipD immediately upstream of domain 2 ( Figure 1B ) , instead of the two helices at the N-terminus of SipD . Moreover , both proteins in the complex comprised an angle of about 45° ( Figure 3A and Figure S6 ) due to the contact between SipD domain 3 and PrgI . We tested the oligomerization state of the PrgI-SipDΔD1 fusion protein using static light scattering technique . The needle tip complex was monomeric in solution ( Figure S7 ) , suggesting that the supramolecular architecture of the tip complex is influenced by the PrgI assembly of the T3SS needle . This hypothesis is in agreement with our observation that deletion of domain 1 of SipD did not support polymerization of the needle tip protein ( Figure S4 ) but rather allowed interaction with the needle protein PrgI ( Figure 2 ) . The X-ray crystal structure of the PrgI-SipDΔD1 complex compared with the structure of SipD alone and with previous structural studies of needle tip proteins [18] , [21]–[23] showed that both PrgI and SipD changed conformation during complex assembly . As mentioned above , the two helices which showed novel kinked conformation are part of a four helix bundle , thus providing close contact between SipD and PrgI ( Figure 3A and Figure 4A ) . The four helix bundle stabilized by hydrophobic ( Figure S8 ) and polar interactions encompassed a buried surface of 1113 Å2 per protein . An extended hydrogen bonding network connecting conserved amino acids of both proteins stabilizes the twisted helical arrangement found in the PrgI-SipDΔD1 fusion protein ( Figure 4A ) . In total six hydrogen bonds and salt bridges between the C-terminal helix of PrgI and the long helices of domain 2 or the central helix of SipD domain 3 stabilized the tertiary structure of the complex ( Figure 4B ) . A hydrogen bond between Ser333 and Asp11 of SipD and PrgI , respectively , contributed additional stabilization of the complex structure . Spin labelled amino acids Asp136 , Ala144 , Asp147 , Leu318 , Lys324 , Ser328 , Ser331 and Glu335 of SipD are influenced by PrgI as recently shown [24] , consistently with the PrgI-SipDΔD1 crystal structure presented here . We tested whether the interactions between SipD and PrgI are necessary for the T3SS function in HeLa cell invasion assays . SipD knockout cells , which are not invasive , were complemented with plasmids harbouring wildtype or mutant sipD ( Figure 4C ) . We designed sipD mutants based on the structure of the PrgI-SipDΔD1 fusion protein and on conservation of SipD amino acids ( Figure S1 ) . Including control mutants that did not hamper stabilization of the tip complex , we tested 15 SipD point mutations ( Figure 4C ) . Except for Tyr153 and Glu237 , which formed intramolecular hydrogen bonds , tested SipD mutants should not destabilize folding of the apo-protein . Most interactions between SipD and PrgI were essential for a functional T3SS as demonstrated by a dramatically reduced invasiveness ( Figure 4C ) . In contrast , amino acids in the same region of SipD not interacting with PrgI had little or no effect ( S130A , S236A , Q310A , D320A , S327A ) on host cell invasion ( Figure 4C–D ) . Circular dichroism spectroscopy of purified mutants and wildtype SipD indicated same folding , albeit the weaker spectrum of I142S indicates less α-helical content and suggests lower stability at 37°C ( Figure S9 ) . Similarly prgI mutant complemented PrgI knockout cells showed reduced invasiveness ( Figure S10 ) . Our results showed that mutations in the C-terminus of needle proteins can impede host invasion and are in agreement with previous studies in S . typhimurium and other T3SS dependent bacteria [21]–[23] , [25] . We showed that polar interactions at the PrgI-SipDΔD1 interface provide tight and specific contacts between the needle tip complex components . In close proximity of these contacts two helices adopted kinked conformation in the complex , but not in the individual proteins ( Figure 3 ) . By comparing the structures of PrgI-SipDΔD1 fusion protein and SipD , we can propose how assembly of the T3SS needle tip is established . In chain A of both structures , one helix of the coiled coil ( domain 2 of SipD ) showed partial unwinding . This helix anomaly , usually energetically disfavoured , is stabilized in the fusion protein by hydrogen bonds formed between carbonyl oxygen of amino acid Ser148 , a water molecule , and Trp234 of SipD ( Figure 5A ) . The interaction of the Ser148 backbone carbonyl group with Trp234 shifted the typical backbone hydrogen bonding pattern stabilizing architecture of an α–helix ( i→i+4 ) by one position ( i ( Ala144 ) →i+5 ( Tyr149 ) ) ( Figure 5B ) . Local deviation from the backbone hydrogen bonding pattern of an α–helix described as π-bulge plays an important role in many different proteins that require conformational flexibility for function [26] . In our structure of the PrgI-SipDΔD1 fusion protein the π-bulge caused local unwinding and weakening of the α–helix around Ser148 . Furthermore , mutation of either of the two amino acids ( Ser148 or Trp234 ) involved in formation of the π-bulge led to reduced or even loss of bacterial invasion in HeLa cells ( Figure 4C ) . These results support the relevance of the π-bulge for the functionality of the T3SS needle tip complex . In the PrgI-SipDΔD1 fusion protein a water molecule bridges the backbone carbonyl oxygen of Ser148 and Trp234 ( Figure 5A ) . The backbone carbonyl of Ser148 pointing towards the imino group of Trp234 suggested similar hydrogen bond stabilization in SipD as in the PrgI-SipDΔD1 fusion protein ( Figure 5B ) . It is interesting to speculate whether the coiled coil of SipD is intrinsically destabilized even in the absence of PrgI based on the superposition of different SipD copies found in the SipD structure ( Figure S11 ) . Structural flexibility introduced by the π-bulge may account partially for the helix kink observed in the complex . The SipD helix kink is stabilized through interactions between Ser148 side chain with Asn59 of PrgI , which is highly conserved in needle proteins ( Figure 4A and Figure 5A ) . Notably , in the fusion protein the Ser148 side chain formed a hydrogen bond with the backbone carbonyl oxygen of Ala144 , located one helix turn upstream ( Figure 5A ) . This hydrogen bonding network stabilized the helix kink present at amino acid Ala144 of SipD in the complex . Taken together , a π–bulge in SipD may provide conformational flexibility to allow the formation of the T3SS needle tip complex . Conversely , conformational flexibility of SipD may be arrested by the folded domain 1 in cytosolic SipD . As mentioned above , one helix of PrgI had a similar kink to SipD in the complex . Amino acids Val65 and Val67 of PrgI were located at the kink ( Figure 5A ) . Interestingly , both amino acids were recently found to be critically involved in polymerization of the T3SS needle [18] . Briefly , substitution of valines at position 65 and 67 by alanines reduced polymerization kinetics of the needle protein , but did not abolish needle formation or affect bacterial host invasion . Assembly of the T3SS needle is coupled with α-helix-to-β-sheet change downstream of Val67 . Noteworthy , both amino acids were located at the PrgI-SipDΔD1 interface , suggesting a functional importance during assembly of the T3SS needle and tip complex . The visible amino acids downstream of the helix kink in PrgI adopted a helix conformation in the complex . Conformational flexibility , depending whether PrgI molecules interact with each other to form a needle or with SipD at the T3SS needle tip , may be a prerequisite for the function of this protein . Host invasion of enteric bacteria is often dramatically influenced by the presence of bile salts [10]–[13] . Human intestinal bile salts , including deoxycholates , taurodeoxycholates and chenodeoxycholates , can either increase or repress invasion of S . flexneri or S . typhimurium , respectively [10] , [14] , [15] . The bile salt effect on those bacteria is coupled to a functional T3SS . Moreover , recent studies show that bile salts bind to needle tip proteins , corroborating that a T3SS component is affected by ligands released into the human gut [27] . To measure whether the PrgI-SipDΔD1 fusion protein could also bind deoxycholate ( Figure 6A ) , we used surface plasmon resonance assay ( Biacore ) . The binding curve of the immobilized PrgI-SipDΔD1 fusion with increasing concentrations of deoxycholate ( Figure 6B ) indicated a dissociation constant of 59 . 0±2 . 7 µM , assuming a protein ligand ratio of 1 to 1 . These data are in agreement with previous results indicating an affinity between the S . flexneri needle tip protein IpaD and deoxycholate in the micromolar range [14] . Next , we soaked PrgI-SipDΔD1 crystals with sodium deoxycholate and analyzed the corresponding electron density for novel features that could fit the ligand . In the co-crystal structure ( Figure 6C , Figure S12 and Table 1 ) , deoxycholate was bound through its most hydrophobic β-surface ( Figure 6A ) to the cleft formed by SipD and PrgI . Localization of this binding site is about 25 Å away from the previously described deoxycholate binding site in wildtype SipD [28] . The α-surface of deoxycholate , disposing two hydroxyl groups , was facing the bulk medium . Ligand binding induced only minor structural changes related to the side chain of Ser236 in SipD and Gln24 and Gln48 in PrgI . Deoxycholate is a rigid molecule that fit almost perfectly into the cleft provided by the PrgI-SipDΔD1 fusion protein . In the cocrystal , the deoxycholate carboxyl group located at the end of the binding cleft was not as tightly embedded as the rest of the ligand . Therefore bile salts with larger substituents at this position could also occupy the cleft . Indeed , taurodeoxycholate , which is deoxycholate amidated at the carboxyl group with ethansulfonic acid , also binds to SipD and IpaD [14] , [15] . Results presented here are in agreement with recent NMR titration experiments showing that deoxycholate causes chemical shift changes in SipD upon binding to amino acids in the vicinity of the ligand binding site comprised by Arg232 , Gln233 , Ser236 , Glu237 and Asn239 [15] . Moreover , mutation of amino acid Glu229 of IpaD , which is the equivalent of Glu237 in SipD , abolishes binding of deoxycholate [14] . Our data , however , indicate that bile salts bind to SipD similarly to the deoxycholate PrgI-SipDΔD1 fusion protein . The inhibitory effect of this ligand protein interaction for host invasion suggests that other proteins need to bind to the hydrophobic cleft formed by SipD and PrgI .
The cryo-EM map of isolated needles from S . flexneri which is similar to the map obtained from isolated S . typhimurium needle [31] together with the X-ray crystal structure of a needle protomer mutant can be used to build a composite 3-dimensional model [21] . Based on this composite model of the T3SS needle we manually superimposed the similarly structured regions of the PrgI-SipDΔD1 fusion protein with the MxiH subunits of the needle . Superposition of PrgI and MxiH ( PDB code 2V6L ) using the program Coot [32] was feasible without structural clashes . In total , five molecules of the PrgI-SipDΔD1 fusion protein were successfully superimposed with five MxiH subunits at the distal end of the T3SS needle ( Figure 7 ) . As described above , we found that PrgI binds to the concave side of the central coiled coil in SipD . Therefore , the PrgI-SipDΔD1 fusion protein could be mounted at the distal end of the needle without inducing structural changes . Domain1 present in SipD may face the bulk medium either in an unfold state or as a folded entity . In contrast to our model , the SipD-PrgI contact predicted by a previous work [9] would require substantial structural changes at the tip of the T3SS needle . The tip complex is the distal opening of the transport channel provided by the T3SS needle . According to the proposed model , SipD bound to PrgI localizes to the outer surface of the T3SS needle without obstructing the inner channel ( Figure 7 ) . The channel is opened in the three dimensional model of the T3SS tip complex ( Figure 7A ) , adopting a state that permits transport or release of unfolded molecules after passage through the channel inside the needle . For this reason we assign the presented structure-based model as the “open state” of the T3SS needle tip . About 25 Å for the diameter of the T3SS channel may allow the passage of a single α-helix ( or even of a helix-loop-helix motif ) . Deletion of the T3SS needle tip protein causes constitutive secretion of virulence factors but abolishes bacterial invasion [7] . Consequently , it was proposed that the needle tip protein blocks the secretion of virulence factors . In contrast to this hypothesis , our structure based model of the open state suggests that the T3SS tip complex is not necessarily blocking the T3SS channel . Moreover , the tip protein does not need to be released for secretion of virulence factors , as the SipD-PrgI interaction is not clogging the channel . We speculate that the presence of the tip protein enables intermittent closing of the T3SS system thus regulating the process of secretion . The three dimensional model presented here enables the following conclusions: The open state of the SipD-PrgI needle tip must be closed to block the constitutive transport of virulence factors . The closing of the T3SS needle tip can be mediated by either a conformational change of SipD or by its interaction with other effector proteins or lipids . Notably , the structure of the needle tip complex is not in conflict with possible movement of domain 2 and 3 in SipD but further work is required to explain how SipD regulates the secretion of T3SS . Moreover , binding of other effector proteins to the T3SS needle tip was proposed for the control of the needle length [1] , [33] and the translocation of virulence factors across host membranes ( SipB ) . Along these lines , we previously reported that the T3SS needle protein PrgI extends the needle from the distal end in the absence of tip proteins [18] . Moreover , addition of tip proteins prevented further growth of the T3SS needle [18] . It is plausible that addition of SipD avoid needle elongation . Bile salts , including deoxycholate , can prevent S . typhimurium invasion through binding to SipD [12] , [15] . We showed here that deoxycholate binds to the cleft formed by SipD and PrgI close to the constriction of the T3SS channel ( Figure 6 and Figure 7a ) . This interaction may prevent larger conformational changes in SipD , which block closure of the T3SS channel . Likewise bound deoxycholate may impede the binding of channel blocking proteins . Future studies are needed to understand how the T3SS is regulated using deoxycholate . The presented structural studies enable us to construct a three dimensional model of the Salmonella T3SS needle tip , which in turn suggests a secretion mechanism . In the open state of the T3SS needle tip a large cavity , maybe enclosed through contact with the host membrane , is formed . This cavity could act as a folding chamber to facilitate the folding of secreted proteins . Folding of early secreted translocator proteins at the host membrane could improve the delivery of other effector proteins into the host cytoplasm . A similar folding principle was identified in the molecular chaperones , including prefoldin which forms a cavity for the nascent protein chain at the exit channel of the ribosome [34]–[36] . Moreover , the T3SS channel could be closed by contact with host membranes . In this scenario , the host membrane could prevent waste of secreted virulence factors , which otherwise could diffuse away from the point of contact . The T3SS needle tip is crucial for bacterial invasion and searching for substances similar to deoxycholate that prevent functioning or even assembly of the complex could lead to the discovery of novel targets for the development of drugs against pathogenic enterobacteria .
Wildtype and mutant sipD or prgI were amplified from S . enterica serovar typhimurium strain SL1344 ( S . typhimurium ) by standard PCR using oligonucleotide primers with NdeI and XhoI restriction sites at either ends . Single crystallization of SipD132–343 superseded cocrystallization with PrgI in various attempts . Therefore , wildtype prgI and a sipD fragment encoding amino acids 127 to 343 were connected by fusion PCR . N-terminal PrgI was fused by the amino acids Gly-Gly-Ser-Gly-Gly to SipD127-343 . PCR products were cloned into the expression vector pET-28a ( + ) ( Novagen ) or pET-21a ( + ) ( Novagen ) , both containing N-terminal His-tag , and expressed in Escherichia coli BL21 ( DE3 ) cells . Cells were induced with isopropyl-β-D-1-thiogalactopyranoside , harvested after 4 h and His-tagged protein purified using affinity chromatography ( HisTrap , GE Healthcare ) . Bound protein was washed ( 40 mM imidazole ) and eluted using buffer containing 500 mM imidazole . After buffer exchange ( 20 mM HEPES pH 7 . 4 , 50 mM NaCl ) the tag was cleaved with CleanCleave Kit ( Sigma-Aldrich ) . The cleaved product was purified by size-exclusion chromatography ( Superdex 200 or Superdex 75 , GE Healthcare ) and stored at 4°C until use . For functional assays , wildtype or mutant sipD or prgI were cloned into the pASK-IBA5 vector ( IBA ) as BsaI fragments . Point mutants were generated using QuikChange Site-Directed Mutagenesis Kit ( Stratagene ) . All constructs were confirmed by sequencing . Crystals of SipD , SipDΔD1 and PrgI-SipDΔD1 were obtained at 18°C using hanging drop vapour diffusion technique . SipD was concentrated to ∼15 mg/ml and mixed with equal volume of reservoir solution containing 100 mM HEPES pH 7 . 5 and 1 . 5 M Li2SO4 . SipDΔD1 was concentrated to 40 mg/ml and mixed with equal volume of reservoir solution 0 . 1 M MES pH 6 . 5 and 12% ( w/v ) polyethylene glycol 20000 . PrgI-SipDΔD1 was concentrated to 30 mg/ml and mixed with equal volume of reservoir solution containing 0 . 49 M NaH2PO4 • H2O and 0 . 91 M K2HPO4 , pH 6 . 9 . To obtain cocrystals PrgI-SipDΔD1 crystals were soaked for 72 hours in mother liquor containing ∼10 mM deoxycholate . All crystals were flash frozen in liquid nitrogen in the presence of 30% glycerol ( v/v ) . Diffraction data were collected at 100 K and wavelength 0 . 918 Å at BESSY II ( Berlin , Germany ) beamlines 14 . 1 or 14 . 2 , or wavelength 1 . 000 Å at SLS ( Villigen , Switzerland ) beamline X06SA . Diffraction data were indexed , integrated and scaled using the program package XDS [37] . The crystal structure of SipDΔD1 was solved by molecular replacement with the program Phaser [38] using the structure of truncated IpaD ( pdb code: 2J0N ) as template . The structures of SipD and PrgI-SipDΔD1 , apo and with deoxycholate , were solved by molecular replacement using the SipDΔD1 structure as template . The initial models were refined by repeated cycles of manual building and refinement using the programs Coot [32] and CNS [39] . Crystals of SipD have 4 copies in the asymmetric unit and the following Ramachandran statistics: 82 . 7% of residues in most favoured regions , 16 . 6% in additionally allowed regions , 0 . 7% in generously allowed regions . Crystals of SipDΔD1 have 2 copies in the asymmetric unit and 92 . 7% of residues in most favoured regions , 6 . 5% in additionally allowed regions , and 0 . 8% in generously allowed regions . Crystals of PrgI-SipDΔD1 have 2 copies in the asymmetric unit and 92 . 9% of residues in most favoured regions , 7 . 1% in additionally allowed regions . The structure of PrgI-SipDΔD1 complexed deoxycolate has 93 . 4% of residues in most favoured regions and 6 . 6% in additionally allowed regions . Ramachandran statistics were calculated with PROCHECK v . 3 . 3 [40] . Molecular graphics images , including representations of surface electrostatic potential , were produced using PyMOL version 0 . 99rc6 [41] , except Figure S6 which was produced with UCSF Chimera package from the Resource for Biocomputing , Visualization , and Informatics at the University of California , San Francisco [42] . Bacterial knockouts were generated according to Datsenko and Wanner [43] . pASK-IBA5 plasmids harboring wild type or mutant sipD ( psipD ) were used to complement deletions of sipD in S . typhimurium strain SL1344 to generate strains SL1344ΔsipD/psipD . HeLa cells were seeded at 1×105 cells per well and grown for 24 h at 37°C . Prior to infection , growth medium was aspirated , cells were washed twice with phosphate-buffered saline ( PBS ) , and serum-free medium was added . To test for epithelial cell invasion and intracellular growth , HeLa cells were infected with S . typhimurium at a multiplicity of infection ( MOI ) of 10:1 . Expression of sipD wild-type and sipD mutants was induced with 0 . 2 µg ml−1 anhydrotetracycline for 1 h . Bacterial inocula were prepared in PBS and centrifuged onto cells ( 2000 rpm , 10 min ) , and infected cultures incubated for 20 min at 37°C . Cultures were washed three times with PBS , and fresh medium containing 100 µg ml−1 gentamicin was added . After 2 h cells were washed with PBS and lysed with 0 . 1% Triton X-100 . Numbers of viable bacteria were obtained by plating dilutions of lysates on tryptic soy agar plates and counting colonies after overnight incubation at 37°C . For mass determination a combined setup consisting of SEC and subsequent online detection by UV absorption , ( three angle ) static laser light scattering and differential refractive index measurement was used as described earlier [44] . SEC was performed with either a Tricorn Superdex 200 10/300 GL column or a Tricorn Superdex 75 10/300 GL ( GE Healthcare ) equilibrated with 20 mM HEPES ( pH 7 . 5 ) , 150 mM NaCl . For static light scattering and differential refractive index measurements a linear coupled miniDAWN Tristar ( Wyatt Technology ) system and a differential refractive index detector ( RI-101 , Shodex ) , respectively , was used . All calculations were done with the software ASTRA ( Wyatt Technology ) . Each experiment was repeated at least in triplicate . Titration experiments were carried out using a VP–ITC isothermal titration microcalorimeter ( MicroCal , Northampton , MA , USA ) . Aliquots of 12 µl of SipD ( 1 . 35 mM ) were injected consecutively at 20°C into the cell containing 1 . 4 ml of PrgI* ( 0 . 34 mM ) or at 17°C by injecting consecutively 12 µl aliquots of PrgI* ( 1 . 99 mM ) into the cell containing 1 . 4 ml of SipDΔD1 ( 0 . 37 mM ) . The heat of dilution of the injected protein was measured in both cases and subtracted from the heath measured at each injection . Binding stoichiometry , enthalpy , and equilibrium association constants were determined by fitting the corrected data to one set of sites model equation using the evaluation software provided by the manufacturer . Binding of sodium deoxycholate to the PrgI-SipDΔD1 fusion protein was measured using surface plasmon technology-based Biacore X100 biosensor ( GE Healthcare ) according to manufacturer's instruction . Briefly , PrgI-SipDΔD1 fusion protein was immobilized on a sensor chip CM5 ( research grade ) by amine coupling method . Binding experiments were performed at 25°C at continuous flow rate of HBS-N buffer ( 10 mM HEPES , 150 mM NaCl , pH 7 . 4 ) . Deoxycholate was injected in steps with increasing concentrations in a single analysis cycle without regeneration of the surface in between injections . Affinity analysis was performed using single-cycle kinetics . Equilibrium dissociation constant ( KD ) was determined with Biacore evaluation Version 4 . 1 software . During the assays , the signal was corrected against the control surface response to eliminate refractive index changes due to buffer change . CD spectra were collected with a Jasco J-500A spectropolarimeter . Samples buffered in 10 mM HEPES ( pH 7 . 4 ) , 25 mM NaCl were measured either at 20°C and protein concentration1 . 5 mg/ml between 182 and 260 nm in a quartz cuvette with optical path length of 0 . 1 mm or at 37°C and protein concentration 0 . 15 mg/ml between 198 and 260 nm in a temperature controlled quartz cuvette with optical path length of 1 mm . Wavelength scans were carried out at a scan rate of 12 nm/min , with time constant 2 sec . All the spectra were acquired in triplicates . The atomic coordinates and structure factors of the four structures described here are available from the Protein Data Bank under the following accession codes: 2YM0 for SipDΔD1 , 2YM9 for SipD , 3ZQB for PrgI- SipDΔD1 , 3ZQE for PrgI- SipDΔD1 complexed with deoxycholate . | Since the rise of pathogenic bacterial strains resistant to antibiotics , the need to develop potent anti-infective drugs is continually increasing . This necessitates a detailed knowledge of the bacterial host invasion process . Gram-negative bacteria have evolved a protein transport system through which they deliver virulence factors into host cells . These virulence factors influence the signal transduction cascade and metabolism inside host cells in a way that is beneficial for the invading bacteria . The proteins at the transport system needle tip mediate contact with host cells and spatiotemporal coordinated release of virulence factors . In this study , we used biophysical and biochemical methods to understand the structure and function of proteins present at the needle tip of such a virulence factor transport system in Salmonella species . We could show that two different proteins , structurally conserved in many pathogenic bacteria , bind each other to constitute the needle tip of the transport system . Multiple copies of both proteins constitute the tip of the transport system in what may represent the open state of the needle . Our study will serve to provide new insights into the virulence factor transport system essential for many different pathogenic bacteria , and may thus offer novel targets to fight infection . | [
"Abstract",
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"bacteriology",
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"and",... | 2011 | Crystal Structure of PrgI-SipD: Insight into a Secretion Competent State of the Type Three Secretion System Needle Tip and its Interaction with Host Ligands |
A systems understanding of nuclear organization and events is critical for determining how cells divide , differentiate , and respond to stimuli and for identifying the causes of diseases . Chromatin remodeling complexes such as SWI/SNF have been implicated in a wide variety of cellular processes including gene expression , nuclear organization , centromere function , and chromosomal stability , and mutations in SWI/SNF components have been linked to several types of cancer . To better understand the biological processes in which chromatin remodeling proteins participate , we globally mapped binding regions for several components of the SWI/SNF complex throughout the human genome using ChIP-Seq . SWI/SNF components were found to lie near regulatory elements integral to transcription ( e . g . 5′ ends , RNA Polymerases II and III , and enhancers ) as well as regions critical for chromosome organization ( e . g . CTCF , lamins , and DNA replication origins ) . Interestingly we also find that certain configurations of SWI/SNF subunits are associated with transcripts that have higher levels of expression , whereas other configurations of SWI/SNF factors are associated with transcripts that have lower levels of expression . To further elucidate the association of SWI/SNF subunits with each other as well as with other nuclear proteins , we also analyzed SWI/SNF immunoprecipitated complexes by mass spectrometry . Individual SWI/SNF factors are associated with their own family members , as well as with cellular constituents such as nuclear matrix proteins , key transcription factors , and centromere components , implying a ubiquitous role in gene regulation and nuclear function . We find an overrepresentation of both SWI/SNF-associated regions and proteins in cell cycle and chromosome organization . Taken together the results from our ChIP and immunoprecipitation experiments suggest that SWI/SNF facilitates gene regulation and genome function more broadly and through a greater diversity of interactions than previously appreciated .
Chromosomes undergo a wide variety of dynamic processes including transcription , replication , repair and packaging . Each of these activities requires the recruitment and congregation of a particular set of factors and chromosomal elements . For example visualization of nascent mRNA in HeLa cells has led to a model of transcription units being clustered into “factories” thereby facilitating optimal engagement of RNA Polymerase II ( Pol II ) and coordination with other crucial holoenzyme complexes [1]–[3] . In addition to RNA Pol II and transcription factors , transcriptional assemblages include proteins critical to regulating chromatin . The accessibility of nuclear proteins to DNA is often controlled by ATP-dependent chromatin remodeling complexes , which are thought to play a role in a number of different cellular transactions by reshaping the epigenetic landscape . The SWI/SNF ( switch/sucrose nonfermentable ) chromatin remodeling proteins were first discovered in Saccharomyces cerevisiae as components of a 2 MDa complex that repositions nucleosomes for vital tasks such as transcriptional control , DNA repair , recombination and chromosome segregation [4] , [5] . Mammalian SWI/SNF is comprised of approximately ten subunits and the combinations of these subunits , some of which have multiple isoforms , enable multiple varieties of SWI/SNF complexes to exist both within a given cell and across cell types [6] . Among these subunits either of the two ATPases , Brg1 or Brm , is sufficient to remodel nucleosome arrays in vitro , however maximal nucleosome remodeling activity is achieved when the SWI/SNF subunits BAF155 , BAF170 and Ini1 are present in a 2∶1 stoichiometry relative to Brg1 [7] . Whereas the ATPases have an obvious catalytic function , the roles of the other SWI/SNF subunits are largely obscure . Several reports indicate that BAF155 and BAF170 provide scaffolding functions for other SWI/SNF subunits as well as regulating their protein levels [8] , [9] . SWI/SNF also contains β-actin and the actin-related protein BAF53 , suggesting a possible bridge to nuclear organization or signal transduction , e . g . through phosphatidylinositol signaling [10] , [11] . Phosphatidylinositol 4 , 5-bisphosphate has been shown to bind to Brg1 and promote binding to actin filaments [12] . Mutations resulting in loss of Ini1 function are associated with rare but aggressive pediatric cancers [13] , [14] . The SWI/SNF subunits Brg1 [15] and ARID1A [16]–[18] are likewise thought to have tumor suppressor roles based on mutations recovered from other tumor types . Curiously , Ini1 alone has a unique and largely undefined role in HIV-1 infection that includes binding of Ini1 to HIV-1 integrase and the cytoplasmic export of Ini1 and its incorporation into HIV-1 particles [19]–[21] . The role of SWI/SNF components in cancer and tumor suppression is poorly understood despite extensive study . Detailed investigations of individual loci have implicated SWI/SNF in various transcriptional pathways including the cell cycle and p53 signaling [22] , insulin signaling [23] , and TGFβ signaling [24] , as well as signaling through several different nuclear hormone receptors [25] . Although in vitro experiments and single-gene studies have been informative and have laid the foundation for understanding chromatin remodeling , a global analysis of targets of SWI/SNF is expected to yield a more extensive view into the biological roles of SWI/SNF components and their involvement in human disease . In this study we present two complementary global analyses of SWI/SNF subunits to provide a more systematic view of SWI/SNF functions . First we performed ChIP-Seq with the ubiquitous SWI/SNF components Ini1 , BAF155 , BAF170 as well as the Brg1 ATPase . Second , in a parallel set of studies we performed mass spectrometry identification of proteins that co-immunoprecipitate with SWI/SNF components . Using our ChIP-Seq results the resulting chromosomal locations were integrated with published annotations to yield a more complete understanding of SWI/SNF on a genome-wide scale . We find SWI/SNF components frequently occupy transcription start sites ( TSSs ) , enhancers , CTCF regions and many regions occupied by Pol II . Further analyses of the SWI/SNF regions we identified by ChIP-Seq reveals that SWI/SNF factors target genes and signaling pathways involved in cell proliferation and cancer . Our investigation of SWI/SNF protein interactions detected not only the expected co-occurrences of individual SWI/SNF factors with each other but also with cellular components such as nuclear matrix proteins , key transcription factors and centromere proteins implying a ubiquitous role in gene regulation and nuclear function . We find an overrepresentation of both SWI/SNF-associated chromosomal regions and proteins in cell cycle and chromosome organization . Collectively our results suggest that SWI/SNF is at the nexus of multiple signal transduction pathways , essential chromosomal functions and nuclear organization .
We identified the targets of four SWI/SNF components , Ini1 ( SMARCB1 ) , Brg1 ( SMARCA4 ) , BAF155 ( SMARCC1 ) and BAF170 ( SMARCC2 ) , using ChIP-Seq . Chromatin complexes were isolated from HeLa S3 nuclei following independent immunoprecipitations with antibodies for each factor . Each of these antibodies was characterized by both immunoblot and mass spectrometry analyses ( see Materials and Methods ) . Reads that mapped uniquely to the genome were retained ( 29–33 million reads per data set; Table 1 ) and significant binding regions were identified using the PeakSeq program with q-value<0 . 05 [26] . The peaks were compared to a similarly-sized data set of uniquely mapped ChIP DNA reads obtained from control immunoprecipitation experiments using normal IgG ( i . e . a control serum that is not directed to any known antigens ) . Using this approach we identified many Ini1- , Brg1- , BAF155- and BAF170-associated regions ( Table 1 ) . The majority of SWI/SNF binding occurs near ( ±2 . 5 kb ) protein-coding genes , a distribution that is significant relative to a random target list ( p<1×10−16; Genome Structure Correction ( GSC ) test [27]; see Materials and Methods ) . Several examples of SWI/SNF positioning relative to genic regions are shown in Figure 1 and Figure S1 . In order to further examine SWI/SNF locations with respect to gene-rich and gene-poor regions we obtained a set of histone H3K27me3 domains that were identified in HeLa cells ( Table S1; [28] ) because this chromatin mark often occurs in gene-poor and repressed ( i . e . heterochromatin ) regions . Although most SWI/SNF-binding occurs outside H3K27me3 domains , we observed that SWI/SNF is occasionally found in heterochromatin regions , as shown in Figure 2 . In this example a 7 . 5 Mb heterochromatin region on Chr16 contains a single gene , the neuronal cadherin CDH8 , that is repressed and lacks RNA Pol II , however several SWI/SNF binding regions are found nearby . We have performed considerable analyses of the targets for the individual SWI/SNF factors , particularly with respect to elements representing several major classes of genomic features including promoters ( Ensembl protein-coding genes ) , RNA Pol II sites [26] , CTCF sites [28] , and predicted enhancers [29] . All of these features were identified in HeLa cells ( Table 1 , Tables S1 and S2; see Materials and Methods ) . In comparisons between the individual target lists for Ini1 , Brg1 , BAF155 and BAF170 with promoters , RNA Pol II sites , CTCF sites and enhancers we found that each SWI/SNF factor is significantly overrepresented for each of these major classes of genomic elements ( p<1×10−16 , GSC test , see Materials and Methods ) . To arrive at a single unified and more conservative list of SWI/SNF locations , we first took the union of all regions for Ini1 , BAF155 , BAF170 and Brg1 , resulting in 69 , 658 SWI/SNF regions . We then trimmed this list to a high-confidence set of 49 , 555 sites by eliminating those regions where either only a single SWI/SNF subunit was present or that those regions that did not co-occur with either promoters , RNA Pol II sites , CTCF sites or predicted enhancers . We used this list of 49 , 555 SWI/SNF regions for all subsequent analyses unless otherwise noted ( Table S3 ) . The four major classes of genomic features mentioned above were overrepresented in both the 69 , 658 SWI/SNF regions as well as the more conservative list 49 , 555 SWI/SNF regions ( p<1×10−16 , GSC test ) . We next examined the configurations of our 49 , 555 SWI/SNF regions ( Figure 3A and Table 2 ) . Ini1 , BAF155 and BAF170 have been described as forming a ‘core’ based on their ability to stimulate remodeling activity of the Brg1 ATPase in reconstitution experiments [7] . Among our data 30 , 310 regions ( 61% ) have two or more SWI/SNF components and 9 , 760 regions ( 20% ) contain the core of Ini1 , BAF155 and BAF170; for the purposes of this study we call this the ‘core set’ . Among putative complexes comprised of two or more SWI/SNF subunits , we observed BAF155 was the subunit most common to each binding region . Only 770 SWI/SNF subunit co-occurrences were recovered that lacked BAF155 as compared to 6 , 467 for BAF170 and 14 , 824 for Ini1 . This finding is consistent with several previous studies showing that BAF155 is important for SWI/SNF complex stability [8] , [9] . BAF155 may increase the stability of the complex during assembly , or BAF155 may be easier to detect by ChIP . One of the primary functions of chromatin remodeling complexes is to assist in gene regulation . Among the SWI/SNF regions in our high-confidence union set of 49 , 555 sites , 29% correspond to the 5′ ends of protein-coding Ensembl genes , 40% correspond to Pol II sites , 17% correspond to CTCF sites and 43% correspond to predicted enhancer regions ( Figure 3B; Table 3 ) . The various combinations of these four elements account for a total of 90% of the SWI/SNF union regions; 4 , 800 ( 10% ) of the SWI/SNF regions are unclassified using the above elements . Similar trends were observed for the 9 , 760 SWI/SNF “core” regions where Ini1 , BAF155 and BAF170 all co-occur ( Table 3 ) . None of these four particular SWI/SNF subunits or any combinations thereof exhibited a differential preference for one type of element ( Table S4 ) . There are some differences between the SWI/SNF core and union regions . The SWI/SNF core regions are overrepresented for RNA Pol II ( p<9 . 9×10−16; hypergeometric test ) and 5′ ends ( p<6 . 5×10−211; hypergeometric test ) relative to all of the SWI/SNF high-confidence union regions; however the SWI/SNF high-confidence union regions are overrepresented for enhancer regions relative to the Ini1-BAF155-BAF170 core ( p<2 . 4×10−67; hypergeometric test ) . Neither the SWI/SNF core nor the high-confidence union regions were over- or underrepresented for CTCF sites relative to each other ( p>0 . 05; hypergeometric test ) . Enhancers are often characterized by long-range interactions . We examined the locations of SWI/SNF binding regions in the 150 kb CIITA region where numerous chromosomal looping interactions have been mapped at high resolution in HeLa cells using 3C ( Chromosome Conformation Capture ) . Brg1 has been previously mapped at several sites in this locus in these cells [30] . Superimposition of these 3C data on our SWI/SNF ChIP-Seq data ( Figure 4 ) reveals that all six of the 3C interacting regions in the CIITA locus ( −50 kb , −16 kb , −8 kb , pIV , +40 kb and +59 kb ) are bound by SWI/SNF components . Moreover certain individual SWI/SNF component binding regions that appeared initially as orphans may now be seen as part of a complete complex when joined with a distal element . For example Ini1 at pIV when joined with BAF155 and BAF170 regions at the −16 kb element forms a SWI/SNF core . Thus in the CTIIA locus SWI/SNF regions are often associated with 3C regions and many of the regions bound by individual factors may in fact be part of entire SWI/SNF complexes inside the nucleus . Overall our ChIP-Seq results are summarized in Table 1 , Table 2 , Table 3 , and Figure 3 and indicate that SWI/SNF likely contributes to gene regulation through many different avenues in light of its binding to promoters , enhancers and CTCF sites . Furthermore SWI/SNF may facilitate looping interactions among these various elements as it has been shown in vitro that SWI/SNF can interact simultaneously with multiple DNA sites and generate loops between them [31] . Interestingly we found a slightly higher presence of the SWI/SNF core at TSSs and with Pol II than the SWI/SNF union regions with these elements ( Table 3 ) . Thus a complete core of Ini1 , BAF155 and BAF170 may be required for effective promoter function whereas only a subset of these factors may be required for enhancer function . Alternatively a full SWI/SNF core may be more difficult to recover from a single enhancer element as compared to a more compact promoter region due to the enhancer's presumed interaction with many different distal elements . As detailed above SWI/SNF regions are enriched for Pol II . To explore the prevalence of SWI/SNF with transcriptional machinery we asked whether the converse would also be true , namely if regions bound by RNA polymerases are enriched for SWI/SNF . Indeed Pol II regions are enriched for SWI/SNF binding regions ( p<1×10−16 , GSC test ) . Although Pol II overlaps extensively with SWI/SNF it differs from SWI/SNF in its concordance with CTCF and enhancer regions ( Table S5 ) . Pol II regions lacking SWI/SNF show a five-fold decrease in CTCF sites and a two-fold decrease in enhancer regions as compared to those Pol II regions containing SWI/SNF . We further compared our SWI/SNF regions with binding intervals identified for RNA polymerase III ( Pol III ) , which in addition to transcribing tRNA and other non-protein coding RNAs has an emerging role in the formation of boundary elements [32] , [33] . Pol III localization data were obtained from published ChIP-Seq studies using HeLa cells ( [34] , [35]; Tables S1 and S6 ) and constitute 478 known and novel Pol III-associated regions . Pol II is often associated with Pol III ( Table 4; reviewed in [32] . Therefore we examined whether SWI/SNF was associated with Pol III binding regions independently of Pol II . Of the 478 Pol III regions , 253 Pol III intervals lack Pol II and among these 39% ( 98/253 ) contain one or more SWI/SNF components . These results suggest that SWI/SNF association with Pol III can occur independently of Pol II . Overall 65% ( 309/478 ) of Pol III regions and 84% ( 19 , 541/23 , 320 ) of Pol II regions have at least one SWI/SNF factor associated with them . The Ini1-BAF155-BAF170 core is found at 41% ( 195/478 ) of Pol III regions and 52% ( 12 , 079/23 , 320 ) of Pol II regions . From the colocalizations of SWI/SNF , Pol II and Pol III we see that there is substantial overlap among these factors yet each of these factors also has distinct characteristics . SWI/SNF is known to act as both an activator and repressor of transcription [36] . We examined the locations of four SWI/SNF components relative to transcribed regions in HeLa S3 cells using the RNA-Seq data of Morin et al . [37] , Ini1 , Brg1 , BAF155 and/or BAF170 are present at or near the 5′ ends ( ±2 . 5 kb ) of 71 to 92% of active protein-coding genes . As noted above , SWI/SNF occupancy in promoters is similar to that of Pol II and each of the factors is individually enriched in promoter regions ( p<1×10−16 , GSC test ) . Although the majority of Ini1 , Brg1 , BAF155 and BAF170 target genes are expressed , an appreciable fraction of gene targets have little or no detectable mRNA in HeLa cells . A closer examination of the union regions where a SWI/SNF component is located in the promoter of an inactive gene reveals that 58% ( 2 , 063/3 , 565 ) of these promoters are co-associated with Pol II suggesting transcriptional stalling ( reviewed in [38] , [39] . Considering that SWI/SNF components bind near many expressed regions and that SWI/SNF factors occur in a multitude of configurations ( Figure 3 and Table S3 ) , we examined transcript expression levels for all possible combinations of Ini1 , Brg1 , BAF155 and BAF170 occurrences . Using the RNA-Seq data of Morin et al . [37] , we examined transcript expression levels corresponding to each of these configurations ( Figure 5 ) . We see that the highest levels of transcription are associated with the following four configurations: 1 ) the complete core of Ini1 , BAF155 and BAF170; 2 ) the complete core plus Brg1; 3 ) Ini1 and BAF155 only and 4 ) Brg1 , BAF155 and BAF170 . Although BAF155 is the subunit that is common to all of the configurations associated with the highest levels of transcription , it does not appear to be the sole driver of transcriptional activity . Compared against each other , all three components of the core complex taken individually have nearly indistinguishable profiles . Despite the involvement of Brg1 in two of the four configurations with the highest expression levels , most other configurations involving Brg1 are restricted to profiles associated with the lowest expression levels . One inference from these data is that certain combinations of SWI/SNF subunits are likely synergistic in promoting transcription whereas other combinations may be inhibitory or unstable . We also examined SWI/SNF occurrences relative to 48 , 403 non-canonical small RNAs from HeLa cells ( ≤156 bp; Table S1 ) where most ( 83%; p<1×10−16 , GSC test ) of these small RNAs are near protein-coding genes [40] . Approximately one third ( 30% ) of this entire small RNA set is within 1 kb of a target from our high-confidence union list of 49 , 555 SWI/SNF regions . The incidence of small RNA-SWI/SNF co-associated regions was nearly equivalent in protein-coding genes and intergenic regions . From this we surmise that SWI/SNF may contribute to gene regulation of a variety of transcripts , many of which are newly annotated and of unknown function . Prior research has shown that a variety of signaling cascades are linked to SWI/SNF [25] . To gain further insights into potential actions of SWI/SNF components we examined the underlying Gene Ontology ( GO ) and Kyoto Encyclopedia of Genes and Genomes ( KEGG ) designations of their gene targets to determine significantly overrepresented annotations and pathways ( Table 5 and Table S8 ) . SWI/SNF gene targets were associated with ‘Pathways in cancer’ and several specific cancers types , e . g . chronic myeloid leukemia and pancreatic cancer . A number of signaling pathways and cellular processes that are “hallmarks of cancer” [41] were also overrepresented among the gene targets of Ini1 , Brg1 , BAF155 and BAF170 . These include the Wnt , ErbB , p53 , MAPK , and insulin signaling pathways , and processes endemic to oncogenesis and cancer progression such as DNA repair , the cell cycle and apoptosis . From these analyses we surmise the recruitment of SWI/SNF components is likely to influence the molecular basis of cancer through several potential mechanisms . The SWI/SNF-enriched pathways are highly interconnected . Using the 49 , 555 SWI/SNF targets we identified a total of 24 KEGG signaling or biochemical pathways ( Figure 6 , yellow nodes ) . Interestingly , these pathways partition into three groups ( Figure 6A–6C ) . Two of the groups ( Figure 6A and 6B ) comprise sets of pathways exhibiting at most one degree of separation , e . g . ‘inositol phosphate metabolism’ and ‘amino sugar and nucleotide sugar metabolism’ . The third group ( Figure 6C ) consists of three pathways that are unrelated to any other pathways in the KEGG database . As displayed in Figure S2 directly related pathways such as ‘p53 signaling’ and the ‘cell cycle’ have shared components and many of the genes encoding these components are occupied by SWI/SNF factors . Thus , our results demonstrate that SWI/SNF is involved in many closely related signaling pathways and cellular processes and may help serve to coordinate expression of genes involved in these processes . The genomic binding data demonstrates that SWI/SNF localization is coupled with a broad range of functional elements , suggesting that SWI/SNF may also be found with a broad range of associated proteins . To further examine the scope of SWI/SNF's roles in the nucleus we analyzed proteins associated with SWI/SNF subunits using co-immunoprecipitation followed by mass spectrometry . The SWI/SNF components Ini1 , BAF155 , BAF170 , Brg1 , Brm and ARID1A were immunoprecipitated from HeLa S3 nuclei , the resulting proteins were gel-separated and peptides were generated for analysis by mass spectrometry ( See Materials and Methods; Table S9 ) . In addition to the factor-specific antibodies , parallel immunoprecipitations were performed using non-specific IgG antibodies . Proteins identified in these “control IgG” immunoprecipitations were excluded as potential SWI/SNF co-purifying factors . We identified a total of 101 proteins that were specifically associated with at least one of the SWI/SNF components assayed ( Figure 7 , turquoise edges; Table S10 ) . Of the non-SWI/SNF subunits detected , 5 of these interactions were found previously in HeLa cells ( e . g . estrogen receptor alpha [42] , and 96 were new to this study . Interestingly one of the novel interactions we observed in HeLa cells , BAF155 with NUF2 , has been previously observed in yeast between the yeast homolog of BAF155 ( SWI3 ) and NUF2 [15] . Using the 101 nodes that we identified as proteins co-purifying with SWI/SNF in our undirected approach we ascertained overrepresented GO categories ( Table 6 ) . Several of these designations such as ‘cell cycle’ and ‘chromosome organization’ coincide with the categories obtained from GO and pathway analyses of SWI/SNF ChIP-Seq targets , suggesting the possibility of highly-interactive network structures . Many of the proteins that were novel to this study reinforce and expand upon other published reports of SWI/SNF characterizations . For example SWI/SNF components have been localized by immunofluorescence to mitotic kinetochores and spindle poles [43] , and Brg1-deficient mice show dissolution of pericentromeric heterochromatin domains [44] . From our immunoprecipitations BAF155 and BAF170 were associated with a number of kinetochore and centrosomal proteins ( e . g . BUB1B , CENPE and NUF2 , Figure 8 , green circles ) . The role of SWI/SNF in the maintenance of kinetochore and spindle function is unknown . We detected a variety of transcription factor activators and repressors ( e . g . NFκB1 , NFκB2 , RelA , PML and NFX1 ) as well as DNA repair ( ERCC5 and RAD50 ) and cell cycle ( e . g . CCNB3 and CDCA2 ) proteins ( Figure S3 ) . Some of the SWI/SNF interacting proteins themselves interact with one another . For example we detected several different proteins integral to estrogen and insulin signaling ( Figure 7; Table S10 ) . We also identified proteins associated with only one SWI/SNF factor; these may either be interactions with a specific SWI/SNF component or an inability to detect the protein in the immunoprecipitations . We developed an expanded network of SWI/SNF associations by including proteins that were found by others to co-purify with SWI/SNF subunits ( Figure 7 , black edges ) . Only those factors that showed a one-degree separation with a SWI/SNF component in HeLa cells are displayed and all interactions are annotated in Table S10 . SWI/SNF interacting proteins are associated with numerous UniProt keywords ( Figure 8; [45] ) . Overall these results suggest a role for SWI/SNF components in a wide array of nuclear processes and diseases . Some of these processes may take place in nuclear substructures . Higher order chromatin structure is facilitated by the nuclear lamina and tethering of genes to the nuclear periphery is one epigenetic mechanism of gene regulation [46] , [47] . Intriguingly we and others have detected SWI/SNF components with various nuclear envelope-associated proteins ( Figure 7 and Table S10 ) including lamin A , EMD ( emerin ) and BAF/BANF1 ( Barrier to Autointegration Factor , which although similar in name is not a SWI/SNF subunit ) . Two of the nuclear membrane proteins , SYNE1 and C14orf49 , that we isolated in association with BAF155 are part of LINC complexes that link the nucleoskeleton and cytoskeleton [48] , [49] . Numerous studies point to a high degree of functional organization in cell nuclei [46] . Emerging nuclear organization models would benefit greatly from a catalogue of processes and chromatin characteristics mapped to particular genomic elements . For example , the nuclear lamins are thought to influence chromatin organization , DNA replication and transcription [47] , [50] . Our immunoprecipitation results demonstrating that SWI/SNF components are associated with lamin A/C ( Figure 7 and Table S10 ) along with immunoprecipitation , immunolocalization and cell fractionation experiments from others demonstrating an association between SWI/SNF and nuclear lamina ( e . g . emerin Figure 7; [51] ) prompted us to investigate whether SWI/SNF and the lamins can be located to the same genomic sequences . We isolated lamin A/C and lamin B ChIP DNA from HeLa S3 nuclei and performed ChIP-chip on tiling arrays covering the ENCODE pilot regions ( see Materials and Methods and Table S1 ) . Most of the 1 , 770 lamin A/C regions mapped to H3K27me3 domains ( 76%; 1 , 337/1 , 770 ) whereas the 1 , 270 lamin B regions were less commonly associated with H3K27me3 ( 29%; 372/1 , 270 ) . Comparing regions where signal was detectable for the SWI/SNF , lamin A/C and lamin B experiments revealed that SWI/SNF has a much higher overlap with lamin B than lamin A/C . We found that 38% ( 297/784 ) of SWI/SNF sites are within 100 bp of a lamin B region whereas only 5% ( 41/784 ) of SWI/SNF sites are within 100 bp of a lamin A/C region ( Table 7 ) . For both lamin types the colocalization with SWI/SNF regions is significant relative to random target lists ( lamin B , p<1×10−16; lamin A/C , p<1×10−15; GSC tests ) . SWI/SNF-lamin B intersecting regions contained approximately the same proportion of CTCF sites in the ENCODE regions as did all SWI/SNF sites in the ENCODE regions ( p>0 . 05 hypergeometric test; Table 7 ) . Enhancers are underrepresented in the SWI/SNF-lamin B regions relative to all SWI/SNF locations in the ENCODE regions ( p<1 . 9×10−36; hypergeometric test ) . The SWI/SNF-lamin B regions are overrepresented for Pol II ( p<2 . 9×10−39; hypergeometric test ) and 5′ ends ( p<7 . 3×10−37; hypergeometric test ) relative to all SWI/SNF locations in the ENCODE regions . In crosslinked chromatin SWI/SNF is detected primarily with lamin B , but as noted from the above mass spectrometry experiments , in solubilized , non-cross-linked cells SWI/SNF is detected with lamin A/C and not lamin B ( Figure 1 and Figure 7 ) . We interpret these results to indicate that SWI/SNF , lamin A/C and lamin B co-associate in different nuclear contexts but are all part of a broader interacting network with specific sub-associations . SWI/SNF and the lamins have each been implicated in DNA replication ( see above; [52] , [53] ) . One of the proteins we detected as associated with SWI/SNF is the replication protein RepA and another regulator of DNA replication , geminin , has been found to co-purify with SWI/SNF in HeLa cells ( Figure 7 , red circles; [54] ) . We investigated whether there might be a relationship among SWI/SNF , lamins and DNA replication origins . We obtained a set of 282 DNA replication origins identified in HeLa cells for the ENCODE regions ( [55]; Table S1 ) . Of these 282 replication origins , 90 ( 32% ) occur within 100 bp of a SWI/SNF region ( p<1×10−16 , GSC test ) , 86 ( 31% ) occur at the 5′ ends of protein-coding genes and 151 ( 54% ) occur within 100 bp of a lamin B region . In contrast to lamin B , only 17% ( 48/282 ) of the replication origins were near a lamin A/C region . These results are consistent with nuclear staining patterns observed in mouse 3T3 cells showing colocalization between lamin B and sites of DNA replication whereas the same colocalization patterns were not observed for replication foci and lamin A [52] . Of the 86 replication origins in promoter regions , 88% ( 76/86 ) intersected a lamin B region and most ( 78% or 67/86 ) were within a 100 bp of a SWI/SNF region . These data indicate that SWI/SNF components are located near many DNA replication origins , particularly those located in promoter regions . The coincidence of chromatin remodeling factors , promoters , lamins and replication origins at the same subset of genomic regions suggests that these loci may be particularly favorable for the formation of both DNA and RNA polymerase assembly and chromatin tethering . As shown in Figure 1 the interplay among these elements as well as with Pol II , CTCF and heterochromatin regions is complex and interwoven , such that each may share many different supporting and counteracting roles .
SWI/SNF performs a crucial function in gene regulation and chromosome organization by directly altering contacts between nucleosomes and DNA . In the work presented here we undertook a two-pronged approach ( ChIP-Seq and IP-mass spectrometry ) to move towards a more thorough understanding of these functions . Our ChIP-Seq analyses demonstrate that SWI/SNF components overlap extensively with important regions that require tight control of the dynamics of nucleosome occupancy such as promoters , enhancers and CTCF sites . Not only does the SWI/SNF complex change the accessibility of DNA but it also acts in concert with an extensive host of cooperating factors , thereby facilitating combinatorial control among various genomic elements . In addition to our ChIP-Seq results , the diversity and number of proteins that co-purify with SWI/SNF as identified in our mass spectrometry experiments further supports SWI/SNF's involvement with a variety of functionally distinct complexes . RNA polymerases II and III are extensively colocalized with SWI/SNF components . Studies of transcription in HeLa cells have estimated that the number of active RNA II polymerases exceeds the number of transcriptionally active sites by at least one order of magnitude , leading to the proposal of “transcription factories” [1]–[3] . The number of RNA Pol II transcription factories in HeLa cells has been estimated between 5 , 000 and 8 , 000 where each factory can be typified by several looped loci , their resulting transcripts and distal elements such as enhancers . We infer that SWI/SNF regions are prevalent in transcriptional assemblages and their associated regulatory loops , given that >90% of our high-confidence union targets are associated with genic or regulatory regions and that 65% of Pol III and 84% of Pol II regions colocalize with at least one SWI/SNF factor ( Table 4 , Tables S5 and S6 ) . Interestingly we observed that SWI/SNF components often occur independently of each other and in various configurations across the genome , and similarly our mass spectrometry data point to heterogeneity of SWI/SNF complexes . We speculate that several mechanisms may underlie these various configurations and their associated genomic features , including 1 ) synergism or antagonism of the individual SWI/SNF factors in influencing expression ( e . g . Figure 5 ) ; 2 ) failure to detect individual subunits due to epitope masking as a consequence of variation with local environments; 3 ) the capture of incomplete complexes that may in fact be completed upon superposition of genome-wide 3C data once such data become available ( e . g . Figure 4 ) ; 4 ) the existence of SWI/SNF sub-complexes that deviate from the conventional composition of SWI/SNF assemblies ( e . g . [56] ) or 5 ) the capture of intermediates in a multistep assembly or remodeling process . This last view is consistent with a model of stochastic assembly that may occur through intermediate interactions and that has been described for several other large , multifactor complexes such as RNA polymerases and associated transcription factors [57] , spliceosomes [58] , and DNA repair complexes [59] . As shown in Figure 6 SWI/SNF occurs throughout many interconnected pathways . The assembly of functional SWI/SNF complexes at many locations in the genome may require the activation of one or more of these related pathways . Consequently some of the SWI/SNF associated regions we observed may reflect constitutive binding of partially assembled complexes that may be poised to receive additional signal inputs for subsequent regulatory activity . Indeed it has been shown that SWI/SNF components are present at regulatory regions even in the absence of stimulatory conditions or tissue-specific cofactors . For example Brg1 is present constitutively at the interferon-inducible genes IFITM3 [60] and CIITA [30] in unstimulated HeLa cells , which is consistent with our own finding of Brg1 and Ini1 at IFITM3 and various combinations of BAF155 , BAF170 , Ini1 and Brg1 at different elements in CIITA . In solution SWI/SNF factors are associated constitutively with RelB ( HEK293 cells , [61] ) , RelA , NFkB1 and NFkB2 ( HeLa cells , this study ) , the glucocorticoid receptor ( T4D7 cells , [62] ) and estrogen receptor alpha ( HeLa cells , this study and [42]; SW13 cell extracts , [63] ) . The prevalence of SWI/SNF and the high degree of connectivity of its overrepresented pathways implies that SWI/SNF may assist in many related processes and may even facilitate crosstalk across many constituents of the transcriptional machinery . Notably SWI/SNF binds in the genes of its own subunits ( Table S19 ) suggesting that SWI/SNF may contribute to auto- and cross-regulation of its subunit levels . Loss-of-function of a particular subunit , as may occur in certain cancers , could initiate oscillations and alter the relative abundance of the levels of the other SWI/SNF subunits through a variety of feedback and feed-forward loops . Aberrant SWI/SNF expression has been proposed to result in new combinatorial assemblies of SWI/SNF , some of which may deleterious [64] . The gene attributes revealed by our ChIP-Seq data substantiate that SWI/SNF is proximal to targets that comprise sets of fundamental biological processes . Many of the functional categories we found to be significantly overrepresented have disease implications , especially as related to cancer ( Figure S2 ) . For example failures in DNA repair and unchecked cell cycle activity are common characteristics of pre-cancerous cells , and our SWI/SNF analyses identified the p53 and MAPK signaling pathways , which are well known for maintaining checkpoint functions . Growth dysregulation particularly in the context of hormone signaling is another common cancer phenotype . Extracellular growth signals are transduced from the cell membrane to the nucleus by the ErbB , insulin and phosphtidylinositol signaling pathways , all of which we recovered as overrepresented ( Table 5 ) . The existence of phosphoinositide signaling in the nucleus and the ability of Brg1 to act as an effector for phosphatidylinositol 4 , 5-bisphosphate ( PIP2 ) raises the prospect of several levels of control of this signaling pathway with respect to SWI/SNF [65] , a hypothesis that can be examined in future studies . Several of the overrepresented pathways we identified through our ChIP-Seq analyses share proteins detected in SWI/SNF co-purification experiments , thereby providing a resource to explore potential , highly-interactive network structures . For example we found that genes with products critical for ‘nucleotide excision repair’ were enriched using our SWI/SNF union list ( Figure 6 ) . Within this pathway the excision repair protein ERCC5 co-purified with both BAF155 and BAF170 in our IP ( immunoprecipitation ) -mass spectrometry experiments . The excision repair protein , XPC , associates with SWI/SNF in response to UV irradiation in HeLa cells , and BRCA1 and ATR also cooperate with SWI/SNF in DNA repair ( Figure 7; Table S10; [66] ) . Thus we speculate SWI/SNF may participate in DNA repair through both transcriptional regulation as well as recruitment to regions undergoing repair . Our study uses two strategies to attempt to comprehensively collect a SWI/SNF interaction network . We limited our network to a single model system , HeLa cells , because many attributes of SWI/SNF have been documented in these cells and it has been noted that SWI/SNF associations vary by cell type [67] . We extensively collated SWI/SNF protein interactions described in the literature . This undertaking was necessary because many of the proteins described in the literature as co-associated with SWI/SNF factors are not represented in interaction databases such as BioGRID , Molecular Interactions Database ( MINT ) , IntAct , Human Protein Reference Database ( HPRD ) , Nuclear Protein Database ( NPD ) and Interologous Interaction Database ( I2D ) . Therefore we attempted to comprehensively collect such information to overcome these limitations . In total 158 SWI/SNF interacting proteins have been described in HeLa cells ( Figure 8 and Table S10 ) , which is similar to the number of SWI/SNF interacting proteins that have been described in other cell types [67] . Published molecular associations that were not discerned here might be due to interactions that are: 1 ) transient or of low affinity , 2 ) dependent on a specific set of biochemical conditions or 3 ) undetectable due to masking by the presence of more abundant protein ( s ) of similar size . In working with protein interaction data , similar degrees of overlap have been noted when comparisons are made across data sets [68] , [69] and even in a well-studied model such as yeast , mass spectrometry analyses have found a plasticity of complexes and many previously undetected interactions [70]–[72] . From the ChIP-Seq and ChIP-chip results we expected that CTCF and lamin B may be among the proteins that co-associate with SWI/SNF , however neither of these factors was recovered in any of the non-directed experiments ( Table S10 ) , including a CTCF immunoprecipitation-mass spectrometry experiment performed in HeLa cells . In addition to the above considerations one possibility is that CTCF or lamin B may associate more strongly with one of the SWI/SNF factors not studied , e . g . BAF53A or one of the BAF60 subunits . SWI/SNF is most often described in a chromatin remodeling context however data derived from a variety of sources suggests that SWI/SNF has other facets . It is possible that not all of SWI/SNF's functions involve DNA localization and therefore other types of global experiments , such as the IP-mass spectrometry , are valuable as first steps towards recognizing previously unknown roles . Unlike cytoplasmic compartments , nuclear compartments are not separated by a physical barrier but rather are functional assemblies that are typically organized around sets of molecules engaged in common functions . Data from both ChIP-Seq and IP-mass spectrometry illuminate the sectors in which SWI/SNF operates and the integration of these two methods is better than each alone for furnishing a broad comprehension of SWI/SNF action . For example ChIP-Seq enables the global identification of SWI/SNF chromosomal elements except for those regions with highly repetitive sequence such as human centromeres ( Figure 2A ) . In this respect IP-mass spectrometry is complementary to ChIP-Seq because it strongly suggests that SWI/SNF occurs at kinetochores as evidenced by its co-purification with CENPE , NUF2 , BUB1B and CLASP2 ( Figure 7 and Figure 9 ) . In addition to kinetochore proteins the SWI/SNF co-purification experiments also uncovered proteins from other substructures including centrosomes , microtubules , the nuclear periphery and PML nuclear bodies , the latter of which is characterized by cryptic foci of PML ( promyelocytic leukemia protein ) and has been implicated in a variety of diseases [73] . The ChIP-Seq and IP-mass spectrometry data are synergistic as well . Notably both methods found an overrepresentation of regions or proteins enriched for ‘cell cycle’ and ‘chromosome organization’ . One possible inference from these studies is that SWI/SNF is well positioned to integrate signals across multiple signaling pathways both by its presence in a variety of cellular structures and its role in gene regulation through chromatin remodeling . A fraction of SWI/SNF complexes co-associate with elements of the nuclear periphery where they are well situated to contribute to the nuclear organization and position-dependent gene expression ( Figure 7; [74] ) . We found that in crosslinked cells SWI/SNF localizes more widely with lamin B than lamin A whereas in non-crosslinked cells SWI/SNF co-purifies with lamin A . As mentioned above lamin B may have escaped detection in SWI/SNF protein interaction studies . A related possibility is that SWI/SNF may exist in different nuclear pools that have varying solubilities and associations , such that recovery of particular SWI/SNF complexes depends upon the proteins with which SWI/SNF is associated . For example lamins A and B are known to have different nucleoplasmic mobilities and localization patterns [50] , [52] . Immunolocalization experiments in HeLa nuclei have revealed that the A/C- and B-type lamins form distinct meshworks with occasional points of intersection [50] , which is consistent with the interspersed patterns of lamin A/C and B that we detected ( Figure 1 ) . Hence it is reasonable to expect that SWI/SNF associated with lamin A would behave differently than when associated with lamin B . We surmise that in a chromatin context the dominant association of SWI/SNF with the nuclear lamins occurs in regions where lamin B is present . The purification of SWI/SNF with lamin A may indicate other biological roles , such as cell cycle progression or nuclear assembly [75] , [76] . Gaining a more detailed understanding of SWI/SNF's activities in or near various heterochromatin environments will be central to comprehending nuclear events over the cell cycle as well as during development . Among the numerous molecular and epigenetic factors that have been found to affect heterochromatin formation or maintenance , the heterochromatin protein 1 alpha ( HP1α , also known as CBX5; Figure 7 ) and Polycomb complexes ( PcG ) are of particular relevance to SWI/SNF [77]–[79] . Polycomb complexes promote gene silencing by catalyzing the trimethylation of H3K27 in its target regions , and SWI/SNF antagonizes this epigenetic silencing [80] . It is tempting to speculate that SWI/SNF found near the edges of H3K27me3 domains ( Figure 1A and 1C ) may be contributing to the establishment or maintenance of boundary elements . SWI/SNF may also engage in heterochromatin dynamics through its interaction with HP1α , which is often located in the centromeric regions ( reviewed in [81] ) . Curiously HP1α interacts with the lamin B receptor [82] thus providing a potential bridge between heterochromatin and the inner nuclear membrane . Both H3K27me3 and lamin B are associated with spatially regulated genes whose conversion between active and inactive states depends on access to their regulatory regions , as may be conferred by SWI/SNF . The work presented here provides new insights into the scope of SWI/SNF's influence in gene regulation and nuclear organization . The integration of numerous studies is beginning to reveal the complexities contributing to the regulation of any given locus . Contemporary models of transcriptional control propose that a series of factors transiently associate with a regulatory region before a decisive event tilts these intermediate reactions towards a productive outcome [57] , [83] . SWI/SNF may contribute to such intermediate reactions or trigger switches between inactive and active states . The capacity for SWI/SNF to preserve many aspects of homeostasis also makes it vulnerable to being ensnared for aggressive cell proliferation . Our work demonstrates that SWI/SNF in particular and perhaps chromatin remodeling proteins in general will contribute unique insights to our understanding of gene regulation and disease mechanisms through the integration of target regions , spatial positioning and functional annotations . For example the co-occurrence of SWI/SNF with centrosomes , microtubules , kinetochores and the nuclear periphery may suggest that a pool of SWI/SNF is sequestered by these structures during mitosis to assist in the post-mitotic reformation of chromosomal territories . Our collective findings help inform a comprehensive view of SWI/SNF function as well as form a valuable compendium for future studies of nuclear functions as related to chromatin remodeling .
Suspension HeLa S3 cells were cultured by the National Cell Culture Center ( Biovest International Inc . , Minneapolis , MN ) in modified minimal essential medium ( MEM ) , supplemented with 10% FBS at 37°C in 5% CO2 , to a density of 6×105 cells/mL . Cells were fixed with 1% formaldehyde at room temperature for 10 min . Fixation was terminated with 125 mM glycine ( 2 M stock made in 1x PBS ) . Formaldehyde-fixed cells were washed in cold Dulbecco's PBS ( Invitrogen ) and swelled on ice in a 10-mL hypotonic lysis buffer [20 mM Hepes ( pH 7 . 9 ) , 10 mM KCl , 1 mM EDTA ( pH 8 . 0 ) , 10% glycerol , 1 mM DTT , 0 . 5 mM PMSF , and Roche Complete protease inhibitors , Cat#1697498] . To isolate nuclei , whole cell lysates were homogenized with 30 strokes in a 7 mL Dounce homogenizer ( Kontes , pestle B ) . Nuclear pellets were collected by centrifugation and lysed in 10 mL of RIPA buffer per 3×108 cells [RIPA buffer: 10 mM Tris-Cl ( pH 8 . 0 ) , 140 mM NaCl , 1% Triton X-100 , 0 . 1% SDS , 1% deoxycholic acid , 0 . 5 mM PMSF , 1 mM DTT , and protease inhibitors] . Chromatin was sheared with an analog Branson 250 Sonifier ( power setting 2 , 100% duty cycle for 7×30-s intervals ) to an average size of less than 500 bp , as verified on a 2% agarose gel . Lysates were clarified by centrifugation at 20 , 000× g for 15 min at 4°C . Clarified nuclear lysates from 1×108 cells were agitated overnight at 4°C with 20 µg of one of the following antibodies: 1 ) anti-Ini1 ( C-20 ) , Santa Cruz Biotechnology , sc-16189; 2 ) anti-BAF155 ( H-76 ) , Santa Cruz Biotechnology , sc-10756; 3 ) anti-BAF170 ( H-116 ) , Santa Cruz Biotechnology , sc-10757; 4 ) anti-Brg1 ( G-7 ) , Santa Cruz Biotechnology , sc-17796; 5 ) anti-lamin A/C ( H-110 ) , Santa Cruz Biotechnology , sc-20681; 6 ) anti-lamin B antibody , EMD Biosciences , NA12; or 7 ) normal IgG , Santa Cruz Biotechnology , sc-2025 . Antibody incubations were followed by addition of either protein A ( Millipore #16-156 ) or protein G agarose beads ( Millipore #16-266 ) . Beads were permitted to bind to protein complexes for 60 min at 4°C . Immunoprecipitates were washed three times in 1x RIPA , once in 1x PBS , and then eluted in 1xTE/1%SDS . Crosslinks were reversed overnight at 65°C . ChIP DNA was purified by incubation with 200 µg/ml RNase A ( Qiagen #19101 ) for 1 h at 37°C , with 200 µg/ml proteinase K ( Ambion AM2548 ) for 2 h at 45°C , phenol:chloroform:isoamyl alcohol extraction , and precipitation with 0 . 1 volumes of 3 M sodium acetate , 2 volumes of 100% ethanol and 1 . 5 µL of pellet paint ( Novagen #69049-3 ) . ChIP DNA prepared from 1×108 cells was resuspended in 50 µL of Qiagen Elution Buffer ( EB ) . Three biological replicates were prepared per antibody . ChIP-Seq libraries were prepared and sequenced as previously described [26] , [84] . Biological replicates for each factor were converted into separate and distinct libraries . To summarize , ChIP DNA samples were loaded onto Qiagen MinElute PCR columns , eluted with 15 µL of Qiagen buffer EB , size-selected in the 100–350 bp range on 2% agarose E-gels ( Invitrogen ) and gel-purified using a Qiagen gel extraction kit . DNA was end-repaired and phosphorylated with the End-It kit from Epicentre ( Cat# ER0720 ) . The blunt , phosphorylated ends were treated with Klenow fragment ( 3′ to 5′ exo minus; NEB , Cat# M0212s ) and dATP to yield a protruding 3′-‘A’ base for ligation of Illumina adapters ( 100 RXN Genomic DNA Sample Prep Oligo Only Kit , Part# FC-102-1003 ) , which have a single ‘T’ base overhang at the 3′ end . After adapter ligation ( LigaFast , Promega Cat# M8221 ) DNA was PCR-amplified with Illumina genomic DNA primers 1 . 1 and 2 . 1 for 15 cycles by using a program of ( i ) 30 s at 98°C , ( ii ) 15 cycles of 10 s at 98°C , 30 s at 65°C , 30 s at 72°C , and ( iii ) a 5 min extension at 72°C . The final libraries were band-isolated from an agarose gel to remove residual primers and adapters . Library concentrations and A260/A280 ratios were determined by UV-Vis spectrometry on a NanoDrop ND-1000 spectrophotometer ( Thermo Fisher Scientific ) . Purified and denatured library DNA was capture on an Illumina flowcell for cluster generation and sequenced on an Illumina Genome Analyzer II following the manufacturer's protocols [85] . Immunoprecipations were performed using the same conditions as for ChIP experiments except the HeLa S3 cells were not crosslinked . In addition to the ChIP antibodies described above we also used anti-Brm , Abcam Cat# ab15597 and anti-BAF250a ( PSG3 ) , Santa Cruz Biotechnology , sc-32761 . Complexes were resolved on BioRad 4–20% precast Tris-HCl gels ( Cat#161-1159 ) such that a single gel was used for each specific antibody and normal IgG immunoprecipitation pair . Gels were silver stained using Pierce SilverSNAP stain for mass spectrometry ( Cat#24600 ) and each lane was excised into 10–12 molecular weight regions . Gel slices were destained , dried in a Savant speed-vac and digested overnight at 42°C with Sigma's Trypsin Profile IGD kit for in-gel digests ( Cat# PP0100 ) . Following the overnight incubation the liquid was removed from each gel piece and volume reduced by drying to approximately 10 µL . The individual gel slices were analyzed separately . The samples were subjected to nanoflow chromatography using nanoAcquity UPLC system ( Waters Inc . ) prior to introduction into the mass spectrometer for further analysis . Mass spectrometry was performed on a hybrid ion trap LTQ Orbitrap mass spectrometer ( Thermo Fisher Scientific ) in positive electrospray ionization ( ESI ) mode . The spectra was acquired in a data dependent fashion consisting of full mass spectrum scan ( 300–2000 m/z ) followed by MS/MS scan of the 3 most abundant parent ions . For the full scan in the orbitrap the automatic gain control ( AGC ) was set to 1×106 and the resolving power for 400 m/z of 30 , 000 . The MS/MS scans were done using the ion trap part of the mass spectrometer at a normalized collision energy of 24 V . Dynamic exclusion time was set to 100 s to avoid loss of MS/MS spectral information due to repeated sampling of the most abundant peaks . Sequence data from MS/MS spectra was processed using the SEQUEST database search algorithm ( Thermo Fisher Scientific ) . The resulting protein identifications were brought into the Scaffold visualization software ( Proteome Software ) where the information was further refined resulting in improved protein id conformation . Scaffold search criteria were set at 98% probability and required at least 2 unique peptides per id . All ChIP-Seq data sets ( Ini1 , Brg1 , BAF155 , BAF170 , and Pol II ) were scored against a normal IgG control using PeakSeq [26] with default parameters ( q-value<0 . 05 ) to determine an initial set of enriched regions . These lists were then filtered by removing those regions that did not meet all of the following requirements: 1 ) the q-value from PeakSeq was further restricted to a q-value of<0 . 01; 2 ) a minimum of 20 sequencing reads per peak from the specific antibody ChIP; 3 ) an enrichment of 1 . 5-fold of the specific antibody relative to the normal IgG control; and 4 ) an excess of at least 10 of the specific antibody reads relative to the normal IgG control reads . Enriched regions satisfying these criteria comprised our initial list of enrichment sites for each factor ( Table 1 and Tables S11 , S12 , S13 , S14 , S15 , and S16 ) . Among these data sources , Pol II and the normal IgG control have been published as part of prior studies and are available in GEO ( accession numbers GSE14022 and GSE12781 , respectively ) [26] , [84] . Data for Ini1 , Brg1 , BAF155 and BAF170 can be accessed through GEO series GSE24397 . After obtaining our initial list of enriched regions for each factor subjected to chromatin immunoprecipitation , we generated a union list of SWI/SNF component targets . Using the method described in Euskirchen et al . [86] , we formed the union of Ini1 , BAF155 , BAF170 , and Brg1 enriched regions as identified by ChIP-Seq and merged any unioned regions that were separated by ≤100 bp . Each union region was then classified by whether it intersected with one or more of BAF155 , BAF170 , Ini1 , and Brg1 . The resulting list consists of 69 , 658 SWI/SNF union regions ( Table S2 ) . We compared our ChIP-Seq target lists for the 69 , 658 SWI/SNF union regions against genomic features at which chromatin remodeling is expected to play a prominent role: RNA polymerase II sites [26] , 5′ ends of Ensembl protein-coding genes , CTCF sites [28] , and regions predicted to be enhancers in HeLa cells [29] . We also compared individual SWI/SNF component lists against each other . Only those SWI/SNF regions which intersect another SWI/SNF component or which intersect at least one of the above genomic features were retained for the ‘high-confidence’ union list . For gene promoter regions , we define overlap as a target region with at least 1 shared bp within ±2 . 5 kb of the annotated transcription start site ( TSS ) . SWI/SNF region intersections were calculated both for all genes in the Ensembl 52 database build using annotations from NCBI36 ( human genome build hg18 ) as well as for a subset of genes that Ensembl identifies as protein-coding . The resulting target list consists of 49 , 555 ‘high-confidence’ SWI/SNF union regions ( Table S3 ) . Union regions containing all three of the BAF155 , BAF170 , and Ini1 subunits are designated as the 9 , 760 ‘core’ SWI/SNF regions ( Table 3 ) . To determine the co-occurrences of features of interest we used a similar intersection strategy as was used for determining the high-confidence SWI/SNF regions . For all pairwise comparisons , one of the two data sets was extended by 100 bp on each side of the region and then intersected against the other , non-extended dataset . We required an overlap of at least 1 bp to deem two regions as associated . Using a Perl script , the intersection results for all comparisons were combined to form the co-occurrence table . The same procedure was followed to generate SWI/SNF-centric ( Tables S2 and S3 ) , Pol II-centric ( Table S5 ) and Pol III-centric ( Table S6 ) co-occurrence tables . Using the HeLa RNA-Seq data of Morin et al . [37] , we subdivided each list by the expression status of the corresponding gene targets . Expressed genes were defined as any Ensembl gene with an associated Ensembl transcript having an adjusted depth of ≥1 , representing an average coverage of 1x across all bases in the transcript . A total of 9 , 711 expressed protein-coding genes satisfied these criteria . We created a series of lists based upon the combinations of SWI/SNF components that could co-occur using the 49 , 555 high-confidence SWI/SNF regions derived from Table S3 . Using the RNA-Seq data of Morin et al . [37] , we intersected each list against the 5′ ends of transcripts queried by that study and recorded the corresponding adjusted depth for any transcript with a 5′ end within ±2 . 5 kb of a SWI/SNF region . Morin et al . treats adjusted depth as a measurement of transcription level for the corresponding transcript . For each list , these measurements were used to build a series of violin plots showing the probability distribution of transcription levels associated with different compositions of SWI/SNF subunits . Note that each SWI/SNF region from Table S2 can only be assigned to one list ( e . g . a region containing BAF155 , BAF170 , and Ini1 is not also assigned to the list of regions containing BAF155 and BAF170 ) . Overrepresented GO categories [87] and KEGG pathways [88] were determined using DAVID tools [16] . Figures S2 and S3 were drawn using KGML-ED [89] . The ENCODE tiling arrays ( NimbleGen Systems Inc . , Madison , WI ) interrogate the regions from the pilot phase of the ENCODE project [90] and tile the non-repetitive forward strand DNA sequence with 50-mer oligonucleotides spaced every 38 bp ( overlapping by 12 bp ) for a total of approximately 390 , 000 features . For array hybridizations ChIP DNA samples from 1×108 cells were labeled according to the manufacturer's protocol by Klenow random priming with Cy5 nonamers ( lamin A/C or lamin B ChIP DNA ) or Cy3 nonamers ( normal IgG ChIP DNA ) . Biological replicates , defined as ChIP DNA isolations prepared from distinct cell cultures , were each hybridized to separate microarrays . Each lamin data set consists of three biological replicates . ChIP DNA labeling and array hybridizations were conducted by the NimbleGen service facility ( Reykjavik , Iceland ) . Briefly , arrays were hybridized in Maui hybridization stations for 16–18 h at 42°C , and then washed in 42°C 0 . 2% SDS/0 . 2x SSC , room temperature 0 . 2x SSC , and 0 . 05x SSC . Arrays were scanned on an Axon 4000B scanner . For each pair of arrays the files ( in GFF file format ) corresponding to the two channels for ChIP DNA ( 635 nm ) and reference DNA ( 532 nm ) , were uploaded to the TileScope pipeline for normalization and scoring [91] . Data were scored with the following TileScope program parameters: quantile normalization of replicates , iterative peak identification , window size = 500 , oligo length = 50 , pseudomedian threshold = 1 . 0 , p-value threshold = 4 . 0 , peak interval = 1000 , and feature length = 1000 . Regions called by Tilescope were then filtered and corrected for multiple hypothesis testing by false discovery rate ( FDR ) . To generate our set of background regions for FDR analysis , we randomly shuffle the probe values within each replicate , ensuring that the same probes are swapped for each replicate . This shuffled data set is then used as input to Tilescope and the scores compared against the lamin A/C and the lamin B data sets . The final lists of enriched regions for lamin A/C and lamin B have a final FDR of 0 . 1 . Target coordinates were converted to hg18 using the UCSC ‘liftOver’ utility ( http://genome . ucsc . edu/cgi-bin/hgLiftOver ) . Lamin A/C and lamin B data are available through GEO series GSE24382 and Tables S17 and S18 . To facilitate comparisons between sequencing and array data we retained only those regions that could be queried by both platforms . To this end , we first identified sequences represented on the ENCODE tiling array that possess less than 25% mappability in ChIP-Seq experiments using 30 bp reads . Any enriched regions in the lamin A/C and the lamin B data sets that were entirely contained within these regions of low mappability were removed from our lists , as corresponding signal levels are unlikely to be detected accurately via ChIP-Seq . Mappability was determined using a 30 bp read length and reported in 100 bp windows according to [26] . The end result is a list of lamin A/C and lamin B enriched regions identified by ChIP-chip in areas of the genome that can be queried by ChIP-Seq . Accordingly , regions that are not represented on the ENCODE tiling arrays were also removed from our SWI/SNF ChIP-Seq experiments for this comparison . Because our ChIP-Seq data covers the entire genome , we began by restricting our enriched SWI/SNF regions only to those that occur in the ENCODE pilot regions . We further refined our ChIP-Seq data set by discarding any SWI/SNF regions that occur in a region of the tiling array for which a signal level of 0 was observed via ChIP-chip . Once our SWI/SNF , lamin A/C , and lamin B lists were limited to those regions that could be queried by both platforms , we intersected the remaining lamin regions and the SWI/SNF regions using the same method that generated the all features table for enhancers , Pol II , and other elements , as described above . Similar procedures were followed for intersections with DNA replication origins identified in the ENCODE regions using tiling arrays [55] . To determine whether SWI/SNF components , core regions , and union regions are enriched for factors such as enhancers , small RNAs , lamin A/C and B , CTCF sites , Pol II regions , Pol III sites , 5′ ends and DNA replication origins , we used the genome structure correction test ( GSC ) . This test determines the significance of observations where there “exists a complex dependency structure between observations” and was specifically designed for large-scale genomic studies [27] . Given two lists of genomic regions to compare and a list of coordinates defining the overall sample space ( i . e . the length of each chromosome ) , a p-value for the significance of the overlap of the two lists is calculated and we report this value where noted . All data produced for this study can be accessed through GEO and accession numbers for individual series are provided in the relevant sections . Alternatively , data from the lamin ChIP-chip experiments and the Ini1 , Brg1 , BAF155 , and BAF170 ChIP-Seq experiments can be accessed through GEO using the SuperSeries accession number GSE24398 . | Genetic information and programming are not entirely contained in DNA sequence but are also governed by chromatin structure . Gaining a greater understanding of chromatin remodeling complexes can bridge gaps between processes in the genome and the epigenome and can offer insights into diseases such as cancer . We identified targets of the chromatin remodeling complex , SWI/SNF , on a genome-wide scale using ChIP-Seq . We also identify proteins that co-purify with its various components via immunoprecipitation combined with mass spectrometry . By integrating these newly-identified regions with a combination of novel and published data sources , we identify pathways and cellular compartments in which SWI/SNF plays a major role as well as discern general characteristics of SWI/SNF target sites . Our parallel evaluations of multiple SWI/SNF factors indicate that these subunits are found in highly dynamic and combinatorial assemblies . Our study presents the first genome-wide and unified view of multiple SWI/SNF components and also provides a valuable resource to the scientific community as an important data source to be integrated with future genomic and epigenomic studies . | [
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"biol... | 2011 | Diverse Roles and Interactions of the SWI/SNF Chromatin Remodeling Complex Revealed Using Global Approaches |
Molting is an essential process in the nematode Caenorhabditis elegans during which the epidermal apical extracellular matrix , termed the cuticle , is detached and replaced at each larval stage . The conserved NIMA-related kinases NEKL-2/NEK8/NEK9 and NEKL-3/NEK6/NEK7 , together with their ankyrin repeat partners , MLT-2/ANKS6 , MLT-3/ANKS3 , and MLT-4/INVS , are essential for normal molting . In nekl and mlt mutants , the old larval cuticle fails to be completely shed , leading to entrapment and growth arrest . To better understand the molecular and cellular functions of NEKLs during molting , we isolated genetic suppressors of nekl molting-defective mutants . Using two independent approaches , we identified CDC-42 , a conserved Rho-family GTPase , and its effector protein kinase , SID-3/ACK1 . Notably , CDC42 and ACK1 regulate actin dynamics in mammals , and actin reorganization within the worm epidermis has been proposed to be important for the molting process . Inhibition of NEKL–MLT activities led to strong defects in the distribution of actin and failure to form molting-specific apical actin bundles . Importantly , this phenotype was reverted following cdc-42 or sid-3 inhibition . In addition , repression of CDC-42 or SID-3 also suppressed nekl-associated defects in trafficking , a process that requires actin assembly and disassembly . Expression analyses indicated that components of the NEKL–MLT network colocalize with both actin and CDC-42 in specific regions of the epidermis . Moreover , NEKL–MLT components were required for the normal subcellular localization of CDC-42 in the epidermis as well as wild-type levels of CDC-42 activation . Taken together , our findings indicate that the NEKL–MLT network regulates actin through CDC-42 and its effector SID-3 . Interestingly , we also observed that downregulation of CDC-42 in a wild-type background leads to molting defects , suggesting that there is a fine balance between NEKL–MLT and CDC-42–SID-3 activities in the epidermis .
Members of the NIMA-related kinase ( NEK ) family are conserved serine/threonine kinases found in fungi , plants , and animals . The original member of the family , Never in Mitosis A ( NIMA ) , was discovered in the filamentous fungus Aspergillus nidulans , where it promotes cell cycle progression [1 , 2] . More recent analysis has indicated that NIMA interacts with components of the Endosomal Sorting Complex Required for Transport ( ESCRT ) to control normal polarized growth [3] . Most mammalian genomes encode eleven NEKs , termed NEK1–NEK11 [4] , which have a number of distinct functions including roles in cell cycle progression , ciliogenesis , DNA damage response , and inflammasome activation [5–14] . Correspondingly , a number of NEKs have been implicated in human diseases including cancer , polycystic kidney disease , situs inversus , cardiopathies , paucity of bile duct syndrome , and Majewski syndrome [5 , 15–24] . The genome of the nematode Caenorhabditis elegans encodes four NEK-like proteins , termed NEKL-1–NEKL-4 [25] . NEKL-3 is a close ortholog of mammalian NEK6 and NEK7 , whereas NEKL-2 is most similar to NEK8 and NEK9 [25 , 26] . We have previously shown that NEKL-2 and NEKL-3 , together with three conserved ankyrin repeat partners , MLT-2/ANKS6 , MLT-3/ANKS3 , and MLT-4/INVS , are required for the completion of molting [25–27] . During molting , larvae release their old apical extracellular matrix ( ECM ) , termed the cuticle , and synthesize a new one underneath , thereby allowing for further growth and development [28] . Whereas null mutations in nekls or mlts lead to a complete failure to shed the old cuticle during the first larval molt , hypomorphic alleles typically arrest during the second molt and exhibit a partial release of the old cuticle [25–27] . Our previous studies have suggested that the role of NEKL–MLT proteins in molting may be linked to their regulation of endocytic trafficking , which is critical for the normal molting process [25–27] . Whether the perturbation of normal trafficking is primarily responsible for molting defects in nekl and mlt mutations , however , is currently unresolved . In this study , we further clarify the mechanism through which the NEKL–MLT pathway is involved in molting . More specifically , by using two independent screens , we have linked the NEKL–MLT pathway with the Rho-like GTPase , CDC-42/CDC42 , and its conserved effector protein , SID-3/ACK1 . CDC42 was initially identified as an essential cell polarity and budding regulator in the yeast Saccharomyces cerevisiae and its activity is in large part controlled through its association with GTP ( in the active state ) and GDP ( in the inactive state ) [29 , 30] . Studies in mammals have shown that CDC42 becomes activated in response to different intracellular and extracellular signals and , depending on the type of the stimulus , can interact with a wide variety of effectors to control diverse cellular functions . These include actin reorganization and polarization , intracellular trafficking , polarized growth , microtubule polarization , and septin organization [31–41] . CDC42 is thus considered to be a key regulator in the establishment and maintenance of cellular polarity , a function that is essential for normal proliferation , differentiation , and morphogenesis [34] . ACK1 is a non-receptor tyrosine kinase and serine/threonine protein kinase that is implicated in cell morphology , migration , survival , growth , and proliferation [42–44] . More specifically , ACK1 functions in trafficking and clathrin-mediated endocytosis through binding to the epidermal growth factor receptor and clathrin [45–47] , and also regulates the formation of actin filaments by phosphorylating both the Wiskott-Aldrich syndrome protein ( WASP ) and cortactin [48 , 49] . In addition , ACK1 has been reported to promote the activation state of CDC42 [50] . In C . elegans , mutations in sid-3 affect the transfer of dsRNA between cells , suggesting a possible connection to endocytosis and RNA import [51] . Nevertheless , the functions of SID-3 are not well characterized in C . elegans , and previous studies have not linked SID-3 functions to CDC-42 in this species . Our study for the first time functionally connects CDC-42/CDC-42 and SID-3/ACK1 with the NIMA-related kinase network . Moreover , we demonstrate that these pathways are critical for the regulation of actin-dependent ECM remodeling and vesicular trafficking in the polarized epithelium of C . elegans .
We have previously shown that mutations in components of the NEKL–MLT network lead to a spectrum of molting defects during larval development . In the case of nekl-3 ( sv3 ) hypomorphic mutants , larvae typically arrest at the second ( L2/L3 ) molt and exhibit a stereotypical corset phenotype , whereby the unshed L2 cuticle constricts a significant portion of the central body region [25–27] . To gain insights into the underlying mechanisms controlling molting , as well as targets and components of the NEKL–MLT network , we carried out a screen for RNAi suppressors of nekl-3 ( sv3 ) molting defects . Among the genes identified , an RNAi clone targeting cdc-42 led to penetrant suppression of nekl-3 ( sv3 ) , such that most treated animals progressed through L3 and L4 stages as compared with control animals that arrested at the L2/L3 transition ( Fig 1A and 1B ) . We also tested the ability of cdc-42 ( RNAi ) feeding to suppress defects in nekl-3 ( gk506 ) null mutants , which arrest during the L1/L2 molt and exhibit complete encasement within both the L1 and L2 cuticles [25] . In contrast to the nekl-3 ( sv3 ) hypomorphic allele , suppression of nekl-3 ( gk506 ) by cdc-42 ( RNAi ) was relatively weak but did lead to an increase in the average width of treated animals ( S1A Fig ) . To test if a reduction in CDC-42 can suppress loss-of-function phenotypes in other members of the NEKL–MLT network , we examined the effects of cdc-42 ( RNAi ) feeding on the hypomorphic alleles of nekl-2 ( fd91 ) and mlt-4 ( sv9 ) . In addition , we tested cdc-42 ( RNAi ) on nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) double mutants . Notably , single mutants of nekl-2 ( fd81 ) and nekl-3 ( gk894345 ) are aphenotypic , whereas ~ 99% of double mutants arrest as larvae with molting defects [26] . On control RNAi plates , all three strains displayed near uniform L2/L3 arrest ( Fig 1C–1E ) , consistent with previous reports [25 , 26] . RNAi feeding of cdc-42 led to partial suppression of molting defects in all three strains , whereby animals developed through L3 and L4 stages and in some cases progressed to adulthood ( Fig 1C–1E ) . Suppressed adult animals , however , appeared to be sterile , which is likely due to a requirement for cdc-42 in the germline [52 , 53] . In addition , we tested for cdc-42 ( RNAi ) suppression of the nekl-2 ( gk839 ) null allele , which leads to arrest with a complete encasement phenotype at the L1/L2 molt [25–27] . Similar to findings for nekl-3 ( gk506 ) , cdc-42 ( RNAi ) led to a significant increase in larval width ( S1B Fig ) , although suppression was quite weak . The inability of cdc-42 ( RNAi ) to robustly suppress null alleles of nekl-2 and nekl-3 indicates that reduction of cdc-42 cannot fully compensate for the complete loss of NEKL activity . Because hypomorphic alleles are not available for the two additional characterized members of the nekl–mlt network , mlt-2 and mlt-3 , we used a combined RNAi-feeding approach to assay for cdc-42 suppression . Animals were fed mixtures of bacteria expressing RNAi against either mlt-2 or mlt-3 , together with either cdc-42 or control RNAi . Although suppression under these conditions was weaker than that observed for the hypomorphic alleles described above , cdc-42 ( RNAi ) led to a significant increase in body width ( S2 Fig ) , which may be due to partial release of the unshed cuticle . Taken together , these results demonstrate that depletion of CDC-42 can alleviate molting defects in all tested members of the conserved nekl–mlt network . Given our observation that partial loss of cdc-42 activity can lead to suppression of molting defects in nekl–mlt mutants , we were interested to determine if perturbation of CDC-42 levels might have an effect on molting in an otherwise wild-type background . Injection of cdc-42 dsRNA , which induces a strong reduction in CDC-42 activity in the germline , leads to severe defects in the early embryonic development of F1 progeny because of disruptions in cell polarity and spindle orientation [52 , 54] . In contrast , cdc-42 ( gk388 ) null homozygous mutants derived from heterozygous mothers complete embryogenesis but arrest later in development , likely because maternal CDC-42 provided by the germline is sufficient for progression through early stages of development [52–55] . Interestingly , we found that 32% ( n = 79 ) of cdc-42 ( gk388 ) homozygous progeny derived from heterozygous mothers displayed clear molting defects . Most commonly , cuticle detachments were observed in the head and tail regions , but in some cases shedding defects were visible in other locations ( Fig 2 ) . Based on P-cell divisions , the majority of cdc-42 homozygotes arrested at the L3 stage , although 14% of examined animals progressed to L4 or adult stages ( n = 79 ) . Injection of genomic DNA encoding wild-type cdc-42 into a balanced cdc-42 ( gk388 ) strain resulted in normal molting in the F1 generation of transformed larvae ( n>20 ) , demonstrating that the molting defects in this strain are specifically attributable to a lack of zygotic cdc-42 expression . Taken together , our findings indicate that whereas weak inhibition of CDC-42 can partially compensate for a reduction in nekl–mlt activities , CDC-42 is nevertheless required for normal molting . Because the epidermis is the primary tissue that modulates molting in C . elegans [25–27 , 56] , we next examined the pattern of CDC-42 expression in the larval epidermis . A functional GFP::CDC-42 reporter ( S1 Table ) was expressed in epidermal syncytia ( hyp1–hyp11 ) as well as in lateral specialized epithelial seam cells ( Fig 3A ) . GFP::CDC-42 accumulated in rounded , rod-like , or irregularly shaped structures throughout the cytosol that were enriched in the apical region of hyp7 . GFP::CDC-42 was also observed in ring-shaped structures , suggesting that CDC-42 is associated with the membranes of vesicles ( Fig 3A ) . Furthermore , smaller CDC-42 puncta accumulated at the boundary between the main epidermal syncytium hyp7 and seam cells ( Fig 3A ) . We failed to observe any obvious differences in the pattern of GFP::CDC-42 expression in molting versus intermolt animals ( Fig 3B ) , suggesting that CDC-42 localization may not be grossly altered during the molting program . We did , however , detect a statistically significant increase in mean levels of GFP::CDC-42 expression in molting versus intermolt animals , although these changes were relatively modest ( Fig 3C ) . We next asked if CDC-42 colocalized with components of the NEKL–MLT network that we previously examined using functional CRISRP-generated reporters ( S1 Table ) [25 , 26] . In apical regions of hyp7 , extensive colocalization between MLT-2::mKate2 and GFP::CDC-42 puncta was observed in 100% of animals ( n = 33 ) , primarily at the boundary of seam cells ( Fig 3D and S3A Fig ) , although colocalization was also observed in more medial regions of hyp7 ( Fig 3E and S3B Fig ) . GFP::CDC-42 also colocalized with a subset of bright NEKL-3::mKate2 puncta in 8/8 animals . Specifically , co-localization was observed in the apical portion of hyp7 in regions non-adjacent to the seam cell . ( Fig 3F and S3C Fig ) . Given that we previously described two independent complexes containing NEKL-2–MLT-2–MLT-4 and NEKL-3–MLT-3 [26] , our results indicate that CDC-42 partially colocalizes with both complexes during development . Given the observed functional and spatial connections between CDC-42 and NEKL–MLT components , we next determined if the localization of CDC-42 is dependent on the NEKL–MLT network . Notably , GFP::CDC-42 epidermal localization was altered in 89% ( n = 53 ) of nekl-2 ( fd91 ) and 79% ( n = 53 ) of nekl-3 ( sv3 ) molting-defective larvae ( Fig 4A–4C ) . In particular , apical expression of CDC-42::GFP was altered either throughout the entire epidermis or within limited regions . Changes included smearing ( diffuse expression ) of the GFP::CDC-42 marker as well as increased aggregation . Although GFP::CDC-42 was highly enriched at seam cell boundaries in nekl-2 ( fd91 ) and nekl-3 ( sv3 ) mutants , consistent with wild type , seam cells in these mutants were abnormally small and round relative to wild-type controls ( Fig 4A–4C ) , suggesting a non-autonomous role for NEKL-2 and NEKL-3 in seam cell processes . In addition , we observed severe defects in GFP::CDC-42 localization in 62% ( n = 21 ) of mlt-3 ( RNAi ) animals , including smearing and aggregation of the marker within the apical epidermis ( S4A and S4B Fig ) . These findings are consistent with a role for the NEKL–MLT network in directly or indirectly regulating epidermal CDC-42 subcellular localization . To address the possibility that the observed changes in GFP::CDC-42 localization in nekl and mlt mutants were due to indirect effects caused by improper shedding of the cuticle , we examined GFP::CDC-42 expression in several unrelated molting-defective backgrounds depleted for FBN-1 , a fibrillin-like protein ( Fig 4D ) ; MLT-11 , a putative protease inhibitor ( S4C Fig ) ; or QUA-1 , a hedgehog-like signaling molecule ( S4D Fig ) . We observed that the pattern of GFP::CDC-42 localization and seam cell morphology appeared normal in 92% ( n = 26 ) of fbn-1 ( tm290 ) mutants , as well as 71% ( n = 14 ) of mlt-11 ( RNAi ) , and 100% ( n = 10 ) of qua-1 ( RNAi ) worms , indicating a specific role of the NEKL–MLT protein network in the control of CDC-42 localization . In addition to gross changes in CDC-42 expression , we were interested to determine if CDC-42 activation was affected by loss of nekl function . As a dynamic switch , CDC-42 cycles between GTP-bound active and GDP-bound inactive forms . To assay CDC-42 activity , we made use of two independent reporters of CDC-42 activation . We first examined a GST::GFP::WSP-1 ( GBD ) reporter that associates with GTP-bound active CDC-42 and is expressed under the control of the cdc-42 promoter [57] . In the case of wild type larvae we observed a punctate pattern of expression near the apical surface , which was very similar to our observations for the GFP::CDC-42 reporter ( Fig 4E ) . Notably , we observed both qualitative and quantitative differences in patterns of GST::GFP::WSP-1 ( GBD ) in wild-type larvae and nekl-2 ( fd91 ) and nekl-3 ( sv3 ) single mutants . GST::GFP::WSP-1 ( GBD ) tended to form aggregations in the mutants ( Fig 4F and 4G ) . In addition , apical expression levels of GST::GFP::WSP-1 ( GBD ) in mutants were nearly twice the levels observed in wild type larvae ( Fig 4H ) , suggesting increased CDC-42 activity in the mutants . To confirm these results , we used another reporter for CDC-42 activity , WSP-1 ( CRIB ) ::mCherry , which is expressed exclusively in the epidermis under the control of the lin-26 promoter [58] . In wild-type larvae , WSP-1 ( CRIB ) ::mCherry was primarily observed in small puncta and filament-like structures throughout the epidermis of molting and intermolt animals , with occasional localization within slightly larger aggregates ( S5A Fig ) . The different expression pattern of this reporter in comparison to both GFP reporters for active and total CDC-42 may be due to difference in the fluorescence tag and its C terminal localization . Although we observed some variability in the pattern of WSP-1 ( CRIB ) ::mCherry between individual larvae , no consistent differences were detected between molting ( n = 25 ) and intermolt ( n = 31 ) animals . In contrast , we observed a strong increase in the size ( S5 Fig ) of WSP-1 ( CRIB ) ::mCherry puncta in 93% of nekl-2 ( fd91 ) ( n = 28 ) and 96% of nekl-3 ( sv3 ) ( n = 28 ) larvae , as well as an increase in the number of puncta in some animals . This included the formation of very large accumulations that were never observed in wild type ( S5 Fig ) . In contrast to nekl mutants , we observed a wild-type pattern for WSP-1 ( CRIB ) ::mCherry in 91% of fbn-1 ( tm290 ) molting-defective mutants ( n = 21 ) ( S5D Fig ) , and the size of mCherry puncta in fbn-1 ( 290 ) mutants was much closer to that of wild type ( S5 Fig ) , suggesting that the observed effects on CDC-42 activity are likely specific to perturbation of the NEKL–MLT network . Collectively , these findings indicate that NEKL–MLT components affect both CDC-42 subcellular localization as well as its activity levels . We also tested the effect of expressing two predicted CDC-42 hyperactive variants ( G12V and Q61L ) [54 , 59–61] in the epidermis using the dpy-7 promoter , which drives specific expression within epidermal cells [62] . Whereas expression of wild-type CDC-42 from transgenic arrays was well tolerated and gave rise to transmitting lines , both the G12V and Q61L CDC-42 variants were highly toxic , leading to severe morphological defects and accompanying embryonic and L1 larval lethality in most GFP-marked F1 progeny ( n>1000 ) . In one representative experiment using cdc-42 ( G12V ) with the co-injection marker sur-5::GFP , we observed 57% embryonic lethality and 27% early L1 lethality ( n = 165 ) of GFP-positive worms whereas cdc-42 ( Q61L ) led to 55% embryonic lethality and 22% L1 lethality ( n = 176 ) . This level of lethality was observed using a range of DNA concentrations and is likely due to the deleterious effects of hyperactive CDC-42 on the epidermis of embryos undergoing morphogenesis . Approximately 20% of F1s did not arrest as embryos or L1 larvae but failed to produce stable transmitting lines . F1s that escaped early arrest were typically highly mosaic for expression of the GFP-containing array with a majority displaying some form of morphogenetic defect including molting deficits ( S6 Fig ) . In some cases , we observed complete entrapment within the old cuticle at the L2/L3 stage , whereas other worms failed to shed only a portion of the cuticle or became entrapped at later stages ( S6 Fig ) . These findings are consistent with our above data and indicate that epidermal-expressed hyperactive CDC-42 can cause defects in molting . One caveat to these studies , however , is that the observed effects on molting could be indirectly due to defects in epidermal cells induced by the CDC-42 gain-of-function mutants earlier in development . Nevertheless , molting phenotypes were observed in some animals that did not display obvious morphological defects ( S6 Fig ) , suggesting that these effects are separable . In a parallel forward genetic screen [63] , we identified a mutation , fd139 , that strongly suppressed molting defects in nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) double mutants ( Fig 5A and 5B ) . Molecular identification of fd139 revealed it to affect sid-3 , the ortholog of activated Cdc42-associated kinase 1 ( ACK1 ) . ACK1 was initially described as a positive regulator of CDC42 activity , although later studies have shown that it also functions as a CDC42 effector and has roles in actin regulation and intracellular trafficking [45–50] . In C . elegans , SID-3 regulates systemic RNAi and is expressed in the epidermis in a pattern that is similar to what we observed for CDC-42 [51] . fd139 is a 5-bp deletion in sid-3 exon 13 , leading to a frameshift following Ser 989 of the SID-3 protein ( B0302 . 1a ) . Using CRISPR/Cas9 methods , we generated five additional alleles of sid-3 , all of which induced strong suppression of the corset phenotype in nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) animals ( Fig 5A and S2 Table ) . This included two C-terminal ( fd213 and fd214 ) and three N-terminal ( fd218 , fd219 , fd221 ) alleles , of which the latter group caused frameshifts within 60 bp downstream of the start codon . Notably , none of these alleles has any obvious phenotype on their own , consistent with a previous report [51] . The ability of the N-terminal sid-3 alleles to suppress nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) mutants indicates that complete inactivation of SID-3 can suppress molting defects in the double mutants . These results further strengthen the functional connection between NEKL–MLT components and CDC-42 and also suggest that SID-3 cooperates with CDC-42 in the molting network . The viability of null mutations in sid-3 , however , indicates that unlike CDC-42 , SID-3 is not required for normal molting [51] . In mammals , CDC42 and Ack1 have important roles in the regulation of the actin cytoskeleton . Importantly , epidermal actin is reorganized during molts to form circumferentially oriented arrays , which have been proposed , though not formally demonstrated , to be critical for molting [64] . The observation that inhibition of both cdc-42 and sid-3 can suppress defects in nekl–mlt mutants further suggested that the NEKL–MLT network may control actin localization during molting . We first sought to recapitulate the observations of Costa et al . [64] using phalloidin staining of actin in fixed wild-type worms . We observed that intermolt L2 larvae generally showed very weak actin staining in the epidermis ( Fig 6A ) , suggesting that actin in these animals may be largely diffuse . Occasionally , intermolt animals contained dispersed epidermal actin puncta as well as some accumulation at the seam cell boundary ( Fig 6A ) . In contrast , phalloidin staining in molting animals was much stronger , and actin was clearly organized into parallel apical bundles , consistent with Costa et al . ( Fig 6B ) [64] . To test if epidermal actin organization is regulated by the NEKL–MLT network , we first examined actin staining in nekl-2 ( fd91 ) ( n = 29 ) and nekl-3 ( sv3 ) ( n = 18 ) hypomorphic animals . We failed to observe normal actin bundles in any of the examined animals from both genetic backgrounds . In most larvae , the pattern of actin differed dramatically from wild type in that puncta were more numerous and tended to form aggregations ( Fig 6C and 6D ) . Moreover , although mutants occasionally contained short filamentous-like structures , they failed to complete the formation of parallel bundles observed in wild-type molting animals ( S7 Fig ) . In 24% of nekl-2 ( fd91 ) and 11% of nekl-3 ( sv3 ) mutants we observed some actin filaments or rows of actin puncta in the dorsal and ventral areas of hyp7 , usually above the body wall muscles ( S7B and S7C Fig ) . Notably , patches with actin filaments were more frequently observed in body regions constricted by the old cuticle; it is possible that actin filaments formed and rapidly disassembled in the non-constricted regions . In addition , actin accumulations at the hyp7–seam-cell boundary were frequently undetectable . Consistent with the above findings , actin was mislocalized in 100% ( n = 38 ) of nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) double mutants , although to a lesser extent than in nekl-2 ( fd91 ) and nekl-3 ( sv3 ) single mutants ( Fig 6E ) . Likewise , we observed that formation of filaments was initiated to varying extents in 68% ( n = 38 ) of nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) mutants ( S7D Fig ) but failed to form complete rings . Similar to the single mutants , filaments were prevalent within , but not exclusive to , regions constricted by the cuticle . Large actin aggregates were observed in the posterior regions of nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) larvae , which are typically free of old cuticle . As a control , we also performed phalloidin staining with fbn-1 ( tm290 ) molting-defective animals and observed a normal molting-specific pattern of actin organization ( n = 8 ) ( Fig 6F ) , indicating that the effects observed for nekl-2 and nekl-3 depletion are not likely to be an indirect consequence of improper molting . Collectively , our findings suggest that formation of actin bundles can be initiated but not completed in nekl hypomorphs . Furthermore , filament formation is more severely disrupted in stronger loss-of-function nekl mutants . The strong loss of function allele nekl-3 ( sv3 ) shows very subtle and rare formation of actin filaments , whereas actin bundle formation was more extensive when two weak loss of function alleles , nekl-2 ( fd81 ) and nekl-3 ( gk894345 ) , were combined . From these studies we conclude that NEKL-2 and NEKL-3 play an important role in actin organization during molting . Given that cdc-42 or sid-3 depletion can suppress molting defects in nekl-2 and nekl-3 mutants , we next determined if epidermal actin organization was restored in suppressed larvae . We observed that cdc-42 ( RNAi ) had a strong effect on actin organization in both nekl-2 ( fd91 ) and nekl-3 ( sv3 ) backgrounds , both by reducing the occurrence of actin accumulations and by promoting the formation of actin filaments ( Fig 6G and 6H ) . In the case of nekl-2 ( fd91 ) ; cdc-42 ( RNAi ) , actin filaments were observed in 96% ( n = 24 ) of animals , however , these filaments were frequently disorganized . 92% ( n = 12 ) of nekl-3 ( sv3 ) ; cdc-42 ( RNAi ) suppressed animals also contained numerous actin filaments , but these did not form parallel circumferential bundles and in some cases were in an anterioposterior orientation ( Fig 6H ) . The observed differences between nekl-2 and nekl-3 strains may be due to differences in their specific allele strengths or because cdc-42 ( RNAi ) can more efficiently suppress reduced NEKL-2 functions . To further analyze effects of CDC-42 downregulation on actin organization in the apical epidermis , we carried out actin staining on cdc-42 ( gk388 ) molting defective mutants . Although many of these animals were mostly impermeable to phalloidin , we observed a range of abnormal actin patterns . Importantly , in 60% of mutants ( n = 25 ) actin puncta were organized in parallel rows , reminiscent of molting-specific actin bundles in wild type ( S8C and S8D Fig ) . Nevertheless , filaments connecting these puncta were often missing . Furthermore , the boundary with the seam cells in cdc-42 ( gk388 ) mutants was not always enriched in actin filaments . In the remainder of cdc-42 ( gk388 ) molting defective animals ( 40% ) , actin puncta and rare filaments showed abnormal actin organization patterns and did not form parallel rows ( S8E and S8F Fig ) . Thus , loss of cdc-42 activity in an otherwise wild-type background leads to alterations in the normal pattern of actin staining , although this phenotype was generally less severe than that observed in nekl-2 and nekl-3 mutants . We also examined actin localization in suppressed and nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) sid-3 ( fd139 ) animals and observed either a normal molting-specific actin pattern in animals that contained filaments ( n = 6 ) ( Fig 6I ) or a normal punctate intermolt actin pattern ( n = 13 ) . Taken together , our data imply that NEKL-2 and NEKL-3 influence the formation of epidermal apical actin structures during development and that this function is tightly balanced by the activities of CDC-42 and SID-3 . Because NEKL–MLT components partially colocalize with CDC-42 and because nekl depletion led to a strong perturbation of the actin cytoskeleton , we next asked whether members of the NEKL–MLT network colocalize with actin . For these studies , we used a strain carrying an integrated vab-10 actin-binding domain ( ABD ) mCherry fusion reporter , which can serve as a proxy for actin expression in live animals [65] . A CRISPR-generated reporter for NEKL-2::NeonGreen ( S1 Table ) strongly colocalized with the VAB-10 ( ABD ) ::mCherry reporter in 100% of worms ( n = 15 ) at subapical regions of the epidermal–seam cell boundary , with little colocalization observed in other regions ( Fig 7A and S9A Fig ) . Likewise , a CRISPR-generated reporter for MLT-4 ( S1 Table ) , which forms a putative complex with NEKL-2 [26] , showed a similar degree of overlap with VAB-10 ( ABD ) ::mCherry in the same region ( n = 16 ) ( Fig 7B and S9B Fig ) . Lastly , a CRISPR-generated reporter for NEKL-3 ( S1 Table ) was also found to strongly colocalize with VAB-10 ( ABD ) ::mCherry in hyp7 in 100% of examined animals ( n = 11 ) , however , in contrast to NEKL-2 and MLT-4 , this overlap occurred throughout the cytoplasm and rarely at the seam cell boundary , where NEKL-3 is largely absent ( Fig 7C and S9C Fig ) . Collectively , our data place members of the NEKL–MLT complex in close proximity to both actin and CDC-42 , consistent with a role for NEKL–MLT proteins in regulating the actin cytoskeleton through the CDC-42–SID-3 pathway . We previously reported that inhibition of NEKL–MLT components leads to defects in endocytosis in the epidermis [25 , 26] . Because orthologs of CDC-42 and SID-3 in other systems regulate several steps of endocytosis [40 , 41 , 45–47 , 66 , 67] , we wanted to test if inhibition of CDC-42 and SID-3 could suppress trafficking defects in nekl mutants . To examine effects on trafficking , we used a marker for clathrin , GFP::CHC-1 , which localizes to clathrin-coated pits at the apical membrane [25 , 26] and was also distributed in a punctate pattern throughout the epidermis ( Fig 8A and 8B ) . Consistent with our previous findings , 100% ( n = 57 ) of nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) double mutants contained abnormal accumulations of the GFP::CHC-1 marker . Specifically , nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) mutants exhibited fewer total GFP::CHC-1 puncta but these puncta were larger ( Fig 8C and 8D and S10 Fig ) [26] . In some cases , GFP::CHC-1 accumulations were observed to extend from the apical hyp7 surface to more medial regions of the epidermis , whereas other accumulations were exclusively medial ( Fig 8C and 8D ) . cdc-42 ( RNAi ) was observed to fully restore normal GFP::CHC-1 morphology in small percentage ( 15%; n = 34 ) of suppressed nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) animals that reached adulthood ( Fig 8E ) . In addition , we observed a slight increase in the number of GFP::CHC-1 puncta in nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) animals treated with cdc-42 ( RNAi ) versus control , although these differences were not statistically significant ( S10 Fig ) . In fact , abnormal clathrin aggregations were prevalent in the epidermis of many cdc-42 ( RNAi ) animals that were partially or fully suppressed for molting defects ( S11B Fig ) , suggesting that cdc-42 ( RNAi ) may suppress molting defects through a mechanism that is at least partially independent of trafficking as judged by clathrin localization . In contrast , sid-3 ( fd139 ) , which induces stronger suppression of molting defects in nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) mutants than cdc-42 ( RNAi ) ( compare Fig 1D and Fig 5B ) , led to restoration of normal GFP::CHC-1 expression in 82% of animals ( n = 39 ) ( Fig 8F and S10 Fig ) . Very rarely , some aggregates could be observed in nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) ; sid-3 ( fd139 ) triple mutants , although they were typically smaller than in non-suppressed double mutants ( S11C Fig ) . In summary , our results indicate that inhibition of CDC-42 and its partner SID-3 can suppress molting phenotypes in mutants of the nekl–mlt network and can at least partially correct associated defects in actin patterning and trafficking .
We have shown that inhibition of the small Rho GTPase CDC-42 leads to the suppression of molting defects in nekl–mlt hypomorphic backgrounds . Likewise , we independently found that mutations in sid-3 , the C . elegans ortholog of activated Cdc42-associated kinase 1 , also suppress nekl mutants , indicating that CDC-42–SID-3 functions are closely connected to NEKL–MLT signaling in the epidermis . Furthermore , we have observed that cdc-42 downregulation in wild type can lead to molting defects and developmental arrest , suggesting that there is a functional balance between NEKL–MLT and CDC-42 activities in the epidermis . This balance becomes particularly important during molting , when inhibition of either component impairs normal ECM remodeling . Moreover , our observation that inhibition of CDC-42 and SID-3 can suppress molting defects in nekl–mlt mutants indicates that NEKL kinases are most likely negative regulators of CDC-42–SID-3 . This control could be at the level of CDC-42–SID-3 intrinsic activity , through effects on subcellular localization , or both . Consistent with a strong functional link , we observed that CDC-42 partially colocalizes with components of both NEKL–MLT complexes , NEKL-2–MLT-2–MLT-4 and NEKL-3–MLT-3 . Furthermore , normal GFP::CDC-42 localization and activity depend on NEKL-2 and NEKL-3 , suggesting that there is both a spatial and functional interaction between CDC-42 and the NEKL–MLT protein network . Interestingly , independent proteomic studies to identify binding partners of mammalian NEK6 and NEK7 both identified CDC42 [68 , 69] . These findings suggest that CDC42 interacts directly or indirectly with NEK6 and NEK7 , the highly conserved mammalian orthologs of NEKL-3 . Furthermore , a CDC42 modifier , Rho-GAP SNX26 ( ARHGAP33 ) , which is implicated in regulation of vesicular trafficking , has also been identified as interacting with NEK6 [68] . Because the phosphorylation targets of NEK kinases are largely unknown , we hypothesize that CDC42 , its regulators , or its effectors could be controlled by NEK phosphorylation ( Fig 9 ) . This is consistent with findings that the phosphorylation status of CDC42 and its GEFs is important for their localization and activity [70–72] . CDC42 has important functions in the establishment of cell polarity , cytoskeletal reorganization and the regulation of endocytosis [34 , 59 , 73 , 74] . In this study we analyzed the effects of nekl–mlt depletion on two CDC-42-dependent processes , actin remodeling and endocytosis , which are known to be important for molting in C . elegans [27 , 64 , 75] . It has been previously proposed that apical actin bundles reorganize at each molt to facilitate proper ECM formation [64] . In support of this model , we have shown that loss of NEKL-2 and NEKL-3 activity leads to defects in the pattern of apical actin , a failure to form molting-specific arrays of actin bundles , and defective molting ( Fig 9 ) . These apical actin bundles are proposed to contract as new cuticle is synthesized between the actin filaments , thus allowing for the formation of a new cuticle that is larger than the previous one [64] . Our finding that NEKL-2 and NEKL-3 depletion strongly perturb apical actin organization suggests a mechanism by which both detachment of the old cuticle and new cuticle synthesis might be impaired in nekl–mlt mutants . Namely , disorganization of the actin cytoskeleton may alter the distribution and timing of new cuticle synthesis and create an abnormal juxtaposition of the old and new cuticles , resulting in a failure to fully separate . Our observation that nekl mutants often contain incompletely formed molting-type actin bundles within constricted body regions supports this idea . In addition , colocalization data from this study indicate that components of the NEKL–MLT network and actin are partially coexpressed in the same subcellular compartments , which , together with CDC-42 colocalization data , further support a model for the specific control of actin organization by NEKL kinases through CDC-42 . Consistent with this model , inhibition of CDC-42 and SID-3 can suppress actin localization defects in nekl mutants , suggesting that NEKL–MLT components are required to spatially and/or temporally repress CDC-42–SID-3 activity in the epidermis . Interestingly , studies in mammalian cells have implicated MLT-4/INVS in apical actin regulation as INVS knockout mouse fibroblasts form excessive actin-rich filopodia during interphase as well as in mitosis [76] . Notably , filopodia formation is CDC42 dependent , and filopodia overproduction by mammalian cells is a characteristic of mutations that lead to higher CDC42 activity [77 , 78] . Moreover , CDC42 activity increases in mammalian cells following depletion of INVS , consistent with INVS acting as a negative regulator of CDC42 [79] . Our study suggests that this function for MLT-4/INVS may be conserved in C . elegans and demonstrates for the first time that other components of the NEKL–MLT network control CDC42 activity and actin organization . Both CDC42 and the actin cytoskeleton have been previously implicated in multiple aspects of vesicular trafficking . For example , actin may control the spatial organization of endocytic machinery components , promote the formation of membrane invaginations , create forces required for vesicle fission and internalization , and provide a barrier function that negatively regulates the rate of endocytosis [80–82] . Actin is essential for clathrin-mediated endocytosis in yeast , although its role in mammalian endocytosis may be more limited . Interestingly , actin is required for mammalian endocytosis at cell surfaces that are enriched for actin filaments [81] . Notably , such cortical enhancement of actin filaments is found at the apical surface of epidermal syncytium in C . elegans [64] , providing a potential connection between actin and the regulation of vesicular trafficking during molting . Importantly , endocytosis is a critical process for ECM remodeling in C . elegans , and inactivation of core endocytic components , including CHC-1 , leads to molting defects [25–27 , 75 , 83–86] . One possible role of endocytosis in ECM remodeling may be in resorption and recycling of old cuticle components [27] . Furthermore , epidermal endocytosis has been implicated in the process of sterol uptake from the surrounding environment , and sterols have a proposed role in the synthesis of hormonal cues that initiate molting in nematodes [27 , 56] . In addition , endocytosis is closely coupled to exocytosis [87] , a process critical for cuticle synthesis [27] . Namely , the rate of exocytosis must be balanced with a commensurate level of endocytosis to allow for the recycling of membrane components back to intracellular compartments and to maintain a relatively constant cell surface area [87] . Previously , we have shown that the NEKL–MLT protein network regulates endocytosis in the epidermis [25 , 26] . In this study , we found that highly penetrant trafficking defects in nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) double mutants can be partially or completely suppressed by CDC-42 and SID-3 downregulation . Nevertheless , we observed that suppression of molting defects was not always well correlated with the restoration of normal clathrin localization patterns . This could be due to different threshold requirements for the suppression of molting defects versus the normalization of clathrin-mediated endocytosis . For example , a partial correction of vesicular trafficking may be sufficient to prevent overt molting defects . Alternatively , impaired endocytosis may not be a primary cause of molting defects in nekl–mlt mutants , consistent with our earlier finding that trafficking alterations are not detected in all molting-defective animals depleted for components of the NEKL–MLT network [25 , 26] . Instead , the observed endocytosis defects could be a secondary consequence of actin disorganization in nekl–mlt mutants , given that apical actin assembly and disassembly are critical for normal endocytosis [81 , 82 , 88–93] . In addition , it is possible that perturbation of both endocytosis and apical actin organization contribute to the observed molting defects in nekl–mlt mutants and that suppression can be achieved through compensatory effects on either process . In summary , our data implicate the C . elegans orthologs of NIMA-related kinases NEK6 , NEK7 , NEK8 , and NEK9 , along with their conserved ankyrin-repeat binding partners , in the regulation of actin remodeling and intracellular trafficking through functional interactions with CDC42 and its effector ACK1 . We propose that , by modulating CDC42–ACK1 activity , NEK kinases may control a wide variety of cellular processes including effects on actin morphology and endocytosis ( our study ) , along with potential downstream effects on cell cycle–related processes and ciliogenesis . Future studies will be required to determine the generality of our observations along with the specific nature of this regulatory connection .
C . elegans strains were maintained according to standard protocols [94] and were propagated at 21°C . Strains used in this study include FT1459 [unc-119 ( ed3 ) ; xnIs506 ( cdc42p::gst::gfp::wsp-1[gbd]; unc-119[+] ) ] , GR1395 [mgIs49 ( mlt-10::gfp-pest ) ] , N2/Bristol ( wild type ) , LH373 [nekl-3 ( gk506 ) ; mnEx174 ( F19H6; pTG96 ) ] , NL2099 [rrf-3 ( pk1426 ) ] , RT1378 [pwIs528 ( gfp::chc-1 ) ] , SP2734 [mlt-4 ( sv9 ) V; mnEx173 ( mlt-4 ( + ) ; pTG96 ) ] , SP2736 [nekl-3 ( sv3 ) X; mnEx174 ( F19H6; pTG96 ) ] , WS5018 [cdc-42 ( gk388 ) ; opIs295 ( cdc-42p::gfp::cdc-42; unc-119[+] ) II] , WY1061 [nekl-2 ( gk839 ) ; fdEx257 ( WRM0639aE11; WRM0636aD02; pTG96 ) ] , WY1145 [nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) ; fdEx286 ( pDF153[nekl-3 ( + ) ]; pTG96 ) ]; WY1165 [nekl-2 ( fd91[Y84L , G87A]; fdEx278[pDF166 ( nekl-2[+]; pTG96 ) ] ) ] , WY1217 [nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) ; sid-3 ( fd139 ) ] , WY1242 [nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) ; pwIs528 ( gfp::chc-1 ) ; fdEx257 ( WRM0639aE11; WRM0636aD02; pTG96 ) ] , WY1331 [nekl-2::NeonGreen::3xFlag ( fd100 ) ; mcIs40 ( plin-26::vab-10 ( abd ) ::mCherry; myo-2::gfp ) ] , WY1332 [nekl-3::NeonGreen::3xFlag ( fd118 ) ; mcIs40 ( plin-26::vab-10 ( abd ) ::mCherry; myo-2::gfp ) ] , WY1333 [mlt-4::gfp::3xFlag ( fd114 ) ; mcIs40 ( plin-26::vab-10 ( abd ) ::mCherry; myo-2::gfp ) ] , WY1337 [mlt-2::mKate2::3xFlag ( fd95 ) ; cdc-42 ( gk388 ) ; opIs295 ( cdc-42p::gfp::cdc-42; unc-119[+] ) II] , WY1338 [nekl-3::mKate2::3xFlag ( fd106 ) ; cdc-42 ( gk388 ) ; opIs295 ( cdc-42p::gfp::cdc-42; unc-119[+] ) II] , WY1345 [nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) ; sid-3 ( fd139 ) ; pwIs528 ( gfp::chc-1 ) ] , WY1352 [nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) ; sid-3 ( fd213 ) ] , WY1353 [nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) ; sid-3 ( fd214 ) ] , WY1360 [cdc-42 ( gk388 ) ; opIs295 ( cdc-42p::gfp::cdc-42; unc-119[+] ) II; nekl-2 ( fd91 ) ; fdEx278 ( pDF166 [nekl-2 ( + ) ]; pTG96 ) ] , WY1361 [cdc-42 ( gk388 ) ; opIs295 ( cdc-42p::gfp::cdc-42; unc-119[+] ) II; nekl-3 ( sv3 ) X; mnEx174 ( F19H6; pTG96 ) ] , WY1374 [sajIs31 ( lin-26p::wsp-1[crib]::mCherry; unc-119R ) ] , WY1376 [cdc-42 ( gk388 ) ; opIs295 ( cdc-42p::gfp::cdc-42; unc-119[+] ) II; fbn-1 ( tm290 ) ; fdEx250 ( sur-5::rfp , fbn-1 cDNA pMH281 ) ] , WY1377 [nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) ; sid-3 ( fd218 ) ] , WY1378 [nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) ; sid-3 ( fd219 ) ] , WY1380 [nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) ; sid-3 ( fd221 ) ] , WY1381 [nekl-2 ( fd91 ) ; fdEx278 ( pDF166 [nekl-2 ( + ) ]; pTG96 ) ; sajIs31 ( lin-26p::wsp-1[crib]::mCherry; unc-119R ) ] , WY1382 [nekl-3 ( sv3 ) X; mnEx174 ( F19H6; pTG96 ) ; sajIs31 ( lin-26p::wsp-1[crib]::mCherry; unc-119R ) ] , WY1385 [fbn-1 ( tm290 ) ; fdEx250 ( pMH281 , sur-5::rfp ) ; sajIs31 ( lin-26p::wsp-1[crib]::mCherry; unc-119R ) ] , WY1422 [nekl-2 ( fd91 ) ; unc-119 ( ed3 ) ; fdEx278 ( pDF166 [nekl-2 ( + ) ]; pTG96 ) ; xnIs506 ( cdc42p::gst::gfp::wsp-1[gbd]; unc-119[+] ) ] , and WY1424 [nekl-3 ( sv3 ) X; unc-119 ( ed3 ) ; mnEx174 ( F19H6; pTG96 ) ; xnIs506 ( cdc42p::gst::gfp::wsp-1[gbd]; unc-119[+] ) ] . RNAi was performed using bacterial strains from Geneservice using standard protocols [95] . In the study that identified cdc-42 , we screened all available clones on LGII ( ~3000 clones ) for clones that allowed nekl-3 ( sv3 ) mutants to bypass L2/L3 arrest . Control RNAi experiments were carried out using the bacterial strain HT115 carrying either a vector plasmid ( pPD129 . 36 ) or gfp ( RNAi ) . Marker localization studies involving mlt-3 ( RNAi ) were carried out using strains that were previously grown for several generations on lin-35 ( RNAi ) plates , which increases RNAi sensitivity [96] . Combined RNAi treatments in S2 Fig were performed using RNAi-hypersensitive rrf-3 ( pk1426 ) mutants . Bacteria expressing mlt-2 , mlt-3 , cdc-42 , and gfp ( RNAi ) constructs were grown to the same density and combined at specific ratios . A bacterial culture expressing mlt-2 ( RNAi ) was combined at a 2:1 ratio with bacteria expressing cdc-42 ( RNAi ) or gfp ( RNAi ) , whereas the mlt-3 ( RNAi ) bacterial strain was mixed with the above RNAi clones at a 1:2 ratio . Plasmids were generated by amplifying the dpy-7 promoter region from plasmid pCFJ1662 ( gift of Barth Grant ) using the primers 5’- tcgacaaagctttctcattccacgatttctc-3’ and 5’- tcgacactgcagttatctggaacaaaatgtaagaa-3’ . Following digestion with HindIII and PstI , PCR product was inserted into pD95 . 75 to create plasmids pDF400 . Genomic cdc-42 coding sequences was amplified from fosmids WRM069cE02 and WRM0610aC01 using primers 5’-tgattcggatccatgcagacgatcaagtgcgtc-3’ and 5’-tgattcggtacctcgttccaattcacccactca-3’ , digested with BamHI and KpnI , and ligated into PDF400 to produce pDF406 . G12V and Q61L cdc-42 mutants were generated using the Q-5 Site-Directed Mutagenesis Kit ( New England Biolabs ) and the following primers: For G12V primers 5’-gttggagatgtagctgtcggtaaaac-3’ and 5’-gacgacgcacttgatcgt-3’; for Q61L primers 5’-actgctggactggaagattac-3’ and 5’- atcaaacaatcctaatgtgtatg-3’ . G12V ( pDF409 ) and Q61L ( pDF412 ) Plasmids were sequence confirmed and injected with sur-5::GFP ( pTG96 ) into 20–25 N2 worms at the following concentrations: 50 ng/μl sur-5::GFP + 100 ng/μl cdc-42 , 100 ng/μl sur-5::GFP + 50 ng/μl cdc-42 , 100 ng/μl sur-5::GFP + 20 ng/μl cdc-42 , 100 ng/μl sur-5::GFP + 15 ng/μl cdc-42 . Each injected worm was transferred to a new plate every 24 hours and its progeny were scored and imaged . sid-3 ( fd139 ) was identified as described in Joseph et al . [63] . CRISPR/Cas9 technology was used to generate additional loss-of-function alleles of sid-3 in the nekl-2 ( fd81 ) ; nekl-3 ( gk894345 ) background . Specifically , genome engineering was performed using CRISPR/Cas9 ribonucleoproteins in combination with the dpy-10 co-CRISPR method [97–99] . crRNAs were designed to target sequences in the N terminus ( 5’-AGTGCTCCGCAAAGCACAGT-3’ ) or C terminus ( 5’-TGAGCCGATTCTCTCGTCTG-3’ ) of sid-3 . Absence of repair templates allowed for non-homologous repair mechanisms to generate random frameshifts and premature stop codons in the desired regions . Six loss-of-function alleles were isolated , including fd213 , a 16-bp insertion and one point mutation in exon 13 , causing a frameshift after L987 and premature stop at amino acid position 1034; fd214 , a 7-bp deletion in exon 13 , causing a frameshift after L987 and premature stop at amino acid position 1006; fd218 , a 5-bp deletion in exon 1 , causing a frameshift after K19 and premature stop at amino acid position 25; fd219 , a 1-bp deletion in exon 1 , causing a frameshift after A20 and premature stop at amino acid position 101; and fd221 , a 31-bp insertion and 1-bp deletion in exon 1 , causing a frameshift after A20 and premature stop at amino acid position 24 ( also see S2 Table ) . Suppressed nekl mutants were analyzed at the last molt ( Fig 6 ) or as L4 or adult worms ( Figs 5 and 8 ) . Actin staining was performed following a modified procedure from Costa et al . [64] . Animals were washed in M9 solution , and fixation was carried out by incubating animals in fixative ( 2% paraformaldehyde in 0 . 05 M Na3PO4 , pH 7 . 4 ) for 2 h at room temperature . Worms were washed three times in PBS and stored at 4°C . Phalloidin-TRITC ( 0 . 1 mg/ml , dissolved in methanol ) was diluted in permeabilization solution ( 0 . 05% Triton X-100 , 0 . 1 M NaCl , 3 . 7% sucrose ) at a ratio of 1:500 . Equal amounts of liquid containing fixed animals and the phalloidin-containing solution were mixed and incubated in a dark chamber for at least 1 h . To avoid possible post mortem artifacts in our analyses , we scored only the animals that did not show any alterations in the morphology of their internal organs . Fluorescent images were acquired using an Olympus IX81 inverted microscope with a spinning-disc confocal head ( CSUX1 Yokogawa ) . Confocal illumination was provided by an ILE-4 laser launch ( SpectralAppliedResearch ) . MetaMorph7 . 7 software was used for image acquisition . DIC images were acquired using a Nikon Eclipse epifluorescence microscope and Open Lab software . This setup was also used for measuring body length and width . Body width was measured in the central region of animals; 50 animals were scored for each category . Animals were immobilized using a 0 . 1 M solution of levamisole . Distinguishing molting larvae from intermolt larvae was achieved using 0 . 5-μm red beads mixed with food ( Sigma L3280 ) , following the protocol of Nika et al . [100] . For Fig 6 , wild-type molting and intermolt animals were categorized using a mlt-10p::gfp-pest reporter [101 , 102] . Fluorescence intensity of GFP::CDC-42 in Fig 3 was measured using the FIJI program . For each animal , average fluorescence intensity was measured in 10 equal , randomly selected regions of hyp7 and average values were determined . GFP::CDC-42 was analyzed in 27 molting and 30 intermolt wild-type animals . Fluorescence intensity of GST-GFP::WSP-1 ( GBD ) in Fig 4 was measured using ImageJ program . Mean intensities in selected areas were calculated for 11 animals or more and averaged . Background mean intensities were subtracted . Average area of puncta in S5 Fig and number of puncta in S10 Fig were calculated using FIJI program , using ten animals for each genetic background . Colocalization analyses were performed on L4-stage larvae to allow for better visualization of fluorescent structures . Line scanes in S3 and S9 Figs were made using Metamorph . Colocalization with VAB-10 ( ABD ) ::mCherry marker was performed in apical regions of epidermis that are slightly more medial than the plane containing actin bundles , as this region showed the greatest extent of colocalization . Transverse plane reconstructions of GFP::CHC-1 morphology were created using the FIJI program 3D projection function in selected 1000-pixel × 7-pixel regions of 24-frame z-stacks . Distances between neighboring planes in each z-stack were 0 . 2 μm . Composite sagittal plane projections were created from the same z-stacks in FIJI , using the Temporal-Color Code function . | Protein kinases are key molecular regulators that act by modifying the structures and activities of proteins within the cell . Members of the NEK family of protein kinases regulate cell division and the formation of specialized organelles called cilia . Accordingly , mutations in the human NEK genes have been implicated in a number of diseases including cancer and maladies that result from defective cilia . To better understand the biological functions of NEK kinases , we have undertaken studies in the model system nematode , Caenorhabditis elegans . In this study , we found that NEK kinases regulate the organization of actin , a major component of the cytoskeleton and a player in many cellular processes . In the absence of normal NEK kinase activity , actin fails to form proper filamentous structures during worm development , which leads to growth arrest . Interestingly , these defects in nekl mutants can be partially reversed by simultaneously reducing the activities of CDC42 or ACK1 , two conserved proteins that are key regulators of actin dynamics . Other findings suggest that NEK kinases control actin organization through functional interactions with CDC42 and ACK1 . These studies provide a new link between NEK kinases and the actin cytoskeleton and , together with other reports , suggest that these functions may be conserved in humans . | [
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"developmental... | 2018 | Actin organization and endocytic trafficking are controlled by a network linking NIMA-related kinases to the CDC-42-SID-3/ACK1 pathway |
RNA editing describes the process in which individual or short stretches of nucleotides in a messenger or structural RNA are inserted , deleted , or substituted . A high level of RNA editing has been observed in the mitochondrial genome of Physarum polycephalum . The most frequent editing type in Physarum is the insertion of individual Cs . RNA editing is extremely accurate in Physarum; however , little is known about its mechanism . Here , we demonstrate how analyzing two organisms from the Myxomycetes , namely Physarum polycephalum and Didymium iridis , allows us to test hypotheses about the editing mechanism that can not be tested from a single organism alone . First , we show that using the recently determined full transcriptome information of Physarum dramatically improves the accuracy of computational editing site prediction in Didymium . We use this approach to predict genes in the mitochondrial genome of Didymium and identify six new edited genes as well as one new gene that appears unedited . Next we investigate sequence conservation in the vicinity of editing sites between the two organisms in order to identify sites that harbor the information for the location of editing sites based on increased conservation . Our results imply that the information contained within only nine or ten nucleotides on either side of the editing site ( a distance previously suggested through experiments ) is not enough to locate the editing sites . Finally , we show that the codon position bias in C insertional RNA editing of these two organisms is correlated with the selection pressure on the respective genes thereby directly testing an evolutionary theory on the origin of this codon bias . Beyond revealing interesting properties of insertional RNA editing in Myxomycetes , our work suggests possible approaches to be used when finding sequence motifs for any biological process fails .
RNA editing describes the process in which individual or short stretches of nucleotides in a messenger or structural RNA are inserted , deleted , or substituted . As a consequence , the final RNA product translated into a protein or functional by itself is different from its genomic template in organisms with RNA editing . RNA editing is widely spread across species , including plants , mammals , slime molds , viruses and many other organisms [1]–[7] . In some organisms , RNA editing is essential for their survival while for others it provides another layer of fine tuning the genetic program . Although some distinct editing mechanisms have been identified , in many instances the mechanisms of RNA editing are not understood at all [2] , [5] , [7] . A high level of RNA editing has been observed in the mitochondrion of the slime mold Physarum polycephalum [1] , [4] , [8]–[10] . In this organism , the mRNA of nearly every mitochondrial protein coding gene is edited at a rate of approximately one out of 25 nucleotides , while structural RNAs are edited at a rate of on average one out of every 40 nucleotides [1] , [4] , [8] . The by far most frequent editing type in Physarum is the insertion of individual Cs . However , the mitochondrion of Physarum performs a whole set of other editing types , including insertion of individual Us , insertion of certain dinucleotide pairs , deletion of nucleotides and substitutions of Cs by Us [10]–[12] . It has been shown in vivo that RNA editing in Physarum is extremely accurate [13] , i . e . , that nearly every transcript is completely edited at exactly the correct position . While the machinery inside the mitochondrion of Physarum recognizes the editing sites with extreme precision , we know neither the mechanism by which these editing sites are recognized nor what machinery is actually performing the editing . It is challenging to decipher the code that determines the editing sites and identify the machinery that performs the actual editing . As far as the machinery is concerned , it has been determined that editing in Physarum is co-transcriptional , i . e . , that the RNAs are edited as they are synthesized [14] , [15] . Thus , the RNA editing machinery should be part of the RNA polymerase itself or very closely associated with it . As far as the location of the editing sites is concerned , it has been determined that the recognition of editing sites and the actual editing are two independent processes [16] . In order to understand the RNA editing machinery , it is necessary to identify how the RNA editing machinery knows which sites to edit . It is known that only the DNA in close proximity of the site is necessary in order to obtain editing [17] . Rhee et al . [18] demonstrated that DNA necessary for C insertion is within 9 or maybe 10 base pairs on either side of the editing site . But no sequence patterns have been identified that could explain how the sites are recognized . Although no patterns have yet been identified , computational methods for the prediction of insertional editing sites have been developed [19]–[21] . These prediction methods do not require any knowledge about the mRNA . They have been shown to predict the protein sequence with an accuracy of as much as 90% [19] . Here we present the comparison of insertional RNA editing in two related organism in Myxomycetes: Physarum polycephalum and Didymium iridis . Sequence information on mitochondrial genes of Didymium iridis has recently become available [22]–[24] and can be compared to the also recently determined complete edited transcriptome of Physarum polycephalum [25] . Being able to compare sequences from two related organisms promises better understanding of the editing machinery that is vital to the successful function of these organisms . Our contributions include the prediction of RNA editing sites in Didymium genes based on the knowledge of the Physarum mitochondrial transcriptome , the investigation of sequence conservation in the vicinity of editing sites , and an analysis of codon bias . We use a computational approach to “predict” RNA editing sites in 15 Didymium genes for which the editing sites are known and find that using the Physarum protein sequence can greatly increase the prediction accuracy of Didymium editing sites . We also predict 7 new Didymium genes and their editing sites , and the prediction results suggest one of these genes may be unedited . We investigate the sequence conservation in the vicinity of editing sites between two organisms . Our data implies that a local RNA editing recognition mechanism that is based only on the information contained in any combination of the 18 nucleotides in immediate vicinity of an insertional editing site , even one that uses a different recognition agent ( such as a guide RNA or a protein ) for every single site , is unlikely . In addition , we show that if such a mechanism exists it has to use nearly all of the 18 positions to specify the site . Finally , we examine the codon position bias in C insertional RNA editing of these two organisms . A strong relationship between the strength of the codon bias and the overall sequence conservation is reported: more conserved genes tend to have more significant codon bias . This result verifies a previous mutation-selection theory for the codon bias . The recognition mechanism of insertional RNA editing in Myxomycetes is an example where searches for common sequence motifs in a single organism have failed in spite of a very large number of training sites leading to the conclusion that site specific recognition mechanisms must be at work . This situation is not specific to the case of insertional RNA editing but can occur generically in any search for biological sequence motifs . Thus , the work presented here is not only interesting in terms of the specific results on insertional RNA editing in Myxomycetes but also much more broadly in terms of strategies to be employed if biological sequence motif searches in individual organisms fail .
The first issue we address is to what extent the recently achieved complete knowledge of the edited Physarum transcriptome [25] improves computational prediction of genes and editing sites in the Didymium mitochondrial genome . Computational prediction of insertional RNA editing in the absence of a reference transcriptome has been presented before [11] , [19] , [20] . The method uses a position specific scoring matrix ( PSSM ) built from protein sequences from other organisms . It finds the editing sites for a given genomic sequence that translate to the putative protein with maximum similarity to the protein family described by the PSSM . With this method , we prepared predictions of editing sites in Didymium using the PSSMs made before the Physarum transcriptome was known [11] , [19] , [21] . Since the edited Physarum transcriptome is now known [25] , new predictions utilizing that information were created and compared to these baseline PSSM predictions . Given that Physarum and Didymium both exhibit RNA editing and are closely related , the purpose of this process was to see how much the new predictions , which include transcriptome information from Physarum , improve when compared to the baseline . In order to be able to evaluate and compare the prediction quality we applied our prediction methods to mitochondrial genes in Didymium for which the editing sites had already been determined experimentally [22] , [23] . In total we created five predictions of editing sites for every gene . The first was the baseline prediction using the PSSM developed before the Physarum transcriptome was known as described above . The second was a Physarum based PSSM . The Physarum based PSSMs were created from the NCBI website by starting a PSI-BLAST search [26] with the homologous Physarum protein sequence for each gene rather than with a homologous protein sequence from a more distant organism as in the creation of the original PSSMs . Three iterations of a protein PSI-BLAST search were run for each of the sixteen genes for which the Didymium editing sites are known ( see Methods section ) . During this process we manually excluded the Didymium protein for which the prediction was being made from the model building . Since the first round of PSI-BLAST did not find any homologs when starting from the Physarum protein sequence for atp8 , we could not create a Physarum based PSSM for atp8 and excluded it from further analysis . The remaining three predictions did not use a PSSM summarizing the properties of a whole family of homologs . Instead , the plausibility of a putative Didymium protein sequence ( generated by inserting Cs into the Didymium genomic sequence and translating the result ) was quantified by aligning the putative Didymium protein directly to the known Physarum homolog . Since alignment scores depend on the scoring matrix and different matrices are tuned toward different evolutionary distances , we prepared one prediction each using the BLOSUM62 , BLOSUM75 , and BLOSUM90 matrices . We scored the accuracy of a prediction by counting the number of correct and incorrect predictions made by each prediction method ( see Methods ) . We report the results as a percentage of editing sites in each category relative to the number of predictions the method included overall . The percentage of predicted editing sites is a better indicator than the absolute number since the computational model often does not make predictions for editing sites near the ends of genes . The results for each of the five prediction methods among all fifteen considered genes are shown in Figure 1 ( a ) . Figure 1 ( a ) indicates that the PSSMs created without using Physarum generate the worst predictions showing that the inclusion of the Physarum genome does increase the accuracy of the prediction; a finding that is expected due to the similarities between the organisms and their shared RNA editing . Interestingly , the results show that predictions using the Physarum protein alone outperform the predictions using either of the two PSSMs . Among the predictions that use only the Physarum protein the prediction accuracy increases as the BLOSUM matrices are tuned toward more closely related organisms . This result implies that the organisms are so similar that the inclusion of the genetic information of other organisms into the PSSMs actually decreases the accuracy of the editing site predictions . Since the prediction method relies on sequence homology we wanted to determine the influence of sequence similarity on the prediction quality . To this end we separated the fifteen genes into a more conserved and a less conserved group ( see Table S1 ) based on the nucleic acid conservation of the second codon position between the known Physarum and Didymium mRNA sequences . The prediction accuracy for all five methods in each of these two groups is shown in Figure 1 ( b ) and ( c ) . The overall trend in prediction accuracy is the same for the more highly conserved and the more diverged set of genes . However , although one might have expected that using the Physarum protein works better for genes where the two organisms have diverged less from each other , the improvement in prediction accuracy by using the Physarum protein sequences is actually bigger for the more diverged set of genes than for the more conserved set of genes . We rationalize this by arguing that genes with less conservation between Physarum and Didymium are generally under less evolutionary pressure and thus will also have diverged more between the Myxomycetes in general and the other organisms used to build the baseline PSSMs . Thus , including proteins from other organisms in the prediction hurts the prediction accuracy more for less conserved genes . Encouraged by the quality of the predictions on the already known Didymium genes , we proceeded to use the method to search for other genes in the Didymium mitochondrial genome and to predict the editing sites that are present in those genes . In order to do this , the genes first had to be located within the partial Didymium mitochondrial genome available to us . This was accomplished by scoring segments of the partial genome against the corresponding proteins from Physarum as described before [20] . We used only the Physarum sequence and the BLOSUM90 matrix since this approach performed best on the known genes as described above . Once the location of the genes were identified as the segments with the highest score , the editing sites were predicted . The results are shown in graphical form for each of the eight genes we identified in Figure 2 . The predicted mRNA sequences with C insertions indicated as upper case C's are given in Table S2 . We note that the predictions for nad2 only include part of the gene; a region at the 5′ end is not present as it is missing from the partial genomic DNA sequence available to us . The two genes that stand out by their very low number of editing sites are nad3 and rpS11 . Indeed , nad3 is already known to be unedited [24] . Our prediction resulted in a single editing site in nad3 toward the end of the gene . While this addition of a single predicted editing site was not expected , it is understandable since the prediction of editing sites becomes more challenging toward the ends of the gene . Six editing sites were found in rpS11; one was found near the 5′ end , three in close proximity of each other in the middle , and two were at the 3′ end . Because of the low number of editing sites and the striking pattern of the predicted editing sites we hypothesize that rpS11 is also unedited just like nad3 . The additional predicted editing sites at the end are easily understood based on the overall low prediction accuracy at the end of genes . Since the three predicted editing sites in the middle of the gene are close to each other they can also be a prediction artifact; omitting them would only change the protein sequence over the range of 5 amino acids . We verified that omitting the three editing sites would not create an in frame stop codon in the middle of the protein . Thus , is is plausible that the edited rpS11 mRNA could have been reverse transcribed and inserted into the genome of Didymium as it has been hypothesized for nad3 [24] . We note , however , that while nad3 also has a very much reduced number of editing sites in Physarum ( around one every nucleotides rather than the usual one every nucleotides ) , rpS11 shows the normal level of editing in Physarum . While the graphs presented in the preceeding section convey the successes of the various computational methods for predicting the editing sites in the genes of Didymium with known editing sites , there can be no similar comparison of the successes of the predictions in the new genes shown here as their exact editing sites are not known . However , sequence alignment of the new genes does show that of these genes , rpS4 and rpS11 fall into the less conserved group while cox3 , nad1 , nad2 , nad4 , and nad5 ( as well as nad3 ) all fall into the more conserved category . Using this information , an estimate about the success of the BLOSUM90 computational method can be made for the new genes based the known results of the BLOSUM90 prediction method for the sequenced genes . This estimate results in Figure S1 which show the expected number of correct sites , the expected number of sites one or two sites from the actual editing site , and the expected number of more wrongly predicted sites with the associated errors . A true assessment of the success of these predictions will of course have to wait until these RNAs are fully sequenced and their editing sites are known . As indicated in the introduction , one of the major questions to be resolved is how the RNA editing machinery knows which sites to edit . Previous studies [10] , [11] , [25] have looked for sequence patterns in Physarum alone . One property within the mitochondrial genome of Physarum is that editing sites have a strong preference to occur after a combination of a purine and a pyrimidine [10] , [11] . However , many editing sites do not follow this pattern and many purine-pyrimidines are not followed by an editing site . Thus , this pattern alone cannot explain the extremely reliable recognition of editing sites and the problem of editing site recognition remains unsolved . The absence of discernable sequence patterns among the Physarum editing sites might suggest that every site ( or small groups of them ) are recognized individually . Such mechanisms exist in the kinetoplastids in the form of guide RNAs [27] , [28] and in plant chloroplasts and mitochondria in the form of PPR proteins [6] , [7] , [29] . If every editing site is recognized individually , no sequence pattern will emerge when comparing the sequences surrounding all the editing sites in one organism consistent with previous studies in Physarum [10] , [11] , [25] . However , when comparing organisms at sequences surrounding their shared editing sites , sequence positions that play a role in site recognition are under increased evolutionary pressure and should thus show more conservation across species than sequence positions not involved in editing site recognition . Thus , instead of looking at one organism at a time , we here used the edited genes of two related organisms with insertional RNA editing , Physarum and Didymium , and examined the patterns of sequence conservation between the two organisms . We looked at the nucleotide identities at fixed positions relative to the editing site and investigated whether these nucleotides were conserved between the two organisms or not . In this analysis , we tried to identify positions relative to the editing site with statistically significantly increased degree of conservation from the background , which would indicate functional importance . We studied the sixteen genes for which the editing sites are known in both organisms as described in the Methods section . Comparing the complete mRNA sequences of the two organisms , we determined the overall degree of conservation for the first , second and third codon position . This yielded the background frequencies or the “expected” frequencies at the first , second , and third codon position . Table 1 presents these background frequencies for conservation between Physarum and Didymium . The degree of conservation in these genes is relatively high and may not leave enough room to be significantly increased . Thus , we also studied as another group the subset of the 8 less conserved among the 16 genes ( i . e . , the genes the background frequency at the 2nd codon position of which is less than 85% , see the Methods section ) . It can be seen in table 1 that the two groups share similarities in their background levels of conservation that are to be expected: the second codon position has the highest conservation , while the third codon position is the least conserved . However , there is a clear difference in the amount of conservation between the two groups as expected by construction of the less conserved group . In order to study the vicinity of the editing sites shared by the two organisms , we first identified those C insertional editing sites that are shared and unambiguous ( i . e . , at least in one of the two organisms the neighboring nucleotides are not Cs ) . Because of the variations of background frequencies among different codon positions , these editing sites were separated by codon position . Table 2 shows the number of these shared editing sites for each codon position . A previous study demonstrated that the DNA necessary for C insertion is contained within 9 or maybe 10 base pairs on either side of the editing site [18] . Thus , we first determined the conservation information of the flanking sequences within a window of 9 positions upstream and downstream of each of the shared editing sites . Then we examined the difference between the observed sequence conservation in the vicinity of the shared insertional editing sites ( at positions −9 to +9 relative to editing sites ) and the background conservation . Figure 3 shows the observed and the expected degree of conservation for positions −9 to +9 ( relative to the shared editing site ) for all genes separately for the shared editing sites at the first and third codon position ( we do not show the data for the second codon position because of the small number of these editing sites which results in very low statistical significance ) . Results for the less conserved group are similar to the results for all genes ( see Figure S2 ) . From these figures , we can see that both the observed frequency and the background frequency are position dependent with codon position being the dominant factor . The observed frequency is higher than the background frequency at some positions , while at other positions the observed frequency is lower . In order to see whether these variations are statistically significant , we calculated the probabilities for observing increased or decreased sequence conservation in the vicinity of the shared insertional editing sites based on the binomial distribution ( see details in the Methods section ) . Figure 4 shows those probabilities for positions −9 to +9 for all genes . No significant -values are obtained ( to take into account multiple testing , we use as the -value cut off ) , which implies that there are no statistically significant variations between the observed frequencies and the background frequencies . In spite of the larger room for increased conservation , the results for the less conserved group are similar to the results for all genes ( see Figure S3 ) . We also extended our study to positions that are further away from the editing site than 9 nucleotides . For these positions , the analysis is complicated by the fact that additional editing sites can occur between the position to be studied and the editing site of interest thereby mixing different codon positions at the same position relative to the editing site of interest [10] . We circumvent this problem by eliminating all primary editing sites from the analysis that have an additional editing site between the primary site and the position we are interested in . The disadvantage of this approach is that as one studies positions that are further and further away from the primary editing site , there are less and less sequences that contribute to the analysis and thus the statistical power decreases . In practice , we reached a limit of contributing sequences at a distance of nucleotides for editing sites at the third codon position and at a distance of nucleotides for editing sites at the first codon position . However , even for these distances no statistically significant increase of sequence conservation was found ( data not shown ) . This suggests that the information on editing site location is not contained within the sequence in the immediate vicinity of the editing site at least at the level of statistical significance set by our sample size . This leaves us with the conundrum that on the one hand Rhee et al . [18] demonstrate experimentally that only 9 nucleotides of DNA on either side of the editing site are required for editing and on the other hand our results suggest that there is no statistically significant pattern of conservation within 9 ( or even more ) base pairs on either side of the editing site . Thus , we propose several possible explanations for the discrepancy between Rhee et al . 's findings and our results . The first and second explanation lead us to consider how much increase in conservation for recognition sites of editing events we should see given the size of the mitochondrial genome of Physarum ( 62862 bp ) . Due to the extreme precision of RNA editing in Physarum , the recognition site of each editing event should be unique in the mitochondrial genome of Physarum . According to Rhee et al . , the 9 nucleotides immediately upstream DNA and 9–10 nucleotides immediately downstream DNA of the editing sites are necessary and sufficient for editing site recognition . If the actual information on the editing site position is stored within these nucleotides this implies that the pattern recognized within the set of 18–19 nucleotides should occur at the rate of at most 1/60000 in a random DNA sequence . Based on our calculations ( see Methods section ) , the lowest conservation for a set of 19 nucleotides that still allows specification of a site within the genome is 80 . 7% , i . e . , at least we should see 80 . 7% conservation in a 19 nucleotide region responsible for editing site recognition . To test whether a conservation of 80 . 7% or more would show up as a significant difference between the expected frequencies and the background frequencies at our sample size , we set 80 . 7% ( the lowest expected conservation ) as the “observed frequency” for positions −9 to +9 ( i . e . , we used 80 . 7% to replace the real observed frequencies ) . Then we calculated the -values for observing increased sequence conservation relative to the background frequencies in Table 1 . For putative motif positions at the third codon position in the vicinity of editing sites at the third codon position we found a highly significant ( compared to the cutoff of ) -value of . Thus , according to this analysis , we should have seen statistically significant variations between the actual observed frequencies and the background frequencies at our sample size even if the editing machinery does not recognize the same positions relative to the editing site at all of the sites . We thus conclude that the observed degree of conservation is significantly lower than what is to be expected when only the 9 nucleotides upstream and 10 nucleotides downstream of the editing sites contain the information for editing site recognition even if different sites use different combinations of the 19 nucleotides to specify the editing site location . These studies therefore suggest that the first and second of the hypotheses above can be ruled out . As another test of which aspect of sequences around editing sites could determine the editing position , we tested the specificity of sequences around editing sites . In practice , we started by looking only at sequences immediately downstream of editing sites and examined the uniqueness of these sequences in Physarum , that is , we tested for every sequence of nucleotides ( -mer ) downstream of an unambiguous C insertion site in the known transcriptome of Physarum if this -mer only occurs downstream of C insertional editing sites , but does not occur following non-edited sites of the sequences . This analysis is especially powerful since the full transcriptome has recently been determined by a high throughput sequencing experiment [25] thereby giving complete access to all editing and non-editing sites for this analysis that compares all editing sites to all non-editing sites in all transcripts . Since it is unknown if editing site recognition occurs at the DNA or RNA level , we tested the -mers in both the unedited sequences and the edited sequences . We asked which is the largest for which we can still find a -mer that occurs at least once immediately downstream of an unambiguous C insertion site and at least once in a position that is definitely not preceded by an editing site . Both on the RNA and on the DNA level the largest -mer we found was a -mer . Thus , we conclude that a mechanism that uses only the downstream sequence of an editing site to specify the editing event , even if it is a different mechanism for every editing site , must use at least nucleotides downstream of the editing site . Similarly , we found one -mer combination that is not unique for unambiguous C insertional editing sites when testing the unedited sequences and one -mer when testing the edited sequences when studying only sequences immediately upstream of the unambiguous editing sites . Given that Rhee et al . found that the 9 or maybe 10 nucleotides of DNA both downstream and upstream of the editing sites are responsible for the editing event , we also investigated the specificity of all the possible nucleotide combinations upstream and downstream of the unambiguous C insertional editing sites within 9 nucleotides on either side ( describing them as , where is the number of nucleotides upstream of the editing site and is the number of nucleotides downstream of the editing site with and ) . Since in this case the unambiguous C is inserted inside the motif , testing the uniqueness of these motifs is different between the unedited sequences and the edited sequences . It is the same as before for the unedited sequences . For the edited sequences , we asked if the motif without the inserted C occurs anywhere in the transcriptome ( in addition to the at least one occurence with the inserted C ) . We found possible combinations that are not unique for the unambiguous C insertional editing sites for both unedited sequences and edited sequences up to 9+5 , 8+7 , 7+8 and 6+9 nucleotide combinations . Therefore , the recognition region ( if it exists ) includes at least 9+6 , 8+8 , or 7+9 positions in agreement with Rhee et al . 's finding [18] that the whole 9+9 nucleotides are required for editing . It is important to note that since this particular analysis does not rely on the comparison of two organisms but uses only Physarum data it even in the case of hypothesis 3 ( that the mRNA itself templates the editing sites ) implies that the recognition has to involve at least the 9+6 , 8+8 , or 7+9 nucleotides surrounding an editing site . We would like to conclude by noting that while we did not find a non unique 9+9-mer which would rule out the “9+9” ( or any larger ) model ( the identity of the 9 nucleotides both downstream and upstream of the editing sites carry the information for the editing event ) , we would not have expected to find one on statistical grounds alone since the probability for an 18-mer to occur in the mitochondrion of Physarum is extremely small even if there is no biological reason ( uniqueness of the recognition sequence ) that prevents it from occurring . We want to emphasize again , that the conclusions in this section do not rely on a common mechanism that simultaneously recognizes all editing sites in one organism but apply even to mechanisms that recognize every editing site individually such as guide RNAs or site recognition proteins . Of course , all these considerations only exclude the information for the editing site positions to be stored within the identities of the 9+5 , 8+7 , 7+8 , or 6+9 nucleotides surrounding the editing site - it is still possible that the DNA in the immediate vicinity of the edited site carries a “marker” that is placed based on information elsewhere in the genome . For editing sites within the coding regions , a significant codon bias is known . It has been found that in the mitochondrial genome of Physarum , the third codon position has the largest number of C insertional editing sites , while the second codon position has the lowest number [9] , [10] , [25] , [30] . As shown in Table 2 the codon bias is also significant for the shared C insertional editing sites in both groups of genes . A previous study proposed an evolutionary model which explains this codon position bias [31] . The general idea of this model is the following . During the proliferation of Physarum , nucleotide mutations ( including substitutions , insertions , and deletions ) occur at random positions in the mitochondrial DNA sequence . In the case of random deletions , the offspring can not survive because of the incorrect protein sequence since the mutated DNA sequence is out of frame . However , the editing machinery sometimes may insert back nucleotides to the positions of deletions and preserve the correct reading frame . In this case , the offspring can survive and proliferate . This idea of random creation of new editing sites is also consistent with phylogenetic data [32] . The net effect of a nucleotide deletion followed by the creation of a new insertional editing site is that the original nucleotide will be replaced by a C . The genetic code is organized such that the third codon position is the most irrelevant for the identity of the amino acid while the second codon position is the most relevant . Therefore , the third codon position is the least sensitive to nucleotide changes to C generated in editing events while the second codon position is the most . Thus , random deletions at the third codon position will have the highest survival rate and the lowest for the second codon position . According to this model , the codon position bias in Physarum is mainly a consequence of random mutations with selection at the protein level [31] . This implies that genes that are under stronger selection should have a stronger codon position bias in their editing sites as well . Since for this study we have two organisms , we can directly determine the strength of selection on each gene from the sequence conservation . Thus , to test the theory proposed in [31] , we examined the relationship between the strength of the codon bias and the overall sequence conservation in both Physarum and Didymium . As described in the Methods section , the 16 genes were divided into several groups according to their overall sequence conservation at the second ( most conserved ) codon position between Physarum and Didymium . The detailed group information is shown in Table S1 ( since the conservation at the second position of the 16 genes ranges from 60% to 100% , we separated them into four groups by splitting the range from 60% to 100% into four intervals of equal length ) . We used the ratio of the number of third codon position editing sites and the number of second codon position editing sites as a measure of codon bias , and used the overall sequence conservation at the second codon position as a measure of the conservation within different genes . We examined all unambiguous C insertional editing sites in Physarum and Didymium , i . e . , shared editing sites as well as editing sites specific for either of the organisms . The solid black squares in Figure 5 illustrate the relationship between the codon bias and the conservation at the second codon position . In order to reduce statistical fluctuations , we also considered a grouping of the genes into only two groups by combining data of all genes with conservation between 60% and 80% at the second codon position into one group and the genes with conservation between 80% and 100% into the other group . Figure 5 shows that , whether the 16 known genes were separated into four groups or into two groups based on their conservation at the second codon position , genes with higher conservation at the second codon position have a higher ratio of . This difference is significant even when statistical errors within the ratios are taken into account . This demonstrates that genes under stronger selection ( or with higher conservation ) should have a stronger codon position bias in their editing sites; thus reinforcing the theory that codon bias is a consequence of evolutionary pressure on the protein sequence . In order to increase the statistical significance it would be beneficial to include more genes in the study . Given our work presented above , the seven newly predicted Didymium genes and nad3 are likely candidates to add to the study . The problem with this idea is that the predicted mRNA sequences most likely deviate from the ( unknown ) true mRNA sequences , which might affect the accuracy for both the overall sequence conservation and the codon bias . Since we have the predicted Didymium mRNA sequences for all the 16 genes for which the actual mRNA sequences are known , we can test how much the estimates of overall sequence conservation and the codon bias differ between the predicted sequences and the true sequences . To this end , we aligned the predicted Didymium mRNA sequences and the real Physarum mRNA sequences ( see Table S3 for accession numbers ) to obtain the conservation at the second codon position . Then we plotted the conservation data between the predicted Didymium mRNA and the real Physarum mRNA versus the conservation data between the real Didymium mRNA and the real Physarum mRNA . ( see Figure 6 ( a ) ) . We found , that the overall conservation at the second codon position for the predicted sequences ( predicted Didymium mRNA and real Physarum mRNA ) and the real sequences ( real Didymium mRNA and real Physarum mRNA ) are very close to each other except for possibly two genes – atp8 and atp9 – which are much shorter than the other genes . This implies that estimating the overall sequence conservation from the predicted Didymium mRNA sequences is a valid procedure since the difference in conservation by using the predicted sequences and the real sequences is small . In the same way , we compared the codon bias between the predicted sequences and the real sequences . We treated the predicted Didymium sequences in the same way as the real Didymium sequences before ( we examined all unambiguous C insertional editing sites and used four and two groups ) . As can be seen from Figure 6 ( b ) , the codon biases for the predicted sequences and the real sequences are equal within the error bars . This suggests that codon biases calculated from the predicted Didymium sequences can reasonably be used in lieu of exactly known codon biases . However , the agreement between predicted and true sequences is not as strong for the codon bias as it is for the conservation at the second codon position . Since the overall sequence conservation and the codon bias show only small deviations between the predicted sequences and the true sequences , we can add the seven newly predicted Didymium genes and nad3 to the codon bias analysis . In the same way as described for the 16 known genes , we analyzed the codon bias of these eight genes using the predicted Didymium sequences and real Physarum sequences . Since the determination of conservation at the second codon position from predicted sequences is more robust with respect to prediction errors than the determination of codon bias ( see Figure 6 ) we performed this analysis twice . First , we only used the ( known ) codon bias in Physarum for the eight predicted genes ( indicated in Figure 5 as 16 known genes + 8 genes in Physarum ) , thus only using the predicted Didymium mRNA sequences to determine the overall conservation for group division of each gene , but not using the codon bias in the predicted Didymium mRNA sequences which is less robust with respect to prediction errors than that for overall conservation . Second , we also included the predicted editing sites in Didymium for the eight additional genes in the analysis ( indicated in Figure 5 as 16 known genes + 8 genes in Physarum and Didymium ) . In this case , all unambiguous C insertional editing sites in Physarum and Didymium for all 24 genes are counted . Figure 5 illustrates the relationship between codon bias and the overall conservation for the 16 known genes ( already described above ) , 16 known genes + 8 genes with editing sites in Physarum and 16 known genes + 8 genes with editing sites in Physarum and Didymium with 2 and 4 groups , respectively . As can be seen from this figure , the strength of codon bias of the 24 genes ( including the known genes and predicted genes ) is not as strong as in the 16 known genes . However , given the reduced error bars the dependence of codon bias on selection pressure remains statistically significant . We have thus shown that more conserved genes have more significant codon bias in all unambiguous C insertional editing sites in Physarum and Didymium as suggested by the previous theory [31] .
The mitochondrial genomes of two related organisms with insertional RNA editing , Physarum polycephalum and Didymium iridis were studied . Sixteen genes and their mRNA sequences from the two organisms were included in this study: atp1 , apt6 , atp8 , atp9 , cox1 , cox2 , cytb , nad4L , nad6 , nad7 , rpL2 , rpL16 , rpS3 , rpS7 , rpS12 , and rpS19 . All the sequences were downloaded from GenBank; see Table S4 for accession numbers . For several of our studies the sixteen genes were divided into groups according to their overall sequence conservation at the second codon position between Physarum and Didymium , which was obtained by aligning the mRNA sequences of each gene between the two organisms . Table S1 indicates for each gene which group it was assigned to . The predicted editing sites were scored as either correct , one away , two away , or three or more sites away from the actual editing sites by comparison with the known mRNA sequences . We only scored C insertion sites , i . e . , we ignored predicted insertion sites in close vicinity of thymine , adenine , guanine , or dinucleotide insertion sites in the known mRNA sequences . Also recorded was the number of editing sites included in each prediction due to occasionally missed editing sites at the beginning or at the end of a gene . Omissions of editing sites at either end of a gene was caused by not having a significant number of bases either before or after the input basis sequence . Therefore , the missed editing sites in these instances were due to the lack of information input into the computational method which results in poor conservation of the protein sequence of the gene . Thus , missed editing sites at the beginning and end of a gene sequence were not scored . While these types of predictions were not scored , occasionally an editing site would be missed or added by the prediction in the interior of a gene . Missed or added interior editing sites most often occurred in threes which preserves the reading frame and is most likely to conserve the protein sequence; interior missed or added editing sites were scored as three or more sites from the actual site . For each gene , four sequences ( Physarum-DNA , Physarum-mRNA , Didymium-DNA , and Didymium-mRNA ) were aligned in Clustal X [33] . From these alignments , the C insertional editing sites that are unambiguous ( i . e . , at least in one of the two organisms the neighboring nucleotides are not Cs ) and shared between Physarum and Didymium were identified . The flanking sequences within the window of 9 positions upstream and downstream of each of these editing sites in both organisms were investigated . Then these flanking sequences were turned into patterns of “0” and “1” where “1” means that the two organisms have the same base at the same relative positive and “0” means that they do not . The shared and unambiguous editing sites were separated by codon position . Comparing the mRNA sequences of the two organisms , we obtained the overall conservation information for the first , second and third codon position by counting the “1”s in each codon position across the whole genes . This yielded the background frequencies or the expected frequencies of “1”s at the first , second , and third codon position . In order to see whether variations from background were statistically significant , the probabilities for observing increased ( or decreased ) sequence conservation in the vicinity of the shared insertional editing sites were calculated . These probabilities were calculated based on the binomial distribution: The background frequency or the expected frequency of “1”s at the codon position is . The total number of shared editing sites at the 'th codon position is . For a specific position in the vicinity of the shared editing site , we can easily identify its codon position ( see Table S5 ) and the actual number of “1”s in these samples . Thus , the observed frequency of “1” at this specific position is . Therefore , the probability of the observed increased sequence conservation is:If the observed frequency of “1” is less than the “expected” frequency , the -value was calculated analogously as the probability of observing the decreased sequence conservation . In order to determine the codon bias in insertional RNA editing , the number of third codon editing sites and the number of second codon editing sites were counted . The codon bias was then quantified as their ratio . If we assume that the error for is just counting error given by the square root of ( i . e . , ) , the statistical error of is In order to know how much increase in conservation for recognition sites of editing events we should see in the mitochondrial genome of Physarum ( 62862 bp , NC_002508 ) if the region containing the editing site information is limited to the 18–19 nucleotides surrounding an editing site identified in Rhee et al . [18] , we calculated the lowest conservation for a set of 19 nucleotides that allows specification of a site within the genome . Since the effect of GC content in the Physarum mitochondrial genome is strong ( the GC content is approximately 25% [34] ) , we considered the frequency that a set of 19 nucleotides occurs in a random DNA sequence with the same length ( 62862 bp ) as well as the same GC content as the mitochondrial genome of Physarum . In such a sequence , the probability of two nucleotides being equal by chance is . Therefore , the probability for two sets of 19 nucleotides in the sequence described above being the same is ( i . e . , the occurring rate for such a combination is ) , which is much lower than one in Physarum's mitochondrial genome . Thus , we relax the constraints on a set of 19 nucleotides that will still specify the editing site ( decrease the number of nucleotides that are fixed ) as long as the frequency of the relaxed constraints is not ( much ) higher than . We found that the occurring rate of a motif in which only 9 of the 19 nucleotides are fixed ( and the other nucleotides could occur randomly ) is , which is close to one per Physarum mitochondrial genome . We thus conclude that to uniquely specify a site by a 19 nucleotide motif , at least 9 of these nucleotides have to be fixed while the others can be variable . We do not know which 9 ( or more ) of the 19 nucleotides are fixed for a given editing site , but we can calculate the average conservation generated by these fixed nucleotides . This average conservation for a set of 19 nucleotides is calculated as following: The conservation for each of the 9 fixed nucleotides is 100% while it is at least 63 . 3% ( the lowest conservation between Physarum and Didymium we obtained , see Table 1 ) for the 10 random nucleotides . Thus , the average conservation is at least . | RNA is an important biomolecule that is deeply involved in all aspects of molecular biology , such as protein production , gene regulation , and viral replication . However , many significant aspects such as the mechanism of RNA editing are not well understood . RNA editing is the process in which an organism's RNA is modified through the insertion , deletion , or substitution of single or short stretches of nucleotides . The slime mold Physarum polycephalum is a model organism for the study of RNA editing; however , hardly anything is known about its editing machinery . We show that the combination of two organisms ( Physarum polycephalum and Didymium iridis ) can provide a better understanding of insertional RNA editing than one organism alone . We predict several new edited genes in Didymium . By comparing the sequences of the two organisms in the vicinity of the editing sites we establish minimal requirements for the location of the information by which these editing sites are recognized . Lastly , we directly verify a theory for one of the most striking features of the editing sites , namely their codon bias . | [
"Abstract",
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] | [
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] | 2012 | Comparison of Insertional RNA Editing in Myxomycetes |
Young age is a risk factor for prolonged colonization by common pathogens residing in their upper respiratory tract ( URT ) . Why children present with more persistent colonization is unknown and there is relatively little insight into the host-pathogen interactions that contribute to persistent colonization . To identify factors permissive for persistent colonization during infancy , we utilized an infant mouse model of Streptococcus pneumoniae colonization in which clearance from the mucosal surface of the URT requires many weeks to months . Loss of a single bacterial factor , the pore-forming toxin pneumolysin ( Ply ) , and loss of a single host factor , IL-1α , led to more persistent colonization . Exogenous administration of Ply promoted IL-1 responses and clearance , and intranasal treatment with IL-1α was sufficient to reduce colonization density . Major factors known to affect the duration of natural colonization include host age and pneumococcal capsular serotype . qRT-PCR analysis of the uninfected URT mucosa showed reduced baseline expression of genes involved in IL-1 signaling in infant compared to adult mice . In line with this observation , IL-1 signaling was important in initiating clearance in adult mice but had no effect on early colonization of infant mice . In contrast to the effect of age , isogenic constructs of different capsular serotype showed differences in colonization persistence but induced similar IL-1 responses . Altogether , this work underscores the importance of toxin-induced IL-1α responses in determining the outcome of colonization , clearance versus persistence . Our findings about IL-1 signaling as a function of host age may provide an explanation for the increased susceptibility and more prolonged colonization during early childhood .
Respiratory tract infections remain a leading cause of childhood morbidity and mortality worldwide . In 2016 , pneumonia accounted for 16% of all deaths in children younger than five years of age [1] . Epidemiological studies have demonstrated young age to be a risk factor for colonization of common respiratory tract pathogens , including Streptococcus pneumoniae ( pneumococcus ) [2 , 3] . Carriage of respiratory pathogens on the mucosal surface of the upper respiratory tract ( URT ) is a necessary first step in the pathogenesis of all invasive pneumococcal diseases [4] . Clinically , 25–65% of healthy children are colonized with S . pneumoniae in the URT , in contrast to <10% of adults [5 , 6] . Furthermore , pneumococcal colonization of the URT is prolonged in young children compared to adults [7–9] . This increased persistence of S . pneumoniae in young children is suggestive of a more commensal relationship between bacteria and host . The decline in pneumococcal carriage beyond childhood correlates with a general decrease in the complexity and density of the URT flora with increasing age [10] . Why young children present with more prolonged colonization by bacteria residing on the mucosal surfaces of the URT , like S . pneumoniae , is unknown . Most pneumococci are encapsulated with one of >95 antigenically distinct capsular polysaccharides ( CPS ) , the determinant of serotype . In animal models of URT colonization , expression of CPS , which inhibits phagocytic clearance , is necessary for colonization duration to last more than a few days [11] . A recent bacterial GWAS study from a large infant-mother cohort found capsular serotype to be the major determinant of carriage duration [12] . Other studies in adult mouse models of pneumococcal colonization have shown that expression of its other major virulence factor , the toxin pneumolysin ( Ply ) , is inversely correlated with carriage duration [13 , 14] . Ply is a cytolysin that inserts into cholesterol-containing membranes where it oligomerizes to form large pores , however it is an unusual toxin as it lacks an N-terminal secretion signal sequence or other secretion mechanism [15 , 16] . The release of Ply , therefore , requires bacterial lysis . For example , following uptake and degradation by professional phagocytes , Ply is released into the phagosome where , by forming pores into the membrane of the phagosome , it allows microbial products to access the host cytosol . Cytosolic access activates inflammatory pathways that signal the recruitment and activation of additional phagocytes that eventually promote mucosal clearance [14 , 17–19] . These effects of Ply also eventually result in cell lysis and death . Although many studies have focused on the molecular mechanisms that drive initial host responses to acute infection , there is little knowledge of host and bacterial factors permissive for persistent colonization , particularly during infancy . This study was undertaken to elucidate host and bacterial molecular mechanisms facilitating persistent colonization using S . pneumoniae as a model pathogen . Using the variables of host age , capsule serotype and pneumolysin expression , we assessed how pneumococcal colonization is either cleared or persists . We demonstrate that Ply-mediated mucosal IL-1 signaling via the release of IL-1α is critical for clearance of otherwise persistent colonization and that infants are deficient in IL-1 signaling compared to adults . Our observations of reduced IL-1 signaling early in life provides mechanistic insight into the altered dynamics of pneumococcal colonization with age .
All animal studies were performed in compliance with the federal regulations set forth in the Animal Welfare Act ( AWA ) , the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health , and the guidelines of the New York University School of Medicine and the University of Pennsylvania Institutional Animal Use and Care Committee . All protocols used in this study were approved by the Institutional Animal Care and Use Committee at the New York University School of Medicine ( protocol #160622 and #161219 ) and the University of Pennsylvania ( protocol #804928 ) . S . pneumoniae isolates serotypes 4 and 23F and previously described isogenic Ply mutants were used throughout the study [18 , 20 , 21] . In the serotype 4 background , these mutants included an unmarked , inframe deletion of ply , a point mutant expressing PlyW433F , which is deficient at oligomerization to form pores after membrane insertion , and a corrected mutant where the intact gene was restored ( ply+ ) . In the serotype 23F background , we used a mutant expressing PlyTL→AA , which fails to bind to cholesterol and insert into membranes [17 , 18] . The genotype of mutants was confirmed by sequence analysis of PCR products . The phenotype of all strains was confirmed using a previously described horse erythrocyte lysis assay [17 , 18] . The hemolytic activity of Ply-deficient and point mutants was less than 1% of WT levels . The serotype 4 and 23F parent strains showed no differences from one another in hemolytic activity or Ply protein levels as measured in Western blots . Type 4 and 23F isogenic capsule-switch strains 23F4 ( 23F genetic background expressing the serotype 4 capsule ) and 423F ( 4 genetic background expressing the serotype 23F capsule ) were used to study serotype-dependent effects and were previously described [22] . All pneumococcal strains were grown in tryptic soy ( TS ) broth ( BD ) at 37°C to an optical density of 1 . 0 at 620 nm ( OD620 ) . Alternatively , pneumococci were incubated on TS agar plates supplemented with 100 μl of catalase ( 30 , 000 U/ml; Worthington Biomedical ) and streptomycin ( 200μg/ml ) at 37° in 5% CO2 overnight . Wild-type C57BL/6J mice and congenic knockout mice ( muMT-/- , Tlr2-/- , Nod2-/- , Ifnar-/- , Ccr2-/- , Il1r-/- , Nlrp3-/- ) were obtained from Jackson Laboratories ( Bar Harbor , ME ) , and bred and maintained in a conventional animal facility . The Il1a-/- and Il1b-/- mice were generously provided by Dr . Yoichiro Iwakura at the Tokyo University of Science [23] . Pups were housed with a dam until weaned at the age of 3 . 5 weeks . During colonization , all mice appeared healthy and demonstrated normal weight gain similar to uninfected controls . Pups at day 4 of life were infected with 103−104 CFU of S . pneumoniae in 3 μl PBS by intranasal ( i . n . ) instillation divided over both nares without the use of anesthesia . At the time points indicated following challenge , mice were euthanized by CO2 asphyxiation followed by cardiac puncture . The trachea was lavaged with 200 μl sterile PBS collected from the nares to determine URT colonization density of S . pneumoniae . A second URT lavage with 600 μL RLT lysis buffer ( Qiagen ) + 1% β-mercaptoethanol was performed to obtain host RNA from the URT epithelia for gene expression analyses . Uninfected infants , serotype 23F wild-type and ply- mutant infected pups ( aged 1 week ) were euthanized 3 days post-challenge and a URT lavage with RLT lysis buffer was obtained . RNA was extracted according to manufacturer’s directions ( RNeasy kit , Qiagen ) and five replicates per age group were subjected to RNA sequencing ( RNA-seq ) using Hi-seq and the raw fastq reads were aligned to mm10 mouse reference genome using STAR aligner [24] . Fastq Screen was used to check for any contaminations in the samples and Picard RNA-seqMetrics was used to obtain the metrics of all aligned RNA-Seq reads . featureCounts [25] was used to quantify the gene expression levels . All alignments and read metrics are summarized in the supplementary data . The raw gene counts data were used for further differential expression analysis . To identify the differentially expressed genes ( DEGs ) , DESeq2 R package [26] was used and results were subsequently analyzed using the online annotation tool DAVID Bioinformatics Resources [27] . The resulting genes with FDR < 0 . 05 were considered significant . Heatmaps were generated using pheatmap R package . RNAseq data are made available in the GEO repository under project accession number GSE116604 . Anti-pneumococcal IgG titers were assessed in serum from uninfected and infected mice using whole bacterial cells as the capture antigen , as described previously with some adjustments [28] . Type 23F strain was grown to an OD620 of 1 and washed with PBS . Pneumococcal cells were diluted to final OD620 of 0 . 1 in coating buffer ( 0 . 015 M Na2CO3 , 0 . 035 M NaHCO3 ) . Microtiter plates ( 2HB , Immunolon ) were coated with 100 μL suspension/well at 4°C overnight . The next day , the plates were blocked with 1% bovine serum albumin ( BSA ) in PBS at room temperature for 1 hour , after which the plates were incubated with serial serum dilutions in PBS at 4°C overnight . Peroxidase-conjugated goat anti-mouse IgG ( Jackson Immuno Research Laboratories ) was applied and plates were incubated at 37°C for 1 . 5h . Between incubation steps , plates were washed three times with 0 . 05% Brij-35 ( Thermo Fisher Scientific ) in PBS . Plates were developed using 100 μL/well substrate o-phenylenediamine dihydroxychloride ( OPD , Thermo Fisher Scientific , 1 tablet in 7 . 5 ml H2O with 7 . 5 μL 30% H2O2 ) and incubated at room temperature for 30 minutes in the dark . Reactions were stopped with 50 μl/well of 2M H2SO4 and the absorbance was measured at 492 nm . Serum IgG titers were determined by calculating the dilution at which the absorbance was equal to an OD492 of 0 . 1 . RNA extraction ( RNeasy , Qiagen ) from nasal RLT lavages and subsequent cDNA generation using High Capacity cDNA Reverse Transcriptase Kit ( Applied Biosystems , Thermo Fisher Scientific ) were performed as per manufacturer’s instructions . Reaction samples contained ~10ng cDNA and 0 . 5 μM primers in Power SYBR® Green PCR Master Mix ( Applied Biosystems , Thermo Fisher Scientific ) and samples were tested in duplicate . qRT-PCR reactions were run in a 384 well plate ( Bio-Rad ) using CFX384™ Real-Time System ( Bio-Rad ) . Expression of Gapdh was an internal control and fold change in gene expression was quantified according to the ΔΔCt method [29] . The primer sequences used in this study are indicated in S1 Table . As previously described , recombinant pneumolysin ( PLY ) and pneumolysoid ( PdB ) , bearing the PlyW433F point mutation , were expressed in E . coli , after which bacterial cells were lysed using sonication [17 , 30] . The His-tagged proteins from cell suspensions were purified on a HisTrap column ( GE Healthcare ) by FPLC using Äkta Start ( GE Healthcare ) using binding buffer ( 20 mM NaH2PO4 , 500 mM NaCl , 20 mM Imidazole , pH 7 . 4 ) and elution buffer ( 20 mM NaH2PO4 , 500 mM NaCl , 500 mM Imidazole , pH 7 . 4 ) . Cell lysates were loaded on the column after which washing was performed to eliminate binding by non-specific proteins . His-tagged protein was recovered from the column by running a 70–100% gradient of elution buffer over the column after which the protein fractions were collected . Protein desalting was done using Amicon® Ultra-15 30 Kb centrifuge filters ( Millipore ) . Protein concentration was assessed by Bradford protein assay ( Bio-rad ) and hemolytic activity was confirmed by horse erythrocyte lysis assay . Pups infected with the serotype 4 ply- mutant at age 4 days received a first dose of PLY or PdB 1 day after challenge . Infants were treated with 100 ng protein/dose in PBS given by i . n . administration and control pups received PBS alone . Pups received daily treatment for 4 consecutive days and 5 days post-challenge pups were euthanized to determine pneumococcal colonization density and gene expression . Recombinant IL-1α ( Peprotech ) and IL-1β ( eBioscience ) protein was administered daily via i . n . treatment to 4-day old pups infected with the serotype 4 ply- mutant strain from day 1 through 3 post-challenge . Pups received 10 or 100 ng protein in 3 μl PBS , whereas PBS alone was administered to control animals . At day 4 post-challenge , infant mice were euthanized to obtain URT lavages to assess colonization density and for gene expression analyses . All statistical analyses were performed using GraphPad Prism 7 . 0 ( GraphPad Software Inc . , SanDiego , CA ) . Colonization density values were transformed into logarithmic values . Unless otherwise specified , differences in colonization and gene expression were determined using t-test or one-way Anova with Sidak’s multiple comparison test .
Infant mice have an increased density and duration of pneumococcal carriage compared to adult mice , recapitulating the effect of age during natural colonization [31] . To determine whether loss of pneumolysin ( ply- ) prolongs pneumococcal colonization in infant mice , pups were challenged i . n . at day 4 of age with a serotype 23F isolate or ply-deficient mutant . Clearance of the wild-type strain required approximately 9 weeks by which time the pups had become adults ( Fig 1A ) . In contrast , at this time point colonization of the ply- strain remained at >104 CFU/ml . To determine whether the effect of pneumolysin on colonization was independent of serotype , we infected pups i . n . at day 4 of age with a serotype 4 isolate or ply-deficient mutant . Although the wild-type serotype 4 isolate was cleared earlier than the wild-type 23F strain , at approximately 6 weeks post-challenge , clearance of the serotype 4 ply-deficient strain was significantly delayed compared to the wild-type serotype 4 strain ( Fig 1B ) . Correction of the ply deletion ( ply+ ) restored clearance to wild-type levels confirming the contribution of pneumolysin in attenuating colonization duration ( Fig 1C ) . Together these results indicate that expression of pneumolysin significantly decreases duration of pneumococcal colonization . At 6 weeks post-challenge , most mice remained colonized with the non-hemolytic point mutant ( W433F ) , suggesting that the pore forming activity of pneumolysin contributes to clearance of pneumococcus ( Fig 1C ) . The requirement of toxin-host cell interaction was further supported by persistent colonization of the plyTL→AA mutant , which expresses a toxin deficient in a prior step , attachment to cholesterol , in the serotype 23F background ( Fig 1D ) . Additionally , i . n . dosing of recombinant pneumolysin ( PLY ) , but not the toxoid containing the W433F modification ( PdB ) , accelerated clearance of the ply-deficient mutant ( Fig 1E ) . We concluded that pneumolysin was both necessary and sufficient for pneumococcal clearance in infant mice . Clearance of pneumococcal colonization is generally thought to require adaptive immune responses consisting of specific immunoglobulin or TH17 immunity [32–35] . Colonization increased levels of pneumococcal-specific IgG , however these did not differ between wild-type and ply-deficient mutant colonization ( Fig 2A ) . Moreover , mice deficient in the generation of specific antibody ( muMT-/- ) cleared colonization of wild-type and ply- strain similar to C57BL6 wild-type mice ( Fig 2B ) . Il17a expression was increased during colonization , but expression did not differ between WT and the ply-deficient mutant at day 7 and 21 post-challenge ( Fig 2C ) . These observations suggested that the increased persistence in infants may largely depend on innate immune responses . To more broadly explore how pneumolysin mediates clearance of otherwise persistent colonization , we performed an RNA-Seq screen on RNA isolated from URT lavages of infant mice infected with the wild-type or ply- deficient mutant at 21 days post-challenge , the time point at which clearance of the serotype 23F isolate is initiated . Large numbers of host genes were significantly affected by pneumolysin expression . For the 100 most differentially expressed genes ( Fig 2D ) , pneumococcal colonization without pneumolysin resembled mock-infected controls indicating pneumolysin affects critical host responses that promote clearance . We then investigated the role of innate immune signaling through TLR2 , NOD2 , IFNAR and IL-1R . These sensors of pneumococcal PAMPs or cytokines are expressed at significantly higher levels in wild-type versus mock infected mice in the RNA-Seq analyses and were previously shown to contribute to pneumococcal clearance in adult mouse models [13 , 14 , 19 , 36] . Colonization of the wild-type and ply- mutant was assessed at 9 weeks post-challenge in wild-type versus knockout mice ( Fig 3A–3C and 3E ) . Only for Il1r-/- pups colonization of the wild-type strain persisted and was indistinguishable from the ply-mutant . IL-1α and IL-1β are two cytokines released under different circumstances that exert identical biological effects downstream of the IL-1 receptor binding [37] . Previous in vitro studies showed that pneumolysin promotes release of both IL-1α and IL-1β by S . pneumoniae infected macrophages [19 , 38] . Daily i . n . dosing of recombinant IL-1α or IL-1β accelerated clearance of ply- colonized pups ( Fig 3F and 3G ) . Quantitative RT-PCR ( qRT-PCR ) of RNA isolated from URT lavages showed that pneumococcal colonization minimally upregulated Il1a expression , whereas the difference in Il1a expression between wild-type and ply- strains did not reach significance ( Fig 3H ) . This was not unexpected , since unlike IL1β , the damage associated molecular pattern ( DAMP ) IL-1α is constitutively expressed in epithelial , endothelial and hematopoietic cells in the airway epithelium , although its transcription can be upregulated by strong inflammatory stimuli [39 , 40] In contrast , colonization of the wild-type , but not the ply-mutant , significantly increased expression of Il1b in the URT ( Fig 3I ) . Although IL-1α levels in URT lavages were detectable by ELISA , there was no detectable difference between infected and uninfected pups . For IL-1β , its levels in the highly diluted URT lavages were below the level of detection in all treatment groups despite using a sensitive ELISA . IL-1β is generated from pro-IL-1β as a result of caspase 1 activation by the inflammasome . In vitro and in vivo models using S . pneumoniae or pneumolysin support a role of the NLRP3 inflammasome in this process [41–44] . However , persistence of the wild-type strain was unaffected in Nlrp3-/- pups ( Fig 3J ) , suggesting a role for another inflammasome or non-canonical pathway for IL-1β processing or redundant role for IL-1β [37] . To determine the roles of individual IL-1 cytokines in clearance , we infected IL-1α and IL-1β deficient pups with the wild-type S . pneumoniae or the ply-deficient mutant ( Fig 3K ) . Loss of IL-1α , but not IL-1β , resulted in impaired clearance of the wild-type strain . Colonization density at 9 weeks post-challenge of the wild-type strain was slightly less ( <1 log ) compared to its ply- mutant in Il1a-/- infant mice ( Fig 3J ) , suggesting that factors other than IL-1α play a minor role in Ply-mediated clearance . We concluded that IL-1 signaling is necessary and sufficient to prevent persistent colonization of wild-type pneumococci in infant mice , with a dominant role for IL-1α . The RNA-Seq screen on RNA isolated from infant URT lavages at 21 days post-challenge confirmed the importance of IL-1 signaling in clearance of persistent colonization . Pathway analyses demonstrated increased expression of gene cluster Signal Transduction Through IL-1 Receptor following colonization with S . pneumoniae , while these gene transcripts were less stimulated in absence of pneumolysin ( Fig 4A ) . This was confirmed by qRT-PCR showing decreased Ccl2 and IL1rn expression during ply- compared to wild-type colonization at 21 days ( D21 ) post-challenge ( Fig 4B and 4C ) . Furthermore , the RNA-Seq data revealed two major pathway upregulated during colonization potentially contributing to clearance of pneumococcal colonization , Chemokine-mediated signaling pathway and Phagocytosis ( Fig 4D and 4E ) . These pathways were also less activated during colonization by the ply- deficient strain . Clearance of pneumococcal colonization requires chemokine-dependent recruitment of professional phagocytes into the lumen of the URT and the expression of multiple chemokines were upregulated by colonization [19 , 34] . There was , however , no non-redundant role for CCR2 in clearance in infant mice , although this chemokine receptor was previously shown to be important in clearance in adult mice ( Fig 3D ) [14] . By comparing transcript levels using qRT-PCR on RNA isolated from URT lavages , we confirmed that representative genes revealed in these pathways analyses involved in phagocyte recruitment ( chemokine Cxcl2 ) , activation ( leukocyte integrin CD11b ) , activity ( nitric oxide synthase , Nos2 ) , and regulation ( Fc gamma receptor , FcγR3 ) were all increased in expression during wild-type compared to ply- colonization ( Fig 4F–4I ) . Additionally , i . n . administration of recombinant IL-1α and IL-1β were both sufficient to enhance transcription of FcγR3 ( Fig 4J ) . As was the case for ply- infection , transcription of FcγR3 following wild-type colonization was also impaired in Il1r-/- mice ( Fig 4K ) . These observations were consistent with a clearance mechanism dependent on professional phagocytes that requires IL-1 signaling . Intranasal administration of recombinant Ply upregulated expression of Il1b , but not Il1a , ( Fig 5A and 5B ) and markers of phagocyte recruitment or activity , including Cxcl2 , Nos2 , Fcγr3 ( Fig 5C–5F ) . These increases in gene expression were not observed for toxoid PdB , deficient in pore-formation . These findings confirm the critical role of pneumolysin cytotoxicity in inducing IL-1 responses that promote clearance of otherwise persistent pneumococcal colonization . In Fig 1 , we showed differences in the duration of colonization for two isolates differing in CPS ( serotypes 4 and 23F ) ( Relevant data juxtaposed in Fig 6A ) . To determine whether this difference was due to IL-1 signaling , we compared the IL-1 stimulating capacity of the two isolates . We found that serotype 4 and 23F isolates induced similar expression of Il1a , Il1b , and IL-1 signaling related transcripts at 7 and 21 days post-challenge ( Fig 6B and 6C , and S2 Fig ) , a result that could be due to similar levels of pneumolysin expression and hemolytic activity in these two isolates . To determine whether CPS type or bacterial genetic background is the determinant of isolate-specific persistence , we made use of capsule-switch strains of these two isolates [30 , 45] . We infected pups at day 4 of life with the isogenic strains 23F4 ( 23F isolate expressing 4 CPS ) and 423F ( 4 isolate expressing the 23F CPS ) . The capsule-switch strains colonized equivalently during early infection ( Fig 6D ) . However , at 6 weeks post-challenge , the 423F strain persisted whereas the 23F4 strain was cleared ( Fig 6E ) , demonstrating that strains carrying the 23F CPS are more persistent , regardless of genetic background . Thus , serotype-dependent differences in clearance appear to act through processes downstream or independent of IL-1 signaling . Given the importance of IL-1 in clearance of otherwise persistent colonization , we questioned whether differences in IL-1 signaling underlie the age-dependent susceptibility to S . pneumoniae colonization . We previously showed that Il1r-/- adult mice have reduced numbers of neutrophils during early colonization , fewer macrophages later in carriage , and prolonged bacterial colonization [19] . We used qRT-PCR to transcriptionally profile the IL-1 signaling pathway in the URT of uninfected infant ( 1 week of age ) and adult ( 8 weeks of age ) mice . As hypothesized , qRT-PCR confirmed significantly decreased expression of multiple IL-1-related signaling transcripts in uninfected infant compared to adult mice ( Fig 7A–7J ) . Although colonization increased its expression , the level of Il1b expression in colonized infant mice only reached that of uncolonized adult mice ( Fig 7K ) . In adult mice with increased IL-1 responses at baseline , colonization of Il1r-/- mice at 3 days post-challenge resulted in impaired initial clearance , consistent with results of prior studies showing the importance of IL-1 signaling in adults at 14 days post-inoculation [19] ( Fig 7L ) . Loss of IL-1α , but not IL-1β , led to impaired initial clearance in adult mice ( Fig 7M ) . Thus , the importance of IL-1 signaling , and in particular , the role of IL-1α could account for why clearance in infancy is delayed until reaching adulthood . Accordingly , for infant mice with dampened IL-1 signaling at baseline there was no effect of the absence of the IL-1R at 3 days post-challenge ( Fig 7N ) . As shown in Fig 3 , stimulation of robust responses with recombinant IL-1 cytokines was sufficient to reduce colonization density in infants during early colonization , suggesting the defect in infants is not due to an inability to respond to IL-1 cytokines ( Fig 3E and 3F ) . Together these results support that diminished IL-1 signaling during infancy enhances susceptibility to S . pneumoniae colonization .
Although epidemiological studies have shown that carriage of S . pneumoniae is significantly prolonged in young children , indicative of a commensal lifestyle , the mechanisms underlying pneumococcal persistence remain unknown . Here we showed that IL-1 signaling was required for efficient clearance but is relatively deficient in infants compared to adults . Prolonged colonization of S . pneumoniae was shown to be mediated by either loss of its sole toxin , pneumolysin , or through repression of the IL-1 pathway . While studies in vitro have associated pneumolysin expression with induction of IL-1 responses , our results provide in vivo evidence that pneumolysin drives IL-1 signaling through IL-1α to promote clearance of S . pneumoniae in the URT . Consequently , absence of pneumolysin or loss of the IL-1 signaling pathway , either by genetic ablation of the IL-1 receptor , IL-1α or young age , was sufficient to promote persistent pneumococcal colonization of the URT . Prompt clearance , therefore , does not appear to be initiated until IL-1 responses mature ( >21 days of age ) and is dependent on the presence of pneumolysin . Several aspects of the effect of pneumolysin merit further comment . Pneumococcal colonization with the Ply point mutant deficient in pore-formation showed a clearance defect . However , a greater effect was observed for the Ply mutant unable to bind membrane cholesterol , an earlier step in its activity . Apparently , binding of Ply to membrane cholesterol also contributes to perturbation of cell homeostasis as proposed from in vitro studies and this impacts toxin-mediated clearance [46] . Without the toxin , there appears to be little stimulation of host responses that drive eventual clearance . Our findings are consistent with prior reports that the gradual process of Ply-mediated clearance requires a mild sustained phagocyte influx for clearance [19 , 34] . This raises the question of why the organism expresses a toxin that precipitates its own clearance . Although the expression of Ply may be disadvantageous for S . pneumoniae colonization duration in the current host , inflammation induced by the toxin is necessary for pneumococcal shedding at levels sufficient for transmission to a new , susceptible host [18] . Some naturally circulating pneumococcal strains lack ply or express low or non-cytolytic Ply variants [47 , 48] . These might have a deficit in transmission , but an advantage in carriage duration , although natural colonization dynamics of these strains have not yet been assessed . In Fig 3K we show that in absence of IL-1α the wild-type strain colonizes at lower levels than the ply- mutant , indeed suggesting immune mechanisms other than IL-1 also contribute to clearance . A caveat of this study is that we were unable to measure colonization-induced differences in IL-1 protein in the URT , perhaps because of the mild , gradual nature of the inflammatory process requiring many weeks to months for clearance to complete . Our colonization model contrasts with more dramatic infections where such events transpire over days . Pulse dosing with recombinant Ply , however , was sufficient to facilitate more rapid clearance . In vitro studies have reported that during pneumococcal infection Ply can regulate IL-1 responses by inducing rapid cell death through necroptotic or pyroptotic pathways due to disruption of the cell membrane [49–51] . Recently , pore-forming toxins including Ply were found to trigger necroptosis , the major cell death pathway in respiratory epithelial cells , in mice and non-human primates during bacterial lung infection [50] . In in vitro macrophage models , Ply pore-formation also causes a passive release of alarmin IL-1α following rapid cell death [38 , 52] . Our RNA-seq analyses show more functional clustering of cell death pathways , including necroptosis and apoptosis , following pneumococcal colonization . The IL-1α precursor protein , which is constitutively expressed in many cell types at the mucosal barrier , including both epithelial and myeloid lineages [38–40 , 52 , 53] , does not require cleavage for binding to the IL-1 receptor [39 , 54] , thus Ply-mediated passive release of IL-1α precursor could directly activate IL-1 signaling . In contrast , IL-1β precursor expression is induced in response to TLR stimulation , TNFα , and IL-1 itself , and requires active processing in order to bind the IL-1 receptor [37] . Canonical IL-1β processing and release is mediated by caspase 1 following activation of an inflammasome [37 , 55 , 56] . In contrast , cell death induced by bacterial virulence factors that result in the release of IL-1α protein does not require the inflammasome but may depend on caspase-11 [57] . Despite the extensive evidence of Ply and necroptosis in activating the NLRP3 inflammasome , we observed that Ply-mediated clearance of pneumococcal colonization was NLRP3 independent [42–44 , 51 , 58] . This result , along with the lack of a contribution of TLR2 upstream of IL-1β is consistent with a dominant role for IL-1α , which does not require increased expression or the inflammasome for its activity [19] . Similar to IL-1β , signaling downstream of cytosolic sensing ( via NOD2 or Type I Interferons ) affects clearance in adult mice but showed no contribution in infant mice , which exhibit more persistent colonization [14 , 36] . Ply-dependent cytosolic sensing may only occur following uptake by professional phagocytes and , as noted above , the recruitment of these is attenuated in infant compared to adult mice [17 , 31] . By dosing with recombinant pneumolysin the lack of a secretion mechanism for the toxin would have been bypassed potentially allowing for direct effects on non-professional phagocytes , including epithelial cells . Our study raises the question of how signaling downstream of IL-1α affects carriage . IL-1-dependent activation of chemokines from neighboring nonhematopoietic cells or tissue-resident macrophages triggers the recruitment and activation of inflammatory hematopoetic cells to the site of damage [39] . This in turns initiates a positive feedback loop whereby the recruited myeloid cells respond to the inflammatory process by the release of more IL-1 cytokines . This loop could explain why we observed increased transcription of IL-1β , a cytokine transcribed and released predominantly by cells of hematopoetic origin . We were unable to detect significant differences in numbers of neutrophils or monocytes/macrophages in URT lavages by flow cytometry , perhaps due to the relatively mild and prolonged nature of the inflammatory process . Additionally , IL-1 signaling downstream of IL-1α has potent effects on phagocytic cells . Transcriptional profiling during persistent pneumococcal colonization demonstrated pneumolyin-dependent upregulation of factors involved in both phagocyte recruitment and function . IL-1 signaling also contributes to the differentiation of IL-17+ T cells and could impact eventual Th17-mediated pneumococcal clearance when these responses mature [59] . Colonization persistence was also found to depend on serotype , which is in agreement with clinical observations of serotype-dependent colonization duration in young children [5] . Serotype-specific differences in colonization duration in our model were not attributable to differences in IL-1 signaling . Isolates of serotype 23F and 4 , which are cleared in 9 and 6 weeks , respectively , that express equivalent amounts of Ply generated similar IL-1 responses . Differences in physical properties of the capsule type or its amount may affect processes downstream of IL-1 signaling , such as the deposition of opsonins ( complement and antibody ) or uptake by professional phagocytes as previously documented [4 , 37 , 60 , 61] . The attenuation of IL-1 signaling during infancy could account for why initiation of clearance is delayed until mice are approximately 25 days of age . The association of young age with impaired IL-1 responses observed from our mouse model provides a possible mechanism for enhanced susceptibility to S . pneumoniae during early childhood . However , factors regulating IL-1 signaling during early life remain unknown . We have shown previously that infant mice have an elevated URT mucosal inflammatory profile that depends on the presence of a microbiota [31] . The high mucosal expression of chemokines in infants decreases the gradient for phagocyte recruitment to the airway lumen and delays clearance of colonizing pneumococci . The situation with IL-1 signaling is different as we measured reduced expression of genes in the IL-1 pathway at baseline as compared to adult mice . One possibility is that infants have blunted IL-1 responses to allow for acquisition and establishment of a stable URT microbiota . Alternatively , it seems plausible that the imbalanced infant microbiota could underlie the repressed URT expression of IL-1 signaling genes , or blunted IL-1α responses , and is responsible for increased susceptibility of infants to URT pathogens . Studies in adult mice have shown that the microbiota affects expression of IL-1β precursor protein and is important for regulation of mucosal defense; whereas the impact of the microbiota on IL-1α-dependent effects have yet to be explored [62 , 63] . Alternatively , metabolic and nutritional differences with age could affect mucosal inflammatory responses directly or indirectly through temporal changes in the microbiota [64] . The importance of IL-1 signaling in clearance of otherwise persistent colonization may have broad relevance to other systems [65–68] . IL-1α and IL-1β were found to have overlapping and non-redundant roles in bacterial clearance during lung infection with Legionella pneumophila [57] . Lack of IL-1 signaling , specifically IL-1α , during mycobacterial lung infection led to an inability to control bacterial replication and earlier mortality [66] . Additionally , both IL-1α and IL-1β were necessary for phagocyte recruitment and function to control lung infection of Aspergillus fumigatus [68] . The importance of intact and robust IL-1 responses is further reflected by the increase in severe bacterial infections seen in individuals with inborn errors involving IL-1 signaling or in patients receiving anti-IL-1 treatment for inflammatory diseases [69–73] . In particular , deficiencies in IL-1 signaling , including in IRAK4 , Myd88 and NEMO , are associated with susceptibility to severe and recurrent pneumococcal infections during childhood [69 , 70] . The elderly also suffer from frequent pneumococcal disease . Pneumococcal colonization is prolonged in aged mice [72–75] , and in vitro and in vivo studies demonstrate reduced IL-1 protein release by aged human and mouse monocytes , and in the aged lung , following infection with S . pneumoniae [76 , 77] . Differences in IL-1 signaling , therefore , may be relevant to a number of vulnerable populations . | During early childhood , opportunistic pathogens are often carried in the upper respiratory tract ( URT ) for prolonged periods of time . Why young children experience more persistent carriage is unclear and there is little understanding of host-bacteria interactions that affect persistence , especially in infants . Here , we utilized an infant mouse model of Streptococcus pneumoniae colonization , a common pathogen of the infant URT , that persists for several months . We identified that clearance is dictated by bacterial expression of a single pneumococcal toxin , pneumolysin , and by the host response via a single cytokine , IL-1α , that activates IL-1 signaling . Absence of either of these factors led to increased persistence of S . pneumoniae . We discovered that the infant URT shows repression of IL-1 signaling compared to adults . Our study presents new insight into the importance of IL-1 signaling in clearance of persistent URT carriage and may provide an explanation why infants present with more persistent carriage by common URT pathogens . | [
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"microbiology"... | 2018 | Age-related differences in IL-1 signaling and capsule serotype affect persistence of Streptococcus pneumoniae colonization |
Glossina fuscipes fuscipes , a riverine species of tsetse , is the main vector of both human and animal trypanosomiasis in Uganda . Successful implementation of vector control will require establishing an appropriate geographical scale for these activities . Population genetics can help to resolve this issue by characterizing the extent of linkage among apparently isolated groups of tsetse . We conducted genetic analyses on mitochondrial and microsatellite data accumulated from approximately 1000 individual tsetse captured in Uganda and neighboring regions of Kenya and Sudan . Phylogeographic analyses suggested that the largest scale genetic structure in G . f . fuscipes arose from an historical event that divided two divergent mitochondrial lineages . These lineages are currently partitioned to northern and southern Uganda and co-occur only in a narrow zone of contact extending across central Uganda . Bayesian assignment tests , which provided evidence for admixture between northern and southern flies at the zone of contact and evidence for northerly gene flow across the zone of contact , indicated that this structure may be impermanent . On the other hand , microsatellite structure within the southern lineage indicated that gene flow is currently limited between populations in western and southeastern Uganda . Within regions , the average FST between populations separated by less than 100 km was less than ∼0 . 1 . Significant tests of isolation by distance suggested that gene flow is ongoing between neighboring populations and that island populations are not uniformly more isolated than mainland populations . Despite the presence of population structure arising from historical colonization events , our results have revealed strong signals of current gene flow within regions that should be accounted for when planning tsetse control in Uganda . Populations in southeastern Uganda appeared to receive little gene flow from populations in western or northern Uganda , supporting the feasibility of area wide control in the Lake Victoria region by the Pan African Tsetse and Trypanosomiasis Eradication Campaign .
Human African Trypanosomiasis , or sleeping sickness , is a vector-borne disease that kills thousands of people each year in sub-Saharan Africa [1] . Nagana , a related disease of livestock , can also be fatal and is a major impediment to agricultural development . Both diseases are caused by parasitic trypanosomes transmitted by tsetse flies . No vaccines exist to prevent the disease and drugs currently available to treat sleeping sickness in humans are expensive , can cause severe side effects and are difficult to administer in remote villages . Therefore , controlling the tsetse vector , via methods involving habitat elimination , trapping , insecticide-treated targets , aerial or ground spraying , insecticide-treated cattle , or the release of sterile or transgenic insects , may represent an effective alternative for controlling the disease [2]–[6] . In 2001 , the Pan-African Tsetse and Trypanosomiasis Eradication Campaign ( PATTEC ) was formed with the goal of identifying discrete zones of infestation that could be systematically exterminated , one by one , using area-wide methods [6] , [7] . Geographic features such as mountains and bodies of water were originally proposed as useful boundaries to delineate isolated target populations , however , biologically-relevant boundaries may be better defined by molecular techniques that explicitly account for the degree of interaction between neighboring groups of flies . The application of population genetic methods to disease vectors offers insights into facets of ecology , reproduction , dispersal patterns , population dynamics and genetic diversity that can have direct relevance for guiding control programs . At deeper timescales , population genetics can elucidate the historical processes that have influenced the current distribution of vector populations . Over more recent timescales , population genetics can delineate isolated vector populations that may be appropriate targets for local eradication and can identify dispersal routes that must be disrupted to prevent reinfestation of previously cleared zones . Characterization of barriers to gene flow is particularly important in planning the release of genetically modified or sterile vectors where the scale of success will depend on the scale at which the released vectors mate with wild counterparts [8] , [9] . Beyond this , estimates of genetic diversity and effective size can provide insight into the relative importance of drift or selection in driving important phenotypes such as insecticide resistance and vector competence . Finally , if established early enough , ongoing genetic monitoring can help to characterize the effectiveness of control efforts ( i . e . , by estimating the reduction in effective population sizes resulting from control ) and can help to identify the cause of any failures ( i . e . , whether flies have recolonized from relict populations within controlled areas or from neighboring populations outside of control areas ) . In Uganda , the primary vector of both human and animal forms of trypanosomiasis is Glossina fuscipes fuscipes Newstead 1910 , which occurs in a peninsular distribution terminating around the shores of Lake Victoria at the far eastern edge of its range ( see Figure 1 ) . Given the possibility of severing this peninsula from the remainder of fuscipes' range in central Africa , the Lake Victoria region has been proposed as a potentially suitable target for area-wide control [6] . Although the severe HAT epidemics of the 20th century have largely subsided [10] , the acute form of the disease ( Trypanosoma brucei rhodesiense ) has recently extended its range from a historical focus along the shores of Lake Victoria to several foci extending north into central Uganda [11] . This has raised concern that its distribution will eventually overlap with that of the chronic form of the disease ( T . b . gambiense ) found in northwestern Uganda , complicating both the diagnosis and treatment of the disease . Genetic work by Abila et al . [12] did not find any evidence to suggest that the distribution of trypanosome forms in Uganda is constrained by the distribution of tsetse genotypes . However , in support of proposed vector control activities , this research highlighted a major discontinuity in genotypes between tsetse in central/northern Uganda and those found in the Lake Victoria region to the south . Lake Kyoga , in central Uganda , was identified as the main factor behind this genetic structure , but limited sampling prevented these authors from identifying the actual zone of contact between northern and southern populations and from assessing the level of admixture between these populations in locations where they may coexist . Nuclear data confirmed the genetic discontinuity observed in mtDNA , but the use of only five microsatellite loci prevented finer-scale resolution of population structure . In Uganda , G . f . fuscipes is restricted to discrete patches of riverine and lacustrine habitat distributed among large , uninhabitable tracts of pastures and agricultural fields . The extent to which these apparently discrete populations are actually linked by dispersal is unknown , but preliminary work in southeastern Uganda has suggested that even flies from the same watershed may share little mitochondrial variation [13] . Fully understanding the population structure of tsetse in Uganda at local scales will be important for defining the scale on which vector control is likely to be most effective and , eventually , for elucidating the extent to which local interactions between tsetse and trypanosomes may be influencing disease epidemiology . Therefore , we undertook an in depth population genetic study on a geographically comprehensive scale in order to resolve the hierarchical structure of G . f . fuscipes in Uganda .
Tsetse were caught in biconical traps [14] set amongst low bushes or in gallery forests along rivers , lakes , seeps or other semi-permanent water sources . In general , traps from a single site were placed within a 1–5 km2 area . We collected flies from 21 sites across Uganda as well as single sites in Ndere Island , Kenya ( ND ) and Kurmuk , Sudan ( KU; see Figure 1 ) . S . Torr kindly provided specimens of G . f . quanzensis from the Lukaya River in the Democratic Republic of Congo ( LR ) . All samples were collected between 2006 and 2009 , except for flies from Kigezi ( KZ ) and Kitgum ( KT ) , which had been collected in 1974 and 1997 , respectively , and were obtained from a museum collection stored at the Coordinating Office for the Control of Trypanosomiasis in Uganda . Flies caught between 2006 and 2009 were extracted using DNeasy kits ( Qiagen , Valencia , CA ) following the manufacturer's protocols . Museum specimens were extracted using the DNA-IQ system ( Promega , Madison , WI ) in a dedicated ancient DNA laboratory in order to prevent contamination with more abundant sources of modern DNA . Attempts to amplify fragments of cytochrome oxidase I ( COI ) and cytochrome b ( cytb ) genes using primers designed previously for G . fuscipes [12] yielded multiple peaks in sequence chromatograms ( K . Dion pers . comm . ; unpubl . data ) , suggesting the presence of nuclear copies . Therefore , we designed new primers COIF1 ( 5′ – CCT CAA CAC TTT TTA GGT TTA G – 3′ ) and COIIR1 ( 5′ – GGT TCT CTA ATT TCA TCA AGT A – 3′ ) , which consistently amplified just a single sequence . To help insure that this sequence originated from the mitochondrial genome and not a nuclear copy ( NUMT ) , we compared sequences obtained with COIF1/COIIR1 ( 570 bp ) to sequences obtained from the amplification of an ∼1 . 7 kb fragment ( using C1-J-1751/C2-N-3389; [15] ) , which is larger than most NUMTs found in eukaryotic genomes [16] . In all cases , sequences matched identically . We amplified COIF1/COIIR1 in a 30 µl PCR reaction employing 1X buffer ( Applied Biosystems , Foster City , CA ) , 0 . 2 mM each dNTP , 0 . 6 µM primers , 2 . 0 mM MgCl2 , 0 . 4 mg/mL BSA and 0 . 6 U AmpliTaq Gold ( Applied Biosystems ) using 50 cycles and an annealing temperature of 51°C . All sequences have been deposited in GenBank ( Table S1 ) . The majority of published microsatellite loci that had been developed for Glossina fuscipes [12] , [17] could not be utilized due to apparent X-linkage ( B03 , D12 , C107 , D6 , D109 , A6 , A9 , A112 ) , amplification problems , or scoring difficulty ( unpubl . data ) . Therefore , we adapted 12 loci that had previously been developed for G . pallidipes [18] , G . palpalis [19] and G . morsitans [20] for use in G . fuscipes . In addition , we developed one new locus ( C07b ) from the G . fuscipes microsatellite library used by Brown et al . [17] . Amplifications were performed using a touchdown PCR ( 10 cycles of annealing at progressively lower temperatures from 60°C to 51°C followed by 35 cycles at 50°C ) in 12 . 5 µl reaction volumes employing 1X buffer , 0 . 2 mM dNTPs , 2 . 0 mM MgCl2 , 0 . 2 mg/mL BSA and 0 . 5 U AmpliTaq Gold . Flourescently-labeled primers were included at a final concentration of 0 . 4 µM . When using the m13 amplification system [21] , the m13–tagged forward primer was included at a final concentration of 0 . 05 µM while the flourescently-labeled m13 primer and reverse primers were used at a concentration of 0 . 4 µM . Primer sequences and original sources for all loci are summarized in Table S2 . Microsatellite data were collected on an ABI 3730 and alleles were scored using the program GeneMarker ( SoftGenetics , State College , PA ) . For mtDNA , we calculated haplotype diversity ( Hd ) and nucleotide diversity ( π ) using the program DnaSP v5 [22] . For microsatellites , we tested for deviations from Hardy Weinberg equilibrium ( HWE ) and for linkage disequilibrium ( LD ) among all pairs of loci across all populations using the program Genepop v4 . 0 [23] . Markov chain parameters were set at 10 , 000 dememorizations , 1000 batches , 10 , 000 iterations per batch for HWE and 100 , 000 dememorizations , 1000 batches , 10 , 000 iterations per batch for LD . We used the program FSTAT v2 . 9 . 3 . 2 [24] to calculate allelic richness as well as observed ( Ho ) and expected ( He ) heterozygosity for each population . We evaluated evolutionary relationships among mitochondrial lineages using a parsimony network generated by the program TCS version 1 . 21 [25] . Next , we employed mismatch distributions [26] to help visualize signatures of population growth within two distinct clades of haplotypes identified in the parsimony network . We calculated the raggedness statistic ( r ) as an indicator of the extent to which the distribution followed the smooth unimodal curve expected to result from a growing population . We also tested for deviations from neutral expectations using Fu's Fs [27] and Ramos-Onsins' R2 [28] statistics . These have been demonstrated to be the most powerful tests available for detecting population growth [28] . Fu's Fs assesses the probability of the observed haplotype distribution occurring under conditions of neutrality and tends to be negative when there is an excess of recent mutations due to population growth , genetic hitchhiking , or background selection . R2 , on the other hand , assesses the difference between the number of singleton mutations observed in a population and the average number of nucleotide differences in that population . The increased number of singleton mutations occurring after a recent episode of severe population growth should result in low values of R2 . Tests of demographic expansion were conducted using DnaSP and significance was evaluated by comparing the observed statistics to a distribution of values generated with 5000 coalescent simulations . We calculated pairwise FST values for both mitochondrial and microsatellite data as a basic index of population differentiation . FST reflects the proportion of variance in haplotype or allele frequencies that is attributable to differentiation between populations relative to the total variance observed across all populations ( see [29] for a review of population genetic definitions ) . We calculated FST using the program Arlequin v3 . 1 [30] and tested for significant differences using 10 , 000 permutations . Significance was assessed using the sequential Bonferroni method . We employed microsatellite data and a Bayesian clustering method implemented in the program STRUCTURE v2 . 2 [31] to estimate the number ( K ) of clusters ( metapopulations ) present and to assign individual tsetse to these metapopulations without using prior information on sampling locality . The latter feature allowed for identification of individuals exhibiting recent ancestry from a metapopulation other than the one from which it was sampled , thereby polarizing the directionality of putative dispersal events . To identify the most likely K , we conducted 3 independent runs for each K from K = 1 to 15 assuming an admixture model with uncorrelated allele frequencies . We used a burn-in of 50 , 000 and replication values of 250 , 000 . We used the guidelines outlined by Pritchard et al . [31] and the second order rate of change in the likelihood distribution ( ΔK; [32] ) to identify a K that was most consistent with the data . For final assignment of individuals to the likeliest number of metapopulations , we reran STRUCTURE with a burn-in of 100 , 000 steps and replication value of 500 , 000 . We confirmed the STRUCTURE results using the program Geneland [33] using a nonspatial model and 500 , 000 steps for K = 1 to 15 . We estimated the proportion of variance explained by populations ( sampling sites ) within metapopulations and also the variance explained by metapopulations ( identified at higher K ) within larger metapopulations ( identified at lower K ) using the program HIERFSTAT [34] . Significance of the differentiation was tested by comparing an observed G-statistic to that obtained with 1000 randomized data sets . In order to further explore the hierarchical relationships among populations , we generated a neighbor-joining tree based on Cavalli-Sforza chord distances . We generated genetic distances using the program MSA v4 . 05 [35] and produced the tree using the program NEIGHBOR in Phylip 3 . 68 . Robustness of groupings was evaluated by using MSA v 4 . 05 to generate 1000 bootstrap replicates and using the program CONSENSE in Phylip 3 . 68 to identify the proportion of replicates exhibiting identical groupings . To assess the relative influences of drift and gene flow , we tested for a positive association between genetic distance ( FST/ ( 1- FST ) ; [36] ) and geographic distance using the Isolation By Distance Web Service v 3 . 16 [37] . We employed a one-dimensional framework given the scale of sampling relative to the width of predicted habitat corridors . Significance of the associations was tested with a Mantel test employing 10 , 000 randomizations . We tested for isolation by distance ( IBD ) using FST , a frequency based measure , as well as ΦST , which also accounts for the evolutionary relatedness of haplotypes ( mtDNA only ) . For mitochondrial data , we tested each of the two distinct groups of lineages ( north and south ) identified in a parsimony network , but excluded populations at the zone of contact between these two groups ( MS , JN and BN; see Figure 1 ) . For microsatellite data , we tested for IBD in the northern and southern clusters identified by STRUCTURE at K = 2 . We also evaluated IBD in each of the two clusters that arose from the southern cluster ( west and southeast ) at K = 3 . In all analyses , we excluded populations from Sudan and the DRC as well as populations represented by fewer than 10 ( mtDNA ) or 20 ( microsatellites ) individuals . We performed single sample point estimates of effective population size ( Ne ) , the number of individuals in an ideal population ( subject only to random genetic drift ) that would exhibit the same distribution of allele frequencies as actually observed . We calculated Ne using both linkage disequilibrium ( LDNE; [38] ) and Bayesian ( ONeSAMP; [39] ) methods . For ONeSAMP , estimates of Ne that rise with increasing upper limits on the prior are indicative of large Ne values ( D Tallmon pers . comm . ) ; therefore , we explored the stability of estimates using priors of both 5 , 000 and 10 , 000 for maximum Ne . We also evaluated all populations for evidence of a recent decline in effective population size using the program BOTTLENECK [40] . We performed the test using both the infinite allele ( IAM ) and two-phase ( TPM ) models of microsatellite evolution . We assessed the significance of the tests using Wilcoxon's test , which is the most appropriate test in cases where fewer than 20 microsatellite loci are used [40] .
After excluding the two least polymorphic loci ( C5b and CAG29 ) and using the sequential Bonferroni method to account for non-independence of multiple tests , we detected no significant tests of linkage disequilibrium among the 55 remaining pairs of loci . Using the generalized binomial procedure implemented in MultiTest v . 1 . 2 [41] to control for multiple tests , we identified two of 13 loci ( D05 and Pgp17 ) that exhibited a significant proportion of populations with FIS values significantly different than zero ( Table S3 ) . Six population-by-locus combinations yielded significant ( p<0 . 002 ) deviations from Hardy Weinberg expectations after Bonferroni correction ( performed by locus ) . Analyses performed with the program Micro-Checker [42] indicated that null alleles at loci D05 and Pgp17 were likely responsible for heterozygote deficiencies observed in these populations . Re-amplification of several homozygous individuals using less stringent conditions for both D05 and Pgp17 did not resolve the problem . We performed all major analyses with and without these loci but report only results for 13 loci since the results were consistent across both modes of analysis . In Uganda and the single population from Kenya , we identified 36 unique mitochondrial haplotypes among 284 individuals for which we sequenced mitochondrial DNA . We recovered two additional haplotypes from 15 Sudan specimens and three haplotypes from 10 specimens of G . f . quanzensis from the DRC . Among samples of G . f . fuscipes , overall haplotype diversity ( Hd ) was 0 . 906 . Across individual populations , Hd ranged from a high of 0 . 778 in the Uganda population from Ogur ( OG ) to a low of 0 in the Kenyan population from Ndere Island ( ND; Table 1 ) . We observed high levels of microsatellite diversity in flies sampled in western Uganda at Arua ( AR ) , Murchison Falls ( MF ) and Moyo ( MY; Table 1 , Table S4 ) . A regression of microsatellite allelic richness on longitude ( Figure 2 ) revealed a significant decline in diversity from west to east across both northern ( R2 = 0 . 80 , df = 7 , p = 0 . 001 ) and southern populations ( R2 = 0 . 41 , df = 9 , p = 0 . 017 ) , however the latter trend was not evident when the southern group was further parsed into western and southeastern groups . Island populations did not exhibit uniformly low diversity . As observed with mitochondrial diversity , Ndere Island flies exhibited the lowest levels of microsatellite variation . Flies from Buvuma Island ( BV ) , on the other hand , exhibited allelic richness and heterozygosity levels on par with neighboring populations from the mainland ( Table 1 ) . Mitochondrial haplotypes obtained from flies collected across Uganda and Sudan fell into five clades ( each consisting of lineages separated by at most two mutations ) that were nested within two major groups based on a parsimony network ( Figure 1A ) . These two groups of haplotypes ( henceforth north and south ) were equally divergent from G . f . quanzensis and were perfectly partitioned in geographical space ( Figure 1B ) . The two major groups co-occurred only at three sites ( Masindi ( MS ) , Junda ( JN ) , Bunghazi ( BN ) ) lying in a narrow band across central Uganda . We estimated the approximate divergence time between northern and southern groups based on the genetic distance between these two groups , accounting for ancestral polymorphism using a correction for molecular diversity observed within each group . We used the formula Dxy = D – 0 . 5* ( Dx+Dy ) where D is the mean nucleotide diversity observed between the two clades , and Dx and Dy are the mean nucleotide diversities within group x and group y [43] . Using a Drosophila mitochondrial mutation rate of 5 . 7×10−8 per silent site per year [44] , we estimated the time of divergence of the two groups to be about 340 , 000 years ago . Given that northern and southern flies appear to have evolved independently for an extended period of time , we performed tests of neutrality/population growth for each group separately . Mismatch tests revealed curves typical of expanding populations , particularly in the south ( Figure 3 ) . This was confirmed by raggedness values that failed to reject the null hypothesis of exponential growth in both the north ( r = 0 . 0389 , p = 0 . 09 ) and the south ( r = 0 . 0673 , p = 0 . 20 ) . A hypothesis of exponential growth was also supported by Fu's Fs in both the north ( Fs = −5 . 5 , p = 0 . 03 ) and the south ( Fs = −11 . 8 , p = 0 ) ; however , Ramos-Onsins' R2 was only marginally significant in the south ( R2 = 0 . 038 , p = 0 . 06 ) . At the finest scale , pairwise FST values calculated from microsatellite data revealed significant differentiation among all sampled localities , with the sole exception of Ogur ( OG ) and Pader ( PD; Table S5 ) . However , Bayesian analysis of population structure evaluated with Evanno's criterion ΔK , indicated that our data were most consistent with the presence of two metapopulations . Because this division simply reflected the strong north/south division previously observed in mitochondrial haplotypes , we explored further partitions . The next highest value of ΔK supported three metapopulations ( Figure 4A ) , in which individuals from the four western populations ( Murchison Falls ( MF ) , Masindi ( MS ) , Kabunkanga ( KB ) , Kakoga ( KK ) ) split from individuals in the southeast . Hierarchical partitioning of variance yielded an FST of 0 . 10 ( p = 0 . 001 ) for the effect of population measured within these three metapopulations . Beyond this level of partitioning , individuals continued to exhibit geographically relevant clustering up to K = 11 ( Figure 4B ) , which corresponded to an inflection point at the maximum likelihood observed across all values of K tested . Pairwise FST values between these metapopulations averaged 0 . 2 and ranged from 0 . 054 to 0 . 574 ( Table 2 ) , all of which were significant following Bonferroni correction ( p<0 . 0008 ) . The variance explained by these 11 metapopulations ( Fst = 0 . 09 , p = 0 . 001 ) was similar to that explained by population when measured within the three metapopulations identified at K = 3 . Hypothetical metapopulations identified at K = 11 were generally robust to alternative modes of analysis . We recovered the same groupings upon excluding data from loci D05 and Pgp17 , which had revealed some departures from HWE . Furthermore , although parallel analyses with Geneland indicated that the posterior probability was highest for K = 9 , the groupings identified by this program were identical to those observed with Structure at K = 11 with the following exceptions: JN grouped with NA/KL and ND grouped with BV/BU/OK . The hierarchical relationships described above were also evident in a NJ tree based on Cavalli-Sforza chord distances ( Figure 5 ) . Despite the structure observed above , tests of isolation by distance revealed regional equilibrium between gene flow and genetic drift [45] , but this signal was stronger in microsatellites than in mtDNA . For mtDNA , a significant signal of IBD was evident only in the north and only when accounting for the evolutionary signal present in sequence data using ΦST ( R2 = 0 . 23 , p = 0 . 014; Table 3 ) . Here , geographical proximity explained only a relatively small proportion of the overall variance in genetic differentiation between populations . In the south , we did not detect IBD among maternal lineages , suggesting that for female tsetse , drift is a strong force relative to gene flow . Relationships contributing to this lack of signal included , for example , the small pairwise FST observed between the distantly located populations Kabunkanga ( KB ) and Okame ( OK ) and the large pairwise FST observed between neighboring populations Ndere ( ND ) and Busime ( BU; Table S6 ) . For microsatellites , on the other hand , we detected a significant pattern of IBD in both the north and the south ( Table 3 ) and this was evident in scatterplots of genetic distance versus geographic distance ( Figure 6 ) . This association was particularly strong when assessed within the southeastern populations alone ( R2 = 0 . 52 , p = 0 . 001; Table 3 ) . Given that two of the populations in the southeast were located on islands ( Buvuma ( BV ) and Ndere ( ND ) ) , we tested whether open water presented a barrier to gene flow beyond that imposed by distance alone . A partial Mantel test implemented in IBDWS revealed a significant effect of island isolation on genetic distance , after controlling for geographical distance ( r = 0 . 66 , p = 0 . 023 ) ; however , this effect could be explained by the isolation of ND alone . Examining the effect of ND only , a second partial Mantel test revealed a strong effect of island isolation after controlling for geographical distance ( r = 0 . 94 , p = 0 . 001 ) and also confirmed the strong relationship between genetic distance and geographical distance in the southeast , after controlling for the effect of ND ( r = 0 . 88 , p = 0 . 001 ) . Partial Mantel tests involving BV alone provided no evidence for significant isolation of this population beyond that attributable to geographical distance . Bayesian estimates of Ne based on single point samples provided evidence for relatively large historical effective population sizes in Busime ( BU ) in southeastern Uganda , and also in several populations from western and northwestern Uganda: Kabunkanga ( KB ) , Kakoga ( KK ) , Murchison Falls ( MF ) and Moyo ( MY; Table 4 ) . Based on the linkage disequilibrium method , only four populations ( BN , BU , MS and ND ) exhibited 95% confidence intervals that excluded infinity . Of these , only Bunghazi ( BN ) and Masindi ( MS ) also exhibited relatively low effective population sizes based on Bayesian estimates , and these estimates were not influenced by the choice of prior . The population from Ndere Island ( ND ) was not evaluated via the Bayesian method due to monomorphism at 4 loci . Detection of recent population bottlenecks was strongly influenced by the model chosen for analysis . After Bonferroni correction ( p<0 . 0024 ) , five populations exhibited significant signatures of a population bottleneck under the IAM model , but only one of these ( Okame; OK ) also tested positive for a bottleneck under the TPM model ( Table 5 ) .
Genetic structure in both mtDNA and microsatellites confirmed the presence of two distinct lineages of G . f . fuscipes in Uganda [12] . This genetic differentiation , if associated with differences in physiology , behavior , and/or symbiont composition affecting vector competence [46]–[49] , could have important implications for the epidemiology of trypanosomiasis , as well as vector control . However , the deep structure of G . f . fuscipes in Uganda appears to reflect an ancient event that divided northern and southern lineages , and in light of evidence for modern gene flow , is likely to be impermanent . The isolation of northern and southern lineages , which appear to have diverged on the order of several hundreds of thousands of years before present , may have been facilitated by habitat fragmentation during extreme drought cycles in East Africa that ended only 70 , 000 years ago [50] . Following the Last Glacial Maximum approximately 20 , 000 years ago [51] , a period when Lake Victoria may have been totally dry [52] , increasingly moist conditions would have favored the expansion of the riverine species G . f . fuscipes from dry period refugia . Given the perfect latitudinal partitioning of mitochondrial groups , the two divergent lineages likely expanded into their present distribution via distinct pathways . These paths may have split to the north and south of the Blue and Rwenzori mountain ranges , both of which form a barrier to direct colonization from the west . As evidenced by the distribution of mtDNA haplotypes , these two groups currently meet along a zone of contact extending from Bunghazi ( BN ) in eastern Uganda to Masindi ( MS ) in western Uganda . This zone of contact falls to the south of Lake Kyoga , which was formerly proposed as a barrier to gene flow between northern and southern flies [12] . Given the absence of obvious alternative geographical barriers to gene flow and the fairly narrow zone of contact , we speculate that northern and southern flies have come into contact only recently . At present , evidence for admixture between the northern and southern flies is not uniform along the zone of contact . In Bunghazi ( BN ) , we did not detect any deviation from HWE and almost all flies exhibited a mix of southern and northern ancestry , irrespective of their maternal lineage . In this portion of the contact zone , therefore , no barriers to mating are evident between northern and southern lineages . Given the hybrid dysgenesis observed among cryptic taxa within G . palpalis palpalis [53] , this observation warrants experimental confirmation . Elsewhere in the contact zone , flies exhibited genetic signatures consistent with the introgression of a northern mtDNA haplotype into a nuclear background that allied almost exclusively with either southeastern ( JN ) or western ( MS ) flies . This scenario is consistent with rare female-biased dispersal into the contact zone from the north and chance amplification of that northern haplotype by drift in a small effective female population . Wolbachia-induced mating incompatibility could also have played a role in driving the northern lineage to relatively high frequency , a possibility currently being investigated . In Junda ( JN ) and Masindi ( MS ) , the homogeneous nuclear background did not support admixture of northern and southern flies . However , just north of the contact zone , flies captured along the Nile River at Murchison Falls ( MF ) and along Lake Kyoga at Bugondo ( BG ) exhibited strong signals of mixed ancestry , confirming our observation in Bunghazi that gene flow between northern and southern lineages is possible and ongoing . Given the evidence of admixture highlighted above , the genetic differentiation between northern and southern lineages may simply represent a signature of historical allopatric fragmentation that has little bearing on the current movement of genes . However , Bayesian assignment probabilities ( Figure 4 ) , which provide an estimate of an individual's recent ancestry , suggested that the directionality of gene flow is constrained , both across the zone of contact and on a wider scale . Importantly , individuals to the south of the zone of contact in southeastern Uganda ( blue ) exhibited negligible recent shared ancestry with either flies just to the north , or even with flies from western Uganda , some of which possessed identical mtDNA lineages . This is indicative of little modern gene flow from the north or west into the southeast and lends support for PATTEC's choice of the Lake Victoria region as a suitably isolated target for tsetse intervention [6] . In contrast , individuals in the north and west tended to exhibit traces of ancestry from populations lying to the south or southeast , suggestive of a north or northwestern bias to gene flow . We hypothesize that this bias in direction may be linked to passive dispersal of pupae via seasonally flooded river systems , such as the Nile , Semliki and Achwa , all of which follow a north or northwesterly course in Uganda . In support of this mechanism , experimental tests have shown that pupae of both G . tachinoides and G . submorsitans , can survive for periods of at least 24 hours while submerged in water or saturated soil [54] . Water-borne dispersal , unlike the bidirectional volant dispersal of adult tsetse through habitat adjacent to rivers and lakes , may constrain the direction of movement , but may also allow G . f . fuscipes to disperse in the absence of contiguous tracts of favorable habitat . This warrants consideration when devising vector control strategies . Whatever the dominant mode of dispersal , analyses of isolation by distance using microsatellites reflected an equilibrium between gene flow and drift in both northern and southern regions . In northern Uganda , the slope of the regression of genetic distance on geographic distance was shallow relative to that observed in southern Uganda , suggesting that the homogenizing influence of gene flow is relatively stronger in the north versus the south . The relatively low genetic divergences observed over large geographic distances may have also been influenced by a recent history of sequential founder events occurring from west to east across northern Uganda , a process that is supported by the significant decline in allelic diversity with longitude across the north . Both scenarios emphasize the vagility of G . f . fuscipes , albeit at different time scales . In southern Uganda , the slope of the regression of genetic distance on geographic distance was steeper , and at large geographic distances , was driven by the major genetic discontinuity between flies on either side of the gulf in G . f . fuscipes' predicted range ( between Kabunkanga ( KB ) and Nkumba ( NA ) ; Figure 1 ) . Although mtDNA signatures reflect a historical connection between western and southeastern populations , currently , there appears to be little gene flow and these populations warrant separate consideration . In western Uganda , IBD was not apparent , perhaps owing to the low sample size , or perhaps owing to the fact that nearest-neighbor geographical distances did not capture actual dispersal distances between sites ( e . g . , measured along riverine corridors ) . In southeastern Uganda , on the other hand , the signal of IBD was relatively strong , suggesting that ongoing exchange of genetic material is moderating the random allelic variation that would otherwise accumulate in isolated populations undergoing genetic drift alone . The exception was the population from Ndere Island ( ND ) , which was more differentiated than expected based on the pattern of IBD observed in neighboring populations , perhaps due to its isolation from the mainland or its small effective population size . The latter may be the critical factor since Buvuma Island ( BV ) did not exhibit the same discontinuity . For Buvuma Island , the open water separating the island from the mainland appeared to be no more of a barrier to dispersal than the habitat separating neighboring mainland populations . The signal of gene flow obtained from IBD analysis is consistent with estimates of dispersal rates for riverine tsetse , which are on the order of tens of kilometers per year [55]–[57] . Reflecting this dispersal capacity , at the smallest scale of analysis , individual populations of G . f . fuscipes in Uganda appeared to be genetically homogeneous over the 1–5 km2 trapping area that formed our fundamental sampling unit . Although we detected significant deviations from HWE for two loci in a handful of these populations , we did not observe consistent trends in FIS that would provide evidence for any finer scale substructuring of tsetse populations , such as that observed in G . palpalis , a related species of riverine tsetse [58] . Extending beyond the immediate trapping locality , we observed significant but relatively small differentiation ( FST <∼0 . 1 ) between most populations separated by less than 100 km . This level of differentiation is similar to the differentiation observed among populations of G . palpalis at similar scales in Burkina Faso and Equatorial Guinea [59]–[63] . Several of these studies have focused on the isolation implied by this differentiation , but with rare exception [57] , absolute values of genetic isolation have yet to be reconciled with actual dispersal rates or the outcomes of vector control . Future efforts should focus on calibrating the consequences of genetic differentiation , perhaps by measuring the rate of reinfestation following eradication of the many tsetse populations for which genetic isolation indices now exist . Previous observation of significant FST values among populations of G . f . fuscipes in Uganda prompted the conclusion that genetic drift is a much stronger force than gene flow and that perhaps dispersal tendencies have been overestimated [12] , [13] . In this study , high FST values and little evidence for IBD in mtDNA provided some support for this conclusion , at least among females; however , for microsatellite data , IBD analyses were in accord with higher levels of current gene flow . Even if our microsatellite-based estimates of FST are deemed to be high , modeling has shown that high FST may persist in the face of high gene flow if environmental heterogeneity contributes to a large variance in the size of individual populations [64] , [65] . Our estimates of Ne for G . f . fuscipes in Uganda , though subject to large confidence intervals , were variable across populations , indicating that this condition may be met . Interestingly though , with the exception of Okame ( OK ) , a site that has experienced continuous trapping of tsetse over the last several years , we did not find strong evidence for bottlenecks in the majority of populations considered . Our power to detect bottlenecks was likely low , since the transient signal of reduced allelic diversity that is indicative of a bottleneck , is unlikely to persist across multiple demographic cycles in populations with historically low Ne [66] . At several sites to which we made repeat visits , we observed large changes in trapping rates over the course of a year ( unpubl . data ) , which were presumably linked to changes in water availability and habitat composition . Therefore , it remains possible that severe dry season demographic contractions and attendant high levels of genetic drift could be muting stronger signals of gene flow [65] . Strategies for control of G . f . fuscipes in Uganda should account for the movement of tsetse consistent with this gene flow . In conclusion , implied levels of gene flow among populations of G . f . fuscipes in Uganda appear to be consistent with high dispersal capacity , though confirmation of this will require the coordination of genetic studies with mark-recapture experiments . Interestingly , open water did not appear to present an unusual barrier to gene flow and we speculate that rivers may serve as a conduit for passive dispersal . Contrary to hypotheses invoking modern geographical barriers , the largest scale genetic structure apparent in Ugandan populations of G . f . fuscipes appears to have arisen from an ancient event that divided northern and southern lineages . Evidence for admixture between these lineages suggests that this structure may be impermanent but future studies should explore the viability of hybrid flies , particularly in light of the northerly-biased gene flow evident across the zone of contact . | Glossina fuscipes fuscipes is the most common species of tsetse in Uganda , where it transmits human sleeping sickness and nagana , a related disease of cattle . A consortium of African countries dedicated to controlling these diseases is poised to begin area wide control of tsetse , but a critical question remains: What is the most appropriate geographical scale for these activities ? To address this question , we used population genetics to determine the extent of linkage between populations of tsetse confined to discrete patches of riverine habitat . Our results suggest that Uganda was colonized by two distinct lineages of G . f . fuscipes , which now co-occur only in a narrow band across central Uganda . Evidence for interbreeding at the zone of contact and movement of genes from the south to the north suggest that this historical genetic structure may dissolve in the future . At smaller scales , we have demonstrated that exchange of genes among neighboring populations via dispersal is at equilibrium with the differentiating force of genetic drift . Our results highlight the need for investment in vector control programs that account for the linkage observed among tsetse populations . Given its genetic isolation and its location at the far edge of G . fuscipes' range , the Lake Victoria region appears to be an appropriate target for area wide control . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"genetics",
"and",
"genomics/population",
"genetics",
"evolutionary",
"biology/evolutionary",
"ecology"
] | 2010 | Phylogeography and Population Structure of Glossina fuscipes fuscipes in Uganda: Implications for Control of Tsetse |
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